European Workshop on Structural Health Monitoring: Special Collection of 2020 Papers [1, 1 ed.] 3030645932, 9783030645939

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Lecture Notes in Civil Engineering

Piervincenzo Rizzo Alberto Milazzo   Editors

European Workshop on Structural Health Monitoring Special Collection of 2020 Papers Volume 1

Lecture Notes in Civil Engineering Volume 127

Series Editors Marco di Prisco, Politecnico di Milano, Milano, Italy Sheng-Hong Chen, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China Ioannis Vayas, Institute of Steel Structures, National Technical University of Athens, Athens, Greece Sanjay Kumar Shukla, School of Engineering, Edith Cowan University, Joondalup, WA, Australia Anuj Sharma, Iowa State University, Ames, IA, USA Nagesh Kumar, Department of Civil Engineering, Indian Institute of Science Bangalore, Bengaluru, Karnataka, India Chien Ming Wang, School of Civil Engineering, The University of Queensland, Brisbane, QLD, Australia

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Piervincenzo Rizzo Alberto Milazzo •

Editors

European Workshop on Structural Health Monitoring Special Collection of 2020 Papers - Volume 1

123

Editors Piervincenzo Rizzo Department of Civil and Environmental Engineering University of Pittsburgh Pittsburgh, PA, USA

Alberto Milazzo Department of Engineering Università degli Studi di Palermo Palermo, Italy

ISSN 2366-2557 ISSN 2366-2565 (electronic) Lecture Notes in Civil Engineering ISBN 978-3-030-64593-9 ISBN 978-3-030-64594-6 (eBook) https://doi.org/10.1007/978-3-030-64594-6 © Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Structural health monitoring (SHM) is the scientific process of identifying damage or ascertaining the static and dynamic characteristics of given structures using remote sensors or a noninvasive network of sensors embedded or bonded to those structures of interest. SHM evolves the maintenance paradigm from “time-based” nondestructive evaluation (NDE) in which a structure is inspected periodically, to continuous monitoring. The European Workshop on SHM (EWSHM) is an International event that started in 2002. Ever since, it has been organized every two years in a different European country, in an alternating fashion with respect to the International Workshop on SHM (IWSHM) held at Stanford University every odd-numbered year and paired to the Asia-Pacific SHM meetings. The 2020 event has been jointly organized by the University of Palermo (Italy) and the University of Pittsburgh (USA) and should have been hosted by the Faculty of Engineering of the University of Palermo, in the beautiful island of Sicily. Over 600 abstracts from 52 countries were accepted for the workshop. Regrettably, the Covid-19 pandemic has forced the organizers and the scientific committee members to postpone the event to the year 2022 to the same great location. The authors of the accepted abstracts have been encouraged to submit a full paper to be included in this special collection. About 170 papers have been accepted after a rigorous peer-review process, and are presented here. The variety and quality of the papers are a testament of the very stimulating event that the EWSHM 2020 would have been. Overall, this EWSHM Special Collection includes the latest developments in key technology fields of sensor development, network design, signal processing, modeling, diagnostics, and prognostics with applications to aerospace, civil, and mechanical engineering. The proceedings are organized in thirty-two sections, split into two volumes, Volume 1 (ISBN 978-3-030-64593-9) including sections 1–15 and Volume 2 (ISBN 978-3-030-64907-4) including sections 16–32. The first 31 sections are the special sessions organized by true experts in their respective fields, whereas the last

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section contains the papers of those abstracts that were not submitted to any of the following special sessions:

1

Title Seismic structural health monitoring for civil structures

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SHM in wind turbine technology

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Nonlinear ultrasonic guided wave methods for SHM Real-time monitoring of built infrastructure

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Nonlinear SHM methods for high sensitivity Toward the next generation of performance indicators supported by SHM

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Standardization and guidelines on SHM and NDT: needs and ongoing activities

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Wireless sensing systems for structural health monitoring

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Integrated approaches for SHM: models, data and experiments Diagnostics and prognostics of composite structures toward a condition-based maintenance framework Vehicle-based indirect SHM for infrastructure

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Guided waves in structures for SHM

Organizers M. P. Limongelli (Politecnico di Milano, Italy) M. Celebi (Earthquake Science Center, USGS, USA) W. Ostachowicz (Polish Academy of Sciences, Poland) N. Yelve (Fr.C. Rodrigues Institute of Technology, Mumbai, India) V. Pakrashi and B. Bhowmik (University College Dublin, Ireland) E. Chatzi (Swiss Federal Institute of Technology, Switzerland) C. Lissenden (Pennsylvania State University) H. Sousa (HS Consulting Ltd/BRISA Group, Portugal) A. Mandic (University of Zagreb, Croatia) A. Strauss (Univ. of Natural Resources and Life Sciences, Austria) S. Kessler (Helmut Schmidt University / University of the Federal Armed Forces Hamburg, Germany) A. Strauss (Boku University) H. Wenzel (WENZEL Consulting Engineers GmbH) H. Fu (Loughborough University, UK) Z. Sharif Khodaei (Imperial College, UK) A. Cicirello (University of Oxford, UK) D. Zarouchas (Delft University of Technology, The Netherlands) T. Loutas (Patras University, Greece) H. Y. Noh (Stanford University, USA) A. Malekjafarian (University College Dublin) C.-W. Kim (Kyoto University) Y. Yang (National Taiwan University) W. Ostachowicz (Polish Academy of Sciences, Poland) A. Pau (Sapienza University of Rome, Italy (continued)

Preface

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(continued) 13

Smart multifunctional materials and systems for SHM of large structures

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Human performance monitoring

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Structural health monitoring of cultural heritage structures New opportunities for structural health monitoring and artificial intelligence

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Acoustic emission for structural health monitoring of civil infrastructure Space-borne health monitoring for civil infrastructure

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Autonomous machine learning-enhanced SHM for aerostructures

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Electromagnetic surface and subsurface sensing methods for SHM Ultrasonic NDTs for the SHM of train wheel-axle and rail

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New trends and challenges of SHM in civil engineering

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Infrared thermography for structural health monitoring Fiber optics sensors

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Robust statistical and probabilistic methods for structural health monitoring

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Optical and computer-vision techniques for SHM and NDT

A. D’Alessandro, F. Ubertini (University of Perugia–Italy) S. Laflamme (Iowa State University– USA) K. Loh (Univ. of California, San Diego, USA) E. García-Macías, F. Ubertini (Univ. of Perugia, Italy) Y-B. Lin (National Center for Research on Earthquake Engineering, Taiwan) T.-K. Lin (National Chiao Tung University, Hsinchu, Taiwan) D. Ozevin (University of Illinois at Chicago, USA) G. Giardina (University of Bath, UK) P. Milillo (NASA Jet Propulsion Laboratory, USA) A. Kundu and R. Pullin (Cardiff School of Engineering, Cardiff University, UK) W. Ostachowicz (Polish Academy of Sciences, Poland) T. Yu (University of Massachusetts Lowell, USA) D. Cerniglia, N. Montinaro (Univ. di Palermo, Italy) G. Epasto (Università di Messina, Italy) A. Formisano (University of Naples “Federico II,” Italy), F. Clementi (Polytechnic University of Marche, Italy), N. Cavalagli (University of Perugia, Italy), and G. Milani (Politecnico di Milano, Italy) G. Pitarresi (University of Palermo, Italy) B. Glisic (Princeton University, USA) D. Zonta (University of Trento, Italy) F. Kopsaftopoulos (Rensselaer Polytechnic Institute, USA) S. Fassois (University of Patras, Greece) J. Sakellariou (University of Patras, Greece) A. Sabato (Univ. of Massachusetts, Lowell, USA) (continued)

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(continued) 27

Carbon nanotube and graphene-based sensors for SHM applications

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Damage identification under changing environment and operational conditions

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Multifunctional materials and composites Structural health monitoring of high-speed rail and Maglev systems Defect imaging algorithms based on guided waves for BVIDs detection: a Round Robin test on a large-scale aeronautical composite structures

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A. Güemes (Univ. Politecnica de Madrid, Spain) Z. Su (The Hong Kong Polytechnic University, HK) D. Li (Shantou University, China) M. Cao (Hohai University, China) P. Kraemer (University of Siegen, Germany) O. I. Okoli, V. O. Eze, M. A. Shohag (FAMU-FSU, Tallahassee, USA) Yi-Q. Ni (The Hong Kong Polytechnic University, Hong Kong) A. Marzani, L. De Marchi (University of Bologna, Italy)

The organization of the workshop and the success of this volume would not have been possible without the contributions of many people who have assisted or provided guidance during the last two years. As such, we acknowledge the contribution of the members of the local organizing committee: Ivano Benedetti, Ph.D., University of Palermo, Italy Guido Borino, Ph.D., Università degli Studi di Palermo, Italy Donatella Cerniglia, Ph.D., Università degli Studi di Palermo, Italy Fabrizio Micari, Ph.D., Università degli Studi di Palermo, Italy Giuseppe Pitarresi, Ph.D., University of Palermo, Italy and the members of the International Scientific Board: G. Akhras, Canada C. Boller, Germany P. Cawley, UK F. Cegla, UK F.-K. Chang, USA B. Chapuis, France E. Chatzi, Switzerland A. Cunha, Portugal A. Cusano, Italy Z. Fan, Singapore S. Fassois, Greece C.-P. Fritzen, Germany M. Giordano, Italy V. Giurgiutiu, USA B. Glisic, USA

M. Gresil, UK A. Güemes, Spain L. Jankowski, Poland I. Kressel, Israel J. Kullaa, Finland V. Le Cam, France M. P. Limongelli, Italy N. Mechbal, France L. Mevel, France Y.-Q. Ni, Hong Kong W. Ostachowicz, Poland C. Papadimitriou, Greece S. Pierce, UK N. Rajic, Australia J. Rodellar, Spain

Preface

M. Salvia, France D. Saravanos, Greece H. Sohn, Korea W. Staszewski, Poland Z. Su, Hong Kong

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N. Takeda, Japan M. Tur, Israel T. Uhl, Poland S. Yuan, China D. Zonta, Italy

Last but not least, we would like to express our sincere gratitude to Collage S.p.A. and in particular Mrs. Antonella Giuggioli, Mrs. Cinzia Gentile, and Mrs. Giusy Ventura for coordinating the effort with the logistics before the Covid-19 pandemic forced us to postpone the event to the year 2022. We believe you will find this volume scientifically stimulating and we trust you will consider submitting your latest work to the EWSHM 2022 scheduled to be held in Palermo (Italy) in the first week of July 2022. Sincerely, November 2020

Piervincenzo Rizzo Alberto Milazzo EWSHM 2020 Chairs Fabrizio Ricci Francesco Lanza di Scalea EWSHM 2020 Co-chairs

Organization

Program Chairs Piervincenzo Rizzo Alberto Milazzo

Department of Civil and Environmental Engineering, University of Pittsburgh, USA Department of Engineering, Università degli Studi di Palermo, Italy

Program Co-chairs Fabrizio Ricci Francesco Lanza di Scalea

Department of Engineering, Università di Napoli Federico II, Italy Department of Structural Engineering, University of California, San Diego, USA

Program Committee Ivano Benedetti Donatella Cerniglia Francesco Lanza di Scalea Fabrizio Micari Alberto Milazzo Giuseppe Pitarresi

Department of Engineering, Università degli Studi di Palermo, Italy Department of Engineering, Università degli Studi di Palermo, Italy Department of Structural Engineering, University of California, San Diego, USA Department of Engineering, Università degli Studi di Palermo, Italy Department of Engineering, Università degli Studi di Palermo, Italy Department of Engineering, Università degli Studi di Palermo, Italy

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Fabrizio Ricci Piervincenzo Rizzo

Organization

Department of Engineering, Università di Napoli Federico II, Italy Department of Civil and Environmental Engineering, University of Pittsburgh, USA

Contents

Seismic Structural Health Monitoring for Civil Structures Effect of Ductility on Performance of Reinforced Concrete Portal Frame Loaded with Lateral Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jahanvi M. Suthar, Antariksh Mohaniya, and Sharadkumar P. Purohit S2HM Must Be Real-Time or Not? . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mehmet Çelebi and Maria Pina Limongelli Structural Behavior Characterization of the Gravina Bridge (Matera, Southern Italy) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vincenzo Serlenga, Maria Rosaria Gallipoli, Rocco Ditommaso, Carlo Felice Ponzo, Nicola Tragni, Tony Alfredo Stabile, Angela Perrone, Giuseppe Calamita, Luigi Vignola, Domenico Pietrapertosa, and Raffaele Franco Carso

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Uncertainty Analysis of Damage Identification Results Based on Finite Element Model Updating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Erkan Durmazgezer, Umut Yucel, and Ozgur Ozcelik

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Experience of Sonic Echo/Impulse Response Testing Difficulties in Timber Piles of Bridge Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . Saman Rashidyan, Tang-tat Ng, and Arup Maji

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Predictive Monitoring and Maintenance of Transportation Infrastructures: Requirements for Delivering Sensing Data over 5G Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Filippo G. Praticò, Sara Pizzi, Rosario Fedele, Domenico Battaglia, and Giuseppe Araniti

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Structural Health Monitoring over 5G uRLLC Network . . . . . . . . . . . . Fabio Franchi, Fabio Graziosi, Andrea Marotta, and Claudia Rinaldi

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Seismic and Structural Health Monitoring of Cahora Bassa Dam . . . . . Sérgio Oliveira, Ezequiel Carvalho, Bruno Matsinhe, Paulo Mendes, André Alegre, and Jorge Proença Concrete Crack Detection from Video Footage for Structural Health Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sushmita Kadarla, Sree Keerthe Beeram, Prafulla Kalapatapu, and Venkata Dilip Kumar Pasupuleti MEMS-Based System for Structural Health Monitoring and Earthquake Observation in Sicily . . . . . . . . . . . . . . . . . . . . . . . . . . Antonino D’Alessandro, Giovanni Vitale, and Salvatore Scudero A Study on Vision Based Method for Damage Detection in Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Narasimha Reddy Vundekode, Prafulla Kalapatapu, and Venkata Dilip Kumar Pasupuleti

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SHM in Wind Turbine Technology Understanding the Influence of Environmental and Operational Variability on Wind Turbine Blade Monitoring . . . . . . . . . . . . . . . . . . . 109 Callum Roberts, David Garcia Cava, and Luis David Avendaño-Valencia Fatigue Life Assessment of Wind Turbine Load Time Series Based on Measurements with Different Sampling Rates . . . . . . . . . . . . . . . . . . 119 Manuel Kim, Hamid Rahimi, and Jörg von Vietinghoff Nonlinear Ultrasonic Guided Wave Methods for SHM Application of Nonlinear Guided Waves for Detecting Loose Flanged Bolted Joints in Pipelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Reza Soleimanpour, Alex Ng, Abbas Amini, and Sayed Mohammad Soleimani Ziabari Damage Imaging Post Processing for Delamination Size Assessment of CFRP Aeronautic Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 William Briand, Marc Rébillat, Mikhail Guskov, and Nazih Mechbal Development of Lamb and Rayleigh Wave-Based Nonlinearity Parameters for Estimating the Remnant Life of Fatigued Plate Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Faeez Masurkar, Peter Tse, Nitesh Yelve, and Javad Rostami Non-linear SHM Based Damage Detection in Doubly-Curved-Shells . . . 161 Sathish Subbaiah Murugesan, Renjith Thomas, C. R. Bijudas, and P. Jayesh

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A Methodology for the Clusterisation of Communication Towers on the Basis of Their Structural Properties and Loads . . . . . . . . . . . . . 172 Lorenzo Benedetti, Simone Cinquemani, Marco Belloli, and Matteo Buonanno An Adaptive Wavelet Library to Detect Surface Defects in Rail Tracks Using a Laser Ultrasonic System . . . . . . . . . . . . . . . . . . . . . . . . 181 Javad Rostami, Faeez Masurkar, Peter Tse, Nitesh Yelve, and Edison Z. Y. Hou Experimental Evaluation of Nonlinear Wave/Damage Interaction for Delamination Detection in Laminated Composites . . . . . . . . . . . . . . 190 Xixi Li, Eric Monteiro, Mikhail Guskov, Marc Rebillat, and Nazih Mechbal Modelling of the Shear Horizontal Waves High-Order Harmonics Generation Using Local Interaction Simulation Approach . . . . . . . . . . . 200 Mariusz Osika, Rafal Radecki, Aleksandra Ziaja-Sujdak, and Wieslaw J. Staszewski Real Time Monitoring of Built Infrastructure Hygrometric Moisture Measurements Based on Embedded Sensors to Determine the Mass of Moisture in Porous Building Materials and Layered Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Christoph Strangfeld and Tim Klewe Continuous Static and Dynamic Strain Measurements on Civil Infrastructures: Case Study on One Pier of the Millau Viaduct . . . . . . . 226 Cartiaux François-Baptiste, Le Corvec Véronique, Cachot Emmanuel, Vayssade Thierry, and Servant Claude Gradient-Boosting Applied for Proactive Maintenance System in a Railway Bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 David García-Sánchez, Francisco Iglesias, Jesus Diez, Iñaki Piñero, Ana Fernández-Navamuel, Diego Zamora Sánchez, and José Carlos Jiménez-Fernandez Vibration-Based SHM Strategy for a Real Time Alert System with Damage Location and Quantification . . . . . . . . . . . . . . . . . . . . . . . 245 Ana Fernández-Navamuel, Diego Zamora-Sánchez, Tomás Varona-Poncela, Carlos Jiménez-Fernández, Jesús Díez-Hernández, David García-Sánchez, and David Pardo Slab Vibration Model Coupled with Pier Structure on Continuous Girder Bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 Shohei Kinoshita, Shigeru Kasai, Murtuza Petladwala, and Hideaki Takaku

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Towards Monitoring of Concrete Structures with Embedded Ultrasound Sensors and Coda Waves – First Results of DFG for CoDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Niklas Epple, Daniel Fontoura Barroso, and Ernst Niederleithinger Ultrasonic Wave Scattering at Liquid-Solid Interface by a Phased Array Sensor Using Distributed Point Source Method (DPSM) . . . . . . . 276 Apuroop Sai Vempati and Rais Ahmad Multi-type Sensor Placement for Structural Health Monitoring of Tied-Arch Bridges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 Bartlomiej Blachowski, Andrzej Swiercz, Mariusz Ostrowski, Piotr Tauzowski, and Lukasz Janowski Detection of Earthquake-Induced Damage in Building Structures Using Earthquake Response Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 Punit Kumar, Ankur Gautam, and Suparno Mukhopadhyay Assessment of CNC Machine-Induced Vibrations on an Industrial Inter-story Floor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 Chiara Bedon, Enrico Bergamo, Marco Fasan, and Salvatore Noé Continuous Dynamic Monitoring System of Foz Tua Arch Dam: Installation and First Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 Sérgio Pereira, Filipe Magalhães, Jorge Gomes, Álvaro Cunha, José Paixão, and José Lemos Compressive Sensing and On-Board Data Recovery for Vibration–Based SHM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Matteo Zauli, Federica Zonzini, Nicola Testoni, Alessandro Marzani, and Luca De Marchi A Novel Time-Frequency Distribution for Real-Time Monitoring of Civil Infrastructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Said Quqa, Giacomo Bernagozzi, Luca Landi, and Pier Paolo Diotallevi Nonlinear SHM Methods for High Sensitivity Non Destructive Auscultation and Imaging of Damages by Distributed Sensor Array: Step Towards Passive SHM Under Real Conditions . . . . 349 Lynda Chehami, Emmanuel Moulin, and Marina Terzi Estimation of Deterioration Due to Corrosion in the RC Members Using Higher Harmonics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Ahmet Serhan Kırlangıç A Damage Detection Method of Bridges Utilizing Vehicle Vibration Time History Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Zhongru Yu, Shuai Shao, Guojun Deng, and Zhixiang Zhou

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Guided Wave Propagation and Breathing-Debond Localization in a Composite Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 Shirsendu Sikdar, Wim Van Paepegem, and Mathias Kersemans Towards the Next Generation of Performance Indicators Supported by SHM Structural Health Monitoring (SHM) Goes to Space . . . . . . . . . . . . . . . 389 Aswin Haridas, Carlos Miguel Giraldo, and Holger Speckmann Standardization and Guidelines on SHM and NDT: Needs and Ongoing Activities Methods to Quantify the Utility of NDT in Bridge Reassessment . . . . . . 403 Stefan Küttenbaum, Sascha Feistkorn, Thomas Braml, Alexander Taffe, and Stefan Maack Structural Health Monitoring System for Furnace Refractory Wall Thickness Measurements at Cerro Matoso SA . . . . . . . . . . . . . . . . . . . . 414 Diego A. Tibaduiza, Jersson X. Leon-Medina, Ricardo Gomez, Jose Ricardo, Bernardo Rueda, Oscar Zurita, and Juan Carlos Forero Numerical and Experimental Assessment of FRP-Concrete Bond System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Emma La Malfa Ribolla, Giuseppe Giambanco, and Antonino Spada Wireless Sensing Systems for Structural Health Monitoring Detecting Road Pavement Cracks Based on Acoustic Signature Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Rosario Fedele and Filippo G. Praticò Development of Autonomous UHF RFID Sensors Embedded in Concrete for the Monitoring of Infrastructures in Marine Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 K. Bouzaffour, B. Lescop, F. Gallée, P. Talbot, and S. Rioual Integrated Approaches for SHM: Models, Data and Experiments Improving the Capability of Detecting Damages in the Early State by Advanced Frequency Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 Nicoleta Gillich, David Lupu, Codruta Hamat, Gilbert-Rainer Gillich, and Dorian Nedelcu False Alarm-Improved Detection Capabilities of Multi-sensor-Based Monitoring of Vibrating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 Daniel Adofo Ameyaw and Dirk Söffker

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A Computer Vision-Based Approach for Non-contact Modal Analysis and Finite Element Model Updating . . . . . . . . . . . . . . . . . . . . 481 Marco Civera, Luca Zanotti Fragonara, and Cecilia Surace On Metrics Assessing the Information Content of Datasets for Population-Based Structural Health Monitoring . . . . . . . . . . . . . . . . 494 Chandula T. Wickramarachchi, Wayne Leahy, Keith Worden, and Elizabeth J. Cross Experimental and Numerical Aspects of Lamb Waves Excitation and Sensing by Rectangular Piezoelectric Transducers . . . . . . . . . . . . . 505 Alisa N. Shpak, Mikhail V. Golub, Inka Mueller, and Claus-Peter Fritzen Recent Results in Active and Passive SHM . . . . . . . . . . . . . . . . . . . . . . 515 Victor Giurgiutiu Comparison of CWRU Dataset-Based Diagnosis Approaches: Review of Best Approaches and Results . . . . . . . . . . . . . . . . . . . . . . . . . 525 Xiao Wei and Dirk Söffker Analyzing the Robustness of Hybrid, Output-Only, Kalman Filtering–Based System Identification Method . . . . . . . . . . . . . . . . . . . . 533 Esmaeil Ghorbani and Young-Jin Cha Production-Induced Variance of Guided Wave-Based SHM Systems – A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Inka Mueller, Alisa Shpak, Claus-Peter Fritzen, and Mikhail Golub Damage Identification by Inverse Finite Element Method on Composite Structures Subject to Impact Damage . . . . . . . . . . . . . . . 553 Luca Colombo, Daniele Oboe, Claudio Sbarufatti, and Marco Giglio Comparison of Hilbert Transform and Complex Demodulation for Defect Identification in Cutting Discs using Vibration-Based Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564 Sebastian Priebe, Lukas Brackmann, Ahmad Alabd-Allah, Sahir Butt, Arne Röttger, Günther Meschke, and Inka Mueller In-Service Inspections of Bondlines in Composite Structures by Distributed Optical Fiber Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Carlos Miguel Giraldo, Juan Zuñiga Sagredo, and Luis Miguel Garcia Vazquez Assessment of a Dual Kalman Filter-Based Approach for Input/Output Estimation in an Aluminum Plate . . . . . . . . . . . . . . . . 584 Afshin Sattarifar and Tamara Nestorović Monitoring Road Acoustic and Mechanical Performance . . . . . . . . . . . . 594 Filippo G. Praticò, Rosario Fedele, and Gianfranco Pellicano

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Diagnostics and Prognostics of Composite Structures Towards a Condition-Based Maintenance Framework Acoustic Emission Based Monitoring of Fatigue Damage in CFRP-CFRP Adhesive Bonded Joints . . . . . . . . . . . . . . . . . . . . . . . . 605 Michele Carboni and Andrea Bernasconi Damage Diagnostics of a Composite Single-Stiffener Panel Under Fatigue Loading Utilizing SHM Data Fusion . . . . . . . . . . . . . . . . . . . . . 616 Agnes A. R. Broer, Georgios Galanopoulos, Dimitrios Zarouchas, Theodoros Loutas, and Rinze Benedictus A Strain-Based Health Indicator for the SHM of Skin-to-Stringer Disbond Growth of Composite Stiffened Panels in Fatigue . . . . . . . . . . . 626 Dimitrios Milanoski, Georgios Galanopoulos, Agnes Broer, Dimitrios Zarouchas, and Theodoros Loutas An Impact Monitoring System for Aeronautical Structures . . . . . . . . . . 636 Alessio Beligni, Kamil Kowalczyk, Claudio Sbarufatti, and Marco Giglio Toward Composite Damage Classification Using in Situ WavenumberFrequency Modal Decomposition of Acoustic Emissions . . . . . . . . . . . . 647 Cédric Rosalie, Nik Rajic, Stephen van der Velden, L. R. Francis Rose, Joel Smithard, and Wing Kong Chiu Vehicle-Based Indirect SHM for Infrastructure Identification of the Elastic Modulus of Simply-Supported Girders from Dynamic Tests: Method and in Situ Validation . . . . . . . . . . . . . . . 661 Angelo Aloisio, Elena Antonacci, Riccardo Cirella, Dante Galeota, Rocco Alaggio, and Massimo Fragiacomo Free Vibration Selection Method in Acceleration Responses for Bridge Health Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674 Murtuza Petladwala, Shohei Kinoshita, Shigeru Kasai, and Satoshi Himoto Deployment of Contact-Based Ultrasonic Thickness Measurements Using Over-Actuated UAVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 Robert J. Watson, S. Gareth Pierce, Mina Kamel, Dayi Zhang, Charles N. MacLeod, Gordon Dobie, Gary Bolton, Tariq Dawood, and Juan Nieto Drive-by Bridge Health Monitoring Using Multiple Passes and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 Abdollah Malekjafarian, Callum Moloney, and Fatemeh Golpayegani

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Guided Waves in Structures for SHM Vectorization of the Code for Guided Wave Propagation Problems . . . . 707 Pawel Kudela and Piotr Fiborek In-situ Strain Monitoring Performance of Flexible Nylon/Ag Conductive Fiber in Composites Subjected to Cyclic Tensile Loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716 Yumna Qureshi, Mostapha Tarfaoui, Khalil K. Lafdi, and Khalid Lafdi Guided Waves Dispersion Analysis in Composite Pipe Using the SAFE Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727 Zhengyan Yang and Zhanjun Wu Machine Learning Algorithms for Health Monitoring of Timber Utility Poles Using Stress Wave Propagation . . . . . . . . . . . . . . . . . . . . . 739 S. Bandara, P. Rajeev, and E. Gad Selective Actuation of Antisymmetric Lamb Waves Using Internal d15 Transducers for SHM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 Hussain Altammar, Parry Carrison, and Nathan P. Salowitz Sensitivity of Ultrasonic Guided Waves to Elastic Constants: A Numerical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759 Jannis Bulling, Georg Franosch, Yevgeniya Lugovtsova, and Jens Prager A Structural-Aware Frequency Division Multiplexing Technique for Acoustic Data Communication in SHM Applications . . . . . . . . . . . . 769 Federica Zonzini, Luca De Marchi, Nicola Testoni, Christian Kexel, and Jochen Moll Strategies for Identification of Elastic Constants in Highly Anisotropic Materials Using Lamb Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 779 Maciej Radzieński, Paweł Kudela, Tomasz Wandowski, and Wiesław Ostachowicz Damage Detection with Ultrasonic Guided Waves Based on Broadband Random Excitation and Stochastic Signal Processing . . . 788 Jonas Brettschneider, Peter Kraemer, Pawel Kudela, and Jochen Moll Structural Event and Damage Diagnosis in a Composite Fuselage Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 798 Alejandro Sánchez Sánchez, Santiago Guerrero Vázquez, Patricia Díaz-Maroto Fernández, Jaime García Alonso, Antonio Muñoz Chamorro, Manuel Iglesias Vallejo, and Daniel Iñesta González

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The Potential of Ultrasonic Edge and Lamb Waves Propagating in Laminates to Detect Defects Near an Edge and Weakened Adhesion Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 809 Mikhail V. Golub, Maria Wilde, Artem Eremin, and Olga Doroshenko Guided Wave Monitoring of Industrial Pipework – Improved Sensitivity System and Field Experience . . . . . . . . . . . . . . . . . . . . . . . . . 819 Thomas Vogt, Sebastian Heinlein, Josh Milewczyk, Stefano Mariani, Robin Jones, and Peter Cawley Composite Leading Edge Monitoring with a Guided Wave System . . . . 830 Joseba Castillero, Gerardo Aranguren, Josu Etxaniz, and José M. Gil-Garcia The Global-Local Approach for Damage Detection in Composite Structures and Rails . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 838 Margherita Capriotti, Francesco Lanza di Scalea, and Antonino Spada Smart Multifunctional Materials and Systems for SHM of Large Structures Recent Advances and Open Issues on the Use of Smart Bricks for Seismic Monitoring of Masonry Buildings: Experimental Tests and Numerical Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 851 Andrea Meoni, Antonella D’Alessandro, and Filippo Ubertini Graphite-Cement Composites as Low-Cost Strain Sensing Multifunctional Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 861 H. Borke Birgin, Antonella D’Alessandro, Simon Laflamme, and Filippo Ubertini Combining Ultrasound and Surface Treatments for an Efficient Ice Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 870 Leandro Maio, Filomena Piscitelli, Salvatore Ameduri, Antonio Concilio, and Fabrizio Ricci Human Performance Monitoring An Aircraft Pilot Workload Sensing System . . . . . . . . . . . . . . . . . . . . . 883 Andrea Alaimo, Antonio Esposito, Alberto Milazzo, and Calogero Orlando Site-Specific Quality Assessment of Trabecular Bone Using Highly Nonlinear Solitary Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 Tae-Yeon Kim, Sangyoung Yoon, Andreas Schiffer, In Gwun Jang, and Sungmun Lee

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Structural Health Monitoring of Cultural Heritage Structures Vibration-Based Novelty Detection of Masonry Towers Using Pattern Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905 Gabriele Marrongelli, Carmelo Gentile, and Antonella Saisi Health Assessment and Modal Analysis of Historical Masonry Arch Bridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915 Abhinav Kolla, Ravi Naga Sai Kurapati, Sree Satya Venkat Meka, Venkata Sai Madhu Dinesh Vitakula, and Venkata Dilip Kumar Pasupuleti Novel Structural Health Monitoring Software Systems Exploiting Heterogeneous Sensing Solutions and Data Fusion for Enhanced Local/Global Damage Identification of Historic Structures . . . . . . . . . . . 927 Enrique García Macías and Filippo Ubertini One-Year Dynamic Monitoring the Main Spire of the Milan Cathedral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937 Carmelo Gentile and Antonello Ruccolo A Transfer Learning Application to FEM and Monitoring Data for Supporting the Classification of Structural Condition States . . . . . . 947 G. Coletta, G. Miraglia, P. Gardner, R. Ceravolo, C. Surace, and K. Worden Earthquake-Induced Damage Localization and Quantification in Historic Masonry Towers Using OMA and IDA . . . . . . . . . . . . . . . . 958 Alban Kita, Nicola Cavalagli, Ilaria Venanzi, Laura Ierimonti, and Filippo Ubertini Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 969

Seismic Structural Health Monitoring for Civil Structures

Effect of Ductility on Performance of Reinforced Concrete Portal Frame Loaded with Lateral Load Jahanvi M. Suthar1(&) , Antariksh Mohaniya2, and Sharadkumar P. Purohit1 1

Department of Civil Engineering, School of Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India {jhanvi.suthar,sharad.purohit}@nirmauni.ac.in 2 SECMEC Consultants Private Limited, Mumbai, India

Abstract. Earthquake is a natural hazard which is inevitable. Ductility ensures large inelastic deformation without significant loss of strength. It has been proven that ductile detailed structures give better performance than non-ductile detailed structures. In India after Bhuj 2001 earthquake, there is a change in design practices and lots of emphasis are being on ductility based design. But majority of structures which are constructed before 2001, do not satisfy criteria of ductile detailing. Experiment is performed for performance evaluation of Reinforced Concrete (RC) portal frames under lateral loading. Out of two portal frames, one frame confirms ductility based criteria given in Indian seismic code and another frame is not satisfying the criteria for ductile detailing. Experiments are performed on these portal frames to evaluate its performance under lateral load. Nonlinear static analysis is carried out. Results obtained through experiments are compared with numerical study performed through commercially available software. Study also confirms that the ductile detailed structure gives better performance than the non-ductile loaded under lateral load. Keywords: Ductile detailing evaluation
Inelastic


Portal frame
Lateral load
Performance

1 Introduction Fundamental change in the present seismic design procedure has become essential because of occurrence of recent earthquake in countries across the world [1]. Current seismic design codes are force- based in which forces and displacements within elastic limits, are calculated and used to design structural and non-structural components. In Performance Based Seismic Design (PBSD), various performance objectives are achieved through design criteria when structure is subjected to stated levels of seismic hazard [2]. PBSD gives real assessment and provides structural response under any earthquake ground motion. Current code applications do not provide design criteria such that structure withstands major earthquake ground motion with yielding in components to inelastic range, absorb energy and improves its ductility [3]. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 3–11, 2021. https://doi.org/10.1007/978-3-030-64594-6_1

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Ductility is measure of energy dissipation by inelastic deformation during major earthquakes. Ductility confirms effective redistribution of moments at critical sections as the collapse load is approached. Ductility depends mainly on the Moment-Curvature (M-u) relationship at critical sections where plastic hinges are expected to be imposed. Behaviour of reinforced concrete sections can be assessed by M-u analysis [4]. Sharma et al. [5] have presented experimental and numerical work carried out on a full-scale four storey reinforced concrete (RC) structure for seismic assessment by Pushover Analysis. The structure was detailed as per non-seismic reinforcement detailing norms of Indian Standards. This paper reports the details and results of the experiment and focuses on the need of modelling various structural non-linearities to obtain realistic results. It is evident that a vital step towards good seismic performance estimation of the structure is reliable and accurate determination of force-displacement curve, generally known as Pushover curve. A 3D full-scale four storey structure was loaded with monotonic lateral load. The experiment was performed at tower testing facility of Central Power research Institute, Bangalore. Gwalini and Singh [6] have given comparison of the dynamic response of four storey RC frame building designed as per the current Indian standard codes IS1893 Part-1 (2016) [10] and IS 13920 (2016) [13] using lumped plasticity and fibre-type component models. The comparison of the two modelling approaches highlight flexibility and capabilities of the models to predict the non-linear behaviour of structures. A brief review on different modelling approaches available in the literature is presented in this study. The Non Linear Static Analysis (Pushover Analysis) and Non Linear Dynamic Analysis (Incremental Dynamic Analysis) are carried out to determine the seismic capacity of the building. The results are compared, and final remarks are made to identify the advantages and limitations of the two Non Linear modelling strategies. This study shows that Fibre Hinge model provides a better estimate of the building response, since it accounts for all the modes of deterioration i.e., strength and stiffness deterioration, and energy dissipation. Mander et al. [7] have developed Stress-Strain model for concrete subjected to uniaxial compressive loading and confined by transverse reinforcement. The concrete section may contain confining steel either spiral or circular or rectangular hoops with or without cross ties. A single equation is used for the Stress-Strain equation. The influence of various types of confinement is taken into account by defining an effective lateral confining stress, which is dependent on the type of the transverse and longitudinal reinforcement. In this paper unified Stress-Strain approach for confined concrete with monotonic loading at slow strain rates has been given. Effect of cyclic loading and rate of Strain on Stress-Strain relation is also described in this paper. Patel [8] has given analytical and experiment study to evaluate Moment Curvature relationship for singly and doubly reinforced beam specimens.

2 Design and Detailing of a Portal Frame In order to assess the effect of confinement a portal frame is separated from a building [9]. A building is situated in Ahmedabad which is of one storey with a storey height 3.6 m. Seismic zone is III and important factor is 1. It is special moment resisting frame

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with a response reduction factor 5 according to codal provision [10]. Building is modelled in a commercially available software SAP 2000 [11]. Grade of concrete is M25 and steel of Fe500. Beams and columns are reduced to 1/3rd scale and dimensions are 0.1 m  0.15 m. Various load combinations are considered according to IS 456:2000 [12]. Governing load combination is 1.5DL + 1.5LL. Portal frame is analysed and designed according to Indian Standards [10, 12, 13]. To verify effect of ductility on a frame under lateral load, portal frame is casted in two different types. Type-1 is ductile detailed and spacing between transverse reinforcement near the joint are less. Spacing between transverse reinforcement is kept 50 mm. Type2 is not detailed for ductility and spacing between transverse reinforcement is 100 mm throughout for beam and column. Reinforcement details of type-1 is shown in Fig. 1a and its sectional details is given in Fig. 1b. In type-2, spacing is uniform for transverse reinforcement which is shown Fig. 2a and Fig. 2b shows its sectional details.

Fig. 1. (a) Reinforcement details of portal frame type-1 (b) Sectional details of portal frame type-1

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Fig. 2. (a) Reinforcement details of portal frame type-2 (b) Sectional details of portal frame type-2

3 Preparation of RC Portal Frame To cast reduced scale specimens concrete of M25 and steel of grade Fe500 is used. As the cross section dimensions are very small, Self-Compacting Concrete (SCC) is prepared instead of normal concrete. Concrete cubes of 150 mm  150 mm  150 mm are prepared and tested to measure the compressive strength of concrete. A wooden formwork is used for the concreting work of the portal frame. 12 mm plywood was coated with red film to protect it from water. Strain gauges are mounted on beam and column of both frames as shown in Fig. 3a and Fig. 3b respectively. To apply strain gauges, surface of reinforcement bar was smoothen at 10% and 90% distances of the total length of the element. Small pedestal is prepared for fixity at the base. Resistance of strain gauges are checked before their application and after completion of 28 days of curing. Mortar cover of 1:3 proportion are prepared and used to ensure cover of concrete.

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Fig. 3. (a) Strain gauge on beam element (b) Strain gauge on column element

4 Preparation of RC Portal Frame Lateral load is applied at beam column junction using hydraulic jack having a capacity of 100 kN. To fix pedestal at the one side of base, Z type assembly is prepared which can avoid sliding as well as rotation of frame at the base. Z type assembly is shown in Fig. 4. Other side of pedestal is fixed with the help of 12 mm thick plate using studs. Hydraulic jack was attached with the reaction wall as shown in Fig. 5. To avoid any kind of out of plane bending, reaction wall is attached with inclined props. Various instruments like, Load cell, Linear Variable Differential Transducer (LVDT), Dial gauge, Electric resistant strain gauge, Data acquisition system are used to measure deformation because of load applied. LVDT has a capacity to measure deformation up to 200 mm. Dial gauges have capacity of 50 mm deformation. Two types of strain gauges are used, i.e. 90 mm and 5 mm. 90 mm length strain gauge are used to measure strain in concrete and 5 mm are for steel. To measure all quantities digitally, Data acquisition system of 16 channels is used. Pictorial diagram to show placement of various instruments with frame and its supporting assemblies are given in Fig. 6 and Fig. 7.

Fig. 4. Z–type assembly for fixity at pedestal

Fig. 5. Reaction wall with hydraulic jack

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Fig. 6. Instrumentation to support horizontal and vertical displacement

Fig. 7. Placement of strain gauges to measure strain in concrete and steel

5 Result and Discussion In the present study, behavior of Portal Frame Type-1 and Type-2 are compared. The effect of confinement of the concrete on the failure pattern is studied with the help of this experiment. Placing of lateral reinforcement is different in both the specimens

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whereas longitudinal reinforcement is same. Monotonic lateral load is applied from right end top corner of frame. Deflections in frames are shown after a peak load where supporting instruments such as dial gauges and LVDTs are removed to avoid any damage in them. Inner side of near end column and outer side of far end column are in compression. Similarly, outer side of near end column and inner side of far end column are in tension. Crushing of concrete at beam column joints is observed in both frames. This demands modelling and detailing of joints. Even in pedestal also shear cracks are observed for both frames which demands more depth in pedestal. Column bottom at near end and far end columns are shown in Fig. 8a and Fig. 8b respectively.

Fig. 8. (a) Column bottom at near end (b) Column bottom at far end

In portal frame type-1, minor cracks are observed at the beam – column junction at 4 kN. At 10 kN cracks are observed in beam-column junction of far end column also. Up to 12 kN, there was not a loss of strength. Maximum load taken by frame was 15.7 kN and corresponding displacements at far end of column is 120.4 mm. Displacement corresponding to maximum load is 45 mm. In portal frame type-2, minor cracks are observed at the near end beam column junction at 2.6 kN. At 2.8 kN, first crack is developed in column at neat end. At 8 kN, cracks are observed at far end beam column junction. Maximum load taken by frame is 14.03 kN. Maximum displacements of far end column is 236 mm. Displacement corresponding to maximum load is 62.5 mm. Load vs Displacement graphs for far end column of frame Type-1 and Type-2 are shown in Fig. 9a and Fig. 9b respectively.

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Fig. 9. (a) Load v/s deformation curve of far end column of frame type-1 (b) Load v/s deformation curve of far end column of frame type-2

Type-1 has 10.63% higher load carrying capacity as compared to Type-2. Displacement corresponding to maximum load is 48.98% higher in type-2 for far end column. Displacement corresponding to maximum load is 28% higher in type-2 in far end column.

6 Conclusion This paper gives experimental study of portal frames having variation in transverse reinforcement. Lateral load is applied and deformation quantities corresponding to each load have been measured. Following points are concluded from the experimental study. In type-2, spacing of transverse reinforcement is similar and in type-1 spacing is less near the junction but in both these cases diameter of transverse reinforcement is same which gives difference in overall behavior of frame. Type-1 shows crushing of

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concrete and higher load carrying capacity compared to type-2. Equally placed transverse reinforcement in type-2 suffices the requirement of transverse reinforcement, shows yielding of it and probable tensile behavior which ultimately gives higher deformation compared to type-1. In case of type-1, transverse reinforcement did not get chance to yield and concrete got crushed. In both these frames, crushing of beamcolumn joint is observed which shows requirement of proper detailing of junction. There is a failure at pedestal and it demands proper fixity which can avoid crushing and improves its behavior.

References 1. Ghobarah, A.: Performance-based design in earthquake engineering: state of development. Eng. Struct. 23(8), 878–884 (2001). https://doi.org/10.1016/S0141-0296(01)00036-0 2. ATC-40: Seismic evaluation and retrofit of concrete buildings, Applied Technology Council, Redwood city, California (1996) 3. Freeman, S.A.: Review of the development of the capacity spectrum method. ISET J. Earthq. Technol. 41(1), 1–13 (2004) 4. Chandrasekaran, S., Nunziante, L., Serino, G., Carannante, F.: Seismic Design Aids for Nonlinear Analysis of Reinforced Concrete Structures, 1st edn. CRC Press, Boca Raton (2016) 5. Sharma, A., Reddy, G.R., Vaze, K.K., Eligehausen, R.: Pushover experiment and analysis of a full scale non-seismically detailed RC structure. Eng. Struct. 46, 218–233 (2013). https:// doi.org/10.1016/j.engstruct.2012.08.006 6. Gwalani, P., Singh. Y.: Comparative study on reinforced concrete component modelling using lumped plasticity and fibre models. In: 16th Symposium on Earthquake Engineering, pp. 164–173. 16SEE-Roorkee (2018) 7. Mander, J.B., Priestley, M.J.N., Park, R.: Theoretical stress-strain model for confined concrete. J. Struct. Eng. 114(8), 1804–1826 (1988). https://doi.org/10.1061/(ASCE)07339445(1988)114:8(1804) 8. Patel, J.A.: Evaluation of moment-curvature relationship for reinforced concrete beam element. M Tech Dissertation, Institute of Technology, Nirma University (2014) 9. Kothari, H.: Study the behavior of precast portal frame under lateral loading. M Tech Dissertation, Institute of Technology, Nirma University (2017) 10. IS 1893 (Part-1): 2016. Criteria for Earthquake Resistant Design of Structures. Bureau of Indian Standards, New Delhi, India (2016) 11. SAP2000: Integrated Software for Structural Analysis and Design, California, USA 12. IS:456 (2000): Plain and Reinforced Concrete-Code of Practice, Bureau of Indian Standards, New Delhi, India (2000) 13. IS 13920:2016, Ductile Design and Detailing of Reinforced Concrete Structures Subjected to Seismic Forces-Code of Practice. Bureau of Indian Standards, New Delhi, India (2016)

S2HM Must Be Real-Time or Not? Mehmet Çelebi1 and Maria Pina Limongelli2(&) 1

Senior Research Civil Engineer, U.S. Geological Survey, Menlo Park, CA, USA 2 Politecnico di Milano, Milan, Italy [email protected]

Abstract. Seismic structural health monitoring (S2HM) has advanced significantly in the last three decades. However, currently there is no consensus on the need for real-time processing of data acquired during an earthquake. Numerous applications exist whereby S2HM-equipped systems record valuable seismic response data. A delayed use of the seismic data prohibits timely discovery of hidden damages in a structure which, in turn, possibly increases its vulnerability during events to follow – with increased risk to occupants. Such risks are of particular concern when, for example, there are long-distance/long period effects e.g. for tall buildings and long-span bridges that are significantly affected by events that originate at far distances. These phenomena necessitate near realtime monitored data to make timely data-based informed decisions on the health or performance of the affected structure. The paper discusses criteria for functionality and occupiability thresholds in actual applications. Keywords: Seismic SHM Damage indicators


Real time data
Performance assessment


1 Introduction A monitored structure can provide information to support several types of decisions. Some of the decisions require real-time processing of data, for others a delayed analysis may be performed. Emergency management decisions about the need to evacuate a building or to issue traffic restrictions for a bridge require real time assessment as do the prompt assessments of the damage conditions to decide about priorities of interventions. The needs of owners to rapidly, informedly and accurately assess the damage, and therefore the functionality of a building during and soon after an event is of paramount importance to stakeholders, which include owners, leasers, permanent and/or temporary occupants, city officials and rescue teams that are concerned with the safety of those in the building, and those who may be affected in nearby buildings and infrastructure. Property damage and economic loss due to lack of permission to enter and/or reoccupy a building may be significant. Hence stakeholders require prompt answers to key questions such as: (a) is there visible or hidden damage? (b) If damage occurred, what is its extent? (c) Does the damage threaten other neighboring structures? and (d) Can the structure be occupied immediately without compromising life safety or is life safety questionable? An additional important point is that if there is damage, what © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 12–22, 2021. https://doi.org/10.1007/978-3-030-64594-6_2

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is the vulnerability of the structural system to potential aftershocks that follow a mainshock? The impacts of aftershocks are discussed in Bath (1965), Reasenberg and Jones (1989), and Hardebeck, et al. (2018). For example, Reasenberg and Jones (1989) say that there is roughly a 5–10% chance of an earthquake triggering an aftershock of the same magnitude or larger than itself over the next 5 days. It is clear from such research that the impacts of aftershocks on damaged buildings can be significant and disastrous – hence, the need for near real-time S2HM. One of the main objectives of this paper is to demonstrate, as depicted in Fig. 1, that it is necessary to distinguish real-time monitoring with specific objectives and requisites versus “any-time” assessment the type of which has been going on since the 1970s. In this second approach, the goals were (a) the improvement of seismic design code provisions; (b) better and improved assessment of fundamental structural vibrational periods and critical damping percentages that are requisite parameters of design processes in every design code across the globe. This type of monitoring does not require special handling in real-time. Real-time monitoring and assessment are today possible thanks to the development of data acquisition systems with specific software that can record, digitize, transmit and process the structural response in real-time or near real-time, and can package structural health monitoring algorithms to meet stakeholders’ needs as described above. This paper describes the past and current status of the structural instrumentation applications for seismic monitoring and the types of current building arrays and responses to be captured. Furthermore, recent developments in instrument technology and implications and issues for the future are discussed. We do not include detailed cost considerations.

Fig. 1. Distinguishing real-time and “any-time” monitoring of structures

2 Historical Perspective Seismic Structural Health Monitoring (S2HM) has been going on since 1970 with the main goal of providing information to: (a) reconstruct the response of the structure in sufficient detail to compare it with the response predicted by mathematical models and with those observed in laboratories, and thus improve the models; (b) make it possible to explain the reasons for any damage to the structure; (c) facilitate decisions to retrofit/strengthen the structural systems when warranted; (d) provide data to improve the technical design code provisions. Seismic monitoring of structural systems constitutes an integral part of the National Earthquake Hazard Reduction Program in the

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United States and it is one of the main activities of the Italian Seismic Service of the Department of Civil Protection (OSS-DPC)1. In the United States, the California Strong Motion Instrumentation Program (CSMIP) of the California Geological Survey and the United U.S. Survey (USGS) manage the largest two structural instrumentation programs. Data from both programs are readily available through the internet2. Similar hazard reduction strategies exist in seismically active regions of the world: extensive seismic monitoring of structures programs have been established in Japan, China, Taiwan, Korea and Mexico and other active programs exist in Turkey, Greece and Chile. Except for a few cases which can instead be described as array deployments with real-time data transmittal capabilities that serve special purposes (an example of which is provided later in the paper), the overwhelming majority of the deployments follow the non-telemetered (hereinafter called “classical”) method whereby the data are not transmitted in real-time. In general, until recently, accelerometers have been used to capture the time-variant level of shaking at strategically selected orientations and locations within a structure and, if feasible, the free-field tri-axial acceleration to quantify the interaction between the soil and the structure. Recordings of acceleration responses from instrumented structures have served the scientific and engineering community well, and have been useful in assessing design/analysis procedures, improving code provisions, and in correlating the system response with damage. Assessments of damage following an earthquake, until recently were carried out primarily through inspections by city-designated engineers following procedures similar to ATC-20 tagging requirements (ATC, 1989). Tagging usually involves visual inspection only, and is implemented by posting colored tags indicative of potential hazard to occupants: green indicating the building can be occupied - that is, the building does not pose a threat to life safety; yellow indicating limited occupation - that is, hazardous to life safety but not so as to prevent limited entrance to retrieve possessions; and red indicating entrance prohibited - that is, hazardous to life. However, one of the impediments to accurately assessing the damage level of structures by visual inspection is that some serious damage may be hidden by building finishes and fireproofing. In the absence of visible damage to the building frame, most steel or reinforced concrete moment-frame buildings will be tagged based on visual indications of deformation, such as damage to partitions or glazing. Lack of certainty regarding the actual deformation may lead an inspector toward a relatively conservative tag. In such cases, additional (and commonly expensive) inspections may be recommended to building owners (e.g. following the Mw = 6.7 1994 Northridge, California earthquake, approximately 300 buildings ranging in height from 1 to 26 stories were subjected to costly and intrusive inspections (FEMA352, SAC 2000)).

1 2

OSS data are available at www.mot1.it/ISS. USGS data are available via www.nsmp.wr.usgs.gov and CSMIP data are available via www. strongmotioncenter.org.

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Real-Time Assessment: A Different Perspective in Damage Identification

Advances in computation, communication and data transmission capabilities as well as the adoption of performance-based approaches to structural design have prompted development of new goals for structural monitoring that now include rapid, informed and accurate assessment of the structural condition, enabling the immediate occupancy of a building during and soon after an event. These new goals require not only the quick estimation of a ‘damage indicator’ – which is enabled today by advanced hardware capabilities - but also the rapid assessment of the structural performance. This prompts for a choice of damage indicators that can be directly measured or computed through signal processing of measured quantities and for which thresholds - corresponding to different performance levels - can be defined. This perspective differs from the traditional approach to damage identification which aimed to describe the condition (damage state) of the structure. Damage is defined as ‘changes that adversely affect the structural performance’ and its identification is performed using changes in parameters sensitive to damage. Several damage indicators that can be retrieved from the structural responses during earthquakes have been proposed and, among them, many are defined as functions of the modal parameters that can be readily identified from the response to vibration. Modal parameters are stiffness-dependent therefore their changes can be used to detect losses of stiffness (Limongelli 2019). In this case, since damage is described through a change, the damage-sensitive parameters are chosen as much as possible, independently from the seismic excitation (e.g. modal parameters or Frequency Response Functions). For these damage-sensitive parameters, one challenge is to relate to the effect of sources – for example temperature or soil-structure interaction - which induce their variation and that may hinder or falsely describe damage. The expected outcome of damage identification processes is the description, at different levels of refinement achieved with increasing computational effort, of the condition – or damage state – of the structure. The assessment of the structural performance is a further step that is usually carried out through a calibrated finite element model. When ‘real-time monitoring and performance assessment’ is sought, damage identification and performance assessments are joined in one process. A consistent choice of the damage parameter requires the definition of performance thresholds but does not require that the damage parameter is independent from the seismic excitation since it is its absolute value and not its variation that is needed. Interstory drift is a damage parameter that can be readily estimated through double integration of processed accelerations and for which performance thresholds corresponding to different performance levels can be computed. The establishment of the performance thresholds that are related to a building’s performance is indeed a critical part of the damage identification process. This is the part that requires extensive engineering input based on: (a) nonlinear analyses using actual structural members, geometry and joint characteristics, (b) FEMA-based recommendations (FEMA 352 [SAC2000]) derived from experimental data, (c) code-imposed drift ratios (e.g. Table 1), and (d) engineering experience (Kubo et al. 2011).

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M. Çelebi and M. P. Limongelli Table 1. Example of threshold stages and corresponding drift ratios Threshold stage 1 2 3 Adopted drift ratio 0.2% 0.8% 1.4–2.0%

Figure 2 shows the hypothetical thresholds imposed on a performance curve as adopted from FEMA-274. Threshold stages of damage conditions as defined by drift ratios can be pre-computed using relevant structural parameters such as the type of connections and story geometry (e.g. story height). Thus, once drift ratios can be readily computed in near real-time, an assessment of the damage condition of a building can be made.

Fig. 2. Hypothetical displacement time-history as related to performance (modified from Figure C2-3 of FEMA-274 (ATC 1997)) (from Çelebi et al. 2004; Çelebi 2008).

The rationale for real time computation of drifts is that, for example, a building owner and designated engineers are expected to use the response data acquired by a real-time health monitoring system to justify a reduced inspection program as compared to that which would otherwise be required by a city government for a similar non-instrumented building in the same area3 (e.g. BORP in San Francisco, California (BORP 2001)). An example of the development of such a solution and its algorithm is presented later in the paper. The second challenge in the adoption of drifts as damage parameters – beyond the definition of the performance thresholds – is the fact that the direct dynamic measure of relative displacements between floors is still very difficult and, except for tests conducted in a laboratory (e.g., using displacement transducers),

3

The City of San Francisco, California, has developed a “Building Occupancy Resumption Program” (BORP 2001) whereby a pre-qualified occupancy decision-making process, as described in this paper, may be proposed to the City as a reduced inspection program and in lieu of detailed inspections by city engineers following a serious earthquake.

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has yet to be readily and feasibly achieved for a variety of real-life structures. It is pertinent to add herein that currently there is no efficient and/or commercially available displacement sensor to avoid double-integration of accelerations, that are dominantly used worldwide. Although global positioning systems (GPS) can be used to obtain displacements directly, the fact that GPS units must see the sky to be tracked by satellites confines its deployment at best only to the roof of a building (Çelebi and Sanli 2002), which of course is not sufficient for a S2HM system. However, recent technological developments have already made it possible to successfully develop and implement two approaches, which will be described in the next sections, to dynamically measure and/or compute real-time displacements from which drift ratios or average drift ratios can be computed. Both approaches can be used for performance evaluation of structures and can be considered as building health-monitoring applications.

3 Near Real-Time Monitoring About two decades ago, a real-time monitoring system was developed to meet the needs of a building owner and his consultants (Çelebi et al. 2004). The system was installed in a 23-story reinforced concrete (RC) building in San Francisco, California. The monitoring system includes an array of accelerometers strategically deployed throughout the building to compute near-real time drift ratios between several pairs of floors (Çelebi et al. 2004; Çelebi 2007a and b, 2008). To our knowledge, at that time this was the first such application of S2HM using real-time data. Later, in 2008 and therafter, several bank buildings and California state-owned buildings were instrumented using a similar algorithm to facilitate health monitoring needs [R. Hamburger, pers. comm., and C. Goings, pers. comm., 2008]. Such methods are now being applied in the Unites States and in Japan (Hisada and others, 2012), Abu Dhabi and Turkey (Safak et al. 2014; Kaya and Safak 2014) and in other areas as well, (Skolnik et al. 2014). The algorithms as described in Fig. 3, show the fastest ways to determine if (a) occupants should permanently evacuate following significant shaking, or (b) if occupants should remain out pending additional evaluations or (c) if occupants may resume use of the facilities immediately. In San Francisco, following advanced approval by the city, and in lieu of classical “tagging” by city engineers, such a monitoring and alert system is being used by building owners and their consultants, and may soon be used by other owners for speedy decisions to resume occupancy (BORP 2001). S2HM real-time systems that employ the algorithm are being commercially marketed by two companies in the United States. Since both functionality and knowledge of occupiability are needed to achieve a desirable performance and minimized economic loss due to actual structural damage and/or downtime, it is reasonable to assert that real-time S2HM systems are becoming a requisite and not a luxury.

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Fig. 3. Flow-chart for observation of damage levels based on threshold drift ratios (from [Çelebi 2008]).

4 Pioneering Application A new approach for obtaining displacements in real-time is depicted in Fig. 4, which also shows the distribution of thirty accelerometers in a 23-story building in San Francisco, California. The array is designed to provide data from several pairs of neighboring floors to facilitate drift computations. This monitoring system was primarily realized because of the needs of the building owner to utilize real-time data to meet the goals of the previously discussed BORP (2001) program in San Francisco. The system has a server that (a) digitizes continuous analog acceleration data, (b) pre-processes the 1000 sps digitized data with low-pass filters (herein called as the preliminarily filtered uncorrected data), (c) decimates the data to 200 sps and streams it locally, (d) monitors and locally records (with a pre-event memory) when prescribed triggering thresholds are exceeded, and (e) broadcasts the data continuously to remote users via high-speed internet. The system employs software based on the general flowchart (as depicted in Fig. 2) developed to compute displacements and drift ratios in real-time from signals of accelerometers strategically deployed throughout a building (Çelebi, 2008). Thus, the objective of timely assessment of performance level and damage conditions of the

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Fig. 4. General schematic of data acquisition and transmittal for real-time seismic monitoring of a 23-story building in San Francisco, California.

building can be fulfilled. In addition, to facilitate studies while waiting for strong shaking events, data can also be recorded locally or remotely on demand. Displays of screen-shots of the actual software are provided in Fig. 5. This approach is now implemented in several other buildings in San Francisco. As seen in Fig. 5, the broadcast-streamed real-time acceleration data are acquired remotely using a “Client Software” configured to compute velocity, displacement and a selected number of drift ratios. In the two PC screen snapshots shown in Fig. 5, the client software is configured for 12 channels of streaming acceleration or velocity or displacement or drift ratio time series. In the left frame, each paired set of acceleration response streams is displayed with a different color. In that same frame, also shown is the amplitude spectra of acceleration for one of the channels selectable by the user. Several frequency peaks are clearly identifiable. In the lower left of the frame, the time series of drift ratios are shown for 6 locations, with each color corresponding to the same pair of data from the window above. In order to compute drift ratios, displacements are computed by realtime double integrations of filtered acceleration data. Filter options are built into the client software for processing of the acceleration data. To compute drift ratios, the story heights, as shown in the right frame of Fig. 5 need to be manually entered. This figure also shows the computed pairs of displacements that are used to compute the drift ratios. Corresponding to each drift ratio, there are 4 stages of colored indicators. When only the “green” color indicator is activated, the computed drift ratio is below the first of three specific thresholds. The thresholds of drift ratios for selected pairs of data must also be manually entered in the boxes. As drift ratios exceed the three designated thresholds, additional indicators are activated with a different color (Fig. 5, right). The drift ratios are calculated using data from pairs of accelerometer channels oriented in the same direction. The threshold drift ratios are computed and decided by structural engineers using structural information and are compatible with the performance-based theme, as illustrated in Fig. 4 (Figure C2-3 of

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Fig. 5. (Left) Screen snapshot of client software display showing acceleration streams and computed amplitude and response spectra. (Right) Screen snapshot of client software display showing 12-channel (six pairs with each pair a different color) displacements and corresponding six drift ratios (each corresponding to the same color displacement) streams. Also shown to the upper right are alarm systems corresponding to thresholds that must be manually input. The threshold for the first drift ratio is hypothetically exceeded to indicate the start of the recording and consequent change in the color of the alarm from green to yellow.

FEMA-274 (ATC 1997)) and summarized in Table 1 for this example building. Figure 5 (right) hypothetically shows that the first level of threshold is exceeded, and the client software is recording data as indicated by the illuminated red button (in the upper right). The monitoring and alert systems installed in buildings in San Francisco and vicinity are operational, but no significant earthquakes have yet occurred to test their performance. However, monitoring arrays in Abu Dhabi with a Matlab-based algorithm, using a similar process and alert systems, recorded data from earthquakes *800 km away in Iran (Kaya and Safak 2014), and one tall building in Istanbul recorded data from an earthquake *200 km away in another part of Turkey. Based on low-shaking records obtained by systems in San Francisco, Abu Dhabi and Istanbul, we expect the systems to perform successfully during stronger shaking. In Japan, similar systems (using accelerometers and computing drift ratios) recorded data with average drift ratio of *0.5% (Hisada et al. 2012).

5 Summary In this paper, real-time seismic structural monitoring is discussed to highlight potentialities and differences with respect to the older method of “any-time” monitoring. The need to provide prompt information about the structural performance and to support immediate decisions related to the functionality and occupiability of a building requires not only the quick estimation of a ‘damage indicator’ – enabled today by advanced computation, communication and data transmission capabilities – but also the rapid assessment of the structural performance. This prompts for a choice of damage indicators that can be directly measured or computed through signal processing of measured quantities and for which thresholds corresponding to different performance levels

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can be defined. In this perspective, interstory drift is a promising choice, already implemented in several applications of real-time S2HM that have been described in the paper.

References Applied Technology Council (ATC): NEHRP Commentary on the Guidelines for the Seismic Rehabilitation of Buildings, prepared for the Building Seismic Safety Council, published by the Federal Emergency Management Agency, FEMA 274, Washington, D.C (1997) Applied Technology Council (ATC), 1989. Procedures for Post-Earthquake Safety Evaluation of Buildings, ATC-20, Redwood City, CA. Bath, M., 1965, Lateral inhomogeneities of the upper mantle, Tectonophysics - Elsevier Publishing Company, Amsterdam Printed in The Netherlands Tectonophysics, 2(6), pp. 483–514 (1965) Building Occupancy Resumption Program: City and County of San Francisco, Department of Building Inspection, Emergency Operation Plan (Rev. 2001) (2001). www.seaonc.org/ member/committees/des_build.html Çelebi, M., Sanli, A., Sinclair, M., Gallant, S., Radulescu, D.: Real-time seismic monitoring needs of a building owner and the solution – a cooperative effort. J. EERI Earthq. Spectra 20 (2), 333–346 (2004) Çelebi, M.: Health monitoring of buildings using threshold drift ratios – now an established method. In: International Conference on Structural Health Monitoring, Vancouver, B.C., Canada (2007a) Celebi, M.: Real-time strong shaking health monitoring of buildings using drift ratios. Abstract for SSA Annual Meeting, Kona, Hawaii, 10–14 April 2007 (2007b) Çelebi, M.: Real-time monitoring of drift for occupancy resumption. In: Proceedings of 14WCEE (CD-ROM), Beijing, China, 13–17 October 2008 (2008) Çelebi, M., Sanli, A.: GPS in pioneering dynamic monitoring of long-period structures. Earthq. Spectra 18(1), 47–61 (2002) FEMA-352: Recommended post-earthquake evaluation and repair criteria for welded steel moment framed buildings (also SAC joint Venture 2000 prepared for FEMA), Washington D. C Hardebeck, J.A., Llenos, A.L., Michael, A.J., Page, M.T., Van der Elst, N.: Updated California aftershock parameters. Seismol. Res. Lett. (SRL early edition) (2018). https://doi.org/10. 1785/0220180240 Hisada, Y., Yamashita, T., Murakami, M., Kubo, T., Shindo, J., Aizawa, K., Arata, T.: Seismic response and damage of high-rise buildings in Tokyo, Japan, during the Great East Japan earthquake. In: Proceedings of the International Symposium on Engineering Lessons Learned from the 2011 Great East Japan Earthquake, 1–4 March 2012, Tokyo, Japan, paper 198, pp. 1110–1119 (2012) Kaya, Y., Safak, E.: Real-time analysis and interpretation of continuous data from structural health monitoring (SHM) systems. Bull. Earthq. Eng. 13(3), 917–934 (2014). https://doi.org/ 10.1007/s10518-014-9642-9 Kubo, T., Hisada, Y., Murakami, M., Kosuge, F., Hamano, K.: Application of an earthquake early warning system and a real-time strong motion monitoring system in emergency response in a high-rise building. Soil Dyn. Earthq. Eng. 31, 231–239 (2011) Limongelli, M.P.: Damage localization through vibration based S2HM: a survey. In: Limongelli, M.P., Çelebi, M. (eds.) Seismic Structural Health Monitoring. Springer Tracts in Civil Engineering, Springer (2019). https://doi.org/10.1007/978-3-030-13976-6_9

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Reasenberg, P.A., Jones, L.M.: Earthquake hazard after a mainshock in California. Science 243 (4895), 1173–1176 (1989) Safak, E., Kaya,Y., Skolnik, D., Ciudad-Real, M., Al Mulla, H., Megahed, A.: Recorded response of a tall building in Abu Dhabi from a distant large earthquake. In: Paper 483, Proceedings of 10NCEE, Anchorage, AK, July 2014 Skolnik, D.A., Ciudad-Real, M., Sinclair, M., Graf, T., Swanson, D., Goings, C., Megahed, A., Almarri, A., Kaya, Y., Safak, E.: Enhanced rapid post-event assessment of buildings. In: Proceedings of the 10th National Conference in Earthquake Engineering, paper no. 50. Earthquake Engineering Research Institute, Anchorage (2014)

Structural Behavior Characterization of the Gravina Bridge (Matera, Southern Italy) Vincenzo Serlenga1(&), Maria Rosaria Gallipoli1, Rocco Ditommaso2,3, Carlo Felice Ponzo2, Nicola Tragni1,2, Tony Alfredo Stabile1, Angela Perrone1, Giuseppe Calamita1, Luigi Vignola4, Domenico Pietrapertosa5, and Raffaele Franco Carso5 1

5

IMAA, Italian National Research Council, Tito Scalo, PZ, Italy [email protected] 2 School of Engineering, University of Basilicata, Potenza, Italy 3 SISIA S.r.l., Venosa, PZ, Italy 4 Mallet S.r.l., Villa d’Agri, PZ, Italy ANAS Spa Area Compartimentale Basilicata, Via N. Sauro, Potenza, Italy

Abstract. We applied an integrated, non-invasive and non-destructive seismic and electromagnetic sensing for understanding the static and dynamic properties of the Gravina bridge and its interaction with foundation soils. The ‘Gravina’ is a bow-string bridge located in the city of Matera (Southern Italy) that extends for 144 m along a steel-concrete deck. For foundation soils characterization we executed 3 high-resolution geoelectrical tomographies, 1 bi-dimensional seismic array and two single station seismic noise measurements. The main structural characteristics of the bridge were evaluated through seismic and electromagnetic sensing. The seismic sensing was carried out with four accelerometers and twelve velocimeters (standard and low cost sensors) installed with different geometrical arrangement for real-time and on-demand ambient noise recordings, vibration tests and earthquake recordings. The electromagnetic data have been collected by placing the IBIS-S system below the deck of the bridge. Acquired data have been analyzed in frequency domain with the aim to evaluate the eigenfrequencies and equivalent viscous damping factors. Keywords: Gravina bridge


Geophysical approach
Eigenfrequency

1 Introduction In this study we aim to the global characterization of the Gravina bridge located in Matera (Southern Italy) and of its interaction with foundation soils by using noninvasive and non-destructive seismic and electromagnetic techniques with standard and low-cost sensors [1–4]. First, the structural behavior of Gravina bridge has been evaluated through seismic sensing: in particular, we used four accelerometers for Permanent Real-Time Earthquake Monitoring and twelve velocimeters installed with different geometrical arrangements for On-Demand Ambient Vibration Tests [5–7]. In order to perform the electromagnetic sensing, we have used the Ground-Based © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 23–31, 2021. https://doi.org/10.1007/978-3-030-64594-6_3

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Microwave Radar Interferometry. The IBIS-S system (Image by Interferometric Survey of Structures) has been adopted, since it is well known to be a remote sensing instrument for engineering purposes. The integration of several techniques allowed us to estimate the eigenfrequencies and the related equivalent viscous damping factors. The subsoil characterization has been achieved by performing 3 high-resolution geo-electrical tomographies, one bi-dimensional seismic array and two seismic noise single station measurements. 1.1

Description of Gravina Bridge

The “Gravina” is a recently built bow-string bridge in Matera (Southern Italy), positioned at the km 136 + 675 of the Strada Statale 655 “Bradanica”. This infrastructure, which was completed in the spring of 2015, is located in correspondence of the outcropping lithological units of Bradanic Through, but it directly insists on Calcarenites of Gravina Fm (upper Pliocene - lower Pleistocene) [8, 9]. From a geomorphological point of view, the area is typical as a canyon about twenty meters deep. The bridge structure is made of a pair of slanted steel tubular arches with a diameter of 1.7 m, connected each other by means of nine circular beams. The deck consists of a composite steel-concrete section, which extends for 144 m and having a total width of 18.80 m. The suspension system has been realized according to Langer method and consists of 19 pairs of stay-cables, with fixed end on the arch side and adjustable on the deck end. The structure is externally isostatic and the restraint system is attained with 8 elastomeric isolators: four of them are located underneath the arch bases, whereas the remaining ones are placed underneath the main girders (Fig. 1).

2 Foundation Soils: Geophysical Characterization The stratigraphy nearby the bridge is described by the succession, from the bottom to the top, of Cretaceous Limestones Fm, Calcarenites of Gravina Fm (upper Pliocene lower Pleistocene) and Argille Subappennine Fm (Pleistocene). The resistivity tomography carried out on the terrain SW of the infrastructure has confirmed the presence of a very thin conductive body, associated with Argille Subappenine Fm. This conductive feature completely disappears moving toward the bridge foundations, which lay on the solid calcarenitic layer. These observations are also confirmed by seismic noise measurements performed in the western portion of the ERT profile: indeed, HVSR analysis provides a spectral curve with a strong amplitude peak at about 21 Hz, which is strictly related to the very thin layer of clayey-silt deposits. In addition, the S-wave velocities retrieved from inversion of dispersion curves obtained by means of ESAC analysis are consistent with the geological and geophysical observations already described, highlighting a first geological contrast after 3 m, representing the transition between clay deposits and calcarenites. It is worth noting that the information coming from ESAC analysis is only an average representation of the subsoil properties over a length of about 300 m west of the infrastructure. On the other hand, the resistivity profile describing the subsoil properties of the soil east of the bridge depicts a

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Fig. 1. a) Geographical position of the bridge. From the left to the right: the location of the bridge in the industrial area of Matera: the small black rectangle, enlarged in figure b), shows the exact position of the bridge; cartographic map of Matera; map of Basilicata Region: the thin black line identifies the position of Matera in Basilicata Region; map of Italy, with Basilicata Region depicted in pink. b) Picture of the Gravina Bridge. c) Scheme of the geophysical acquisitions executed for characterizing the structural behavior of the bridge. The triangles represent the velocimetric sensors of the SARA array, arranged in different geometrical configurations, both for Ambient Vibration Test in Operational Conditions (AVT-OC) and for Ambient Vibration Tests by using Special Vehicles (AVT - SV). The different colors of the triangles represent different acquisitions and geometrical configurations: red = configuration 1a; green = configuration 2a; orange = configuration 3a. The grey filled circles identify the position of the Sentinel – Lunitek array, adopted for AVT – SV (configuration 4a). The magenta diamonds represent the accelerometers installed in different positions of the bridge for Permanent Real-time Earthquake Monitoring (PREaMo). The positions of the IBIS-S system (IBIS-S) adopted for Ground Based microwave Radar Interfeometry (GBRI) are indicated through the small picture of the system below the deck. d) Some pictures of the geophysical instrumentations adopted in this study and installed in different points of the structure. From the top to the bottom: accelerometric station on the top of the tubular arch and velocimeter close to the seismic isolator placed underneath the main girder of the bridge.

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high-resistivity layer just in correspondence of the bridge foundation, corresponding to the Calcarenites of Gravina Fm.

3 Structural Health Monitoring To retrieve the structural dynamic parameters (eigenfrequencies and related equivalent viscous damping factors) of the Gravina bridge two kinds of monitoring have been performed: seismic and electromagnetic sensing. As regard to the seismic acquisition, we executed two types of monitoring: Permanent Real-Time Earthquake Monitoring and On-Demand Ambient Vibration Tests. Figure 1b shows the position of the seismic equipment installed. Four accelerometric stations (SARA-Electronic Instruments SL06-SA10 with 1 g full scale) have been used for the Permanent Real-Time Earthquake Monitoring. Three sensors were positioned at the quarter and at the mid-point of the deck (L/4 and L/2, respectively, with L representing the whole length of the infrastructure) and on the top of the arch. As reference station, an accelerometer was located close to the elastomeric isolator placed under the girder. 19 seismic events were collected in the period June 2019 – October 2019; their epicentral distances range from 5 km to 950 km whereas their magnitudes range in the interval Ml = 1–5. The highest acceleration was recorded on the longitudinal component of the reference station (about 1.1  10−2 g). Being very noisy, the accelerometric station installed on the top of the arch was not used for our analyses. Twelve velocimeters, with a corner frequency of 4.5 Hz (five SARA-Electronic Instruments SR04DA sensors, and seven Lunitek – Sentinel GEO) were used with different equipment arrangement for the On-Demand Ambient Vibration Tests [10, 11] in order to acquire high quality ambient vibration signals both in operational conditions (AVT-OC) and by using special vehicles as exciting sources (AVT-SV). These ondemand tests were carried out on the 12th of July 2019, on the 29th of October 2019, and on 22 January 2020. The seismic sensors were placed according to the following configurations: configuration 1a) The adopted sensors are SARA velocimeters: three of them were located on the deck, at L/2, L/4 and at the support (0L, hereinafter), respectively. The fourth node of the array was placed close to the elastomeric isolator, and the last one on the soil, as reference station, east of the infrastructure, on the Matera side (Fig. 1c red triangles). This configuration was adopted both for AVT – OC and AVT – SV; configuration 2a) The used sensors are SARA velocimeters, located along the deck: at 0L (the support of the bridge, on Matera side), L/4, L/2, L3/4 and L positions (Fig. 1c green triangles). We have adopted this configuration only for AVT – OC without any reference station; configuration 3a) Two velocimeters were placed inside the arch (at L/2 and L/4), one velocimeter was placed directly outside the arch, above the isolator underlying the arch basis. The last velocimeter was located on the soil, as reference station (Fig. 1c orange triangles). The sensors belong to SARA array and were adopted for AVT – OC;

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configuration 4a) The seven sensors of Lunitek array were placed in the following way: five velocimeters were located inside the arch, at the ends, at the quarters (both L/4 and L3/4) and at the top of the structure (L/2). One velocimeter was positioned close to the elastomeric isolator, beneath the arch basis; the last one was placed on the soil, quite far from the bridge, as a reference station (Fig. 1c grey filled circles). This array was used only for the AVT-SV. Finally, the electromagnetic sensing was performed through IBIS-S, by configuring the sensor in order to measure targets up to a radial distance of 70 m. The system was placed in two configurations: 1b) the system was located in a longitudinal position with respect to the main extension of the deck: many points of the infrastructure were illuminated, from L/4 to L/2 (Fig. 1c); 2b) the sensor was positioned below the middle point of the deck, pointing toward the target with an angle of 90° with respect to the horizontal plane (Fig. 1c). 3.1

Parameters Estimation: Methods

This monitoring has allowed us to collect different kind of data to estimate the modal parameters, i.e., eigenfrequencies and the related equivalent viscous damping factors. The eigenfrequencies have been estimated by using Operational Modal Analysis (OMA) [12], the Standard Spectral Ratio techniques (SSR) [10, 11, 13] and Microwave Radar Interferometry. On the other hand, we retrieved the equivalent viscous damping factor by adopting the Logarithm Decrement Method (LDM) [14] applied to the Impulse Response Function (IRF) [15]. 3.2

Parameters Estimation: Results

Operational Modal Analysis has been applied on AVT-OC data recorded on 12 July, 2019 providing an overall characterization of the dynamic behaviour of the bridge. Indeed, the analysis of data recorded by velocimetric sensors deployed along the whole extension of the infrastructure (configuration 2a) allowed us to retrieve the first 6 eigenfrequencies of the Gravina Bridge. In detail, OMA technique identified the 1st mode at 0.76 Hz on the vertical component, the 2nd at 0.97 Hz on the transversal component, the 3rd vibration mode at 1.35 Hz on the vertical component, the 4th mode on the transversal component at 1.50 Hz, the 5th one on the vertical component at 2.0 Hz and, finally, the 6th mode at 2.2 Hz on the transverse component. We have monitored in more details the dynamic properties of the Gravina Bridge in five different points of the Gravina bridge: three on the deck (0L, L/4 and L/2) and two on the arch (L/4 and L/2). In each point the SSR and LDM analyses have been performed using both AVT-OC and AVT-SV signals. Where an accelerometric station was also installed (L/4 deck and L/2 deck) the SSR analyses have been carried out on earthquake data as well. Figure 2a–b report the main results of SSR analyses carried out on data recorded at the L/4 point of the deck: - the 1st mode is at 0.75 Hz, both on the longitudinal and vertical component; - the amplitude of the 1st mode peak is higher on the vertical than on the longitudinal one; - the 2nd and the 4th vibration modes, at 0.97 Hz and 1.50 Hz

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respectively, are clearly visible on the transverse component; - there is a very good agreement between results obtained from the analyses of different data. The large standard deviations of SSR functions estimated on earthquake data (Fig. 2a) are due to a large variability of energy content, location and magnitudes of selected seismic events. We have compared the eigenfrequencies estimated through seismic sensing with electromagnetic ones provided by Microwave Radar Interferometry. Figure 3 shows the result obtained from the analyses of data collected during AVT – SV in configuration 2b: in particular, from the displacement spectrum, the peak at 1.35 Hz is clearly visible, corresponding to the 3rd mode of vibration. As in configuration 2b the system is placed right below the deck, only the vertical component of the bridge motion has been detected and, therefore, only a vertical mode of vibration could be highlighted. In particular, the 1st eigenfrequency is not visible from this analysis because L/2 represents a node for the 1st vibration mode. As regard to the equivalent viscous damping factor, the LDM provided a damping factor equal to about 2–3% for all six eigenfrequencies; Fig. 2c–d show the LDM estimation on AVT-OC data related to the position L/2 on the deck.

Fig. 2. Standard Spectral ratios computed at L/4 point, on the deck of the bridge and damping factor estimated at L/2, on the vertical component. a) Results retrieved from the analysis of earthquake data. b) Results retrieved by using both AVT–OC and AVT–SV data. The vertical dashed lines highlight the main frequency peaks retrieved by means of SSR analyses, at 0.75 Hz, 0.97 Hz and 1.5 Hz. c) Application of the LDM to the IRF evaluated from AVT-OC signal recorded at L/2. d) Fourier transform of the IRF, showing the peak at 1.35 Hz (vertical blue dashed line), representing the 3rd mode of vibration.

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Fig. 3. Displacement amplitude spectrum of the signal recorded by IBIS-S, placed below the point at L/2, during AVT – SV.

4 Discussion and Conclusion The Table 1 is a synthesis of the main results obtained by applying seismic and electromagnetic techniques on several kinds of data acquired in different time periods on the Gravina Bridge. For sake of completeness, also estimations not shown in the previous paragraph are presented. Table 1. Dynamic characteristics of the Gravina Bridge estimated by OMA, SSR, MRI and LDM analyses. Eigenfreq. SSR 0.76 Hz 0.97 Hz 1.35 Hz 1.5 Hz

1st mode 2nd mode 3rd mode 4th mode 5th mode 6th mode 2.1 Hz

Eigenfreq. OMA 0.76 Hz 0.97 Hz 1.3 Hz 1.5 Hz 2.0 Hz 2.2 Hz

Eigenfreq. MRI 0.75 Hz 1.35 Hz

Damping LDMIRF 2–3% 2–3% 2–3% 2–3% 2–3% 2–3%

Direction of motion VERTICAL TRANSVERSE VERTICAL TRANSVERSE VERTICAL TRANSVERSE

It is evident that different methods provided consistent results in the estimation of all eigenfrequencies of the Gravina Bridge. A maximum difference of 0.1 Hz has been retrieved in the obtained values of principal modes of vibration due to little differences of data analysis. The SSR method does not identify the 5th eigenfrequency, detected by OMA at about 2.0 Hz. Furthermore, electromagnetic sensing by means of IBIS-S system was able only to highlight the 1st and the 3rd mode of vibration, because of the intrinsic limits of the techniques: indeed, it could detect only displacements along the sensor-target line of sight and, therefore, it cannot observe displacements in the

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transverse component. As regard to damping factors, estimated percentages are consistent with the ones that usually characterize steel structural systems. Summing up, in this study we have provided a static and dynamic characterization of the Gravina Bridge, a bow-string infrastructure located in Matera, Southern Italy. The structural behaviour of the Gravina Bridge was performed by means of seismic and electromagnetic sensings: several kinds of data were collected and different methods were applied for retrieving the main frequencies of vibration of the bridge and the related equivalent viscous damping factors. The very good agreement between results obtained through different methods allows us to assert that our estimations on dynamic properties of the infrastructure are very reliable. According to the results of geophysical prospecting performed on foundation soil, the infrastructure lays on a solid layer of calcarenitic rocks, where no clear seismic amplification and resonance effects have been observed. We propose this integrated geophisical approach consisting of fast-executable and non-invasive techniques to obtain a reference point of the structural parameters of the Gravina Bridge in its early age; it is a very useful information for better monitoring the performances of the bridge and for setting up the most appropriate upkeep strategies.

References 1. Proto, M., Bavusi, M., Bernini, R., et al.: Transport infrastructure surveillance and monitoring by electromagnetic sensing: the ISTIMES project. Sensors 10(12), 10620–10639 (2010). https://doi.org/10.3390/s101210620. ISSN 1424-8220 2. Bavusi, M., Soldovieri, F., Di Napoli, R., et al.: Ground penetrating radar and microwave tomography 3D applications for the deck evaluation of the Musmeci bridge (Potenza, Italy). J. Geophys. Eng. 8(3), S33–S46 (2011). https://doi.org/10.1088/1742-2132/8/3/s04. ISSN 1742-2132 3. Stabile, T.A., Perrone, A., Gallipoli, M.R., et al.: Dynamic survey of the Musmeci Bridge by joint application of ground-based microwave radar interferometry and ambient noise standard spectral ratio techniques. IEEE Geosci. Remote Sens. Lett. 10(4), 870–874 (2013). https://doi.org/10.1109/LGRS.2012.2226428 4. Cuomo, V., Soldovieri, F., Ponzo, F.C., et al.: A holistic approach to long term SHM of transport infrastructures. Int. Emerg. Manage. Soc. Mem. Newsl. 33 (2018). ISSN 20331614 5. Green, M.F.: Modal test methods for bridges: a review. In: Proceedings of the 13th International Modal Analysis Conference, Nashville, Tennessee, 13–16 February 1995 (1995). ISBN 0912053488 6. Salawu, O.S., Williams, C.: Review of full-scale dynamic testing of bridge structures. Eng. Struct. 17(2), 113–121 (1995) 7. Farrar, C.R., Duffey, T.A., Cornwell, P.J. et al.: Excitation methods for bridge structures. In: Proceedings of the 17th International Modal Analysis Conference, Kissimmee, Florida, vol. 1, 1063–1068 (1997) 8. Tropeano, M., Sabato, L.: Response of Plio-Pleistocene mixed bioclastic-lithoclastic temperate-water carbonate systems to forced regressions: the Calcarenite di Gravina Formation, Puglia, SE Italy. In: Hunt, D., Gawthorpe, R.L. (eds.) Sedimentary Responses to Forced Regressions, London, Geological Society, vol. 172, pp. 217–243. Spec. Publ. (2000)

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9. Beneduce, P., Festa, V., Francioso, R., et al.: Conflicting drainage patterns in the Matera Horst Area, southern Italy. Phys. Chem. Earth. Part A/B/C 29(10), 717–724 (2004) 10. Parolai, S., Facke, A., Richwalski, S., et al.: Assessing the vibrational frequencies of the Holweide Hospital in the city of Cologne (Germany) by means of ambient seismic noise analysis and FE modelling. Nat. Hazards 34, 217–230 (2005) 11. Gallipoli, M.R., Mucciarelli, M., Vona, M.: Empirical estimate of fundamental frequencies and damping for italian buildings. Earth. Eng. Struct. Dyn. 38, 973–988 (2009) 12. Shipfors, M., Fabbrocino, G.: Operational Modal Analysis of Civil Engineering Structures. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0767-0. ebook ISBN 9781-4939-0767-0 13. Borcherdt, R.D.: Effects of local geology on ground motion near San Francisco bay. Bull. Seism. Soc. Am. 60, 29–61 (1970) 14. Clough, R.W., Penzien, J.: Dynamics of Structures, 2nd edn., p. 738. McGraw-Hill, New York (1993). https://doi.org/10.1002/eqe.4290240311. ISBN 0-07-011394-7 15. Snieder, R., Safak, E.: Extracting the building response using seismic interferometry: theory and application to the Millikan library in Pasadena. California. Bull. Seis. Soc. Am. 96(2), 586–598 (2006)

Uncertainty Analysis of Damage Identification Results Based on Finite Element Model Updating Erkan Durmazgezer1, Umut Yucel2(&), and Ozgur Ozcelik2 1

Department of Construction Technology, Izmir Kavram Vocational School, Izmir, Turkey [email protected] 2 Department of Civil Engineering, Dokuz Eylul University, Izmir, Turkey [email protected]

Abstract. This paper aims to investigate uncertainties in damage identification results due to errors in modal parameter estimation results. Structural damage is simulated as regional stiffness loss at the column(s) and beam ends of a numerical frame type structure. In the damage identification stage, the first 4 modal parameters are used. Two different levels of noise are added to them to simulate uncertainty in modal parameter estimations. Noise levels are controlled by the coefficient of variation (C.O.V.). In order to quantify the uncertainty of the identified damage due to the variability of modal parameters, a full factorial analysis of variance (ANOVA), resulting in 16 combinations of input factors, is used. For each combination of input factors, 20 noise realizations are generated using Gaussian normal distributions with standard deviations scaled to the level of modal parameters. The results are presented in two formats: (1) Spread of the identified damage factors for all 320 identification runs with their statistical measures and (2) R2 values of the mean and standard deviation of the identified damage factors due to the variability of each input factor. The results of this investigation demonstrate that specific modal parameters have only influence on specific damage factors. Keywords: Uncertainty analysis
ANOVA
Damage identification element model updating
Reinforced concrete frames


Finite

1 Introduction In engineering applications, the use of finite element (FE) models is very common in the prediction of the structural system behavior and the design stage. In the civil engineering community, FE models are indispensable tools in the determination of the internal forces and displacements of structures in various structural limit states. Besides, FE models are frequently used in predictive analysis of responses of structures subjected to dynamic effects such as wind, traffic, and earthquake. These models can be used to design the entire structure or any of the components; however, one needs to make assumptions regarding material characteristics, geometrical properties, loading conditions, and boundary conditions (a.k.a. model parameters) which may result in © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 32–40, 2021. https://doi.org/10.1007/978-3-030-64594-6_4

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accuracy problems. Despite the recent advances in the structural modeling field, an initial FE model is often a poor representation of the real structure due to simplifying assumptions. The model updating method can be used to amend the aforementioned problems. The method is based on the calibration of uncertain model parameters in the numerical (FE) model to accommodate the actual, measurable behavior of the system. In the context of structural mechanics, data measured or extracted from the vibration tests (e.g., acceleration records, frequency response functions, modal parameters, etc.) provides real and detailed information about the operational state of the structure of interest; therefore, these parameters can be used in model calibration and damage assessment applications using finite element model updating method [1–4]. These methods are mainly based on adjusting the set of pre-selected physical (model) parameters of a FE model by minimizing an objective function created by the differences of numerical and experimental data. The minimization process is generally performed by the implementation of iterative optimization techniques [5]. Since model updating methods are aiming to determine the optimal FE model parameters that produce a particular output, they are classified in the branch of inverse engineering. However, the contamination of measurement errors on the data can adversely affect the existence or stability of the solution for the optimization problem; therefore, it is necessary to handle the uncertainties due to modeling and measurement errors in the model updating applications appropriately [6, 7]. In this study, the damage identification results of a single-story, single-bay reinforced concrete (RC) frame are obtained by finite element model updating and the uncertainty in damage identification results is quantified through full factorial analysis of variance (ANOVA) technique. Accordingly, the simulated damage, which is defined as the loss of regional stiffness at the column(s) and beam ends (i.e., substructures), is obtained (verified) by finite element model updating method, and uncertainty bounds of the damage identification results are investigated by ANOVA. The damage identification method used in this study is based on the iterative minimization of an objective function created by using the differences of modal parameters belong to undamaged and damaged (numerically simulated) FE models. This process is performed by updating the finite element stiffness values of the model representing the undamaged condition. Using the stiffness values before and after the model updating, the stiffness reductions (i.e., damage factors) of the elements are calculated, so the location and the extent of the simulated damage are verified. The damage assessment study is carried out using the first 4 structural modes of the frame model. This study investigates the uncertainties in damage identification results due to the estimation (measurement) errors in modal parameters. In this context, two levels of uncertainty, defined by 0.5% and 1.0% coefficient of variation (C.O.V.), are considered for natural frequencies and mode shape components of these 4 modes. The results are presented in two formats: (1) Statistical evaluation of the damage factors determined for 320 identification runs and (2) comparison of the R2 values of the mean and standard deviation of the damage factors for each considered substructure. As a result of the presented research, noise effects in certain modes are shown to be effective only on damage factors defined in specific substructures.

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2 FE Model, Damage Scenario, and Problem Definition In Table 1, the FE model of the frame and modal frequencies for both the undamaged and damaged (simulated) cases are presented. Three design variables (stiffness reduction factors assigned to substructures) are used in updating the undamaged FE model, namely for beam ends, column(s) bottom ends and column(s) top ends (Fig. 1). Here, damage is simulated as a reduction in bending rigidity (EI) for column(s) bottom ends, column(s) top ends and beam ends with the rate of 30%, 40%, and 50%, respectively. The support conditions of the undamaged frame model are represented by simple supports complemented with rotational spring elements assigned in X, Y, and Z directions (i.e., Rot-X = 5894 kNm/rad, Rot-Y = 7444 kNm/rad, and Rot-Z = 20000 kNm/rad). Note that the undamaged FE model is obtained by adjusting the rotational spring coefficients considering the experimental modal data of the laboratory tested undamaged frame. However, for the sake of brevity, the details are not presented here; but more information about the experiments and model calibration work can be found in the author’s previous study [8]. The model updating algorithm is programmed in Matlab - based FEDEASLab [9] environment. Young’s modulus of 32 GPa and a density of 25 kN/m3 are used for the RC members. As shown in Table 1, the FE model of the frame has 17 free nodes; therefore, each mode shape has 17  3 = 51 translational DOFs. In practice, the main source of the uncertainty in damage identification arises from modal parameter estimation errors (due to an insufficient number of sensors, improper sensor placement, measurement errors, etc.). However, several other factors can be found as possible candidates, such as the type of finite elements used in the modeling stage, mesh size, residual types (i.e., types of components used in the objective function), residual weights (i.e., coefficients applied on the residuals), number of design variables. This study investigates the uncertainties in damage identification due to estimation errors in modal parameters. Here, two levels of uncertainty, 0.5% and 1.0% coefficient of variation (C.O.V.), are considered for natural frequencies and mode shape components for the first 4 modes. 20 different noise realizations are generated using Gaussian distributions with standard deviations scaled to the level of modal parameters for the considered C.O.V. Across the realizations, the generated random errors assigned to modes are statistically independent. The noise realization vector for each structural mode contains 2 noise levels  (1 frequency + 51 mode shape components) = 104 components. Table 2 summarizes the input factors and their uncertainty levels. Full factorial ANOVA is applied in order to quantify the uncertainty effects of 24 = 16 (2 is the number of uncertainty levels and 4 is the number of considered modes) combinations of input factors on the updated design variables (stiffness parameters). In this context, 16 input combinations  20 noise realizations = 320 identification runs are performed.

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Table 1. FE model, modal parameters of the undamaged & damaged RC frame .

Support Conditions: Simple Support + Rotational Springs in 3 Directions Rotational Spring Properties Rot-X=5894 kNm/rad, Rot-Y=7444 kNm/rad, Rot-Z=20000 kNm/rad Undamaged FE Model Frequencies Out-of-Plane Mode Torsional Mode Lateral In-Plane Mode Vertical In-Plane Mode

Mode #1) Mode #2) Mode #3) Mode #4)

5.58 Hz 9.40 Hz 14.98 Hz 65.90 Hz

Damaged FE Model Frequencies Out-of-Plane Mode Torsional Mode Lateral In-Plane Mode Vertical In-Plane Mode

Mode #1) Mode #2) Mode #3) Mode #4)

5.14 Hz 8.31 Hz 12.63 Hz 57.68 Hz

Design Variables: Beam ends Column(s) bottom ends Column(s) top ends

Fig. 1. Design variables (stiffness reduction factors) assigned on the FE model substructures for model updating study.

36

E. Durmazgezer et al. Table 2. Description of the input factors and their uncertainty levels.

Input factor Description M1 Uncertainty in modal parameters M2 Uncertainty in modal parameters M3 Uncertainty in modal parameters M4 Uncertainty in modal parameters

of of of of

1st mode 2nd mode 3rd mode 4th mode

Uncertainty levels 2 levels (0.5%, 1.0% 2 levels (0.5%, 1.0% 2 levels (0.5%, 1.0% 2 levels (0.5%, 1.0%

C.O.V.) C.O.V.) C.O.V.) C.O.V.)

3 Uncertainty Quantification Study of the Damage Identification Results As stated in Sect. 2, 320 identification runs are performed using the model updating algorithm. The set of the identified stiffness parameters (i.e. damages) are later then used in ANOVA in order to investigate which input factor contributes more to the uncertainty in damage identification results. Figure 2 shows the spread of the damage identification results of all substructures for all 320 identification runs. Note that, design variables (damage factors) are obtained by modal updating algorithm using 4 input factors (M1, M2, M3, M4), namely the first 4 structural modes polluted with two levels of noise.

Fig. 2. The spread of the identified damage factors at column(s) bottom ends, column(s) top ends, and beam ends for all 320 identification runs (20 realizations).

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37

Figure 3 shows the distributions of the identified damage factors (design variables) for each substructure. On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles (lower and upper quartiles), respectively. The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the ‘+ ’ symbol. Table 3 also reports the mean and standard deviation (STD) of the 320 sets of identified damage factors for each substructure. Accordingly, the mean values of the damage factors are 30.6%, 39.9%, and 50.5%; whereas STD values are 2.14%, 0.94%, 1.11% for column(s) bottom ends, column(s) top ends and beam ends, respectively. The large standard deviation in the identified damage factors shows that the residuals used in the optimization problem are less sensitive to the design variables that represent these substructures.

Identified Damage Factor

0.5 0.45 0.4 0.35 0.3 0.25 Column(s) Bottom Ends Column(s) Top Ends

Beam Ends

Fig. 3. Box plot representation of the identified damage factors at each substructure for 320 identification runs.

Table 3. Mean and standard deviation (STD) of the identified damage factors of each substructure for 320 identification runs Substructures Beam End(s) Column(s) Top Ends Column(s) Bottom Ends

Exact damage [%] Mean [%] STD [%] 50 50.5 1.11 40 39.9 0.94 30 30.6 2.14

Figure 4 shows the spread of mean and STD of the identified damage factors at different substructures for all 16 combinations of input factors. Herein, each circle represents the results (mean and STD) of the 20 noise realizations. However, from the results presented in Fig. 4, it is not possible to quantify the contributions of input factors M1, M2, M3, M4 to the total uncertainty of the mean and STD of the identified

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damage factors; therefore ANOVA is used for the uncertainty quantification. ANOVA helps the analyst to determine the effects of the independent input variables (M1, M2, M3, and M4) on dependent variables (damage factors) in a regression study. Accordingly, the total variances of the output features (damage factors) are decomposed into the sum of partial variances, which result in different values for each independent input variable. In this technique, the input factor contribution to the uncertainty is determined by partial variances estimated from R2 value. The input factor with a larger R2 value for an output feature means a higher contribution to the uncertainty. The R2 results of mean and STD of the identified damage factors (obtained by 16 combinations based on 20 noise realizations) for all substructures are presented in Fig. 5. Results are obtained by the aid of Matlab [10] command ‘anovan’.

Fig. 4. The spread of the mean and STD of identified damage factors at different substructures based on 16 combinations of input factors calculated from 20 noise realizations.

In Fig. 5, R2 values are normalized such that their sum over all factors equates to 100. Accordingly, M1 (uncertainty in modal parameters of the out-of-plane mode) results in a significant amount of uncertainty on the mean and STD values of the damage factor for the column(s) bottom ends; whereas M2, M3, and M4 (uncertainties

Uncertainty Analysis of Damage Identification Results

39

in modal parameters of the higher modes) cause less uncertainty on that damage factor. It can also be concluded that M2 (uncertainty in modal parameters of the torsional mode) has almost no influence on the uncertainties of the mean and STD for all damage factors. In general, M3 (uncertainty in modal parameters of the lateral in-plane mode) results in a significant amount of uncertainty for damage factors located on the column (s) top ends and beam ends. M4 (uncertainty in modal parameters of the vertical inplane mode) has almost no influence on the uncertainties of the column(s) bottom ends and the beam ends in the sense of mean and STD.

Fig. 5. R2 values of the mean and STD of identified damage factors at different substructures due to the variability of input factors M1, M2, M3, and M4 (based on 16 combinations of input factors calculated from 20 noise realizations).

4 Conclusions In SHM, the estimation errors on modal parameters are found to be influential on damage identification results; this motivated the authors to conduct uncertainty analysis. The damaged frame structure is simulated by applying stiffness reductions in three locations, such as beam ends, column(s) top ends, and column(s) bottom ends.

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Accordingly, the first 4 modal parameters of the damaged frame model used in model updating algorithm are polluted with two different levels of noise, namely with 0.5% and 1.0% C.O.V. This allows generating 16 different combinations of input factors; therefore, each different combination of modal parameters is used to identify the simulated damage in these substructures. For each combination of the 4 modal parameters polluted with 2 different noise characteristics, 20 identification runs are performed with statistically independent realizations, which results in totally 16  20 = 320 identification runs. Results are quantified through the analysis of variance (ANOVA) and the following observations are made: (1) Compared to M2, M3, and M4, uncertainty in modal parameters of the out-of-plane mode (M1) results in a significant amount of uncertainty on the mean and STD values of the damage factor for the column(s) bottom ends; whereas M2, M3, and M4 (uncertainties in modal parameters of the higher modes) cause less uncertainty on that damage factor. (2) Uncertainty in modal parameters of the torsional mode (M2) has almost no influence on the uncertainties of the mean and STD for all damage factors. (3) Uncertainties in lateral in-plane mode (M3) mainly influence both the mean and standard deviation of the identified damage for column top end(s) and beam end(s). (4) Uncertainty in vertical in-plane mode (M4) is only found to be effective on the STD value of the damage factor for the column(s) top ends.

References 1. Teughels, A., De Roeck, G.: Structural damage identification of the highway bridge Z24 by FE model updating. J. Sound Vib. 278(3), 589–610 (2004) 2. Friswell, M., Mottershead, J.E.: Finite Element Model Updating in Structural Dynamics. Kluwer Academic Publishers, Dordrecht (1995) 3. Nozari, A., Behmanesh, I., Yousefianmoghadam, S., Moaveni, B., Stavridis, A.: Effects of variability in ambient vibration data on model updating and damage identification of a 10story building. Eng. Struct. 151, 540–553 (2017) 4. En Fang, S., Perera, R., De Roeck, G.: Damage identification of a reinforced concrete frame by finite element model updating using damage parameterization. J. Sound Vib. 313, 544– 559 (2008) 5. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (1999) 6. Simoen, E., De Roeck, G., Lombaert, G.: Dealing with uncertainty in model updating for damage assessment: a review. Mech. Syst. Signal Process. 56, 123–149 (2015) 7. Moaveni, B., Conte, J.P., Hemez, F.M.: Uncertainty and sensitivity analysis of damage identification results obtained using finite element model updating. Comput.-Aided Civ. Infrastruct. Eng. 24(5), 320–334 (2009) 8. Durmazgezer, E., Yucel, U., Ozcelik, O.: Damage identification of a reinforced concrete frame at increasing damage levels by sensitivity-based finite element model updating. Bull. Earthq. Eng. 17(11), 6041–6060 (2019). https://doi.org/10.1007/s10518-019-00690-5 9. FEDEASLab getting started guide and simulation examples. http://www.neesgrid.org/news/ documents.php. Accessed 31 Aug 2004 10. MATLAB: The Mathworks Inc., USA

Experience of Sonic Echo/Impulse Response Testing Difficulties in Timber Piles of Bridge Foundations Saman Rashidyan1(&), Tang-tat Ng2, and Arup Maji2 1

2

University of North Texas, Denton, TX 76207, USA [email protected] University of New Mexico, Albuquerque, NM 87131, USA {tang,amaji}@unm.edu

Abstract. Sonic Echo/Impulse response (SE/IR) is an economical nondestructive method to collect information pertaining to the unknown bridge foundations. Studies have shown that factors such as the pile-to-soil stiffness ratio, the length-to-diameter ratio of the pile, the presence of the defects, the anomalies near the pile head, the quality of the sensor attachment, the striking method, and the hammer type affect the success of the SE/IR tests. In the current study, numerous SE/IR tests were performed on three known and unknown bridge foundations and the superiority of the time domain analysis to the frequency domain analysis is concluded as an outcome helping engineers conduct SE/IR tests efficiently. In addition, more affecting factors and difficulties specific to timber foundations are identified and discussed. The results presented in this study shows that factors such as the foundation condition, the environmental conditions, and the improper pile-pile cap attachment can also be the sources of difficulties in determining the depth of timber piles. Keywords: Sonic Echo/Impulse response
Timber


Pile
Bridge foundation

1 Introduction Nondestructive Testing (NDT) methods have been developed and used in the past decades to assess the condition of civil infrastructure [1–5]. Among NDT methods, Sonic Echo/Impulse Response (SE/IR) is a versatile technique with a wide range of applications, including characterizing unknown bridge foundations. In Sonic Echo (SE) method, the longitudinal waves are created by striking the top of the pile using a hammer. Upon striking, a longitudinal wave with speed v is created along the pile. The generated wave travels down with velocity v and reaches to the bottom of the pile. At this time, due to the change in the mechanical impedance of the materials, a part of the wave energy will be transmitted through the interface to continue traveling in the soil (transmitted wave), and the remainder will be reflected at the interface toward the top of the pile. Using a sensor (accelerometer or geophone velocity transducer) coupled to the pile head, the time lapse, t, between the hammer impulse and the arrival of the reflected waves at the pile head from pile tip is measured. The distance traveled by the stress wave will be the product of time lapse t, and propagated wave © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 41–49, 2021. https://doi.org/10.1007/978-3-030-64594-6_5

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velocity v. This distance is twice of the pile length when the sensor is placed at the top of the pile. Finally, the length of the pile, L, can be calculated as: L¼

v  t 2

ð1Þ

where v is the propagated wave speed. The signals obtained from the sensors can also be further investigated by Impulse Response (IR) analysis to support measurements obtained from SE tests. In IR analysis, the force and velocity time history signals are converted into frequency domain using the Fast Fourier Transform. Mobility is then defined as the ratio between the converted frequency-base velocity and the frequency-base force. The result is commonly presented as a plot of mobility versus frequency. For the generated wave lengths greater than the diameter of a prismatic pile, there are resonant frequencies that depend on the pile length and the propagated wave velocity. The length of the pile can be estimated from the difference of successive resonant frequencies (Df) as: L¼

v 2  Df

ð2Þ

Previous studies have shown that factors such as the pile-to-soil stiffness ratio, length-to-diameter ratio of the pile, presence of defects and anomalies near the pile head, quality of sensor attachment, striking method, and hammer type are factors affecting the success of the SE/IR tests [1, 6–10]. In the current study, numerous SE/IR tests were performed on known and unknown wood bridge foundations to identify more affecting factors and challenges influencing the success of the SE/IR method. The results of this study can help engineers to consider such adverse factors and advise appropriate solutions. Although the study is performed on timber piles, some instructions may be helpful in foundations made of other construction materials.

2 Methodology To achieve the goals of this study, 131 SE/IR tests were carried out on 16 wood piles of three highway bridges located in New Mexico, USA. Fourteen piles were supporting bridge decks, whereas two were wing piles on which there were no superstructures. The utilized equipment consisted of a data analyzer, two 100mv/g accelerometers, and a hammer with force transducer. The propagated wave velocity was determined from the time lapse between two accelerometers mounted on the side of the piles. The measured velocities were in accordance with Wood Handbook [18]. The depths of the piles were determined using velocity and mobility graphs obtained from the accelerometers. The accuracy of the methodology was confirmed in one of the bridges for which the foundation information was available.

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3 Observations and Results

Amplitude (cm/sec) (10-6)

In the first step, the depths of the piles were calculated with SE analysis using time domain (velocity) signals. Afterwards, an additional IR frequency domain analysis was performed for each test using mobility graphs to obtain more accuracy in determining the depths of investigated piles. Figure 1 shows examples of good (interpretable) velocity and mobility signals. In Fig. 1a, assuming wave velocity of 4100 m/s the length can be calculated using Eq. (1) and based on the time difference between the identified impulse and echo as L = (5100 − 2120)/2  4100  10−6 = 6.11 m. The length of the pile pertaining to the resonant frequencies indicated on Fig. 1b Eq. (2) can be inferred as L = 4100/[2  (690 − 366)] = 6.32 m.

250

(a)

0

∆t

- 250 2.12 0 5.1

Amplitude (cm/sec)/N

Impulse

Echo

40 3

Time (μs) (10 ) ∆f

0.07

0

20

Resonant Frequencies 0

366 690 Frequency (Hz)

(b)

1000

Fig. 1. Examples of interpretable (a) velocity and (b) mobility signals

In contrast, in some tests, velocity signal with no clear impulse and echo, as well as mobility graphs without resonant frequencies were obtained. Examples of such bad signals are indicated in Fig. 2 (compare Fig. 2 to Fig. 1).

S. Rashidyan et al.

Amplitude (cm/sec)/lbf

Amplitude (cm/sec) (10-4)

44

12 (a)

0 -8 0

20 Time (μs) (103)

0.38

0

40 (b)

0

500 Frequency (Hz)

1000

Fig. 2. Examples of unclear (a) velocity and (b) mobility signals

The observations of our study showed that the depths of 13 out of 16 piles could be determined using SE/IR method. The results also showed that the SE tests could generally produce more successful results compared to IR analysis. Neither SE nor IR analysis were successful in determining the depths of three investigated piles. The challenges and difficulties pertaining to measuring the depths of those piles are discussed here. 3.1

Environmental Conditions

Environmental conditions may affect the SE/IR test results. In one of the investigated piles adjacent to water flow, the results of SE/IR tests were always bad, despite various setups being attempted. Figure 3 shows this investigated pile (Pile No.8, Bridge 2), which was surrounded by severe running water. A velocity signal from the accelerometer attached to the side surface of this pile is shown in Fig. 4. The shape of the signal is different from successful SE tests (see Fig. 1a). The graph shown in Fig. 4 indicates that, while the impulse is clear on both graphs, no echoes from the pile toe is identifiable. The velocity signal indicated in Fig. 4 contains reflections which are not related to the pile toe. Although the SE/IR tests were not successful on this pile, successful tests were found on two other piles (No. 11 and 12) at the same bent. The intensity of the water flow significantly decreased from Pile 8 to Piles 11 and 12. The success rate of those two piles and their distances from Pile 5 are indicated in Table 1.

Experience of Sonic Echo/Impulse Response Testing Difficulties

Pile No.11

Pile No.8

45

Pile No.12

Intense Water Flow

Amplitude (cm/sec) (10-6)

Fig. 3. Street view of the foundation showing running water surrounding Pile no. 8

250 0 - 250

20

0

40 3

Time (μs) (10 ) Fig. 4. A velocity graph obtained from the accelerometer for Pile No. 8 surrounded by running water Table 1. Success rate of SE tests conducted on Piles No. 11 and 12 Pile no. 11 12

Distance from pile no. 8 (m) 6.7 9.9

Number of conducted tests 21 24

Number of successful SE/IR tests 16 24

Success rate (%) 76.2 100

The results show that the success rate significantly improved for the piles farther from Pile 8, as they were adjacent to gentler water flow. Since the overall conditions of all three piles were more or less the same, it can be concluded that the noise due to the intense water flow (flow hits the pile) might be the reason for unsuccessful tests in Pile No. 8. 3.2

Physical Conditions of the Foundation

The condition and quality of the pile have a considerable effect on the clarity of the pile echo on the obtained signals. In one of the piles, there was a large, longitudinal crack in

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the pile, as indicated in Fig. 5. In this pile, it was difficult to identify the correct toe echo because of the multiple wave reflections produced by the crack. An example of the velocity graph obtained from the accelerometer is indicated in Fig. 6. The obtained velocity graph does not show the toe reflections clearly. Closely located valleys and peaks, indicated in the oval, seem to be from the mechanical impedance change due to the presence of the huge, longitudinal crack which can conceal the pile toe echo. When such anomalies exist in piles, the actual depth of the foundation may not be determined by the SE/IR method.

Large verƟcal crack

Amplitude (cm/sec) (10-6)

Fig. 5. Picture of a crack in a wood pile

100 0 - 40

0

20 Time (μs) (103)

40

Fig. 6. A velocity graph obtained from SE tests performed on pile shown in Fig. 5.

3.3

Attachments Conditions

Another source of difficulty can be the poor quality of the pile-pile cap connection. Since one of the investigated bridges was too old, some piles were deformed, and distorted over time. A poor, incomplete pile-pile cap connection in one the piles is shown in Fig. 7. As it is obvious, a full contact between the pile top surface and the bottom surface of the pile does not exist. SE/IR tests conducted on this pile were not successful. An example of velocity signal obtained from the accelerometer mounted on

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47

the side of the pile is indicated in Fig. 7. These signals are very different from the interpretable velocity signals shown in 1a. The consecutive echoes (black arrows) have large amplitudes and are not significantly smaller than the impulse. A decaying trend of amplitudes is not seen on the graph. This conflicts with the SE test theory. Based on the principals of the SE method, the amplitudes should decay due to the effect of material and surrounding soil energy damping [12]. Therefore, the consecutive valleys do not seem to be the reflections from the pile toe. Since the pile and pile cap were in contact partially, it was expected that the multiple detachments between the pile and pile cap occurred at the time of striking and consequently made the wave propagation more complicated. Such detachment related vibrations could be detected by the sensors, which may affect the obtained signals as a source of difficulty (Fig. 8).

Large Gap

Amplitude (cm/sec) (10-6)

Fig. 7. Poor and incomplete pile-pile cap connection

450 0 - 350

0

20 Time (μs) (103)

40

Fig. 8. An example of a velocity signal obtained from the accelerometer mounted on the side pile shown in Fig. 7.

4 Conclusion SE/IR tests were carried out on 16 piles with known and unknown depths to study various challenges of the method in wood piles. The depths of 13 piles were successfully determined using SE/IR tests. The success rate of the tests in each pile, however, depended on factors influencing the obtained signals. In the current study,

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factors such as environmental and foundation conditions, as well as the quality of pilepile cap connection were identified as sources of difficulty capable of adversely affect the SE/IR test results. Observations and results related to these factors are as follows: • The results showed that determining the depth of unknown wood bridge foundations can be done with SE tests more accurately than IR analysis. The advantage of using both the SE and IR analysis in the time and frequency domain is that it is possible to measure lengths of piles with more certainty. • Environmental conditions may affect the SE/IR test results. In one of the investigated piles adjacent to water flow, the results of SE/IR tests were always unsatisfactory due to the presence of severe running water. It was expected that the noise due to the water flow (river current hit the pile) interfered with the SE/IR tests in this pile. • The condition of the pile has a considerable effect on the clarity of the obtained signals. In one of the investigated piles, there was a large longitudinal crack in the pile, which caused difficulty in identifying the correct toe echo. Multiple wave reflections produced by the crack prevented the success of the SE/IR tests. When such anomalies exist in piles, the actual depth of the foundation may not be determined by the SE/IR method. In summary, the abovementioned difficulties can warn the field engineers about the presence of factors influencing the results. Care must be taken in performing the tests and interpreting the results if such difficulties are encountered in the field, since they may mislead the engineers in determining the depth of the piles. Although these instructions can inform the engineers about these challenges, more experimental and theoretical investigations are required to reveal more aspects of the challenges and remedies to overcome such difficulties.

References 1. Olson, L., Jalinoos, F., Aouad, M.F.: Determination of unknown subsurface bridge foundations: a summary of the NCHRP 21-5 Interim Report. Geotechnical Engineering Notebook, Geotechnical Guideline. Federal Highway Administration, Washington, DC (1998) 2. Rashidyan, S., Ng, T., Maji, A.: Practical aspects of nondestructive induction field testing in determining the depth of steel and reinforced concrete foundations. J. Nondestr. Eval. 38, 19 (2019) 3. Rashidyan, S., Maji, A., Ng, T.: Performance of nondestructive parallel seismic testing method in determining depth of shallow foundations. J. Perform. Constr. Fac. 33, 06019001 (2019) 4. Rashidyan, S., Maji, A., Ng, T.T.: Accuracy of parallel seismic test performance on steel Hpiles in layered soils. J. Perform. Constr. Fac. 34, 06020004 (2020) 5. Rashidyan, S., Ng, T.T., Maji, A.: Exploratory study of nondestructive parallel seismic testing challenges in estimating the depth of unknown wood bridge foundations. J. Perform. Constr. Fac. 34, 06020002 (2020) 6. Anthony, R.W., Pandey, A.K.: Determining the length of timber piles in transportation structures. In: National Conference on Wood Transportation Structures, Madison, WI (1996)

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7. Huang, Y.H., Ni, S.H., Lo, K.F., Charng, J.J.: Assessment of identifiable defect size in a drilled shaft using sonic echo method: numerical simulation. Comput. Geotech. 37, 757–768 (2010) 8. Kim, D.S., Kim, H.W.: Effects of surrounding soil stiffness and shaft length in the impactecho test of drilled shaft. KSCE J. Civ. Eng. 7, 755–762 (2003) 9. Rashidyan, S., Ng, T., Maji, A.: Bridge foundation depth estimation using Sonic Echo test. In: Sciammarella, C., Considine, J., Gloeckner, P. (eds.) Experimental and Applied Mechanics, vol. 4, pp. 99–106. Springer, Heidelberg (2016) 10. Yin, J., Yuan, J., Liu, M.: Assessment of pile integrity by low-strain stress wave method. HKIE Trans. 6, 42–49 (1999) 11. United States Department of Agriculture Forest Service. Wood handbook, wood as an engineering material, Madison, Wisconsin, United States (2010) 12. Davis, A.G.: Nondestructive evaluation of existing deep foundations. J. Perform. Constr. Fac. 9, 57–74 (1995)

Predictive Monitoring and Maintenance of Transportation Infrastructures: Requirements for Delivering Sensing Data over 5G Networks Filippo G. Pratic` o , Sara Pizzi , Rosario Fedele(B) , Domenico Battaglia , and Giuseppe Araniti DIIES Department, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy [email protected]

Abstract. The predictive monitoring and maintenance of future transportation infra-structures will be based on intelligent technologies, such as smart wireless sensing devices. In order to efficiently manage the delivery of crucial information about the structural and environmental conditions detected by wireless sensing nodes, and to suddenly process or exchange the information above with different stake-holders (e.g., authorities, drivers, etc.), the forthcoming fifth-generation (5G) network should be properly exploited. Consequently, this paper aims at illustrating the main requirements for enabling the transmission of the information gathered by sensing devices specifically designed for monitoring the structural and environmental conditions of road pavements and carrying out maintenance and rehabilitation. Different types of sensors (i.e., able to detect accelerations, noise, temperature, humidity, fire and smoke) are included in each sensing device, located on the shoulder of the carriageway (nondestructive structural health monitoring method). Sensor data are sent to the Edge of the network for further data processing. Proper algorithms are used to derive the vibro-acoustic signature of the monitored road pavement from the vibrational and acoustical data, while environmentalrelated data are processed to carry out the real time detection of unexpected events (e.g., a fire or an accident) on/around the road infrastructure. To this end, based on the sensed data size and on the sensing nodes density, several network-side requirements (such as the amount of deliverable data and cell dimension) for enabling the transmission of sensing data over 5G networks are analyzed in this study. Results demonstrate that monitoring and maintenance activities should be designed bearing in mind communication and energy-related requirements and issues. Keywords: Wireless sensing nodes · 5G network · Predictive structural and environmental monitoring and maintenance · Transportation infrastructures · ITS

c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 50–59, 2021. https://doi.org/10.1007/978-3-030-64594-6_6

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Introduction

Road life cycle includes design, construction, monitoring, assessment, maintenance, and rehabilitation. Maintenance spending on roads accounts for about 0.5% of the Italian GPD, while the corresponding investment spending (new construction and rehabilitation) accounts for about 0.3% of the of the Italian GPD [1]. Future transportation infrastructures will entrust their monitoring and maintenance processes to intelligent technologies (e.g., belonging to the internet of things, intelligent transportation system, artificial intelligence, and Smart City fields [2,3]), moving from the current failure-based approach to the currently utopian predictive management one. For instance, wireless sensing devices based on the forthcoming fifth-generation (5G) network could be used to gather crucial information (structural-, environmental-related) about the asset on/in which are installed, and efficiently manage the delivery of these information to different stakeholders (e.g., authorities, drivers). Characteristics such [4,5]: 1) Data rates of gigabits per second everywhere; 2) Significant reduction of latency; 3) System capacity higher than the current Long Term Evolution 4th Generation (4G/LTE) standard; and 4) Higher density of the connected devices per region, make the 5G technology suitable to manage sensor data, allowing real-time monitoring. The collection of the measurements from a massive number of sensors is a typical example of massive machine-type communications (mMTC), which is one of the three main use cases of the 5G system. Based on the literature [6,7], a system based on wireless sensing devices is characterized by i) a large number of devices; ii) Uplink-dominated data transmissions; iii) Small data packets; iv) Low data rates; v) Sporadic user activity; vi) Low complexity and battery constrained devices. Hence, in theory, the 5G technology perfectly matches the requirements of a wireless sensing system, but further evaluations are needed. The main objectives of the study are to: (1) Present a solution that aims at monitoring the structural and the environmental conditions of road infrastructures; (2) Illustrate the main requirements for enabling the data transmission exploiting the 5G networks technology. Based on the above, the remaining part contains: Sect. 2 Monitoring method and system used for the data gathering; Sect. 3 Simulation scenario; Sects. 4 and 5 Synchronization device-base station, and data transmission emergency conditions; Sect. 5 Results and discussions; Conclusions and References.

2

Data Gathering: Method and System

This study refers to a Non-Destructive Test (NDT)-based, Structural Health Monitoring (SHM) method that aims at monitoring also the environmental conditions of the road pavements over time. It considers the road pavement as a mechanical filter and the traffic as a mechanical source of noise and vibrations (input signals), which can be detected using wireless sensing devices attached along the shoulder of the carriageway [8,9]. Proper transducers can gather the

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filtered signals, i.e., the vibro-acoustic response of the pavement to the vehicular traffic (output signals; a.k.a., vibro-acoustic signature of the road). Consequently, any alteration (due to surface and internal road cracks [10]) of the structural conditions of the pavement (filter alteration) will cause a modification of the signal gathered by the transducers (filtered signal). Based on the dimension of the cracks (small size, which means small wavelength), it is expected that the low-pass property of the filter will be emphasized, i.e., the high-frequency components of the waves will be trapped into the cracks producing a reduction of the signal amplitude and the signal energy [11,12]. The NDT-SHM method requires that the data gathered by the sensors are sent to the edge of the network for the application of a specific designed algorithm (based on predefined thresholds derived from experiments and simulations), which allows (1) deriving, from the seismic and acoustic data, the vibro-acoustic signature of the monitored road pavement, (2) detecting, from the environmental data, the occurrence of unexpected events (e.g., fire or accident) on/around the road infrastructure instrumented with the sensor network, and (3) alerting all the stakeholders of the occurence of an unusual event. Usually, data transmission is more consuming that data gathering [13], but it is possible to save energy and increase the autonomy of the monitoring system, by acting on the system’s hardware (e.g., using low-power data transmitters) and software (e.g., using codes that allow Discontinuous Reception (DRX) [14]). Sustainability, reliability, and efficiency of the system are ensured using (see Fig. 1(a)): 1) an ultra-low-power wireless sensing device (i.e., the IoT board in figure consisting of several MEMS sensors, e.g., a 3D accelerometer, a microphone, a temperature/humidity sensor, etc., a microcontroller and a wireless transmitter that uses different protocols, such as Wi-Fi, Bluetooth, etc., and can exchange data with a 5G node); 2) power supply system able to produce renewable energy (i.e., photovoltaic polycrystalline silicon panel, together with a recharge circuit, and a battery); 3) An additional air-borne noise isolated microphone (see Fig. 1(a), top, right) that is able to listen the acoustic signature of the road; 4) Smoke and flame sensors.

3

Data Management: Simulation Scenario

In this section, the simulation scenario that was used to derive the requirements for enabling the transmission of sensing data over 5G networks is presented (see Fig. 1(b)). It is composed of wireless sensing nodes placed beside the carriageway, that cover a given area, and that can be clustered in subgroups. All the nodes communicate with a base station (BS) that is used to transmit sensor data to the edge of the network (e.g., a remote server). Figure 2 illustrate the DRX activity of transmitting devices in normal (Fig. 2(a)) or critical operating conditions (Fig. 2(b)). In order to save energy, sensors deputed to road monitoring implement a Discontinuous Reception (DRX) mechanism, which consists of on-duration and inactivity periods. They switch off the receiver and power down most of their circuitry (wireless transceiver) during the inactivity period to save energy. When the on-duration period starts, sensors have to wake-up to

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Fig. 1. Wireless sensing device of the monitoring system: (a) power supply system, prototype of wireless sensing node, and detail of the node’s microphone, (b) simulation scenario.

listen to the signaling from the base station (BS), indicating an incoming data transmission. Each device wakes up only once per DRX cycle. In normal operating conditions, devices could wake-up only once a day to transmit collected data. In order to avoid collisions when two or more devices transmit data to the network over the same resources, it is preferable to schedule the wake-ups of devices at different times (see Fig. 2(a)). Device power consumption can be reduced to a minimum value by setting the DRX cycle to the periodicity of data transmission. However, when the BS needs to deliver data to a device (downlink direction), the transmission will inevitably undergo a latency1 that depends on the DRX cycle of that device. We assume that, once the DRX cycle, devices wake up to transmit sensed data to the BS and then check if the base station wants to transmit data towards them. The BS could transmit to devices, for example, a software update or a command for communicating a modification in the setting of the sensor device. In particular, the worst-case latency is almost equal to the DRX cycle of devices (since it is likely that at least one device has just come back to inactivity when the need for delivering downlink data arises). However, critical conditions could be detected by the sensors placed along the roadside that could likely trigger the simultaneous wake-up of a group of sensors out of their regular operation regulated by the DRX cycle (see Fig. 2(b)). As a consequence, in the critical operating conditions above, collisions may occur when a possibly high number of devices try to access the channel at the same time to transmit uplink (UL) data. 1

Latency is calculated as the time that elapses from when the BS wants to transmit downlink data to devices in the cell to when all devices receive data. When dealing with devices implementing DRX, it also includes the time waiting for the device to be reachable by the network.

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Fig. 2. Time diagram of (a) normal, and (b) critical operating conditions.

4

The Random Access Procedure

The analysis focuses on the radio access network, which is the most solicited portion because of many MTC devices. We consider the state-of-the-art random access (RA) procedure [15], which is carried out by devices to synchronize with the BS and demand resources for further communications. A separate channel, named Physical Random Access Channel (PRACH), is provided for the first access to the network. The 3rd Generation Partnership Project (3GPP) defines the RA procedure as a four-message handshake mechanism to get UL synchronization and establish an Radio Resource Control (RRC) connection; it involves the exchange of the following four messages (msg): – Msg1. A user (UE) selects one of the 64 available Random-access channel (RACH) preambles and sends it over the PRACH. If the UE does not obtain any response from the network, it raises its power and sends the RACH preamble again. – Msg2. If the gNB (i.e., the 5G BS) successfully decodes Msg1, it will reply with a Random Access Response (RAR) to the UE. The message carries a Cell Radio Network Temporary Identity (CRNTI) for further communication, a Timing Advance Value to compensate for the round trip delay caused by distance, and lastly, the Uplink Grant Resource (initial resources for the UE). – Msg3. The UE, identified by the CRNTI, sends an “RRC connection request message” to the gNB. The message includes the identity of the UE and the reason to connect to the network. The identity could be the TMSI (Temporary Mobile Subscriber Identity) if the UE were previously connected or a random value in case the CRNTI has been previously assigned to more than one UEs. – Msg4. If no collision happens, the gNB will reply with a contention resolution message.

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The contention-based delivery of preambles in 3GPP RACH is an Aloha-based (Aloha Mobile Ltd.) random access technique, where devices start the access procedure in the first accessible opportunity; however, this could lead to a high probability of collisions in the case of a large number of concurrent access requests.

5

Scheduling Group-Based Transmissions in Critical Operating Conditions

Monitoring promptness is crucial for road safety and management. With the aim to reduce collision and latency of the critical message transmission from a large number of sensing devices to the network, we propose to carry out a group-based approach [16]. When the BS detects a critical situation, the collision could be reduced by grouping devices and properly scheduling the access to the wireless medium for each subgroup. We define a subgroup as a subset of the devices in the cell. We assume that the gNB is aware of the position of the sensing nodes. This assumption is realistic since they are properly placed by an authority in charge of road monitoring and maintenance that can communicate to the gNB the location of all sensing nodes. The BS is informed about a critical event when any of the devices in the cell transmits one of the preambles that are in a set of critical preambles prior communicated by the network to sensing nodes. Indeed, with respect to the legacy 3GPP RA procedure that exploits the entire set of possible preambles, our proposal introduces a critical-dedicated preamble set, whereas a subset of preambles is reserved for critical alarm messages (CAMs, as in [17]). Following a critical event, a device will try to transmit a CAM by initiating a RA procedure. Once received any of the CAM-dedicated preambles, the subgroup creation procedure starts. In particular, the gNB will partition the D devices under its coverage into smaller groups of g members and assign a different Access Time (AT ) value to each group. As a consequence, all the devices participating in the same group will share an identical AT . Devices are grouped according to their proximity to the node which sent the critical preamble. Afterward, the gNB will reply to the received CAM preamble with a control message sent in place of Msg2 that will notify subgroup formation decisions and schedules assigned to the subgroups (i.e., the time instant when each subgroup can start the RA procedure).

6

Performance Analysis

To evaluate the system performance, we have developed a system-level simulation framework in MATLAB. Due to collisions, some devices may fail their access attempt to the network and will have to redo preamble transmission after a random backoff interval; a new attempt will be made in one of the next Random Access Opportunities

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(RAOs). The number of retransmission attempts is restricted to K; if a device exceeds this value, it is considered to be in poor channel conditions. For each device, we run three independent clocks that keep track of: (i) when a device sends data towards the gNB, (ii) when a device receives data from the gNB, and (iii) for how long a device is in idle mode. The individual clocks start when the device forwards the preamble for the first time until it completes data transmission. The metrics of interest are: (i) Preamble Collision Rate, that is the percentage of collided preambles in a single RAO, and (ii) Total delay, calculated as the sum of the three individual clocks defined above. We assume that 64 preambles and 25 RBs in total are available in the system. Since the number of available RA preambles in a time slot is 64, the group size should be lower than 64 to avoid introducing unnecessary extra RA delay. We assume that RA slots periodicity is 5ms, thus 200 access opportunities are available each second. We vary the number of devices D requiring access to the network. Inside each RA slot, a device randomly chooses a preamble from the set P of available preambles. To simulate the sudden wake-up of all devices, the initial access time values for the standard DRX mechanism are assumed identical. Groups start the RA procedure at different time instants that are distantiated according to the gI parameter. All model hypotheses are summarized in Table 1 and are set accordingly to [15]. Our group-based strategy is compared against the legacy 3GPP approach. The first analysis we carried out is by varying the dimension of the subgroups. In particular, Fig. 3(a) highlights that the amount of collided preambles in the standard RACH procedure is more significant than our proposal. By examining the 3GPP RACH, the collision probability is close to 0.5 in case of 1000 concurrently accessing devices. In our group-based strategy, even when each group has the maximum size, the collision probability does not exceed 0.3. In contrast,

Fig. 3. (a) Preamble collision rate, and (b) total delay under increasing device density.

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Table 1. System model parameters Notation Definition

Value

T

Simulation time

100 s

RB

Amount of Resource Blocks

25

P

Number of available preambles

64

D

Number of devices

[200:200:1000]

gS

Size of a group

[15:15:60]

gI

Distance between two consecutive groups

[5:5:15] ms

K

Maximum number of preamble retransmissions 2

AT

Access Time for each device

when groups have the smallest scale, the likelihood of a preamble collision is close to only 0.1. The outcome is remarkably significant for devices with low battery capacity, which could quickly drain battery energy, having to repeat the RA procedure after a collision. The same improvements can be seen in Fig. 3(b), where the depicted total delay (considering 1000 simultaneously accessing devices) is almost double as our proposal. Even when the group size is very close to the maximum amount of available preambles, the delay is much lower using a groupbased approach, because sensors will spend less time in idle mode. It is worth noting that, by raising the number of sensors in the cell, the delay is constant at around 50 ms when groups have less than 30 devices. In Fig. 4, where we consider a group size of 30, it is noticeable and expected that increasing the distance between consecutive groups at values above 10 ms will lead to no significant

Fig. 4. Total delay by varying the distance between two consecutive groups.

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benefits. The scheduling of all devices allows them to complete the RACH procedure even with a low value for K 2 .

7

Conclusions

Productive investment in transport infrastructures and their maintenance is crucial for prosperity and economic development. The monitoring and maintenance of future transportation infrastructures will be based on intelligent and advanced technologies. This study focused on the design of a wireless sensing system that uses the emerging 5G networks technology to manage the data gathered by the sensors. In particular, we presented and assessed a group-based scheduling strategy that aims to overcome the limitations of the standard RACH scheme. The suggested solution assures reduced preamble collisions in critical operating conditions by implementing a group-based approach. Simulations show how our group-based scheduling of transmissions and the use of a critical-dedicated preamble is able to reduce the collision probability and the overall latency. Results demonstrate that future road monitoring and maintenance activities should be designed bearing in mind communication and energy-related requirements and issues, such as energy consumption, device activity (DRX cycle) and density, network latency, and collision capacity.

References 1. OECD: Investment by sector (indicator) (2020). Accessed 13 July 2020 2. Sumalee, A., Ho, H.W.: Smarter and more connected: future intelligent transportation system (2018) 3. Uddin, W.: Pavement management systems (2005) 4. Pizzi, S., Suraci, C., Iera, A., Molinaro, A., Araniti, G.: A sidelink-aided approach for secure multicast service delivery: from human-oriented multimedia traffic to machine type communications. IEEE Trans. Broadcast., 1–11 (2020). https://doi. org/10.1109/TBC.2020.2977512. Print ISSN: 0018-9316. Electronic ISSN: 15579611 5. Rinaldi, F., Pizzi, S., Orsino, A., Iera, A., Molinaro, A., Araniti, G.: A novel approach for MBSFN area formation aided by D2D communications for eMBB service delivery in 5G NR systems. IEEE Trans. Veh. Technol. 69(2), 2058–2070 (2020) 6. Bockelmann, C., Pratas, N., Nikopour, H., Au, K., Svensson, T., Stefanovic, C., Popovski, P., Dekorsy, A.: Massive machine-type communications in 5G: physical and MAC-layer solutions. IEEE Commun. Mag. 54, 59–65 (2016) 7. Festag, A., Hessler, A., Baldessari, R., Le, L., Zhang, W., Westhoff, D.: Vehicleto-vehicle and road-side sensor communication for enhanced road safety. In: 15th World Congress on Intelligent Transport Systems and ITS America Annual Meeting, pp. 7016–7027 (2008) 8. Fedele, R., Pratic` o, F.G., Carotenuto, R., Corte, F.G.D.: Structural health monitoring of pavement assets through acoustic signature. In: 10th International Conference BCRRA 2017, pp. 869–875 (2017) 2

In both Fig. 3 and 4, we consider the maximum number of preamble re-transmission (i.e., after the first attempt) equal to 2.

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9. Fedele, R., Pratic` o, F.G.: Monitoring infrastructure asset through its acoustic signature. In: INTER-NOISE 2019 MADRID - 48th International Congress and Exhibition on Noise Control Engineering (2019) 10. Moghaddam, T.B., Karim, M.R., Abdelaziz, M.: A review on fatigue and rutting performance of asphalt mixes. Sci. Res. Essays. 6, 670–682 (2011) 11. Fedele, R., Pratic` o, F.G., Pellicano, G.: The prediction of road cracks through acoustic signature: extended finite element modeling and experiments. J. Test. Eval. 49, 20190209 (2021) 12. Thomas, M., Tesfamariam, S., Sadri, A.: Use of stress waves in the evaluation of structures affected by ASRM. In: Concrete Durability and Repair Technology: Proceedings of the International Conference Held at the University of Dundee, Scotland, UK, 8–10 September 1999 (1999) 13. Fedele, R., Pratic` o, F.G., Carotenuto, R., Della Corte, F.G.: Energy savings in transportation: setting up an innovative SHM method. Math. Model. Eng. Probl. 5, 323–330 (2018) 14. Stea, G., Virdis, A.: A comprehensive simulation analysis of LTE discontinuous reception (DRX). Comput. Netw. 73, 22–40 (2014) 15. 3GPP TR 38.868: RAN Improvements for Machine-Type Communications, Rel. 11, October 2011 16. Araniti, G., Rinaldi, F., Scopelliti, P., Molinaro, A., Iera, A.: A dynamic MBSFN area formation algorithm for multicast service delivery in 5G NR networks. IEEE Trans. Wirel. Commun. 19(2), 808–821 (2020) 17. Condoluci, M., Araniti, G., Dohler, M., Iera, A., Molinaro, A.: Virtual code resource allocation for energy-aware MTC access over 5G systems. Ad Hoc Netw. 43, 3–15 (2016)

Structural Health Monitoring over 5G uRLLC Network Fabio Franchi(B) , Fabio Graziosi , Andrea Marotta , and Claudia Rinaldi Universit` a degli Studi dell’Aquila, Piazza Santa Margherita 2, 67100 L’Aquila, Italy {fabio.franchi,fabio.graziosi,andrea.marotta,claudia.rinaldi}@univaq.it

Abstract. In this work we propose a Disaster Management System on 5G ultra Reliable Low Latency Networks that targets unprecedented reliability levels as well as low latency. In fact, referring to the 5G vision a Structural Health Monitoring system can be considered depending on the operational scenario: in the case of data collection and processing from sensors in monitored buildings, considering the high number of sensors installed, it can refer to the massive Machine Type Communications context. Vice versa, during a seismic event or just after it, the use case requires high reliability connectivity and, sometimes, low latency. Those features refer to the ultra Reliable Low Latency context. It seems interesting to evaluate and experiment the ability of 5G network to dynamically adapt to the changing scenario that this use case can provide. Moreover this work presents an innovative 5G architecture for Earthquake Early Warning that uses Structural Health Monitoring systems to detect a seismic event and to propagate a message reporting the event detection to all the buildings that may be damaged by the event.

Keywords: 5G

1

· uRLLC · SHM · MEC · EEW · Fog · Cloud

Introduction

5G networks pave the way for a number of services with heterogeneous requirements that can be categorized in i) enhanced Mobile Broad Band (eMBB) ii) massive Machine Type Communications (mMTC) and iii) ultra Reliable Low Latency Communications (uRLLC) [1]. Traditionally, the evolution of mobile systems has been characterized by an increasing demand of traffic and consequently of data rates offered by the mobile network. To this end, uRLLC services impose a new perspective on mobile network evolution focusing on low latency and reliability guarantees. The newly defined 5G New Radio (NR) standard targets transmission of relatively small payloads with very low latency (≤1 ms) and high reliability (99.999%) [2]. Furthermore, due to its native support for mMTC services, 5G represents a valid candidate technology to support the paradigm of the Internet of Things (IoT). The challenges underlining to the IoT support by the mobile networks reside in c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 60–68, 2021. https://doi.org/10.1007/978-3-030-64594-6_7

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the huge number of connected devices and in the low energy available for the transmission of data [3]. This work presents an end-to-end solution developed in the context of the Italian 5G experimentation in the city of L’Aquila [4]. The proposed solution focuses on the monitoring of the health status of buildings and on the earthquake early warning. The development of the proposed system involves design aspects of several domains aiming at a so-called vertical perspective involving the IoT board design, the management of the 5G network, and the fog/cloud software platform. The remaining of the work is organized as follows: in Sect. 2 SHM and EEW are introduced; Sect. 3 describes the key aspects of the management of the 5G network for SHM and EEW and the capabilities of 5G to support the proposed solution; in Sect. 4 the sensor board design is described and in Sect. 5 key aspects of investigated data processes are presented; finally in Sect. 6 conclusions are drawn.

2

Structural Health Monitoring and Earthquake Early Warning

Structural Health Monitoring (SHM) is a vital tool to improve safety and maintainability of critical structures such as bridges and buildings. SHM provides real-time and accurate information about the structural health condition. It is a process of non-destructive evaluations to detect location and extent of a damage, calculate the remaining life, and predict upcoming accidents. Different typologies of monitoring actions (e.g. dynamic analysis oriented monitoring, seismic analysis oriented monitoring, crack growth oriented monitoring, environmental or chemical oriented monitoring) may have different or conflicting requirements. For example, dynamic analysis-oriented monitoring systems must guarantee a precise measurement synchronization [5], while this requirement may result less compelling when monitored quantities are slowly variable (e.g. crack growth monitoring). Traditionally, SHM systems are based on wired grids of sensors deployed along a structure, resulting in high deployment costs, considerable size and poor flexibility. In recent years, the gradual development of wireless sensor networks (WSNs) technology represented a significant innovation opportunity in SHM field [6]. The use of WSN in SHM has various significant advantages; first of all, it allows to eliminate connection cables, reducing total system costs. Moreover, wireless nodes have a reduced footprint and visual impact and can therefore be installed in buildings of historical or artistic relevance. Their high flexibility allows the installation in positions for which cable deployment would be complicated (a relevant problem for structures of large dimensions, such as monumental buildings). Furthermore, wireless sensor nodes micro-controllers can be programmed to constantly analyze sensors response and eventually trigger an alarm signal in case of sudden damage detected.

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Data gathered are exploited in order to verify the buildings performance over time enabling a continuous evaluation of their safety and the opportunity to plan proper restoration activities to reduce their vulnerability1 . Sensor networks are able to constantly transmit measured data to an processing node. The aim of this kind of systems is to build a wide monitoring network with low-cost technologies, allowing the development of processes of qualitative analysis and representation of data collected by the sensors (e.g. geo-referenced triaxial accelerometer data).

Fig. 1. Permanent SHM using 5G network

The idea behind Earthquake Early Warning (EEW) is to use the SHM system to detect a seismic event and to propagate a message reporting the event detection (EEW message) to all the buildings that may be damaged by the disaster event. While the seismic detection in the local building can trigger reactive safety actions to compensate the effect of the disaster without any guarantee of effectiveness, the reception of EEW message triggers preventive safety actions in nearby buildings which, if applied with an adequate timing, can increase the safety of citizens and facilities as shown in Fig. 1 Potential preventive safety actions that can be triggered by EEW include initiation of elevator recall to ground floor procedures, placement of sensitive equipment in safe mode, securing of hazardous materials, halt production lines to reduce damage, unlocking of exit doors, switch on of emergency lights. Tajima et al. [7] provide a deep analysis of the limits of the current used EEW and the Fig. 2 describe their conclusions. In order to reduce the “gray zone” more survey stations have to be deployed and the alert propagation time need to be minimal.

1

Topic of great importance for the municipality and citizens due to the aftermath of the 2009 L’Aquila earthquake.

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Fig. 2. Current limits of EEW systems by Tajima et al.

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5G as an Enabler for SHM and EEW

EEW in SHM systems represents a challenging application for 5G since it leverages the capability of 5G networks to offer guarantees of packet transmission within a certain latency. The newly defined 5G NR standard [8] targets transmission of relatively small payloads with very low latency (≤1 ms) and high reliability (99.999%) [2], thus, it represents an effective supporting infrastructure for the proposed EEW system. Figure 3 shows the segments of the 5G network that the EEW message must pass through the SHM node that detects the seismic event towards the remote building where to apply a preventive action. Differently from 4G LTE, 5G introduces several degrees of flexibility such as that can be leveraged to reduce the delay experienced in the EEW system in both wireless segment (e.g. Grant Free transmission, mini-slot based transmission, Semi Persistent Scheduling and URLLC eMBB multiplexing) and transport network [9,10].

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Fig. 3. 5G architecture and key technologies

Traditionally, the design and deployment of new services has been carried out without particular attention to the integration between network infrastructure management and the service itself. 5G, together with network virtualization and software defined networking opens new capabilities towards a so-called vertical design that spans from the application layer to the physical infrastructure. The system under consideration is characterized by the simultaneous support of the traffic related to the periodical monitoring of the status of the buildings and the traffic generated in alert conditions as an earthquake warning. The structural monitoring represents a massive IoT application whose design concerns many aspects (e.g. number of connected devices, quality of signal) mostly addressed in the network planning phase. On the other hand, the earthquake early warning requires low latency and high reliability, achieved through a vertical integrated management of the network virtualization infrastructure, the transport network, and the mobile network. Parallel to the enhancements in the radio segment, 5G leverages the novel paradigms of Software Defined Networking and Network Function Virtualization to introduce the possibility to dynamically adapt network resources according to services requirements. On this hand, Network Slicing, i.e. the possibility to provision a virtual set of network resources supporting specific service KPIs represents an effective tool supporting uRLLC application. Furthermore, MultiAccess Edge Computing (MEC), provides the ability to dynamically deploy virtual computation elements at a variable distance from the end user, thus varying the experienced delay basing on targeted performance. The proposed solution requires the provisioning of two network slices supporting respectively SHM and EEW services, as shown in Fig. 4. Different domains of the network concur to the composition of the network slices in order to achieve target requirements as described below:

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uRLLC slice mMTC slice

5G CPE SHM Elaboration EEW Elaboration

Fig. 4. uRLLC and mMTC slices

4

IoT Board Design

SHM-Board v2 has been designed as a highly versatile and high performance device for real-time SHM of private and public buildings and infrastructures, able to promptly intervene in order to avoid or limit structural-related risks and direct or indirect consequences for people or things [11]. The board is an evolution of a device designed and used in collaboration with the University of L’Aquila; the previous experience has led to a remarkable improvement of the performances [12]. SHM-Board v2 is based on an ultra-low-power micro-controller (MCU) of the STM32F4 family, produced by ST Microelectronic, which offers numerous communication and high-performance interfaces: through its 12-bit ADC, it is connected to a low-noise MEMS accelerometer (Kionix KXRB5-2050), mounted directly on the board, that is always active and acts as a sentinel in the event of accelerations detected above a certain threshold. External low-noise MEMS accelerometers can also be used in order to improve accuracy or measurements range. SHM-Board v2 is equipped with 4-channels 24-bit ADC able of sampling at 100 Hz and on-board data processing in order to minimize the total amount of data to be transmitted. SHM-Board v2 has an Ethernet interface (from which it can be powered via Power over Ethernet) and other expansion interfaces to connect more peripherals. The board also provides the capability to communicate wireless (RF) with other nodes of the monitoring network through W-MBUS protocol at 169 MHz or 868 MHZ. It can be also equipped by NB-IoT or 5G expansion module. Finally, SHM Board provides the possibility of acquiring signals from two other types of measuring instruments, inclinometers and crackmeters, both of which are widely used in the field of structural health monitoring (especially after seismic events that caused serious damages to the buildings) [13,14]. The temperature sensor, integrated in SHM-Board v2, allows to evaluate the thermal effect on the structure and on the sensor thus allowing to distinguish seasonal variations from real inclinations. The MEMS operating principle guarantees good thermal stability and excellent linearity.

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5

Data Processing

Data-Driven modeling approaches, based on System Identification from Control Theory and Machine Learning from Computer Science, has been investigated within the activities of the INCIPICT project, where it has been initially adopted for research activities in energy optimization of BMS: in particular, we focused on data-driven modeling and control of buildings with the dual goal of energy efficiency and thermal comfort. The idea has been to provide predictive mathematical models of the building behavior to apply optimal control strategies that maximize energy and money savings while guaranteeing thermal comfort for the occupants [15–20]. The development of the research activities related to SHM led to a work based on experimental data taken from an experimental setup consisting of a 2-storey structure equipped with accelerometers, located in the “Laboratorio di prove materiali e strutture” of the University of Basilicata [21]. Using such data we were able to derive, with the algorithm developed in [15] and using only regression trees, dynamical models with high accuracy of the vibrations induced on the structure by an earthquake reproduced via mechanical actuators. Our identified models have been used to construct an optimal predictive control algorithm (Model Predictive Control - MPC) in order to reduce the oscillations in terms of accelerations of the structure by means of active dampers. Subsequently, these results have been extended [19] also using Random Forests and Neural Networks, showing that the use of Random Forests allows to further reduce the acceleration of the structure compared to the results obtained using only Regression Trees, considerably reducing the amount of kinetic energy involved in the process and in particular the effort required by the dampers. The developed model identification techniques have also been exploited in the context of damage detection. Current works are based on the identification algorithms of the models developed were applied by comparing them with the Principal Component Analysis techniques, appropriately combined with Kalman filtering theory. In addition, sensor selection algorithms have been developed based on the concept of Entropy and Information Gain, showing how in some cases there is the possibility to reduce the number of sensors significantly while improving at the same time the accuracy of the predictive model.

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Conclusion

This work deals with the exploitation of a permanent structural monitoring system for buildings and propose an IoT-based system on 5G uRLLC Networks that address also the EEW in case of seismic event. A practical design example has shown how the proposed solution can be applied to a real monitoring problem. The EEW in SHM systems is presented in order to leverage the capability of 5G networks to offer guarantees of reliable packet transmission within a certain latency. The idea behind EEW is to use the SHM system to detect a seismic event and to propagate a message reporting the event detection to all the buildings that may be damaged by the event.

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Acknowledgment. This work was partially supported by the Italian Government under CIPE resolution no. 135 (December 21, 2012), project INnovating City Planning through Information and Communication Technologies (INCIPICT). SHM-Board v2 has been developed thanks to a close collaboration between University of L’Aquila and WEST Aquila S.r.l. (University spin-off).

References 1. Alliance, N.: 5G white paper. In: Next Generation Mobile Networks, White Paper, vol. 1 (2015) 2. 3GPP TS 38.913: Technical specification group radio access network; study on scenarios and requirements for next generation access technologies. Release 15, June 2018 3. Li, S., Da Xu, L., Zhao, S.: 5G internet of things: a survey. J. Ind. Inf. Integr. 10, 1–9 (2018) 4. Antonelli, C., Cassioli, D., Franchi, F., Graziosi, F., Marotta, A., Pratesi, M., Rinaldi, C., Santucci, F.: The city of L’aquila as a living lab: the incipict project and the 5G trial. In: 2018 IEEE 5G World Forum (5GWF), pp. 410–415, July 2018 5. Krishnamurthy, V., Fowler, K., Sazonov, E.: The effect of time synchronization of wireless sensors on the modal analysis of structures. Smart Mater. Struct. 17(5), 055018 (2008) 6. Lynch, J.P., Loh, K.J.: A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vibr. Digest 38(2), 91–130 (2006) 7. Tajima, F., Hayashida, T.: Earthquake early warning: what does “seconds before a strong hit” mean? Progr. Earth Planetary Sci. 5(1), 63 (2018) 8. 3GPP TS 38.201: Technical specification group radio access network; NR; physical layer; general description. Release 15, December 2017 9. Sachs, J., Wikstrom, G., Dudda, T., Baldemair, R., Kittichokechai, K.: 5G radio network design for ultra-reliable low-latency communication. IEEE Netw. 32(2), 24–31 (2018) 10. Kaippallimalil, J., Lee, Y., Saboorian, T., Shalash, M., Kozat, U.: Traffic engineered transport for 5G networks. In: 2019 IEEE Conference on Standards for Communications and Networking (CSCN), pp. 1–6 (2019) 11. D’Errico, L., Franchi, F., Graziosi, F., Marotta, A., Rinaldi, C., Boschi, M., Colarieti, A.: Structural health monitoring and earthquake early warning on 5G uRLLC network. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), pp. 783–786. IEEE (2019) 12. Potenza, F., Federici, F., Lepidi, M., Gattulli, V., Graziosi, F., Colarieti, A.: Long-term structural monitoring of the damaged basilica S. Maria di Collemaggio through a low-cost wireless sensor network. J. Civ. Struct. Health Monit. 5(5), 655–676 (2015) 13. Ha, D., Park, H., Choi, S., Kim, Y.: A wireless MEMS-based inclinometer sensor node for structural health monitoring. Sensors 13(12), 16 090–16 104 (2013) 14. Lorenzoni, F., Caldon, M., da Porto, F., Modena, C., Aoki, T.: Post-earthquake controls and damage detection through structural health monitoring: applications in L’aquila. J. Civ. Struct. Health Monit. 8(2), 217–236 (2018) 15. Behl, M., Smarra, F., Mangharam, R.: Dr-advisor: a data-driven demand response recommender system. Appl. Energy 170, 30–46 (2016)

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16. Jain, A., Smarra, F. Mangharam, R.: Data predictive control using regression trees and ensemble learning. In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), pp. 4446–4451. IEEE (2017) 17. Jain, A., Smarra, F., Behl, M., Mangharam, R.: Data-driven model predictive control with regression trees–an application to building energy management. ACM Trans. Cyber-Phys. Syst. 2(1), 1–21 (2018) 18. Smarra, F., Jain, A., de Rubeis, T., Ambrosini, D., D’Innocenzo, A., Mangharam, R.: Data-driven model predictive control using random forests for building energy optimization and climate control. Appl. Energy 226, 1252–1272 (2018) 19. Smarra, F., Di Girolamo, G.D., Gattulli, V., Graziosi, F., D’Innocenzo, A.: Learning models for seismic-induced vibrations optimal control in structures via random forests. J. Optim. Theory Appl. 1–20 (2020) 20. Smarra, F., Di Girolamo, G.D., De Iuliis, V., Jain, A., Mangharam, R., D’Innocenzo, A.: Data-driven switching modeling for MPC using regression trees and random forests. Nonlinear Anal: Hybrid Syst. 36, 100882 (2020) 21. Di Girolamo, G., Smarra, F., Gattulli, V., Potenza, F., Graziosi, F., D’Innocenzo, A.: Data-driven optimal predictive control of seismic induced vibrations in frame structures. Struct. Control Health Monit. 27(4), e2514 (2020)

Seismic and Structural Health Monitoring of Cahora Bassa Dam Sérgio Oliveira1(&), Ezequiel Carvalho2, Bruno Matsinhe2, Paulo Mendes3, André Alegre1,4, and Jorge Proença4 1

Laboratório Nacional de Engenharia Civil, Av. Brasil 101, Lisbon, Portugal [email protected] 2 Hidroeléctrica de Cahora Bassa, Songo, Maputo, Mozambique 3 Instituto Superior de Engenharia de Lisboa, R. Cons, Emídio Navarro 1, Lisbon, Portugal 4 Instituto Superior Técnico, Av. Rovisco Pais 1, Lisbon, Portugal

Abstract. This paper focuses on presenting a complete study on the dynamic behavior of Cahora Bassa dam (Mozambique), a 170 m high double curvature arch dam which has been under continuous vibrations monitoring since 2010. The installed Seismic and Structural Health Monitoring system was designed to continuously record acceleration time series in several locations in the dam body (crest gallery) and near the dam-rock interface, under ambient/operational vibrations and during seismic events, using uniaxial and triaxial accelerometers. The system was complemented with the development of software for automatic modal identification and automatic detection of seismic vibrations. The numerical simulations are carried out using a 3D finite element program, based on a solid-fluid coupled formulation to simulate the dam-reservoir-foundation system, considering dam-water dynamic interaction and propagation of pressure waves throughout the reservoir. The main experimental outputs are presented and compared with results from 3D finite element analysis, including the evolution of identified natural frequencies over time, vibration mode shapes, and the seismic response in accelerations. Finally the non-linear seismic behavior of Cahora Bassa dam is studied for an input accelerogram with a 0.6 g peak acceleration, considering the joints movements and a damage model for concrete. Keywords: Cahora-Bassa dam
Seismic and structural health monitoring
Natural frequencies
Finite element analysis
Linear and non-linear seismic response
Damage model
Joints’ movements

1 Introduction The safety control and health monitoring of large dams, is nowadays supported by automatic monitoring systems to continuously control their performance for static and dynamic loads, using the so-called Seismic and Structural Health Monitoring (SSHM) systems for measuring vibrations. The concept of SSHM is a quite recent one, referring to the implementation of procedures and strategies to characterize the dynamic behavior of these structures under operation conditions and during seismic events, © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 69–78, 2021. https://doi.org/10.1007/978-3-030-64594-6_8

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based on continuous monitoring data [1]. The main goals of implementing a monitoring system for SSHM are: i) the characterization of the global dynamic behavior; ii) to study the evolution of modal parameters over time; iii) to study the seismic response; iv) to investigate the evolution of material deterioration for structural integrity assessment; v) to provide useful information for stakeholders and technicians/engineers responsible for safety control and health monitoring, to fulfill regular maintenance needs and/or to support decision making in face of exceptional emergency situations.

2 Seismic and Structural Health Monitoring Systems for Dams The application of SSHM methodologies for dam safety control has suffered an important growth over the past decade, because of the undeniable advantages of continuous vibrations monitoring [2] and due to the increasing demands of owners, stakeholders and engineers. Therefore, the installation of monitoring systems for continuously measuring vibrations has been proposed for most of the new large dams, to evaluate their behavior since the early stages of their service life, and for older dams, some built several decades ago, with possible deterioration problems (e.g. swelling reactions) [2–4]. The installation of an SSHM system in large concrete dams aims at continuously measuring accelerations in as many locations in the dam as possible, in various positions along the dam-foundation interface, and, if possible, in the free field, to enable ongoing evaluation of the response under operational/ambient vibrations and under earthquake ground motion. For SSHM, it is essential to design a system with high dynamic range, capable of an accurate measurement of the dam’s response for reduced amplitude motions, i.e. ambient/operational vibrations and low intensity earthquakes, and for movements of greater amplitude, e.g. caused by high intensity earthquakes. Therefore, the systems should be implemented [2–4] using cutting-edge equipment for automatic data measurement, acquisition and transmission, including, e.g., digitizers, recorders, transducers and accelerometers. They should also be complemented with the development of software, adapted and optimized to each dam, to automatically process and analyze the measured vibrations, e.g. for modal identification and earthquake detection (Fig. 1). In addition, reference 3D finite element (FE) models are required, to enable the comparison between measured and computed response, in order to support structural health evaluations and dam safety control studies, while also providing useful data to calibrate/validate new models in development. Taking into consideration the experience accumulated over the years with Cahora Bassa dam vibrations measurement [2–4], the combined use of complete monitoring systems, appropriate data analysis software and 3DFE models can provide important results for engineers/technicians responsible for dam safety and health monitoring, namely: i) to study the evolution of modal parameters over time, enabling to assess the effects of reservoir water level and thermal variations on the measured response; ii) detect structural changes due to material deterioration, given that its evolution over

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time will affect the global stiffness of the dam and cause changes in the natural frequencies; iii) to automatically detect earthquake events and hence analyze the seismic response due to seismic ground motion based on recorded acceleration time histories; and iv) to evaluate the structural effects caused during significant seismic events (e.g. cracking phenomena), by analyzing the dynamic performance in normal operation conditions before and after said events. In resume, based on the comparison between measured and computed response, structural health assessments can be made, and thus immediate actions can be proposed for both regular maintenance and eventual emergency cases.

Seismic and Structural Health Monitoring of large dams

Fig. 1. Seismic and structural health monitoring for large dams. Main components and results.

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3 SSHM System Installed at the Cahora Bassa Dam Cahora Bassa dam (Fig. 2), is located near Songo, in western Mozambique, and was built in late 1974 on the Zambezi river. It impounds Lake Cahora Bassa, a 270 km long lake that extends to the Mozambique-Zimbabwe/Zambia border. Cahora Bassa dam is a 170 m high double curvature arch dam, founded on a gneissic granite mass rock foundation of very good quality. The crest, at el.331 m, has a 303 m long arch. The central cantilever is 23 m wide at the base and 4 m wide at the crest. Concerning structural integrity, a concrete swelling process was detected in the 1980’s, a few years after the dam’s construction (a cracking pattern can be observed at the top of the crest, which is common on dams with internal swelling processes); also, small horizontal cracks appeared at the upstream face due to the high tensile stresses induced by the hydrostatic pressures and/or thermal loading. An SSHM system (Fig. 2) was installed in Cahora Bassa dam in 2010, aiming to characterize its dynamic behavior under ambient/operational vibrations and to measure the response during seismic events for health monitoring over time. Therefore, the system was designed to continuously record acceleration time histories using extremely low noise sensors, at a sampling rate of 50 Hz and considering one-hour series, with a full-scale recording range of ±1 g. Thus, the implemented monitoring scheme includes 10 uniaxial accelerometers (EpiSensor ES-U2), located in the upper gallery below the crest, to measure accelerations in radial direction, and 3 triaxial sensors (EpiSensor EST), one positioned near at the base (dam-foundation interface) and two in the right and left banks. All sensors (19 channels) are connected to a 24 channel Granite unit from Kinemetrics for data acquisition/digitalization, in 24 bits. In total, 19 accelerograms are continuously recorded, every hour, and then sent and stored to the computer server located in the offices at the dam’s control center. Specific software has been developed to analyze data collected with continuous monitoring systems, including the one installed in Cahora Bassa dam. This software comprises various tools, namely for: i) interactive (with user interface) and automatic modal identification, using the Frequency Domain Decomposition (FDD) method [9] with Singular Value Decomposition (SVD), a novel technique for automatic peak selection based on a threshold line procedure, and an optimized clustering technique for distinguishing the different modal frequencies; and ii) for automatic earthquake detection, based on maxima analysis, enabling alert emails to be sent to owners and/or engineers responsible for the safety control. Based on the experimental results obtained with this software, it is possible to evaluate the evolution of the dynamic behavior of dams over time and to carry out comparative studies with the predicted response from FE analysis. The numerical simulations for this paper were conducted using DamDySSA4.0, a 3D FE program developed for linear and non-linear dynamic analysis of concrete dams. The dam-reservoir-foundation system is simulated using a coupled model [10] with massless foundation, based on a formulation in displacements and pressures to simulate the pressure waves’ propagation throughout the reservoir [11]. For modal analysis, a state space formulation with two state matrices and complex modal coordinates is used to solve the eigen problem of the whole system and calculate natural frequencies (eigenvalues) and mode shapes (eigenvectors). For seismic

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analysis, the response is computed by an algorithm for numerical integration in time domain based on the Newmark method, with the seismic accelerograms applied at the base and uniformly distributed along the dam-rock interface.

Seismic and Structural Health Monitoring system in Cahora Bassa dam SSHM software 1. Monitoring data analysis 1.1 Modal identification 1.2 Automatic detection of seismic vibrations 2. Finite element analysis 2.1 Complex modal analysis 2.2 Linear seismic analysis 2.3 Non-linear seismic analysis SSHM equipment

Fig. 2. Seismic and structural health monitoring system installed Cahora Bassa dam.

4 Vibrations Monitoring over Time The dynamic behavior of Cahora Bassa dam under ambient/operational excitation is analyzed for the monitoring period between August 2010 and June 2019, with a reservoir level variation from 312 to 326 m: the evolution of the automatically estimated natural frequencies for five dam vibration modes is presented (Fig. 3). The frequency value of the first mode ranges between 1.95 Hz to 1.78 Hz, considering a water level variation from 312 m to 326 m, respectively; for the second mode, the values vary from 2.4 Hz to 2.16 Hz. As expected, the influence of the water level in the dynamic response of the dam is clearly noted, which can be seen by the variations in natural frequencies (especially for modes with higher frequencies).

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The numerical simulations were carried out using a reference 3D FE model of the dam-reservoir-foundation system, considering a Young’s modulus E = 40 GPa and a Poisson’s ratio m = 0.2 for the dam and the foundation materials and a pressure waves propagation velocity cw = 1500 m/s in water. For dynamic calculations the relation Edyn = 1.3  E was used. Figure 3 presents the comparison between the identified natural frequencies over time and the frequency curves from 3D FE analysis, considering the real water level variations as inputs to the FE model, as well as the mode shapes computed for a water level at el. 319 m. Focusing on the first five vibration modes, modes 1 and 5 are antisymmetric, while modes 2, 3 and 4 present symmetric shapes. The comparative analysis shows an excellent agreement between identified and computed natural frequencies for the first five modes, with differences of less than 0.1 Hz noted for the third and fourth modes.

Fig. 3. Cahora Bassa dam. Evolution of the natural frequencies (2010–2019): comparison between identified and computed (FEM) natural frequencies.

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5 Measured Seismic Response Under Low/Medium Earthquakes With Cahora Bassa dam’s SSHM system, several earthquakes have been automatically identified and recorded, allowing studies on the seismic response to be carried out based on measured accelerations. Some important issues to consider are the base to top amplification of the measured vibrations, the influence of reservoir water level and the damping of the dam-reservoir-foundation system under seismic loading, for different earthquake events. In the present work, the seismic response of Cahora Bassa dam is analyzed for a low magnitude earthquake, near Songo, at about 30 km from the dam, which was measured at the dam site on June 21, 2017. The water level during the seismic event was at el. 319 m, i.e. 12 m below the crest, and the recorded peak acceleration near the dam base was 13.5 mg (0.1324 m/s2), in the upstream-downstream direction. The comparison between measured and computed seismic accelerations of Cahora Bass dam is presented in Fig. 4. The measured accelerations were recorded with the sensor located at the upper gallery (el. 326 m), about 5 m to the right of the center of the dam: a peak acceleration of about 39 mg was measured (base to top amplification factor of 2.9 times). The seismic simulations were performed considering a reservoir level at el. 319 m and the seismic accelerograms measured at the dam base as inputs: a peak acceleration of 40.5 mg was computed (amplification factor of 3 times). In this study, it was possible to fit the computed accelerations to the measured response by using a damping ratio of about 5% in the dam and 20% in the foundation (although these values are high in comparison with standard ratios, analogous conclusions have been drawn by other researchers [12, 13]).

Fig. 4. Cahora Bassa dam. Seismic event detected on June 21, 2017. Measured and computed seismic response

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6 Predicted Seismic Response for a Strong Earthquake In this section is presented a study on the non-linear seismic response of Cahora Bassa dam for a load combination including the self-weight (SW), the hydrostatic pressure (HP) for full reservoir and a seismic accelerogram with peak acceleration of 0.6 g. The numerical simulations are carried out with DamDySSA4.0, using a FE model with joints and concrete non-linear behavior (Fig. 5). The dam concrete and the foundation rock are assumed as isotropic materials, considering the same properties as in the linear model. Damping ratios of about 10% in the dam and 15% in the foundation were assumed. The non-linear concrete behavior is simulated using a strain-softening constitutive law with tensile strength ft = 3 MPa and compressive strength fc = 3 MPa. For the vertical contraction joints, null cohesion is considered, to simulate the opening movements under tensions, and a 30° friction angle is used to consider the existing shear keys in the contraction joints of Cahora Bassa dam. For the dam-rock interface, high values of cohesion and friction angle are used to take into account the dam’s insertion in the valley along the foundation. The seismic input is a 10 s computer generated seismic accelerogram, with a peak acceleration of 0.6 g, which is applied at the dam base in the upstream-downstream direction.

Fig. 5. Cahora Bassa dam. FE model, material properties, joints, concrete damage law and seismic input.

These results show that Cahora Bassa dam’s seismic response under a strong earthquake is clearly influenced by the opening of the vertical contraction joints, for a full reservoir condition, and that significant concrete cracking under tension can occur in the upper part of the dam, at the downstream face, and close the dam base, at the upstream face. Nevertheless, concerning the structural safety for a collapse situation, it is possible to conclude that Cahora Bassa dam is capable of withstanding the strong earthquake applied (Fig. 6).

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Fig. 6. Cahora Bassa. Non-linear seismic response. Tension and compressive damage, and joints movements at the crest.

7 Conclusions The recorded monitoring data from the SSHM system of Cahora Bassa dam was used, in combination with results from 3DFE analysis, to study the dynamic behavior of the dam under ambient/operational vibrations, enabling to evaluate the evolution of natural frequencies over time, considering the influence of the reservoir water level: a good agreement was obtained between identified natural frequencies and the computed frequency curves. The measured vibrations data was also used for analyzing the measured response during a seismic event, based on the accelerations recorded in the central section, at the upper gallery (it was possible to fit the computed accelerations to the measured response by using a damping ratio of about 5% in the dam and 20% in the foundation). A non-linear seismic analysis of Cahora Bassa dam was also carried out. It was considered a load combination involving the self-weight, the hydrostatic pressure for full reservoir and the seismic load, represented by a computer generated seismic accelerogram with a peak acceleration of 0.6 g. The seismic simulations were carried out considering the non-linear behavior of concrete and the joints movements. The comparison between linear and non-linear seismic response showed how taking the movements of the vertical contraction joints and the non-linear behavior of concrete in the model into account has influenced the dam’s structural response. Namely, the opening of the vertical contraction joints led to a significant decrease of the arch stresses at the top of the dam, and hence to an increase of the stresses along the height of the cantilevers (particularly to the right and left of the central section), at the downstream face, and near the base of the dam, at the upstream face. Significant tension damages were computed in these zones. Regarding the structural safety for a collapse situation, the presented results allowed to conclude that the Cahora Bassa dam is capable of withstanding the 0.6 g earthquake, despite the occurrence of important tensile damage. Finally, with this paper it was possible (i) to emphasize the advantages of using SSHM systems, complemented with software for monitoring data analysis, and

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programs for FE analysis to study the dynamic behavior of arch dams over time and thus provide useful information for supporting safety control and health monitoring; and (ii) to demonstrate the potential of DamDySSA4.0 for predicting the non-linear seismic behavior of concrete dams and to support seismic safety verifications.

References 1. Limongelli, M.P., Çelebi, M.: Seismic Structural Health Monitoring: From Theory to Successful Applications. Springer Tracts in Civil Engineering. Springer, Cham (2019) 2. Oliveira, S., Alegre, A.: Seismic and structural health monitoring of dams in Portugal. In: Limongelli, M., Çelebi, M. (eds.) Seismic Structural Health Monitoring - From Theory to Successful Applications. Springer Tracts in Civil Engineering, pp. 87–113. Springer (2019) 3. Oliveira, S., Alegre, A.: Vibrations in large dams. Monitoring and modelling. In: ICOLD 26th Congress and 86th Annual Meeting, Vienna, Austria (2018) 4. Alegre, A., Carvalho, E., Matsinhe, B., Mendes, P., Oliveira, S., Proença. J.: Monitoring vibrations in large dams. In: HYDRO 2019, Porto, Portugal (2019) 5. Carvalho, E.F., Tembe, I.: On-line dynamic monitoring of Cahora Bassa dam. In: International Symposium on Dams for a Changing World, Kyoto, Japan (2012) 6. Carvalho, E.F., Matsinhe, B.T.: First steps on automatic data acquisition and analysis system in Cahora Bassa dam. In: HYDRO 2014, Como, Italy (2014) 7. Carvalho, E.F., Matsinhe, B., Oothuizen, C.: Monitoring system of Cahora Bassa dam … the past, present and way forward. In: International Symposium on “Appropriate technology to ensure proper Development, Operation and Maintenance of Dams in Developing Countries”, Johannesburg, South Africa (2016) 8. Bukenya, P., Moyo, P.: Monitoring the structural behavior of concrete arch dams: the case of Roode Elsberg dam, South Africa. In: SANCOLD Conference on Management of Dams and Reservoirs in Southern Africa, Centurion, Thswane, South Africa (2017) 9. Brincker, R., Zhang, L., Andersen, P.: Output-only modal analysis by frequency domain decomposition. In: ISMA25 Noise and Vibration Engineering, Leuven, Belgium (2000) 10. Zienkiewicz, O.C., Bettess, P.: Fluid-structure dynamic interaction and wave forces. An introduction to numerical treatment. Int. J. Numer. Meth. Eng 13, 1–16 (1978) 11. Zienkiewicz, O.C., Taylor, R.L., Zhu, J.Z.: The Finite Element Method: Its Basis and Fundamentals. 6th edn. Elsevier Butterworth-Heinemann (2005) 12. Proulx, J., Darbre, G.R., Kamileris, N.: Analytical and experimental investigation of damping in arch dams based on recorded earthquakes. In: 13th World Conference on Earthquake Engineering, Vancouver, Canada (2004) 13. Robbe, E., Kashiwayanagi, M., Yamane, Y.: Seismic analyses of concrete dam, comparison between finite-element analyses and seismic records. In: 16th World Conference on Earthquake Engineering, Santiago, Chile (2017)

Concrete Crack Detection from Video Footage for Structural Health Monitoring Sushmita Kadarla(&) , Sree Keerthe Beeram , Prafulla Kalapatapu , and Venkata Dilip Kumar Pasupuleti Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India {sushmita170107,sreekeerthe170102}@mechyd.ac.in, {prafulla.kalapatapu, venkata.pasupuleti}@mahindrauniversity.edu.in

Abstract. Non-destructive imaging is largely encouraged as a preliminary investigation for damage identification on concrete structural surfaces. Cracks are basic signatures for any structure to initiate the damage. As the whole world is currently connected with lot of cameras all around for various purposes either it be for traffic studies, accident analysis, thefts, natural or human disasters. Alternatively, the same video frames obtained from cameras located in or on the structure can be analysed even for the structural health monitoring. This study aims at identifying the cracks from images mined out of the video frames apart from the crack propagation and length of the crack. Convolution Neural Network is used to train over the images from the video captured during the laboratory compressive strength experiment on a concrete cube to examine and estimate the crack properties. This methodology can be extended to the real-life scenario to alert the damages caused in the structures. Keywords: Crack detection
Convolution Neural Network processing
Non-destructive method
Video footage


Image

1 Introduction Almost all the concrete structures such as dams, bridges, roads, and buildings gradually deteriorate due to natural or human effects, leading to loss of their functional properties or decreased serviceability. In general, damage in concrete structures is scaling, spalling, curling, and cracking, resulting in deterioration. Cracks are considered one of the most elemental damage and prominently seen in all the concrete structures but with varying severity. The reasons for cracking could be drying shrinkage, thermal contraction, restraint to shortening, non-uniform settlement, and external loads applied. The underlying science behind cracks is said to be its relatively low tensile strength of concrete [1]. Cracks developed to reduce the structure’s stiffness and durability apart from increasing the deterioration rate [2]. They appear on beams, columns, slabs, foundation walls, which may lead to structural deformation, and in some cases, it might be severe. Early detection of cracks is essential for taking preventive steps and protecting structural health [3]. A considerable amount of research carried worldwide in the last two decades, proposing various solutions like vision-based, image processing, © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 79–88, 2021. https://doi.org/10.1007/978-3-030-64594-6_9

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neural networks, fuzzy, and sensor-based to detect cracks [4–7]. Machine vision-based methods have successfully detected early cracks as a part of advanced structural health monitoring systems [8]. There is less research on differentiating between active and passive cracks, even after a fair amount of research on crack detection algorithms. Nowadays, as most technological devices connect the world in terms of sensors or surveillance cameras, the former is used for structural health monitoring or disaster alerts, whereas the latter is primarily used to observe social safety, traffic studies, and related. Digitally captured videos through surveillance cameras can help structural health monitoring identify various defects and their propagation as they function around the clock [9]. This study is motivated to find solutions through images using AI for robust and faster detection of early cracks with an extension of finding crack propagation and its length.

2 Background The study on structural concrete cracks is as old as concrete itself. Cracks are the initial stage of structural deterioration, and its severity depends on length, width, and depth. Early detection of cracks can save the economy and possible failures. Automatic crack detection using the latest technologies for faster and robust results is being developed by considering human errors and difficulties during the visual inspection [10]. Arun and Sumathi [11] provided information about various research issues that can help other researchers in crack detection using image processing. They have considered 50 research papers related to crack detection and carried a critical review. The authors provided an analysis based on image processing techniques, objectives, accuracy level, error level, and image data set, helping researchers who want to research crack detection further. They have analyzed objectives like the crack’s length, the crack’s width, and the crack propagation direction. Datasets used for testing were examined and concluded that most of the system uses real data sets for convenience and efficiency. Lee et al., [12] has proposed a methodology based on a morphological technique that automatically detects and analyzes the cracks from the digital image and gives length, width, and orientation apart from pattern recognition. Sravya et al., [13] developed a graphical user interface using MATLAB, which can upload an image consisting of its dimensions or distance from which the photograph is taken and gives crack length and type. Their accuracy is 92.94% in giving the crack length and 78.09% in crack width. Adhikari et al. [14] developed the first integrated model based on digital image processing showing the defects in the three-dimensional spatial graphical representation to precisely depict live visual inspection. The author was also able to perform crack length and used neural networks to predict crack depth. Kim et al., [15] surveyed identifying cracks in concrete structures through various image binarization algorithms. Silva and Lucena [16] demonstrated the capability of a deep learning approach for concrete crack detection. Li et al., [17] proposed automatic pixel-level multiple damage detection of concrete structure using a fully convolutional network (FCN) because of deep learning-based structural damage detection methods that overcome the limitation of lighting and shadow changes real-world situations. Deep learning methods detect structural damage at the image and grid cell level. A dataset of

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2750 images of 504  376 pixels, including crack, spalling, efflorescence, and holes in the concrete structure, is considered and labeled manually for their study; Finally, a comparative study was conducted using the SegNet-based method to examine the performance of the proposed FCN-based approach. The results obtained show better performance and can detect multiple concrete damages at the pixel level in realistic situations. Cha and Choi [18] proposed deep learning-based crack detection using Convolutional Neural Network (CNN) and used a 256  256-pixel resolution classifier. It was a vision-based method using a CNNs for detecting cracks without calculating the defect features from the dataset of 40000 images. They used 55 images of 5888  3584-pixel resolutions taken from different structures for testing robustness and adaptability, and they were not used for training and validation processes under conditions like shadows, fragile cracks, extreme light spots. They have compared CNN using traditional Canny and Sobel edge detection methods. Kim and Cho [19] proposed an automated vision-based detection of the crack using deep learning techniques and divided the training set into five classes of cracks on concrete surfaces. Authors have evaluated images taken from the field and real-time video frames taken using an unmanned aerial vehicle. They have implemented Data augmentation skills such as rotation, blurring, and color adjustment to enhance the diversity and quantity of the thousands of training and validation images. Mostly two folded crack detection areas have been an uprising, first neural networks for faster and robust crack detection, and second is digital image processing, which is best in quantifying the physical parameters of the crack.

3 Methodology In general, sensors and some machine learning techniques in SHM can evaluate the structures. These sensors can monitor any structure over time when placed in or around the structures. Convolution neural network (CNN) and Image processing methodologies can be used for early crack detection. CNN’s had always been the prevalent model for object recognition, and as cracks are a fundamental cause of degradation of a structure, it is necessary to inspect them as early as possible. Researchers preferred CNN’s as they are easy to control data and even easier to train. CNN’s do not experience overfitting at an alarming scale when being used on millions of images. The only challenge of using CNN’s, they are hard to apply to high-resolution images. In line with CNN’s, the study also carries Digital Image processing for video footage analysis to find its geometry. Our research is two-folded; hence two different datasets are used to make the analysis. Formerly, the dataset for CNN’s comprises of 40000 images taken from the Imagenet dataset. These images are first trained under two different folders’ labels, crack and No crack. The model’s accuracy is based on the number of images used for training and its size. Thus, some images are randomized and picked in the ratio of 8:2 for training and testing—thirty-two thousand images for training and 8000 images for validating the model. Later, the dataset for DIP was prepared from our lab experiment video. The duration of 20 min is captured and segmented the video into 25 frames per sec and each frame with one image leading to 30000 images in total.

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Convolutional Neural Networks (CNN)

The use of Fully Convolution Neural Network can classify images and detect the objects. CNN is mainly used for processing and recognizing the data from pixels, and the results achieved are more accurate, which can also be helpful in video analysis. It can classify every image into specific classes. The algorithm can be trained to recognize and classify the detected image into a particular class. Within CNN’s, there are many pre-trained architectures such as AlexNet, LeNet, GoogleNet, ResNet, Etc. AlexNet outperforms the rest of the architectures when used on a larger dataset and consists of eight layers: five convolutional layers and three fully-connected layers. Figure 1 represents the proposed architecture for this study. The dataset comprises 40000 images, undergoes training individually, and images are filtered based on its noise. So, they are standardized into the resolution of 227  227  3 RGB before importing into the model. Few images with more significant noise get rejected, and accepted images are taken to the next step for feature extraction and feature learning. As a next step, AlexNet architecture is proposed to classify the dataset further. Firstly, it goes through a large convolutional layer and the max-pooling layer, and the process is repeated twice to detect the object in the images. Then the images go through three smaller convolutional layers followed by a max-pooling layer. In the pipeline, images are flattened and connected with several fully-connected layers. The activation functions are all Rectified Linear Units (ReLU), mostly preferred for accurate predictions.

Fig. 1. Proposed architecture

Training the Model The dataset is loaded into the model, and it automatically names the trained images based on their folder names and stores it as an ImageDatastore object. It can even store the data that does not fit in the memory and reads all the images as batches during the CNN training. Figure 2 Represents the flow considered to train the AlexNet architecture model. Now, these images are divided into training and validation data sets; eighty percent of the images are used for training, and the rest twenty percent are taken

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for validation. Analyze network is used to analyze all the data into the dataset folder. It helps to display an interactive visualization of the network architecture. We also get the visualization of detailed information about the network layers. The last three layers are trained with only the standard categories; these layers must be changed for the new classification set. Multiple iterations can do this, and we used 84 iterations per epoch and six epochs for replacing the last three layers with a fully connected layer. Then all the images are set to have the same size and resolution as per the number of classes.

Fig. 2. AlexNet training model

Testing the Model During the training phase, the dataset is pre-processed so that all the images must be the same in size and resolution, 227  227  3 RGB. It helps to prevent the network from overfitting and to memorize the exact details. The initial learning rate is set to 0.01, but the fully connected layer’s learning rate is increased. It will let new layers in fast learning and others slower. During the learning phase, the number of epochs is less. An epoch is when an entire dataset is passed in a cycle for the entire training dataset. We can also note the frequency while plotting the epochs. Transferred networks and new layers are trained. This whole process uses 10 min on GPU or 42 min on CPU. The images which are taken for testing are classified using a fine-tuned network. Finally, image classification results in images with the labels as Crack and No crack. The results are influenced based on the dataset’s size and the variety of images in the dataset. 3.2

Digital Image Processing (DIP)

A DSLR camera is used to capture concrete cube video footage during its compressive strength experiment in the laboratory, as shown in Fig. 3. The captured video footage is converted into 25 frames per sec and each frame with one image. All these frames

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extracted from the video footage are used as test images for crack classification. At first, the pre-trained Alex net model is loaded, i.e., by running the code for validation, AlexNet gets trained with the classes to detect as Crack and No crack. MATLAB is used for digital image processing in which the Imread function helps to read the images in the file to be tested; all the images in it must be resized.

Fig. 3. Compressive strength testing of concrete cube in the laboratory (a) Test setup with recording (b) Application of load (c) Fully tested cube (d) Initial specimen (e) First crack appearance (f) Full crack formation

It can be done using Imresize, and the Rgbgray function helps us remove the hue and eliminate the saturation information from the image while testing the dataset with retrained Fully Convolution Network (FCN) layers. In contrast, the bwmorph function removes the pixels so that crack’s image without holes shrinks to minimally connected strokes. This operation is repeated ‘n’ times on a binary image until the operation stops changing. Skeletonization is a process of reducing other regions to its skeletal part, which has a large extent of its original region while the other foreground pixel regions get removed. It helps in an accurate segmentation as the noise gets removed, and the images are classified. The results of the images are with their predicted labels. These classified grayscale crack images are saved in a new folder to find the crack’s length in each image. These grayscale images are converted to binary images, and then they are further skeletonized to find the crack length. All the pixels sum up and crack length is estimated.

4 Results and Discussion The proposed methodology has resulted in good accuracy for crack detection and the crack’s physical characteristics.

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Crack Detection

The classifier is trained for epochs and 84 iterations. During this training, CNN and Alexnet classifiers achieved 90% accuracy. For validation, the classifier divides the training set into 8:2 ratios, as mentioned before.

Fig. 4. Training and validation of results for the crack dataset with accuracy and loss while training images

Figure 4 demonstrates the proposed architecture’s outcome using AlexNet, which successfully identified hairline crack to the severe structural cracks. The model results, which are trained using 40000 images with an 8:2 training and testing ratio, have taken 14 epochs and 84 iterations with the learning rate of 0.0001 by achieving 95% accuracy for image classification cracks and no cracks. 4.2

Physical Crack Properties Using Digital Image Processing

Video taken in the lab is converted as.jpg files to calculate the crack’s physical properties. As a first step, the images are segregated into the crack or no crack using CNN’s, as seen in Fig. 5. Then all the images consisting of cracks are iteratively converted to greyscale, which is binarized or converted to ones and zeroes using the ‘imbinarize’ method. Processed images are complemented where cracks are viewed in white color, and remaining in black color, as seen in Fig. 6. Crack lengths are calculated in terms of pixels, and the increment of pixels always gives the active crack indication, as the images are part of the series.

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Fig. 5. Segregation of concrete Crack and non-crack images.

Fig. 6. Calculated length of the cracks in terms of pixels

Figure 7 demonstrates the proposed digital image processing technique’s outcome to calculate the crack’s length. It shows four sets of images, and each set consisting of three images; The first image is the input extracted from the video. The second one is the greyscale image, segmented into white and black pixels and white indicate crack.

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Using regionprops function to read grayscale images clearly distinguishes the number of pixels based on color and showed in each set’s third image. The Crack length is determined using the continuity function, which can trace single or multiple cracks on the crack surface. The increase in the crack’s length compared between any two steps will give a clear picture of crack activity and propagation rate per each frame.

Fig. 7. Four sets of images were processed to find the length of the crack at different time steps.

This method calculates all the crack’s possible characteristics, static and dynamic, which plays a vital role in the structures’ active cracks.

5 Conclusions In this study, a vision-based technique convolutional neural network is used to detect cracks on concrete structures. AlexNet (pre-trained network) was selected as an FCN encoder for crack image classification. The whole fully connected network architecture of Alexnet was trained end to end on a subset of crack images. This subset was divided into a training set and validation set in the ratio of 8:2. The accuracy reached approximately 90% for testing, training, and validation. It helped in training Alexnet with the required categories. By this fully connected network segmentation method, the cracks could be identified accurately. Furthermore, the cracks were identified using video footage captured by a static camera, and results show relatively better performance apart from detecting multiple concrete damages. It can be used to estimate the length of the cracks in a realistic situation as well. This two-folded method can help in continuous monitoring of any structure by using surveillance cameras. Further, this research work is being carried on the videos obtained from the surveillance cameras; even though there is a challenge in the orientation of camera and light on the concrete surfaces, more specific training and methods are being developed. If this can be implemented, all the critical structures that are being monitored, need no extra-special devices for structural health monitoring.

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References 1. Kovler, K., Chernov, V.: Types of Damage in Concrete Structures. In: Failure, distress and repair of concrete structures, pp. 32–56. Woodhead Publishing (2009) 2. Castel, A., Gilbert, R.I., Ranzi, G.: Overall stiffness reduction of cracked reinforced concrete beams due to long term effects. In: Zdeňk, P.B. (eds.) Mechanics and Physics of Creep, Shrinkage, and Durability of Concrete: A Tribute, pp. 443–450 (2013) 3. Chakraborty, J., Katunin, A., Klikowicz, P., Salamak, M.: Early crack detection of reinforced concrete structure using embedded sensors. Sensors 19(18), 3879 (2019) 4. Yao, Y., Tung, S.T.E., Glisic, B.: Crack detection and characterization techniques—an overview. Struct. Control Health Monit. 21(12), 1387–1413 (2014) 5. Prasanna, P., Dana, K., Gucunski, N., Basily, B. Computer-vision based crack detection and analysis. In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace systems 2012, vol. 8345, p. 834542. International Society for Optics and Photonics (2012) 6. Oertle, D.H., Randall, G.I.: Crack detection by electrical resistance. U.S. Patent 4,503,710, 12 March 1985 7. Chen, S.: Crack detection using a frequency response function in offshore platforms. J. Mar. Sci. Appl. 6(3), 1–5 (2007) 8. Liu, Z., Cao, Y., Wang, Y., Wang, W.: Computer vision-based concrete crack detection using U-net fully convolutional networks. Autom. Const. 104, 129–139 (2019) 9. Wong, L.N.Y., Einstein, H.H.: Using high speed video imaging in the study of cracking processes in rock. Geotech. Test. J. 32(2), 164–180 (2009) 10. Fujita, Y., Hamamoto, Y.: A robust automatic crack detection method from noisy concrete surfaces. Mach. Vis. Appl. 22(2), 245–254 (2011) 11. Mohan, A., Poobal, S.: Crack detection using image processing: a critical review and analysis. Alexandria Eng. J. 57(2), 787–798 (2018) 12. Lee, B.Y., Kim, Y.Y., Yi, S.T., Kim, J.K.: Automated image processing technique for detecting and analyzing concrete surface cracks. Struct. Infrastruct. Eng. 9(6), 567–577 (2013) 13. Nedunuri, S., Thota, N., Pasupuleti, V.D.K., Kalapatapu, P.: Investigation of crack properties using image processing: a user interface. In: Emerging Trends in Civil Engineering, pp. 81–90. Springer, Singapore (2020) 14. Adhikari, R.S., Moselhi, O., Bagchi, A.: Image-based retrieval of concrete crack properties for bridge inspection. Autom. Const. 39, 180–194 (2014) 15. Kim, H., Ahn, E., Cho, S., Shin, M., Sim, S.H.: Comparative analysis of image binarization methods for crack identification in concrete structures. Cem. Concr. Res. 99, 53–61 (2017) 16. Silva, W.R.L.D., Lucena, D.S.D.: Concrete cracks detection based on deep learning image classification. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 2, no. 8, p. 489 (2018) 17. Li, S., Zhao, X., Zhou, G.: Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network. Comput.-Aid. Civ. Infrastruct. Eng. 34(7), 616– 634 (2019) 18. Cha, Y.J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural networks. Comput.-Aid. Civ. Infrastruct. Eng. 32(5), 361–378 (2017) 19. Kim, B., Cho, S.: Automated vision-based detection of cracks on concrete surfaces using a deep learning technique. Sensors 18(10), 3452 (2018)

MEMS-Based System for Structural Health Monitoring and Earthquake Observation in Sicily Antonino D’Alessandro1, Giovanni Vitale1,2, and Salvatore Scudero1(&) 1

Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, Rome, Italy {antonino.dalessandro,salvatore.scudero}@ingv.it 2 Dipartimento Scienze della Terra e del Mare DiSTeM, Università degli Studi di Palermo, 90123 Palermo, Italy

Abstract. The implementation of systems for Structural Health Monitoring and Earthquake Observation is increasing in the last years owing to the development of new technologies which enable low-cost and small-size devices to be installed in large-scale or high-density applications. This paper introduces the implementation of monitoring systems, either for structural health assessment and earthquake observation. The applications are based in Sicily (Italy), a region characterized by a high seismic hazard and where the buildings are often old and vulnerable. The system relies on a MEMS (Micro Electro-Mechanical Systems) sensor, a 3-axial accelerometer which has been specifically selected in order to ensure the suitability for the specific applications: accelerations from 100 to 102 Hz. We present the details of the designed monitoring station, of the network architecture, and some of the recorded data. Keywords: Structural Health Monitoring
Urban seismic network Earthquake Observation
Seismic risk reduction




1 Introduction The implementations of systems for Structural Health Monitoring (SHM) and Earthquake Observation (EO) are highly increasing in the last years, when the limitations deriving from the traditional systems have been overcome. Such limitations basically consist in the high costs of the traditional instruments and in the difficulty to maintain, the monitoring systems in the long-term (e.g. a decade or more) after their implementation. For this reason the SHM and EO systems have been usually limited to small-scale or low-density applications with poor technical and scientific results. The advancements in term of miniaturization, sensitivity, and quality of data made possible to overcome the compromise between technical-scientific needs and the economic affordability. As a consequence, the implementation of SHM and EO increased really fast in recent time. Refer to [1, 2] for an overview of the applications in Italy. Kinematic (acceleration, velocity, displacement), physical (temperature, humidity), or mechanical (forces, strain) parameters can be monitored by means of different © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 89–95, 2021. https://doi.org/10.1007/978-3-030-64594-6_10

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types of sensors [3]. The traditional inertial sensors for the measure of kinematic parameters are based on a spring-mass mechanism and have heavy proof masses making them bulky and difficult to transport and manage. The development Micro Electro-Mechanical Systems (MEMS) as inertial sensors (but not only) characterized by reduced size, weight, and cost played a primary role in such rising of SHM and OE systems [4]. Similarly, advancements regarded the robustness and the reliability of the systems, the capability of data transmission in terms of frequency rate and amount of information, the computational capability for data processing, and also the lowering of power consumption. In this paper we introduce the realization of the MEMS-based, real time monitoring system and its implementation for SHM and EO in Sicily (Italy).

Fig. 1. Working scheme of the monitoring station (top left), the assembled station equipped with the digital MEMS accelerometer (top right), and scheme of the monitoring network (bottom). See the text for further details.

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2 The Monitoring System The monitoring system consists of a single-board computer which manages the acquisition and the transmission of data through a dedicated code (Fig. 1). The main sensor is a 3-axial MEMS capacitive accelerometer with digital output. This sensor is suitable for dynamic accelerations in the range of ±2 g, it is characterized by a measure resolution of 76.3 lg, and white noise of 280 lg. Several studies indicate the suitability of such devices for earthquake and structural monitoring systems [5–10] when they are characterized by flat noise response to acceleration and resolution (smallest detectable acceleration) in the order of 10−2 – 10−3 m/s−2 [4]. A devoted software for the acquisition automatically runs cyclically, sampling the data at frequency of 200 Hz. Three waveform files, one for each component, are written in miniSEED format which is a standardized protocol for the exchange of earthquake data used by seismologists worldwide. The data transmission exploits a ring-server conceived by the Incorporated Research Institutions for Seismology (IRIS) which runs continuously and cyclically. The synchronization of the signals between the various stations is fundamental and is ensured by a GPS or, alternatively by a NTP server (Network Time Protocol). Finally, the monitoring station carries a 5,200 mAh power bank (UPS) to stabilize the energy flux and to provide power supply in case of temporary black-out. Every monitoring stations is linked to a main hub by means of internet connection to form a monitoring network. The network topology chosen for the SHM and OE applications is a star network where each host is connected to a central hub with a point-to-point connection (Fig. 1, bottom). This topology has been chosen because it complies with the main needs of our system: flexibility and reliability. From the hub can depart n linear connections, therefore further devices (i.e. monitoring stations) can be added or removed without disturbing the network. Moreover, two or more endpoints can be merged in a sub-network and, similarly, two or more networks can be merged into a unique network simply connecting their hubs. Every node (i.e. monitoring station) can be accessed remotely to fix possible malfunctions or to update the software. The set-up and the arrangement of the monitoring stations at the sites, and within the edifices, have been accurately planned. All the details about the device, the hardware and software components, and about the code and the can be retrieved in the technical report by [11].

3 Case Studies The MEMS-based systems have been implemented in Sicily (Italy), a region where the seismic hazard is high (Fig. 2). In such context, the vulnerable historic buildings often represent the most exposed place to seismic risk. Some prototypal urban seismic networks implemented in relevant public buildings are being installed in municipalities of Acireale, Catania, Messina, Noto, Ragusa, Santa Ninfa, and Siracusa (Fig. 2), located in the areas struck by strong earthquakes (M > 6) several times during history (1169, 1542, 1693, 1818, 1908, 1968).

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The monitored building are the strategic (for their function or value) ones in the selected municipalities. SHM is a fundamental tool to integrate and support conservation strategies of infrastructures and to preserve their strategic function (i.e. security, management, organization) and the architectural heritage. SHM should be considered necessary, at least for public edifices with strategic function, since stress factors acting on all the structures (either natural or anthropogenic) lower their resistance properties and may induce potential risks in the long-tem.

Fig. 2. Seismic hazard map of the Sicily region from http://zonesismiche.mi.ingv.it/. Colours indicate the peak ground acceleration (g) with probability of exceeding equal to 10% in 50 years. The black full squares indicate the installed monitoring networks, the empty squares the planned ones.

Considering the scheme of a generic multi-storey building in reinforced concrete, the literature suggests to install sensors at every level and in correspondence with changes of stiffness for buildings with irregular scheme [12]. The sensors at the base of the building would also provide an almost unaltered record of the input motion which can be used as ground motion reference. For the masonry buildings, the scheme of a regular multi-storey one [13] can be directly reused taking account the numerous irregularities that often occurs in the historic edifices (Fig. 3). Therefore, it is necessary to consider the type and quality of connection between the walls, before to proper positioning the measuring points. All the sensors were levelled on the horizontal plane and the horizontal components accurately oriented along the N-S and E-W directions in order to have a unique reference systems for the signals at every station (Fig. 3).

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Fig. 3. Operation of installation and configuration of a monitoring station at the ground floor of the 18th century “Elefanti Palace” located in the main square of Catania (Sicily Italy). Schemes for the sensors’ distribution into edifices: a) minimum necessary requirements for a regular multistorey building, and b) ideal extensive installations for irregular masonry buildings.

4 Results The MEMS-based monitoring system enables the real-time structural health monitoring of the key infrastructures playing the major role during a crisis. The monitoring represents also the base for the fast damage assessment of the urban area in case of damaging events. The stress factors acting on the structures can be due to natural or anthropogenic factors: seismic events, atmospheric agents (wind, thermal cycles), vibration due traffic flow, applied loads, lowering of the resistance properties (corrosion, alteration, etc.) which effects can be assessed by means the appropriate monitoring (c.f. [3] for a complete review). The SHM allow also to compile a register of historical data, and to create files for postprocessing. Moreover, in case of strong earthquake, the monitoring stations allow to study the site response due to local geological conditions and also to assess the structural health and the characteristic features of the buildings. In fact, the analysis of the recorded signals enables to characterize the input signal (e.g. the earthquake) and the output signals (i.e. the edifice shaking) allowing, in a successive phase, to describe the relationship between the shaking level at the site and the variation of the equivalent structural modal parameters, while keeping into account the effects of soil–structure interaction. As example, we show the amplitude spectra recorded by the MEMS-based monitoring station installed in the second floor of a reinforced concrete building (the Town Hall of Santa Ninfa municipality) after a moderate earthquake (ML 3.1) at about 5 km distance (Fig. 4). The spectra of the horizontal components (red and green lines) show a main peak at about 4 Hz which is ascribable to the resonance of the building under the seismic shaking [11].

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Fig. 4. Amplitude spectra of the seismic signals (unfiltered accelerations) recorded in a monitored building in Santa. Ninfa after the ML 3.1 earthquake occurred on October 10th, 2018. (modified from [11]). Blu line: Up-Down component; red line: North-South component; green: Est-West component.

5 Conclusive Remarks Such designed monitoring systems with real-time transmission and small-scale design (building or urban) represent powerful tools for several tasks in the post-earthquake scenario which can be summarized with a continuous chain of actions, before, during, and after the arrive of the seismic waves at the nodes of the network. These tasks include the rapid assessment of earthquake damage through the automatic production of intensity maps (shakemaps), the procedures for search and rescue, the seismic microzonation. However, the most relevant future development in the near future would the realization of an on-site early warning, systems [14].

References 1. Gattulli, V. Implementation of identification methodologies on large-scale structures. In: Identification Methods for Structural Health Monitoring, pp. 1–34. Springer International Publishing (2016). http://dx.doi.org/10.12989/smm.2016.3.1.071 2. D’Alessandro, A., Costanzo, A., Ladina, C., Buongiorno, F., Cattaneo, M., Falcone, S., La Piana, C., Marzorati, S., Scudero, S., Vitale, G., Stramondo, S., Doglioni, C.: Urban seismic networks, structural health and cultural heritage monitoring: the National Earthquakes Observatory (INGV, Italy) experience. Front. Built Environ. 5, 127 (2019). https://doi.org/ 10.3389/fbuil.2019.00127 3. Moreno-Gomez, A., Perez-Ramirez, C.A., Dominguez-Gonzalez, A., Valtierra-Rodriguez, M., Chavez-Alegria, O., Amezquita-Sanchez, J.P.: Sensors used in structural health monitoring. Arch. Comput. Methods Eng. 25(4), 901–918 (2017). https://doi.org/10.1007/ s11831-017-9217-4

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4. D’Alessandro, A., Scudero, S., Vitale, G.: A review of the capacitive MEMS for seismology. Sensors 19(14), 3093 (2019). https://doi.org/10.3390/s19143093 5. D’Alessandro, A., D’Anna, G.: Suitability of low-cost three-axis MEMS accelerometers in strong-motion seismology: tests on the LIS331DLH (iPhone) accelerometer. Bull. Seism. Soc. Am. 103(5), 2906–2913 (2013). https://doi.org/10.1785/0120120287 6. D’Alessandro, A.: Monitoring of earthquakes using MEMS sensors. Curr. Sci. 107(5), 733– 734 (2014) 7. D’Alessandro, A., Luzio, D., D’Anna, G.: Urban MEMS based seismic network for postearthquakes rapid disaster assessment. Adv. Geosci. 40, 1–9 (2014). https://doi.org/10.5194/ adgeo-40-1-2014 8. Zou, X., Thiruvenkatanathan, P., Seshia, A.A.: A seismic-grade resonant MEMS accelerometer. J. Microelectromech. Syst. 23(4), 768–770 (2014) 9. Saunders, J.K., Goldberg, D.E., Haase, J.S., Bock, Y., Offield, D.G., Melgar, D., Walls, C.: Seismogeodesy using GPS and low‐cost MEMS accelerometers: perspectives for earthquake early warning and rapid response. Bull. Seism. Soc. Am. 106(6), 2469–2489 (2016). https:// doi.org/10.1785/0120160062 10. D’Alessandro, A., Vitale, G., Scudero, S., D’Anna, R., Costanza, A., Fagiolini, A., Greco, L.: Characterization of MEMS accelerometer self-noise by means of PSD and Allan variance analysis. In 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI), pp. 159–164 (2017). https://doi.org/10.1109/iwasi.2017.7974238 11. D’Alessandro, A., Greco, L., Scudero, S., Siino, M., Vitale, G., D’Anna, R., et al.: Sviluppo di una stazione sismica low-cost basata su tecnologiaMEMS. Quaderni di Geofisica, vol. 153, pp. 1–60 (2019). http://editoria.rm.ingv.it/quaderni/2019/quaderno153/ 12. Boscato, G., Dal Cin, A., Ientile, S., Russo, S.: Optimized procedures and strategies for the dynamic monitoring of historical structures. J. Civ. Struct. Health Monit. 6(2), 265–289 (2016). https://doi.org/10.1007/s13349-016-0164-9 13. Celebi, M.: Seismic instrumentation of buildings. US Department of the Interior, US Geological Survey (2000) 14. D’Alessandro, A., Scudero, S., Vitale, G., Di Benedetto, A., Bosco, G.L.: Optimization of low-cost monitoring systems for on-site earthquake early-warning of critical infrastructures. In: Gervasi, O., Murgante, B., Misra, S., Garau, C., Blečić, I., Taniar, D., Apduhan, B.O., Rocha, A.M.A.C., Tarantino, E., Torre, C.M., Karaca, Y. (eds.) ICCSA 2020. LNCS, vol. 12250, pp. 963–975. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58802-1_69

A Study on Vision Based Method for Damage Detection in Structures Narasimha Reddy Vundekode1 , Prafulla Kalapatapu2 and Venkata Dilip Kumar Pasupuleti2(&)

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MVSR Engineering College, Hyderabad, India [email protected] 2 Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India {prafulla.kalapatapu, venkata.pasupuleti}@mahindrauniversity.edu.in

Abstract. To ensure the safety and the usefulness of civil structures, it is fundamental to visually inspect and survey its physical and functional condition. Current techniques in condition and safety assessment of large concrete structures are performed physically promoting to subjective and unreliable outcomes, costly and time-consuming data collection, and safety issues. This paper presents a study on less time consuming and less expensive alternative to the present methods of preliminary assessment for the detection of damages in structures. Henceforth, the focus is set on various vision-based methods for different parameters like cracks, corrosion and spalling which cause damage and deterioration of structures. Thus, a study is made on the current achievements and drawbacks of existing methods as well as open research difficulties are outlined to help both the structural engineers and the computer science researchers in setting a motivation for future research. Keywords: Damage detection
Vision based methods techniques
Structural health assessment


Computer based

1 Introduction Even though the use of concrete has started centuries back, larger light was shed in the 19th century. Many experiments were carried to combine concrete and Iron, later replaced steel in the place of Iron producing effective and economical structures [1]. From the beginning of its use, reinforcement concrete as a construction material has been accepted worldwide and usage is exponential. The amount of Concrete used in last two centuries is equivalent to concrete used in last two decades. As perspective of global development, concrete is highly preferred for various structures, but the major challenge is maintenance throughout their design life for the civil engineer community [2]. Severity of deterioration in structures depends on geographical location and its functional utility like structures nearer to the sea-shore are more prone to corrosion and spalling e.g. harbors, shipyards and bridges [3], whereas structures which undergo cyclic loading or subjected to fatigue stress lead to microscopic to structural cracks e.g. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 96–105, 2021. https://doi.org/10.1007/978-3-030-64594-6_11

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beams, girders and tall buildings [4]. Initiation of internal distress is generally projected on the external surface of the concrete structure in terms of cracks and spalling. If these kind of damages are detected early, can prevent further deterioration and also enhances structural design life [5]. Maintenance of structures in countries like India with huge infrastructure commonly depends on Human visual inspection, which requires subject expertise in structural health assessment, types and severity of damages. At the same time this procedure has limitations related to human error, climatic conditions, time, accessibility and visibility. Computer’s vision in the form of Artificial Intelligence (AI) is playing a vital role to address the mentioned limitations. This study is motivated to find solutions through images using AI for robust and faster visual inspection techniques.

2 Related Work Computer vision based techniques have evolved drastically in last two decades along with the higher degree of improvements, especially as an effective tool in the field of structural health assessment and monitoring. Currently, human visual inspectors are replaced by self-navigating robots with the help of AI to assess all kinds of damages in structures [6]. Hutchison and Chen [7] worked on automatic detection of concrete surface damages like cracks and spalling from images using a statistical based method and relied on a Bayesian method. Lee et al., [8] proposed a model for accurate detection of cracks through integrating different image processing techniques, apart from that, class labels of the cracks were classified using a neural network based model. Kim et al., [9] surveyed on identification of cracks in concrete structures through various image binarisation algorithms. Silva and Lucena [10] demonstrated the capability of deep learning approach for concrete crack detection. Choi and Kim [11] carried research to identify the corrosion type based on the morphology of the corroded surface and for training and testing the classifier, 150 to 200 images were taken under optical microscope considering the features like color, texture and shape. Medeiros et al. [12] demonstrated the segregation of corroded and non-corroded surfaces using texture descriptors obtained from GLCM (grey level co-occurrence matrix) and color. Itzhak et al., [13] was the first to carry out research on pitting corrosion using digital image processing. Kim et al., [14] presented on Terrestrial laser scanner(TLS) for the detection of surface damages on concrete using laser and simultaneously locate and quantify the spalling defects. Similarly, Dawood et al., [15] developed a model to detect and quantify spalling by image data processing and machine learning techniques. Author was successful in getting the results about 89 to 90% accurate, with only limitation of not providing exact shape of damaged region. Hoang et al., [16] proposed a model using hybridization of image texture analysis and machine learning techniques to characterize the condition of concrete wall surface. In line with that many researchers have attempted to develop a model which can classify all types of damages seen on a single structure. Lin et al., [17] studied on different types of damages caused during the natural disasters like earthquake, tsunami and debris flows. Damages can vary in its scale from collapsing of structures, breaking of bridges and cracks on roads, author also discussed the role of computer vision based technique in the prevention of structural

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damages. Spencer et al., [18] did a detail review on recent advances in computer vision techniques applied to various civil infrastructure for structural health assessment considering visual defects like cracks in concrete, concrete spalling, fatigue cracks in steel, steel corrosion and Asphalt damages. Authors also detailed damage detection methods like heuristic feature-extraction, deep learning-based damage detection, and change detection. Apart from the damage detection techniques vision based methods are also currently used in calculation of compressive strength of concrete, which is taken as a video or set of image frames during the lab experiment [19–21].

3 Methodology To understand damages in various locations of a structure, we have experimented with the latest technological advancements by using algorithms in Machine Learning (ML) and Artificial Neural networks (ANN). Considering Images as samples we can define ML as the “Area of study that enables Systems to learn without being explicitly programmed”. At its most fundamental it is the act of utilizing algorithms to tokenize data, learn from it, and afterward estimate or predict something. So instead of handcoding the programming schedules with a particular set of instructions to achieve a specific task, the machine is “trained” utilizing huge amounts of data that gives to the machine the ability to figure out how to learn to perform the task. The challenge is, ML models need to be input with relevant features and it might be complex to find the right combination of features to correctly identify and classify the objects in the images. To overcome the aforementioned problem, Deep Learning (DL), a special discipline in ML is required for classification purposes. Neural Networks are inspired by our understanding of the biology of our brains. In the current work the image processing is carried out in order to automatically crop out the object of interest from the whole images. The crops will feed the DL classification tool and they will be classified into three classes as Crack or No Crack, Corrosion or No Corrosion and Spalling or No Spalling. To train a network from scratch requires hundreds of thousands of labelled images. In order to deal with the availability of a smaller dataset (200 each of crack, corrosion and spalling), pre-trained networks are adopted for the purpose. That process is called Transfer Learning and it is commonly used in deep learning applications. Fine-tuning a network with transfer learning is much faster and easier than constructing and training a new network. The advantage of transfer learning is that the pre-trained network has already learned a rich set of features. These features can be applied to the broad range of other similar tasks. To use that networks for the purpose of damage detection in structures, only the fully connected last layers need to be changed introducing the desired labels. To experiment, we have chosen AlexNet architecture which measures the performances in terms of precision and accuracy as well as in terms of time consumed for training and testing. 3.1

Dataset Building

Images used to build the dataset for this study are collected during the visual inspections of various structures carried in the last four years. Even though images have been

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captured from different devices, they are standardized into the resolution of 227  227  3 RGB before importing into the model. Majority of the images related to crack are obtained from the visual inspection carried out for four hundred national highway bridges and residential buildings, as they have single crack to multiple cracks on the surfaces. Concrete Bridges nearer to sea-shore had high corrosion and spalling when compared to the bridges which are in other part of the country or 300 to 400 km away from the shores. Spalling was also seen in the bridges which were very old and had less maintenance. Figure 1 depicts various damages captured during site investigations for concrete and steel infrastructure. Figure 1(a) shows minor hairline cracks to structural cracks, Fig. 1(b) shows quantity of corrosion increasing from left to right and largely calculated on surface area rather than depth of the corrosion and Fig. 1(c) shows minor spalling to major spalling, this is also calculated on surface area exposure. Figure 1(a–c) are arranged in such way that the severity of damage is increasing from left to right.

Fig. 1. Images captured during the bridge visual Inspections (a) Cracks (b) Corrosion (c) Spalling, images are arranged in the increasing severity of the damage from left to right.

Figure 1 shows images capture from bridge inspection, similarly large database is also created from the visual assessment of residential and historical structures too. Images were added from every perspective and not necessarily taken perpendicular to the damage. So that the code can more robust and implemented directly on live project site investigations.

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Convolutional Neural Networks (CNN)

Convolutional Neural Networks (CNN) are special kind of multi-layer neural networks, designed to recognize visual patterns directly from pixel images with minimal preprocessing. To implement these CNN’s we require complex computational power, so it is easily accessible via software programs or architectures like LeNet, AlexNet, ResNet etc. Among which we have experimented using AlexNet. Figure 2 represents proposed architecture for this research work. Dataset comprises of 600 images in total undergoes training individually and images are filtered based on its noise. Few images with greater noise get rejected and accepted images are taken to next step for feature extraction and feature learning. As a next step AlexNet architecture is proposed to classify the dataset further. Firstly, it goes through a large convolutional layer and max pooling layer and process is repeated twice to detect the object (crack, corrosion and spalling) in the images. Then the images go through three smaller convolutional layers followed by a maxpooling layer. In pipeline images are flattened and connected with the several fully-connected layers. The activation functions are all Rectified Linear Units (ReLU) which is mostly preferred for accurate predictions.

Fig. 2. Proposed architecture

Model built using such small dataset, the training time is around 60 s using a GPU and around 90 min using only the CPU for AlexNet architecture. The training and testing ratio considered in this study is 9:1 for good accuracy, if the ratio is 8:2 or 7:3 the accuracy of the results will be decreased as the trained images are less.

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4 Results and Discussion The proposed methodology has resulted in good accuracies even though the dataset is small. As described experiments were carried on three classes of images i.e. crack, corrosion and spalling. 4.1

Crack Detection

Identification of crack at early stage is important as it effects the strength and stiffness of the structure.

Fig. 3. Model for crack detection

Figure 3 demonstrates the outcome of the proposed architecture using AlexNet which was successful in identifying hairline crack to the severe structural cracks. The model was also able to classify single and multiple cracks with different contrasts. Figure 4 shows the results of the model which is trained using 200 images with 9:1 training and testing ratio which has taken 6 epochs and 204 iterations with the learning rate of 0.0001 by achieving 98% accuracy for image classification in labelling cracks and no cracks. The accuracies can increase or decrease based on the size of the dataset, varieties of the images in the dataset and the ratio of training and testing. So, in this case achieved accuracy is considered to be good. Similar observations were made related to the accuracies while experimenting other two classes of images.

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Fig. 4. Training and validation of results for crack dataset with accuracy and loss while training images.

4.2

Corrosion Detection

Corrosion detection at early stages in steel structures plays a vital role and automatic detection could be one of the most needed solution for the current scenarios considering the scale of infrastructure.

Fig. 5. Model for corrosion detection

Figure 5 demonstrates the outcome of the proposed architecture using AlexNet which was successful in identifying rust and no rust for various given scenarios.

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Fig. 6. Training and validation of results for corrosion dataset with accuracy and loss while training images.

Figure 6 shows the results of the model which is trained using 200 images with 9:1 training and testing ratio which has taken 6 epochs and 210 iterations with the learning rate of 0.0001 by achieving 95.33% accuracy for image classification in labelling rust and no rust. 4.3

Spalling Detection

Detecting spalling at the initial stage can arrest the further deterioration, largely spalling is observed at the seashore or in the old structures.

Fig. 7. Model for spalling detection

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Figure 7 demonstrates the outcome of the proposed architecture using AlexNet which was successful in identifying spalling and no spalling for various given scenarios.

Fig. 8. Training and validation of results for spalling dataset with accuracy and loss while training images.

Figure 8 shows the results of the model which is trained using 200 images with 9:1 training and testing ratio which has taken 6 epochs and 102 iterations with the learning rate of 0.0001 by achieving 97.22% accuracy for image classification in labelling spalling and no spalling.

5 Conclusions The proposed architecture in the study is easy, faster and robust as the obtained results for all the three classes resulted in higher accuracies. Dataset created had varied variety of images and it shows that this method is capable of identifying three categories of damages crack, corrosion and spalling. Increased dataset can lead to higher accuracy and more types of damages can be detected. Can attempt in using different software architectures to observe the accuracies. Faster development in this line of integrating Artificial Neural Networks to structural health assessment problems can provide robust solutions and save man-hours and also economy of any country apart from sudden failures.

References 1. Stuart, T.: The early use of reinforced concrete in India. In: Huerta, S (ed.) I. Juan de Herrera, SEdHC, ETSAM, A. E. Benvenuto, COAM, F. Dragados, Madrid (2003) 2. Gardner, D., Lark, R., Jefferson, T., Davies, R.: A survey on problems encountered in current concrete construction and the potential benefits of self-healing cementitious materials. Case stud. Const. Mater. 8, 238–247 (2018)

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3. Valdez, B., Ramirez, J., Eliezer, A., Schorr, M., Ramos, R., Salinas, R.: Corrosion assessment of infrastructure assets in coastal seas. J. Mar. Eng. Technol. 15(3), 124–134 (2016) 4. Mohan, A., Poobal, S.: Crack detection using image processing: a critical review and analysis. Alexandria Eng. J. 57(2), 787–798 (2018) 5. Dhital, D., Lee, J.R.: A fully non-contact ultrasonic propagation imaging system for closed surface crack evaluation. Exp. Mech. 52(8), 1111–1122 (2012) 6. Hoskere, V., Narazaki, Y., Hoang, T., Spencer Jr, B.: Vision-based structural inspection using multiscale deep convolutional neural networks. arXiv preprint http://arxiv.org/abs/ 1805.01055 (2018) 7. Hutchinson, T.C., Chen, Z.: Improved image analysis for evaluating concrete damage. J. Comput. Civ. Eng. 20(3), 210–216 (2006) 8. Chen, L.C., Shao, Y.C., Jan, H.H., Huang, C.W., Tien, Y.M.: Measuring system for cracks in concrete using multitemporal images. J. Surv. Eng. 132(2), 77–82 (2006) 9. Kim, H., Ahn, E., Cho, S., Shin, M., Sim, S.H.: Comparative analysis of image binarization methods for crack identification in concrete structures. Cem. Concr. Res. 99, 53–61 (2017) 10. Silva, W.R.L.D., Lucena, D.S.D.: Concrete cracks detection based on deep learning image classification. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 2, no. 8, p. 489 (2018) 11. Choi, K.Y., Kim, S.S.: Morphological analysis and classification of types of surface corrosion damage by digital image processing. Corros. Sci. 47(1), 1–15 (2005) 12. Medeiros, F.N., Ramalho, G.L., Bento, M.P., Medeiros, L.C.: On the evaluation of texture and color features for nondestructive corrosion detection. EURASIP J. Adv. Signal Process. 2010(1), 817473 (2010) 13. Itzhak, D., Dinstein, I., Zilberberg, T.: Pitting corrosion evaluation by computer image processing. Corros. Sci. 21(1), 17–22 (1981) 14. Kim, M.K., Sohn, H., Chang, C.C.: Localization and quantification of concrete spalling defects using terrestrial laser scanning. J. Comput. Civ. Eng. 29(6), 04014086 (2015) 15. Dawood, T., Zhu, Z., Zayed, T.: Machine vision-based model for spalling detection and quantification in subway networks. Autom. Const. 81, 149–160 (2017) 16. Hoang, N.D., Nguyen, Q. L., Tran, X.L.: Automatic detection of concrete spalling using piecewise linear stochastic gradient descent logistic regression and image texture analysis. Complexity 20(5), 536–572 (2019) 17. Lin, C.W., Hsu, W.K., Chiou, D.J., Chen, C.W., Chiang, W.L.: Smart monitoring system with multi-criteria decision using a feature based computer vision technique. Smart Struct. Syst. 15(6), 1583–1600 (2015) 18. Spencer Jr., B.F., Hoskere, V., Narazaki, Y.: Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering 5(2), 199–222 (2019) 19. Baygin, M., Ozkaya, S.G., Ozdemir, M.A., Kazaz, I.: A new approach based on image processing for measuring compressive strength of structures. Int. J. Intell. Syst. Appl. Eng. 6 (10), 21–25 (2017) 20. Dogan, G., Arslan, M.H., Ceylan, M.: Concrete compressive strength detection using image processing based new test method. Measurement 109, 137–148 (2017) 21. Choi, S., Shah, S.P.: Measurement of deformations on concrete subjected to compression using image correlation. Exp. Mech. 37(3), 307–313 (1997)

SHM in Wind Turbine Technology

Understanding the Influence of Environmental and Operational Variability on Wind Turbine Blade Monitoring Callum Roberts1(B) , David Garcia Cava1 , and Luis David Avenda˜ no-Valencia2 1

Institute for Infrastructure and Environment, The University of Edinburgh, Edinburgh, UK [email protected] 2 Mechanical Engineering, Department of Technology and Innovation, University of Southern Denmark, Odense, Denmark

Abstract. For data-driven vibration-based structural health monitoring (VSHM) systems to be considered reliable they must overcome the challenge of mitigating the environmental and operational variability (EOV) on the vibration features. This is particularly important in large and exposed structures such as wind turbine blades (WTB). This work aims to understand the influence of EOV, namely quantifying the influence of input variables on the selected vibration features. Understanding the specific sources of influence can facilitate better prediction of outliers as well as leading to a VSHM system less sensitive to EOV. This study uses an operational wind turbine with an undamaged and incrementally damaged WTB under three operating conditions (idle, 32 and 43 rpm). The approach calculates frequency transformation based features on the vibration responses obtained from an array of accelerometers along the WTB. Subsequently, the features are regressed on environmental and operational parameters (EOPs) via multivariate non-linear regression. The difference between the regression predictions and the actual feature values is used as a new feature. In parallel, to understand the influence of the EOV, inclusive and exclusive sensitivity analyses were conducted. These analyses compared the likelihood of a model based on one or all but one EOP, respectively, against a model using all the EOP. The results showed that the temperature has the largest influence, with respect to the considered EOP, on the regression likelihood. Ultimately, the obtained regression model was used to normalise the effects on the features and enhance damage detection. Keywords: Multivariate nonlinear regression · Structural health monitoring · Environmental and operational variations · Sensitivity analysis · Wind turbine blade

c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 109–118, 2021. https://doi.org/10.1007/978-3-030-64594-6_12

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Introduction

As structures become larger, more remote and more complex in design, so does too the size and difficulty of the task of maintaining them. A structure that falls under all three of these categories are wind turbines. Traditionally they are maintained through an out-dated method of visual inspection, done either by engineers on site or via transmitted images. Wind turbine blades (WTB) themselves are prone to damage from impacts leading to cracking and delamination. Therefore, it would be desirable to have an online monitoring system capable of detecting and tracking damage so wind turbines can operate more effectively [2]. One method that has been proposed as an alternative to visual inspection is vibration-based structural health monitoring (VSHM). These methods aim to detect changes in structural behaviour, attributed to damage, by comparing vibration responses from the structure to a reference model. In the early stages of VSHM, these models were typically physical based models [6] which can be computationally expensive and difficult to work with. More recently, data-driven approaches have been applied to account for the some of the drawbacks of the model-based methods [12]. However, environmental and operational variability (EOV) can cause issues for both methods. This topic has been a focus of VSHM for some time and the essence of the problem is to isolate the effects from EOV and remove them without removing any influence from damage [5]. The EOV leads to difficulties in defining a reference system since the structural dynamics, on which damage detection is based, are constantly changing [8]. This issue is amplified as structures become larger since the damage acts only as a local effect whereas the EOV affects to the whole structure [3]. Several methods have been proposed to remove the influence of EOV within VSHM. Some methods, such as principal component analysis (PCA) or cointegration, try to solve the problem without considering the EOV directly. For instance, PCA decomposes a vector of features into a number of components which are then ordered based on their respective contribution to the total dataset variance [7]. The assumption that is often made here is that the EOV effects are embedded in higher variance components and are separate from the influence of damage [11]. As such, the higher variance components can be discarded and, thus, the effect of EOV. Other methods look to solve the problem in an explicit manner. The initial drawback of these methods is that more inputs required for the VSHM system, namely those relating to the EOV. However, the additional information can be used to create models describing the direct effects of EOV on the system. The feature vectors can then be corrected by the new models, effectively normalising them by the EOV. This type of method has the potential to create more robust VSHM systems by effectively isolating the effect of EOV from the effect of damage. To this end, regression models of various sorts may be used [1]. In this work, multivariate non-linear regression is used to model vibrations features which

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have been extracted from an operational wind turbine. One of the advantages of this method is that the input variables can be manipulated in a way that allows a sensitivity analysis to be conducted. By varying which inputs are included, it is possible to see which have the largest influence on the predictability of the features. By having a greater understanding of what influences these systems, it becomes possible to design them to negate such effects. The aim of this work is to gain a better insight into what factors affect the vibration features the most. A secondary goal of the work was to improve damage detectability using corrected features.

2 2.1

Methodology Feature Extraction from Vibration Responses

A vibration response is measured in a system for a number of observations. For each observation it is desirable to have one feature vector containing multiple features with information from many measurements. Considering the data obtained   from the accelerometers, there exists a matrix Yp = y1 y2 . . . yj . . . yM where Yp is matrix made up of M acceleration time series vectors, each represented by yj (dimension S, equal to the length of the acceleration time series). Subscript p represents the number of the observation. Performing a Fourier transform on   where yj = aj + ibj matrix Yp gives a new matrix Yp = y1 y2 . . . yj . . . yM and has a length of S/2. Following this, a new matrix is created by concatenating the real, a, and imaginary, b, parts of yj . To enable the next step, the data is reorganised by accelerometer, as opposed to observation number, to give the following:   a1,j a2,j . . . ap,j . . . aN,j  (1) Yj = b1,j b2,j . . . bp,j . . . bN,j where N is the total number of observations, both undamaged and damaged. Since the dimension of Yj is large, it is appropriate to reduce the dimension of the matrix using principal components analysis (PCA). Full details of a PCA procedure can be found in [7]. PCA is performed on a accelerometer by accelerometer basis, using only the training data, NT (where NT μ → Damaged

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Case Study

The data that was used for this research was taken from an operational Vestas V27 wind turbine located in Denmark. The work was carried out by the Technical University of Denmark in collaboration with Br¨ uel & Kjær. A brief explanation has been included in this work for completeness but a full description can be found in [10]. One of the turbine’s blades was instrumented with 12 accelerometers as well as an actuator (see Fig. 1). The purpose of the actuator was to excite the blade to experience higher frequencies.

Fig. 1. Experimental blade setup shown as (a) an on site picture and (b) as schematic drawing

During the campaign, damage was periodically introduced to the trailing edge of the blade in the form of a crack, emulating delamination in the structure. The blade was tested without damage and then with a 15 cm, 30 cm 45 cm crack and subsequently repaired. The V27 is an older turbine and only has three operational regimes (idling, 32 rpm and 43 rpm), of which only the 43 rpm is used for this work. The turbine is situated in a real environment and is, therefore, naturally exposed to changing environmental conditions such as temperature and wind speed. Since the undamaged state experienced limited variations in EOP, it was chosen to use the repaired data in its place.

4 4.1

Results and Discussion Sensitivity Analysis on the Measured EOP

The structure of the multivariate non-linear regression is optimized by minimization of the leave-one-out cross-validation error in a grid of values, covering model orders from 1 to 5, for each one of the EOPs. Ultimately this leads to an individual structure for each feature dependent on the result of the cross-validation corresponding to the optimal model. The exclusive and inclusive sensitivities for 43 rpm are shown in Fig. 2.

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(a)

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(b)

Fig. 2. Box plot representing the aggregated sensitivity values for the (a) exclusive study and (b) inclusive study

A box plot is used to represent the aggregated sensitivity values, which arise from having multiple features. From Fig. 2 it is clear that the temperature has the largest influence on the vibration features according to the logic outlined in Sect. 2.3. This result is expected as temperature has a direct influence on the stiffness and thus would account for a significant amount of the variation between the signals. Since only the first component of PCA is considered, it is possible that some of the remaining components are more sensitive to other EOPs.

(a)

(b)

Fig. 3. Distribution of feature α1 against (a) temperature and (b) wind speed

The results of the sensitivity study can also be reflected in Fig. 3 by comparing the features to the EOPs directly. There is a much clearer dependency of temperature than wind speed. For simplicity only the first feature has been shown. However, the same can be found for all features in this work. 4.2

Novelty Detection

Using the novelty index as a detection system, as described in Sect. 2.4, it is possible to compare how the multivariate non-linear regression corrected features

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(a)

(b)

(c)

(d)

Fig. 4. Control chart for detecting damage in the 43 rpm operating regime for (a–b) original features and (c–d) multivariate non-linear regression corrected features. (b) and (d) are used to highlight the region around the threshold and damage. The control charts show the MSD for the corresponding p-th observation with a threshold defined to cover 98% of the variance in the training data.

compared to the original ones. Figure 4 displays the MSD calculated in the case of the original and corrected features for the 43 rpm operational condition. There are a few noticeable differences between the control charts in Fig. 4. Firstly, the spread of the MSD in the damage scenarios has increased dramatically for the corrected features. This does not cause any issues since they tend to increase the distance between themselves and the threshold. The second thing is that the damage points that were below the threshold are now above for the corrected features. This could mean that the specific EOPs during those observations caused the structure to behave similarly to the undamaged. Lastly, there is an increased number of outliers from the validation set. A reason for this could be that the EOPs experienced for these observations did not exist in the training set and, as such, are not mapped well. Out of the three noticeable differences explained above, two of them provide an improvement for the corrected features over the original. As with many statistical learning problems, the method could benefit from a more substantial training set with a wider range of conditions experienced, this could help alleviate the problem with the outliers in the validation set. Unfortunately, this is not always possible. Despite this, the system still performs well at detecting dam-

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age. Furthermore, this work provides an introductory analysis using the features described in Sect. 2.1, since only the first component is used. A more detailed analysis is under way, comprising the complete set of principal components and a more comprehensive range of EOPs.

5

Conclusion

The work presented in this paper aimed to provide deeper insight into which environmental and operational variations had the largest influence on a vibrationbased structural health monitoring system. Multivariate non-linear regression was used to model vibration features obtained from an operational wind turbine. By including, and excluding, different environmental and operational parameters, it was possible to determine which variable has the largest influence on the selected vibration features. The results from the analysis show that the temperature has the largest influence on the system. The wind speed seemingly has the lowest influence out of the parameters tested with the azimuth angle having a slightly larger influence. For future investigations, it would be helpful to include a larger range of parameters, as well as more principal components, to gain a deeper understanding of the system. Multivariate non-linear regression models were also used to correct the vibration features to account for the varying environmental and operational conditions. From the control charts, it was shown that the results for the new features can provide an improvement when compared to the original features. Additionally, by modelling the features as a function of EOPs, their influence is better accounted for and the reference state is more robust. Acknowledgements. The authors of this paper would like to acknowledge the generous input of Dr. Dmitri Tcherniak who kindly provided the data from the experimental regime performed on the V27 wind turbine. Furthermore, the author acknowledge The Carnegie Trust for the Universities of Scotland for supporting this project with the Caledonian PhD Scholarship (grant reference number: PHD007700).

References 1. Avenda˜ no-Valencia, L.D., Chatzi, E.N., Tcherniak, D.: Gaussian process models for mitigation of operational variability in the structural health monitoring of wind turbines. Mech. Syst. Signal Process. 142, 106686 (2020) 2. Ciang, C.C., Lee, J.R., Bang, H.J.: Structural health monitoring for a wind turbine system: a review of damage detection methods. Meas. Sci. Technol. 19(12), 122001 (2008) 3. Kojidi, S.M., D¨ ohler, M., Bernal, D., Liu, Y.: Linear projection techniques in damage detection under a changing environment. In: Topics in Modal Analysis, vol. 7, pp. 325–332. Springer (2014) 4. Magnusson, M., Andersen, M.R., Jonasson, J., Vehtari, A.: Leave-one-out cross-validation for Bayesian model comparison in large data. arXiv preprint arXiv:2001.00980 (2020)

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5. Manson, G.: Identifying damage sensitive, environment insensitive features for damage detection. In: Proceedings of the Third International Conference on Identification in Engineering Systems, pp. 187–197 (2002) 6. Montalvao, D., Maia, N.M.M., Ribeiro, A.M.R.: A review of vibration-based structural health monitoring with special emphasis on composite materials. Shock vibr. Dig. 38(4), 295–324 (2006) 7. Mujica, L., Rodellar, J., Fernandez, A., G¨ uemes, A.: Q-statistic and T2-statistic PCA-based measures for damage assessment in structures. Struct. Health Monit. 10(5), 539–553 (2011) 8. Peeters, B., Maeck, J., De Roeck, G.: Vibration-based damage detection in civil engineering: excitation sources and temperature effects. Smart Mater. Struct. 10(3), 518 (2001) 9. Tcherniak, D., Mølgaard, L.L.: Active vibration-based structural health monitoring system for wind turbine blade: demonstration on an operating vestas v27 wind turbine. Struct. Health Monit. 16(5), 536–550 (2017) 10. Ulriksen, M.D., Tcherniak, D., Damkilde, L.: Damage detection in an operating vestas v27 wind turbine blade by use of outlier analysis. In: 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) Proceedings, pp. 50–55. IEEE (2015) 11. Yan, A.M., Kerschen, G., De Boe, P., Golinval, J.C.: Structural damage diagnosis under varying environmental conditions-part I: a linear analysis. Mech. Syst. Signal Process. 19(4), 847–864 (2005) 12. Ying, Y., Garrett Jr., J.H., Oppenheim, I.J., Soibelman, L., Harley, J.B., Shi, J., Jin, Y.: Toward data-driven structural health monitoring: application of machine learning and signal processing to damage detection. J. Comput. Civ. Eng. 27(6), 667–680 (2013)

Fatigue Life Assessment of Wind Turbine Load Time Series Based on Measurements with Different Sampling Rates Manuel Kim1 , Hamid Rahimi1(B) , and J¨ org von Vietinghoff2(B) 1 2

LEINE LINDE SYSTEMS GmbH, Hamburg, Germany {m.kim,h.rahimi}@ll-systems.com DR. Johannes Heidenhain GmbH, Traunreut, Germany [email protected]

Abstract. The flexible design of the rotor and the tower coupled with load based control strategies, increase the need for a load monitoring system. These measurement systems require reliability, availability as well as low latency and high measurement sampling rates to cope with the aeroelastic issues of a load based control system. This work aims to quantify the influence of the strain measurement sampling rates and down-sampling methods on a Structural Health Monitoring (SHM) system for wind turbines. To evaluate these influences, fatigue and spectral analysis were performed on the measurements from elongation sensors which were installed in the rotor blade and tower. The primary results from the analysis showed differences of up to 0.3% in the equivalent fatigue load, which could be seen by comparing datasets recorded with the same sampling rates but down sampled with different methods. Keywords: Wind turbine · Elongation measurement health monitoring · Fatigue load

1

· Structual

Introduction

Nowadays a clear trend towards larger and more flexible rotor designs emerged which makes load measurement systems to be required beyond certification campaigns for design load validation. Among many, load measurements by means of measuring the elongation of the structure to derive stress and load information on the wind components is necessary for the following reasons: – To evaluate the structural integrity of a wind turbine design. Such measurement campaigns are done for validation and certification purposes [3]. – New production possibilities and load based control strategies allow bigger dimensions of structural components, making the load measurements crucial for monitoring and control purposes. – To evaluate extreme and fatigue loads and to estimate a potential lifetime extension of wind turbine structures. c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 119–125, 2021. https://doi.org/10.1007/978-3-030-64594-6_13

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– Updated guidelines are encouraging the load measurement campaigns to be performed for a longer period for fatigue assessment evaluations [5]. For the purpose of elongation measurement different sensor types can be used. Depending on the usage, different requirements on the measurement devices themselves exist. Among many, one of the main specification to consider is the sampling rate of the sensor. The sampling rate is important for new control strategies but also has an influence on the structure analysis. Although structures like rotor blades are known to have eigenfrequencies in the range of 0.2 Hz– 20 Hz, sensors with higher sampling rate can give additional information of the structure’s condition. In this work we aim to quantify the effect of the sampling rate and different down-sampling methods on the fatigue load analysis. Here we use the ESR which is a product developed by LEINE LINDE SYSTEMS [1] based on a stress-free measurement concept to monitor the structural health and which can provide high-resolution measurements up to 1 kHz.

2

Experimental Setup

To monitor the resulting bending moments, ESR sensors applied on the rotor blade and tower surface are used. These sensors are capable of measuring strain with an accuracy of up to 0.025 μ. More details about the characteristics of these sensors can be found in Table 1. Table 1. Specifications of the ESR125 Sensor Description

Value

Reference length

0.2 m

Measuring step - length

5 nm

Measuring step - elongation ±5.000 μ Interface

EnDat 2.2

Clock frequency

= < A2 2 ffi dR ¼ 2 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi     A1 > ; : fR 2  fR 2 > 2

ð12Þ

1



 k þ 2l k ðk 2 þ s2 Þ ¼ ; 2qk  nx s

 k þ 2l  2  2 R f2 ¼ 2k  k þ s2 y n

fR1

ð13Þ ð14Þ

Where, the contribution to fR1 is dominant from the longitudinal wave, and for fR2 is mainly from the shear wave. 2.3

Physics-Based Nonlinear Parameter for Lamb and Rayleigh-Waves

The propagation of longitudinal waves produces longitudinal strain in the material, whereas, Rayleigh waves produces longitudinal and transverse strain in the material simultaneously. Thus, the physics-based nonlinear parameter derived for Lamb or Rayleigh waves is given as,

Development of Lamb and Rayleigh Wave-Based Nonlinearity Parameters

cphy ¼

h   2 i12 2 cxT þ czT

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ð15Þ

ð16Þ and ð17Þ Where, the reference to symbols used can be found in [11].

3 Simulation Studies 3.1

Material Specifications

The present study uses Al 7075-T651 for numerical simulations and its mechanical properties are given in Table 1. The generation of Lamb waves is pretty straightforward, however, for generating a pure Rayleigh wave mode, wedge technique is employed in present study. The Lame’s constants, Rayleigh, longitudinal, shear wave velocities, and Rayleigh critical angle hR are shown in Table 1. The Rayleigh critical angle is provided on the wedge used for actuating Rayleigh waves in the material and it is calculated using the Snell’s law as follows, hR ¼ sin1

V Ljwedge

!

V Rjplate

ð18Þ

The wedge is made of Plexiglas. The properties of the Plexiglas, such as the modulus of elasticity (E), poisons ratio (m), and density (q) are given in Table 2 and their relations with Lame’s constants of the material can be found in [15–17]. The nonlinearity present in the intact state of Al 7075-T651 is calculated using [11] cphy ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðAe2 Þ2 þ ðBe2 Þ2 as 12.16 and shown in Table 3. Table 1. Mechanical properties of Al 7075-T651. Material k (GPa) l (GPa) m [−] VR (Km/s) VL (Km/s) VT (Km/s) hR (Deg) 7075-T651 52.324 26.955 0.33 2.8867 6.1486 3.0972 50.104

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Table 3. Material nonlinearity at pristine state for Al 7075-T651. Material

Ae2 (GPa) Be2 (GPa) cphy

7075-T651 106.234

3.2

106.234

12.16

Finite Element (FE) Models

Thin specimens support Lamb wave propagation, whereas, very thick specimens support a pure Rayleigh wave propagation. Thus, to compute nonlinearity of thin and thick specimens, Lamb and Rayleigh wave nonlinear parameters, respectively should be employed. The FE simulation is conducted here separately for Lamb and Rayleigh wave propagation considering the specimen model of material Al 7075-T651 plate subjected to different stages of fatigue. The thickness and length of the specimen plate are taken as 1.5 mm and 1 m respectively, and it is modeled in two dimensions with the assumption of the plane strain condition as shown schematically in Fig. 1. The use of such a long specimen model helps to avoid the reflections in the response arising from the boundary.

Fig. 1. Schematic of FE model for Lamb wave generation and sensing.

For exciting Lamb waves, 2.5 MHz frequency with 20 Cycles windowed by Gaussian function is selected due to the existence of a resonant S1  S2 mode pair at 2.5 MHz and its second harmonic. The reasons for such selection are given elsewhere [7, 12]. The pressure in the form of a tone burst signal is exerted at left end of the FE model and its right end is fixed as shown in Fig. 1. The mesh size and integration time steps for the transient simulation are chosen in accordance to [7] as kmin =10 and 1/(20fmax) respectively. Here, kmin is the wavelength and fmax refers to frequency. The schematic of the 2D FE model in case of Rayleigh wave generation and sensing is shown in Fig. 2. The model consists of a Plexiglas wedge and specimens with same

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material Al 7075-T651. Wedge technique [13] is one of the efficient techniques to generate the desired Rayleigh waves in the test specimen. To generate Rayleigh waves, L- waves are excited at the flank of the wedge by applying a uniform pressure of magnitude 0.1 GPa. When these longitudinal waves hit the interface between wedge and specimen at a Rayleigh critical angle shown in Table 1, the desired Rayleigh waves are generated. Since the analysis is focused on estimating the nonlinearity of the specimens, wedge material is modeled as linear elastic, whereas, the specimen is modeled as a Hyperelastic material using the same Murnaghan material model.

Fig. 2. FE model for Rayleigh wave generation and sensing.

The lowermost left and right nodes are subjected to fixed boundary conditions (BC) to prevent moving of the assembly as shown in Fig. 2. The minimum element length is selected as 10 nodes per smallest wavelength of interest. A higher resolution is necessary near the top surface of the specimen model to support the Rayleigh wave propagation. Accordingly, the element size is decreased in steps towards the top surface of the specimen. The element size is varied between 0.06 mm and 1 mm in steps from top to bottom surface of the specimen model. This reduced the computational time and memory requirement significantly. The thickness of the specimen is 25.4 mm. The excitation frequency for Rayleigh wave generation is selected as 2.1 MHz frequency. Note that, as Rayleigh waves are non-dispersive, there is a great freedom in choosing the excitation frequency, as the cumulative effect is guaranteed at all frequencies. The results from the numerical simulations are discussed in the following subsection.

4 Results The results of material nonlinearity estimation obtained from the nonlinear parameters and numerical simulations are discussed in this section. The fatigued specimen is simulated by assigning the Murnaghan constants as a function of percent life to the Hyperelastic material model. These constants are evaluated experimentally using the acousto-elasticity measurements conducted on Al 7075-T651 specimens and are

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reported in [18]. However, these constants are given for only three different percent fatigue life, i.e. 0%, 40%, and 80% as shown in Table 4. Firstly, the inherent nonlinearity of Al 7075-T651 specimens using csamp and dR is estimated by assigning the Murnaghan constants evaluated for the specimen with 100% RUL. For this purpose, the Lamb and Rayleigh waves are excited in the specimen model at 2.5 MHz and 2.1 MHz excitation frequencies respectively by applying a pressure tone burst as discussed in Sect. 3.2. This is intentionally done as the anti-symmetric Lamb waves do not support cumulative effect of primary and higher order harmonics. The propagating waves are then recorded at different distances. For brevity, in this paper, time-domain signal for only Lamb waves are shown. A normalized time-domain signal and its normalized frequency spectrum at 100 mm is shown in Figs. 3(a) and 3(b) respectively. The presence of second harmonic is obvious in Fig. 3(b) and it results from the inherent material nonlinearity. Here, as the Murnaghan constants for 0% fatigue life are used, therefore the nonlinearity is referred to as the inherent nonlinearity. Note that, the amplitudes of the harmonics shown in Fig. 3(b) cannot be used to evaluate csamp as there is a contribution of the S0 Lamb mode to the amplitude of S1  S2 mode. Table 4. Murnaghan constants with percent life [19]. Percent fatigue life (%) l (GPa) 0 −252.2 40 −266.8 80 −271.2

m (GPa) −325.0 −332.8 −335.8

n (GPa) −351.2 −358.3 −359.8

The presence of S0 mode is marked in Fig. 3(a) and the first wave-packet highlighted by a rectangle contains both S1 and S2 modes at the frequencies 2.5 MHz and 5 MHz respectively. Thus, a fast Fourier transform is applied only on this wave packet and its normalized version is shown in Fig. 3(c). Amplitudes of these harmonics correspond to S1 and S2 modes respectively are used for computing csamp . Similarly, csamp is computed for several other distances. In a similar way, the simulation is conducted for different stages of fatigue by assigning the Murnaghan constants listed in Table 4.

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Fig. 3. Simulation results at 100 mm. (a) The raw time-domain signal (b) The normalized signal (c) The normalized signal of marked wave packet in (a).

The evaluation of material nonlinearity at different distances using csamp and the spectral amplitudes of harmonics obtained from the FE simulation for the intact specimens are shown in Fig. 4(a). At start, csamp increases until a certain distance called MCPD [11] and then drops. This initial increase in csamp is because of the cumulative building of the generated second harmonic. However, after MCPD, the wave attenuation and mismatch of phases of the first and second harmonics cause a reduction of csamp . For the present case of Lamb wave, a MCPD of around 170 mm is observed. A similar trend is observed for the material nonlinearity estimation using Rayleigh waves.

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Fig. 4. (a) Trend of csamp with distance for Lamb waves (b) Comparison of maximum values of csamp and dR with TNC for different percent of life [Note: In fig. (b), red color data is for csamp and blue color is for dR ] Table 5. Evolution data of nickel with percent life [19]. fv Specimen condition fpsb (% of full life) 0.1 0.103 0.44 1.1 0.145 0.44 11.2 0.158 0.44 100 0.224 0.44 Table 6. Calculated vales of cphy with percent life [11] Percent fatigue life 0 0.1 1.1 11.2 80 12.16 12.7376 13.4184 13.6239 14.6295 cphy

Next, the parameter cphy is evaluated using the sub-structural evolution data of nickel reported in [11] and listed in Table 5. Finally, following the procedure outlined in [11], the physics-based nonlinear parameter is evaluated and its values for different percent of fatigue life are summarized in Table 6. These values of cphy are then used to construct the TNC. In Fig. 4(b), the TNC is constructed using the calculated values of cphy at different percent of life given in Table 6. It was proved in [6–8] that the value of csamp and dR matches cphy at MCPD. Therefore, csamp and dR are computed at MCPD for different percent of life and overlapped onto the TNC as shown in Fig. 4(b). The results show that csamp and dR at their respective MCPDs are matching well with the TNC which signifies a good agreement between simulation and theory. For life estimation, a horizontal line should

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be drawn passing through the computed value of csamp or dR in Fig. 4(b), and the life can be predicted from the respective abscissa where the horizontal line intersects the TNC. Thus, both the parameters csamp or dR , were found to be useful in predicting the RUL of the fatigued specimens. It is also observed that there is a small variation between the simulation and theoretical results which may be attributed to the fact that the TNC is constructed from the evolution data of nickel and it is drawn thru very discrete data points. Nonetheless, the deviation between csamp or dR and TNC is marginal even with this limited number of data. Lastly, it is worth noting, that the TNC plotted by cphy is found to be independent of the stress or strain levels and dependent on the microstructural properties at the percent fatigue life. For higher stress or strain levels, it is obvious that the specimen may fail earlier compared to that under lower stress or strain levels. Whereas, for a concerned percent life, its microstructural state is expected to be similar in both the situations and therefore evaluated cphy will also be roughly the same. It is clear that cphy serves as a reference for csamp or dR and for estimating the RUL in terms of percent fatigue life.

5 Concluding Remarks The amplitude-based nonlinear parameters csamp and dR are used to calculate the inherent and dislocation induced nonlinearity from fatigue loading. The conclusions from the present study are outlined as follows: 1. The material nonlinearity estimated using csamp and dR at MCPD are in close agreement with the TNC constructed from cphy for different percent of fatigue life. 2. The TNC acts a good reference for csamp and dR obtained through simulation and also to predict the RUL. 3. Despite the construction of TNC from the evolution data of nickel obtained for different stress levels, the agreement between nonlinear parameters and TNC signifies that this TNC can also be used for different metals loaded under different loading conditions, however, by using the material properties and burger vector of the material under consideration.

References 1. Jhang, K.Y.: Nonlinear ultrasonic techniques for non-destructive assessment of micro damage in material: A review. Int. J. Precis. Eng. Manuf. 10, 123–135 (2009) 2. Mitra, M., Gopalakrishnan, S.: Guided wave based structural health monitoring: a review. Smart Mater. Struct. 25(5), 053001 (2016) 3. Cantrell, J.H.: Substructural organization, dislocation plasticity and harmonic generation in cyclically stressed wavy slip metals. Proc. Roy. Soc. London Ser. A: Math. Phys. Eng. Sci. 460, 757–780 (2004)

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4. Cantrell, J.H.: Quantitative assessment of fatigue damage accumulation in wavy slip metals from acoustic harmonic generation. Phil. Mag. 86, 1539–1554 (2006) 5. Cantrell, J.H.: Ultrasonic harmonic generation from fatigue-induced dislocation substructures in planar slip metals and assessment of remaining fatigue life. J. Appl. Phys. 106, 093516 (2009) 6. Masurkar, F., Tse, P., Yelve, N.P.: Evaluation of inherent and dislocation induced material nonlinearity in metallic plates using Lamb waves. Appl. Acoust. 136, 76–85 (2018) 7. Masurkar, F., Tse, P.W., Yelve, N.P.: Investigating the critical aspects of evaluating the material nonlinearity in metal plates using Lamb waves: theoretical and numerical approach. Appl. Acoust. 140, 301–314 (2018) 8. Yelve, N.P., Tse, P.W., Masurkar, F.: Theoretical and experimental evaluation of material nonlinearity in metal plates using Lamb waves. Struct. Control Health Monit. 25(6), e2164 (2018) 9. Rostami, J., Tse, P.W., Yuan, M.: Detection of broken wires in elevator wire ropes with ultrasonic guided waves and tone-burst wavelet. Struct. Health Monit. 19(2), 481–494 (2020) 10. Tse, P.W., Rostami, J.: Matching pursuit with novel dispersive dictionary for mode separation in guided wave signals obtained from pipes. In: AIP Conference Proceedings, vol. 2102, no. 1, p. 050010. AIP Publishing LLC, NY (2019) 11. Masurkar, F., Peter, W.T., Yelve, N.P.: Theoretical and experimental measurement of intrinsic and fatigue induced material nonlinearities using Lamb wave based nonlinearity parameters. Measurement 151, 107148 (2020) 12. Rauter, N., Lammering, R.: Investigation of the higher harmonic Lamb wave generation in hyperplastic isotropic material. Phys. Proc. 70, 309–313 (2015) 13. Masurkar, F., Tse, P.: Theoretical and Experimental evaluation of the health status of a 1018 steel I-beam using nonlinear Rayleigh waves: application to evaluating localized plastic damage due to Impact loading. Ultrasonics 108, 106036 (2019) 14. Masurkar, F., Tse, P.: Analyzing the features of material nonlinearity evaluation in a rectangular aluminum beam using Rayleigh waves: theoretical and experimental study. J. Phys. Commun. 3(5), 055002 (2019) 15. Masurkar, F.A., Yelve, N.P.: Optimizing location of damage within an enclosed area defined by an algorithm based on the Lamb wave response data. Appl. Acoust. 120, 98–110 (2017) 16. Masurkar, F.A., Yelve, N.P.: Lamb wave based experimental and finite element simulation studies for damage detection in an aluminium and a composite plate using geodesic algorithm. Int. J. Acoust. Vibr. 22(4), 413–421 (2017) 17. Andhale, Y.S., Masurkar, F., Yelve, N.: localization of damages in plain and riveted aluminium specimens using Lamb waves. Int. J. Acoust. Vibr. 24(1), 150–165 (2019) 18. Stobbe, D.M.: Acoustoelasticity in 7075-T651 aluminum and dependence of third order elastic constants on fatigue damage. Doctoral dissertation, Georgia Institute of Technology (2005) 19. Tse, P., Masurkar, F., Yelve, N.P.: Estimation of remaining useful life of fatigued plate specimens using Lamb wave-based nonlinearity parameters. Struct. Control Health Monit. 27(4), e2486 (2020)

Non-linear SHM Based Damage Detection in Doubly-Curved-Shells Sathish Subbaiah Murugesan1 , Renjith Thomas1(&) C. R. Bijudas1 , and P. Jayesh2 1

,

Indian Institute of Space Science and Technology, Thiruvananthapuram, India [email protected] 2 Liquid Propulsion Systems Centre, Thiruvananthapuram, India

Abstract. Doubly curved shells are used as structural members in space launch vehicles as part of propellant tanks, pressure bulkheads in aircrafts, submarinehull, etc. SHM techniques for these structures are limited in the literature. The present study is based on the fact that higher harmonics will be generated in guided wave propagation in presence of Contact Acoustic Nonlinearity (CAN) type defects such as fatigue crack and de-lamination. The higher harmonics are generated due to non-linear interaction of the crack surfaces when the incident wave passes through these surfaces. Also, the study explores capability of non-linear Vibro-Acoustic Modulation (VAM) technique on doubly curved shell structures for the detection of CAN type defects. VAM is based on the response of the system where effects of modulation of low-frequency vibration (pumping vibration) on high frequency guided wave propagation (probing wave) are studied. In the presence of damage, the frequency spectrum of the response shows sidebands with respect to the frequency of the guided wave excitation. These non-linearity features of guided wave propagation are numerically and experimentally investigated on a doubly-curved-shell structure which has a CAN type defect of partially bonded attachment. Keywords: Nonlinear SHM
Higher-harmonics
Vibro-Acoustic Modulation (VAM)
Doubly-curved shell
Contact acoustic non-linearity (CAN)

1 Introduction Continuous monitoring of a structure to identify formation of any defect at the earliest is important for the safety of the system and also to minimize the maintenance costs and time associated with conventional NDT based approaches, which are schedule-driven inspections rather condition-driven. This continuous monitoring of a structure during its operation is referred to as Structural Health Monitoring (SHM). Among various techniques, guided-wave based SHM techniques have got substantial attention in research mainly because i) transducers used in the technique are cheap and light-weight, ii) need less number of transducers to cover a large area and iii) can detect minute damages compared to low-frequency vibration techniques. Much of the guided wave based SHM techniques available are based on linear features like time

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 161–171, 2021. https://doi.org/10.1007/978-3-030-64594-6_17

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of flight, reflection, attenuation, transmission or scattering of wave at same frequency. However, recently attention has been on non-linear features of guided waves. Non-linearity in waves is classified into classical and non-classical. Material nonlinearities [1] and second harmonic generation at an un-bonded interface between two solids [2] are some of the classical nonlinearity cases [3]. Some non-classical nonlinearities are fatigue, crack detection based on higher-harmonics [4] and detection of de-laminations using vibro-acoustic technique [5]. Doubly-curved-shells are used as structural members in space launch vehicles as part of propellant tanks, pressure bulkheads in aircrafts, submarine-hull, etc. Dispersion relation for two-dimensional circular annulus is derived experimentally in [6]. Guided wave propagation in anisotropic spherical curved plate is solved for the first time in [7]. In [8], dispersion equations for the spherical shell are derived to investigate the effect of coating thickness and visco-elastic damping on dispersion relations. However, SHM techniques for doubly-curved-shell structures are limited in the literature. This paper presents the numerical and experimental study of nonlinear nonclassical SHM techniques applied on a spherical shell. One of the techniques on which the study based on is higher-harmonics generation. In this, a wave packet propagating through the structure at a particular frequency, when encountered by a Contact Acoustic Nonlinearity (CAN) type defect, results in generation of higher harmonics of the excitation frequency. This is due to the non-linear interaction of the defect surfaces when a wave packet propagates through these surfaces. This paper studies the application of this technique on spherical shells via experiments and numerical simulations. Other technique this paper explores is Vibro-Acoustic Modulation (VAM). In VAM, effects of modulation of low-frequency vibration (pumping vibration) on high frequency guided wave propagation (probing wave) are studied. When single-frequency sinusoidal wave packets are used as probing wave in VAM technique on a structure with CAN type defect and is subjected to a pumping vibration, side-banded wave packets get generated at CAN type defect interface. Since the wave propagation based damage detection has to be performed in a dynamic environment where significant structural vibration is involved, VAM based study is further significant. This paper explores the application of VAM technique on spherical shells via numerical simulations.

2 Numerical Simulations This paper, via numerical simulation, explores the nonlinearity of guided waves in presence of CAN type defects on spherical shells using two SHM techniques: i) a higher-harmonics based approach, and ii) vibro-acoustic modulation technique. The doubly-curved-shell structure considered in the study is a spherical shell, which is made up of aluminum alloy, shown schematically in Fig. 1.

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Fig. 1. Schematic view of doubly-curved portion of a propellant tank

2.1

Higher-Harmonics Based Approach

In this approach, a CAN type damage of partially bonded attachment is detected using higher-harmonics based approach. The attachment is a metal plate having 1 mm thickness and 5 cm length, and has same curvature as the spherical shell, which has a radius of curvature of 1.15 m at mid-thickness and thickness of 2.55 mm. Two 7 mm  7 mm  0.5 mm Piezo-electric Wafer Active Sensors (PWAS) of type PZT 5A, one acting as an actuator and the other as a sensor, are used for the numerical study. Two numerical models are studied: 1) for pristine structure and 2) for de-bonded structure. The respective FE model geometries are shown in Fig. 2(a) and Fig. 2(b). The material properties of the shell and PWAS materials are shown in Table 1.

Fig. 2. Geometry of axi-symmetric FE models for (a) pristine structure, and (b) de-bonded structure

The numerical simulations are carried out using ANSYS 14.5. Both the pristine and de-bond models are created using 2D axi-symmetric elements, where PWAS are attached in a pitch-catch configuration. A four-noded 2-D element, PLANE 182, is used for the shell modelling and an eight-noded piezo-electric element type of PLANE 223 is used for the PWAS modelling. A mesh size of 0.25 mm is used everywhere in the model, which is sufficient enough to meet the criteria in Eq. (1), where kmin is the minimum wavelength in the wave propagation. The time-step sizes are varied

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according to the excitation frequency; however the step size met the criterion in Eq. (2), where fmax is the maximum frequency in the wave propagation. Table 1. Materials properties of the doubly-curved-shell structure and PWAS S. No Parts (Qty) Material description 1 Spherical shell Aluminum 2124-T851

2

Material properties Young’s Modulus E = 73.1 GPa Density q = 2780 kg/m3 Poisson’s ratio l = 0.33 PWAS (2 Nos) Lead Zirconite Titanate (PZT-5A) Piezoelectric charge constants d33 = −171  10−12 C/N d31 = 584  10−12 C/N Elastic constants SE11 = 16.4  10−12 m2/N SE33 = 18.8  10−12 m2/N Density q = 7750 kg/m3

kmin 20

ð1Þ

1 20fmax

ð2Þ

le ¼ Dt ¼

Voltage load is applied to one of the PWAS as a function of time as shown in Eq. (3), where a Hanning-window modulated sinusoidal signal of 10 counts is used. The excitation is applied for 10 different frequencies - 50, 75, 100, 125, 150, 175, 200, 225, 250 and 300 (all in kHz).    2pft VðtÞ ¼ A  1  cos  sinð2pftÞ n

ð3Þ

Where, A is maximum amplitude of voltage, f is frequency of excitation, n is no. of counts in the tone burst and t is time step size. For the de-bond model, 3 cm de-bond length over a total attachment length of 5 cm is taken, refer to Fig. 2(b) (note: debond-length in figure is not proportional). In order to model contact mechanism at de-bond location, ANSYS contact elements CONTA 172 and TARGE 169 are used, which simulate the clapping action in de-bond area while wave passes through it. Augmented-Lagrangian method is selected for surface interaction at the de-bond interface.

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Vibro-Acoustic Modulation Technique

The numerical model for vibro-acoustic modulation technique is same as that for the higher-harmonics based approach, refer Sect. 2.1, except that additional low frequency excitation, Eq. (4), is applied to the structure. Refer to Fig. 3 for the VAM model geometry. FðtÞ ¼ A  sinð2pftÞ

ð4Þ

Where, A is maximum amplitude of vibration in Newton, f is excitation frequency in kHz, and t is time in seconds.

Fig. 3. Geometry of VAM FE model

3 Experimental Set-up Experiments are carried out on spherical shell structure to detect the presence of a CAN type defect on it. An attachment of size 4  4  0.1 cm3, bonded partially to the shell structure at 2  2 cm2 area, is used to create a CAN type defect, as shown in Fig. 4, where the bonding is made using an epoxy based adhesive, Araldite. Experimental setup includes a digital oscilloscope, a function-generator and an amplifier. The actuator PWAS is connected to a digital function generator (Agilent Technologies 33522A Series, Santa Clara, California) via an amplifier (Trek model 2100HF). The sensor signal is acquired using a digital oscilloscope (Agilent Technologies DSO-X 2004A).

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Fig. 4. Partially bonded attachment on doubly-curved-shell geometry

The geometrical details are same as given in Sect. 2 for the numerical analysis and also the material properties, which are given in Table 1. A 13-count sinusoidal Hanning-window modulated wave packet, generated from the function-generator and amplified using the amplifier, is applied to one of the PZT. Different excitation frequencies are used in the experiment, which are 65 kHz, 91 kHz, 117 kHz, 143 kHz, 169 kHz, 195 kHz and 221 kHz. The voltage response from other PWAS is extracted using an oscilloscope.

4 Results and Discussions This section presents the results from the two numerical simulation models; i) higherharmonics based approach, and ii) vibro-acoustic modulation technique, and experiments. 4.1

Higher-Harmonics Based Approach

The time-domain signal of the output waveform for the pristine and de-bond models is shown in Fig. 5(a) and Fig. 5(b) respectively. Compared to Fig. 5(a), the output from the de-bond model in Fig. 5(b) shows some decrease in wave amplitudes and some additional waves due to scattering of the wave packet at the de-bond location. Figure 6(a) and Fig. 6(b) show the FFT for the output response in Fig. 5(a) and Figure 5(b) respectively. Figure 6(a), which is for the pristine model, does not shows any higher harmonics, whereas Fig. 6(b), which is for the de-bond model, shows the presence of higher harmonics in the waveform at 2nd, 3rd and 4th harmonics, which are at 204 kHz, 308 kHz, and 412 kHz respectively.

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Fig. 5. Output sensor time-domain signal, (a) from the pristine model and (b) from the de-bond model, at excitation frequency of 100 kHz

Fig. 6. FFT of the output waveform (a) for the pristine model and (b) for the de-bond model, at excitation frequency of 100 kHz

In order to locate the de-bond location, Continuous Wavelet Transform (CWT) of the output waveform is calculated for the de-bond model, where CWT provided both time as well as frequency information. Equation (5) is used to arrive at the location of de-bond. x¼

1 C1

Dt  C12



ð5Þ

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Where, Dt is time difference between fundamental and 2nd harmonic, C1 is velocity at excitation frequency, and C2 is velocity at 2nd harmonic frequency. The de-bond location calculated using the above equation is 265.85 mm from the sensor location. This calculated location of de-bond is coinciding with the de-bond applied location in the model, which is between 245 mm and 275 mm distance from the sensor location. 4.2

VAM Technique

The numerical simulation for VAM technique has been done for both the pristine and de-bond models, where a de-bond length of 60% is used. The output responses for pristine and de-bond models obtained are shown in Fig. 7(a) and Fig. 7(b) respectively, which show the variation in time domain signal between the two. In Fig. 7 (b), the scattering of the input wave at the de-bond location can be seen. Also, these figures show amplitude decreases in time domain signal for the de-bond model. FFT of the output responses for the pristine and de-bond models are shown in Fig. 8(a) and Fig. 8(b). In comparison with the pristine model, higher harmonics are present for the de-bond model as shown in Fig. 8(b), however, there is no significant sideband present in it.

Fig. 7. Time domain output signal using VAM technique (a) for pristine model, and (b) for debond model

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Fig. 8. FFT of the output waveform (a) for the pristine VAM model, and (b) for the de-bond VAM model

4.3

Experimental Results

To verify there is no higher harmonics from sources other than the CAN defect, the experiment is initially conducted without the defect and the FFT for the same is shown in Fig. 9.

Fig. 9. FFT for the pristine structure at an excitation frequency of 91 kHz

FFT of the response for various excitation frequencies are taken and out of which 65 kHz, 91 kHz, 117 kHz and 143 kHz have shown higher harmonics in the FFT. FFT of the response for excitation frequency of 91 kHz is shown in Fig. 10. Other excitation frequencies, 169 kHz, 195 kHz and 221 kHz, which have not shown any higher harmonics response is might be due to tuning character of the spherical shell structure and PWAS.

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Fig. 10. FFT for the de-bond structure at an excitation frequency of 91 kHz

5 Conclusions Numerical and experimental studies are carried out on a spherical shell for the detection of CAN type damages, by exploring the non-linear features of guided wave propagation. Numerical simulations are carried out for two non-linear SHM techniques: i) higher-harmonics approach and ii) VAM technique. Studies used only twodimensional axi-symmetric elements. On comparison of time domain output responses for the pristine and de-bond models, in the case of higher-harmonics approach, scattering of the wave packet at the de-bond location is observed with reduction in overall amplitudes. The comparison of FFT of output responses shows presence of higher harmonics for the de-bond model. In models incorporating VAM technique, similar amplitude reduction is noted in the de-bond model, however, expected sidebands were absent. Experimental results for the higher-harmonics based approach show the presence of higher harmonics due to a CAN type defect, which is de-bond here. Further experiments are ongoing for VAM based SHM technique. Hence, a CAN type damage on a spherical shell can be detected by non-linear feature of higher harmonics generation. More studies are needed to arrive at such a conclusion for VAM technique.

References 1. Breazeale, M.A., Ford, J.: Ultrasonic studies of the nonlinear behavior of solids. J. Appl. Phys. 36(11), 3486–3490 (1965) 2. Buck, O., Morris, W.L., Richardson, J.M.: Acoustic harmonic generation at unbonded interfaces and fatigue cracks. Appl. Phys. Lett. 33(5), 371 (1978) 3. Broda, D., Staszewski, W.J., Martowicz, A., Uhl, T., Silberschmidt, V.V.: Modelling o nonlinear crack–wave interactions for damage detection based on ultrasound—a review. J. Sound Vibr. 333(4), 1097–1118 (2014)

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4. Wan, X., Zhang, Q., Xu, G., Tse, P.W.: Numerical simulation of nonlinear lamb waves used in a thin plate for detecting buried micro-cracks. Sensors 14(5), 8528–8546 (2014) 5. Aymerich, F., Staszewski, W.J.: Experimental study of impact-damage detection in composite laminates using a cross-modulation vibro-acoustic technique. Struct. Health Monit. 9(6), 541– 553 (2010) 6. Liu, G., Qu, J.: Guided circumferential waves in a circular annulus. J. Appl. Mech. 65, 424– 430 (1998) 7. Towfighi, S., Kundu, T.: Elastic wave propagation in anisotropic spherical curved plates. Int. J. Solids Struct. 40, 5495–5510 (2003) 8. Qiaoa, S., Shanga, X., Panc, E.: Elastic guided waves in a coated spherical shell. Nondestr. Test. Eval. 31(2), 165–190 (2016)

A Methodology for the Clusterisation of Communication Towers on the Basis of Their Structural Properties and Loads Lorenzo Benedetti(B) , Simone Cinquemani, Marco Belloli, and Matteo Buonanno Politecnico di Milano, 20156 Milan, Italy {lorenzo.benedetti,simone.cinquemani}@polimi.it, [email protected]

Abstract. Optimising the O&M activities related to the proprietary infrastructure assets is crucial for a company successful management. Nowadays this topic is gaining even major hype due to the accessibility of new technologies such as big data. Such reasons push the industrial market towards a pursue of effectiveness and efficiency of maintenance strategies. This work inserts itself in this context: born from a collaboration with a company involved in the sector of telecommunications, it aims at the determination of an heuristic model able to estimate, as far as their towers are concerned, the probability of a structural failure. The initial phase is about the analysis of the company’s database to identify the variables to be considered in the study. Then the focus is shifted on the research of a synthetic index that could summarise the contribution of the aforementioned parameters. Subsequently, a sensitivity analysis aimed at testing the robustness of such index is carried out, followed by a subdivision of the entirety of the assets into homogeneous clusters in terms of loads and structural properties. The clusterization is intended to feed the “risk” axis of a risk-impact matrix to be used to tailor the O&M best practices to apply to the fleet of communication towers. The predictive capability of such representation will be assessed performing physical checks on a number of towers sampled from the matrix.

Keywords: Communication towers SHM

1 1.1

· Structural health monitoring ·

Introduction Overview of Steel Lattice Masts

Steel masts belong to the class of special steel tower structures that thanks to their mechanical properties become the typical choice in the telecommunication field for data transmission. As Efthymiou E., Gerasimidis S. and Baniotopoulos C.C. clearly explain [1], these type of structures are often lightweight since an c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 172–180, 2021. https://doi.org/10.1007/978-3-030-64594-6_18

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open lattice morphology requires half as much material as a free-standing tubular one with a similar stiffness; yet they are considered as flexible structures. Steel lattice communication masts usually have to be placed in spots that can guarantee a high coverage thus leading the choice for their erection to high hills or mountain peaks; their modularity property becomes particularly handy to cope with difficult terrain. As far as loading conditions concern, self weight and environmental actions have to be considered during the design process. Serving the purpose of telecommunication, this structures will carry additional dead weight related to the mounting of antennas and dish reflectors which will also have a great impact in the calculation of wind forces acting on the structure due to the increased thrust surface available. Indeed, wind action is considered as the most critical load for steel lattice masts. 1.2

Vulnerability

The concept of vulnerability has been defined in physical terms as the sum between the nominal stresses for which the structures are designed and different detrimental factors. Nominal stresses are mainly related to the wind action. Detrimental factors are ascribable to systematic overloads, like the formation of ice on the structure that alters it’s dead weight and the dynamic response with respect to the wind action, but also to the reduction of loading capabilities due to corrosion phenomena or simply to reasonable factors that enhance the probability of being more vulnerable, like e.g. the age of the structure. It is then possible to create a synthetic index expressing the concept of vulnerability by detecting those variable involved in the definition of vulnerability itself. Following this train of thought it is necessary to collect the variables included in the calculation of wind pressure according to the most updated regulation; the most relevant contributions are ascribable to the geographical collocation (necessary to select the parameters needed to calculate the wind velocity), the altitude of the site and it’s exposure. The exposure is function of the height above the ground of the structure considered and also of the orography of the terrain around the latter. All of the variable listed are usually considered and available without uncertainty during the design phase, exception made for the information related to the orography of the terrain. Since the contribution of this variable is particularly relevant when dealing with telecommunication towers (due to their typical placements), in the next paragraph will be shown a methodology to describe and include the orographic information in the calculation of wind pressure. Finally, considering the succession of the different regulations (with different methodologies for the calculation of wind pressure) throughout the last decades, also the year of construction is a fundamental variable. With this selection of variables it will be possible to develop an index describing the invariant vulnerability associated to a given structure. However, it has to be considered also the problem related to corrosion which is always relevant when dealing with metal structures exposed to the environmental action; therefore, it will be created a separated index to take into account the variable in time effect of corrosion.

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Orography

The NTC-2018 express a methodology to consider the contribution of orography in the calculation of wind pressure (through the exposure coefficient). However, this methodology is particularly suitable to analyse a little number of cases, since it requires a great and detailed amount of information which can only be acquired on the spot. In this work it is proposed a methodology to overcome this limitation and deduce the orographic information through an automated process and thus for an unlimited number of structure to monitor. 2.1

Acquisition Methodology and Representative Parameter

In order to develop a parameter able to describe the orographic collocation, it is needed to acquire information on the altitude variations of the terrains around the structures. It is possible to create a simple script (php language in our case) to acquire the value of elevation of a grid of points distributed around the geographical coordinates of the cases under exam. For the sake of this work it was used the web-service found at https://elevation-api.io/, which allow to obtain the elevation of a list of points (expressed in geographical coordinates) with a simple http get request in the following form: https://elevation-api.io/ api/elevation?points=(lat1,lon1),(lat2,lon2),.....&key=api-key. Sub-points. The first step in the acquisition process regards the definition of the number and localization of sub-points. We decided to develop a sub-point grid centred with the site’s marker and ranging from zero to a hundred meters to overcome possible uncertainties with the actual tower position with respect to the marker and to cover an area suitable for all the possible terrain features. The number of sub-points distributed in this area has been selected as a trade off between the desired accuracy for the description of the orographic collocation, the resolution of the service’s database and the time needed to receive data response for our http request. However, the maximum available resolution from the Web service’s database is of 30 m, meaning that two points fixed at less then 30 m from each other will return the same elevation value. Finally, not only the time needed to receive a response from the web-service, but even the processor’s resources usage are really sensitive to the choice of the number of sub-points; indeed, the total number of sub-points that will have to be managed is equal to (nsub + 1)Ntorri , where nsub is the number of sub-points used and Ntorri is the number of the towers of interest. The number of sub-points has to be limited in order to avoid Gateway timeout errors, yet maintaining a good degree of description for the terrain orography. The grid-matrix adopted is show in Fig. 1. Representative Parameter. As confirmed in [2], the standard deviation of elevations is a measure of the altitude variability within a given area; it is then useful as a measure of local relief at the scale specified by the selected area.

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Fig. 1. Representation of the disposition of the grid of sub-points with respect to the reference marker of the site

In this way we are able to create a synthetic variable describing the orographic collocation for the structure.  n ¯)2 k=1 (zi − z (1) σ= n with zi as the elevation of a point, z¯ the mean elevation value and n the number of considered points. 2.2

Visual Investigation and ct Association

The standard deviation of elevations cannot provide information regarding slopes and their orientations; still, it gives the possibility to deduce if we are dealing with an area characterized by a flat, undulating or hilly/mountainous terrain. The calculated values for σ range from 0 to around 150 m and show a predominant distribution in the 15 to 30 m range, with a mean value σ ¯ = 22 m. Since the standard deviation of elevations has been chosen to describe the orographic collocation and thus be a measure of local relief, we expect to be able to visually distinguish specific terrain morphologies within σ ranges. Therefore, to validate this hypothesis, it was performed a visual investigation which results can be seen in Fig. 2. From this preliminary analysis it was clear that the standard deviation of elevation can turn out to be a good indicator to provide a rough description of the local orography; however, as it can be seen in Fig. 3, two sites returning the same σ value might have significantly different orographic features since with the standard deviation we cannot distinguish a positive from a negative difference in elevation with respect to the mean value. The consequence is that we won’t be able to understand if we are dealing with a ridge type terrain or a slope. ct (σ). A methodology for the calculation of the ct coefficient are present in literature and they are reported in the appendixes of the latest regulation but, as it

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Fig. 2. Examples of the different orographic features encountered for different values of σ

Fig. 3. Example of locations with same σ but different orographic features.

was already expressed, it requires the availability of a greater amount of tailoredshape information. Anyway, it is still possible to cover some relevant indications

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on the range of values covered by the topographic parameter by looking at the most critical situations considered in the regulation. The worst possible collocation is represented by the top of an isolated cliff, edge or escarpment. The regulation suggests to calculate the topographic coefficient as in 2:  x (2) ct = 1 + βγ 1 − 0, 1 H where β depends on the height above the ground and of the relief (βmax = 0.5), γ depends on the slope (γmax = 1), x is the distance of the construction from the summit of the relief and H is the height of the relief itself. From Eq. (2) it is evident that the maximum expected value for the topographic coefficient corresponds to ct,mac = 1.5. it was shown how those sites The visual investigation showed those site having σ greater then 60 m represented the most critical orographic collocations; therefore, we can with sufficient certainty consider those locations as belonging to the family of isolated cliffs, edges or escarpments and thus assign to those sites the maximum value for the topographic coefficient. The assignment of a value for the topographic parameter for the remaining σ values proved to be an ambiguous problem that should be solved with the aid of experimental measurements whenever possible. While it is still possible for some of the sites with σ in the 30 to 60 m range to present the shape properties for the maximum ct value, it is clearly not possible, in the perspective of a big case study, to visually investigate one by one each these locations. Our approach on this particular matter aimed at providing the most conservative type of analysis. The calculation methodology considered, expressed in Eq. (3), results in a quite large estimation of the contribution of orography to the wind pressure calculation through the topographic coefficient; other type of relationships between σ and ct have been investigated during the subsequent clustering phase on our testing database but, since no particularly relevant differences were detected, we decided to stuck with the less conservative one.   log σ ct = 1 + 0.5 (3) log σmax The just detected topographic coefficient was then used to recalculate the wind pressure following the latest regulation procedure.

3

Vulnerability Index

The vulnerability associated to the nominal stresses is considered from the calculation of wind pressure with the latest standard and with the contribution of the orographic information. The detrimental factor associated to the possible systematic overloads is considered as the differential between the latest calculated value of pressure and the design one. This two contributions are needed to create the invariant vulnerability index.

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Aerodynamic Index

Therefore, it was created a synthetic index describing the vulnerability associated with the wind action (Eq. 4), being careful to weight each contribution adequately. That’s the reason why it was added a weighting factor for the differential of pressure and with respect to the height of the towers, it was decided to use a logarithmic scale, given that it is not considered in the analysis the exact number and positioning of antennas and dish reflectors along the towers height and therefore the distribution of surfaces available for the wind thrust. Iaero = (P2018 + λ ΔP ) log H

(4)

The weighting factor can be determined through a sensitivity analysis. Objective of this work is the creation of a synthetic index summarizing the concept of vulnerability on the basis of which define distinct vulnerability classes. Knowing the number of classes to be created it is possible to perform the subdivision in distinct vulnerability classes with the K-means algorithm. K-means is an algorithm which aim is to partition a set of n objects into a k user-defined number of clusters by dynamically reassigning objects to clusters until either the minimization of on objective function or the maximum number of iterations is reached. The objective function to be minimized is the distance of objects from one another within the cluster and the optimization goal is to maximize the distance between clusters centroids. Therefore, having a set of n structures under study, it is possible to perform the sensitivity analysis by dividing the towers in k clusters on the basis of the just created Iaero and observing the number of towers leaps from one cluster to the other occurring for variations of the weighting factor λ. The selected value will be the one that minimizes the number of leaps. 3.2

Corrosion Index

The corrosion phenomenon is interesting both from the point of view of the determination of maintenance procedures and of the contribution to vulnerability. It is a problem depending on a large number of factors besides time. Undoubtedly, the first factor to consider is the classification of the corrosion class of the environment in which the structure is positioned since the speed of corrosivity is influenced by the rate of relative humidity in the air and pollution in the atmosphere. The UNI EN ISO 12944 provides the indications to classify the environments in six fundamental classes and identifies the specific annual wear rate in terms of thickness loss. The areas adjacent to the coast (within 10 km) are classified, according to regulations, within the high corrosivity class due to the higher salinity level of the atmosphere. The loss of thickness induced by corrosion is very different if one considers hot-galvanized structures with respect to unprotected structures. In fact, for unprotected structures a thickness loss ranging from 80 to 200 µm per year is indicated alongside this class of corrosivity; instead, if a hot-dip galvanization has been carried out, the loss

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of thickness decreases drastically at 4.2 to 8.4 µm. It is therefore clear that the intensity of the corrosion effect on the structure over time will be closely linked to the type of pre-treatment performed on it and on the maintenance procedure (painting) performed during the tower’s working cycle. From the information concerning the type of pre-treatment and painting process applied the duration of the protection Tp is expressed as in (5): Tp =

tpt + tp c˙

(5)

where tpt represents the thickness of the pre-treatment layer in μm, tp as the thickness of the painting layer in μm and c˙ as the rate of corrosion in μm/year. Knowing the nominal duration of the protection it is possible to create an index for the vulnerability related to the corrosion phenomenon to be added to Iaero . This corrosion index should be zero when the painting is carried out (maximum protection), while it would reach its maximum value for a time interval from the last painting equal to Tp . The analytical formulation for the corrosion index Ic was expressed as in (6): (6) Ic = mI0 ki with m as a boolean variable with positive value only for close to coast locations, I0 as a factor to weight equally the contribution of Ic and Iaero for unprotected structures, k as the inverse of the protection duration and i as the years passed from the last maintenance intervention (imax = Tp ). The final formulation for the vulnerability index is therefore expressed as in (7): I = Iaero + Ic 3.3

(7)

Conclusion

In this work an heuristic model able to delineate a vulnerability profile has been developed. This model is based on an ad hoc index created to account for all the variable that could potentially have an influence on the vulnerability definition of steel lattice masts. Clustering a database of structures using this index gives the possibility to start investigating more effectively the potential critical situations defined on the basis of the most objective and physical criterion available.

References 1. Efthymiou, E., Gerasimidis, S., Baniotopoulos, C.C.: On the structural response of steel telecommunication lattice masts for wind loading and combined effects. In: EACWE 2009, vol. 5, pp. 1000–1009 (2009) 2. Wilson, J., Gallant, J.: Digital Terrain Analysis in Terrain Analysis: Principles and Applications, vol. 479, pp. 1–27 (2000) 3. D.M. 17 Gennaio 2018: Aggiornamento delle norme tecniche per le costruzioni, 20 February 2018

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4. Al Katsaprakakis, D., Christakis, D.G.: Wind parks design, including representative case studies. In: Sayigh, A. (ed.) Comprehensive Renewable Energy, pp. 169–223 (2002) 5. Buzzi, A., Foschini, L.: Mesoscale meteorological features associated with heavy precipitation in the Southern Alpine Region. In: Meteorology and Atmospheric Physics, February 2000 6. Gonz´ alez-Longatt, F., Medina, H., Gonz` alez, J.S.: Spatial interpolation and orographic correction to estimate wind energy resource in Venezuela. Renew. Sustain. Energy Rev. 48(2015), 1–16 (2015) 7. Troen, I., Petersen, E.L.: European wind atlas. Risø National Laboratory, Roskilde (1989) 8. Ruel, J.C., Pin, D., Cooper, K.: Effect of topography on wind behaviour in a complex terrain. For.: Int. J. For. Res. 71(3), 261–265 (1998)

An Adaptive Wavelet Library to Detect Surface Defects in Rail Tracks Using a Laser Ultrasonic System Javad Rostami1,2(&), Faeez Masurkar1, Peter Tse1, Nitesh Yelve3, and Edison Z. Y. Hou1 1 Smart Engineering Asset Management Laboratory (SEAM) and Croucher Optical Nondestructive Testing and Quality Inspection Laboratory (CNDT), City University of Hong Kong, Hong Kong, China [email protected] 2 School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK 3 Structural Health Monitoring Lab, Department of Mechanical Engineering, Fr. Conceicao Rodrigues Institute of Technology, Vashi, Navi Mumbai, Maharashtra, India

Abstract. This study is concerned with locating surface defects that occur in rail tracks. Ultrasonic Rayleigh waves were used to investigate the rail track surface. To generate and sense these waves a fully non-contact laser ultrasonic transduction system was employed. The laser-generated signals are in general more susceptible to environmental noise in comparison with signals generated by other methods. Meanwhile, the quality of signals received from one point may vary in each time of measurement. Continues Wavelet Transform (CWT) is a practical tool in dealing with complicated signals. In this regard, CWT works better if its mother wavelet is carefully selected based on the nature of the analyzing signal. Seeing that, a library of mother wavelets was tailor-made for studying laser-based Rayleigh waves in rail tracks. Mother wavelets were designed based on characteristics of the incident wave packets after extensive measurements on rail tracks. For analyzing a signal, initially, the first biggest wave packet that is the incident wave is recognized. Absolute cross-correlation is then used to pick a mother wavelet from the library that has the maximum resemblance with the incident wave. Using such an approach, the irrelevant wave packets can be easily discarded, and surface defects are exposed. Keywords: Laser ultrasonic
Guided waves
Rayleigh waves
Wavelet
Rail tracks
Signal processing

1 Introduction Rail tracks are important parts of transportation infrastructure whose proper maintenance ensures their safety for continuous everyday operation. Seeing the need for regular inspection of rail tracks, ultrasonic guided wave is an attractive way for defect detection purposes in such structures [1]. For thick structures like rail tracks, guided © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 181–189, 2021. https://doi.org/10.1007/978-3-030-64594-6_19

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waves propagate over the surface of the rail, and in this case, they are called Rayleigh waves [2]. Among different methods of generating and receiving guided waves [3–6], laser ultrasonic because of its non-contact nature is a promising technique for nondestructive testing of rail tracks [7]. Meanwhile, as the focus of this study is on detecting surface defects, it is an ideal way to generate Rayleigh surface waves. Nonetheless, laser-ultrasonic signals can be more contaminated with noise and unwanted components in comparison with other guided wave signals such as the ones generated by PZT [8] or Magnetostrictive sensors [9]. Part of the reason why laser-ultrasonic signals are more complicated arises from the fact that laser naturally generates broadband signals. Broadband guided wave signals contain multiple modes travelling with different velocities that may not be desirable from a nondestructive testing point of view. Addressing the complications of broadband signals, slit masks can be used to limit the frequency band within a narrow range [10]. Using slit masks or any other interferometry setup to make laser signals narrowband, improves the quality of guided wave signals. However, it does not fully purify signals from other sources of contamination. Signal processing methods such as Continuous Wavelet Transform (CWT) that focus on time-frequency analysis are practical tools in explaining and interpreting complicated signals [6, 8, 11]. In using CWT, the mother wavelet must be carefully chosen to get the best result. Rostami et al. previously demonstrated that if the excitation guided wave signal is used as a mother wavelet, the capability of CWT in detecting small defect indications buried in noise increases [6]. Nonetheless, the excitation signal in laser ultrasonic cannot be as easily formulated as the ones in contact methods. Addressing this problem, one solution could be to consider the first wave packet travelling along a waveguide as a proper mother wavelet. However, it is worthy to mention that measurements of laser-ultrasonic signals might slightly differ in each time of measurement. For example, in one measurement we may observe a 5-cycle sine wave as an incident wave and in anther measurement, this might be 6-cycle. Given such an issue, a library of wave-packets with a maximum resemblance with possible incident waves were designed. Using absolute crosscorrelation as a similarity tool between the incident wave and wave packets in the library, the best choice for mother wavelet would be the wave packet with the highest value of absolute cross-correlation. In this paper, the applicability of the proposed wavelet library was studied on Rayleigh surface waves propagating in rail tracks. The Rayleigh surface waves were emitted and recorded by a fully non-contact laser-ultrasonic transduction system. The proposed method facilitates the inspection of rail tracks in finding surface defects.

2 Continuous Wavelet Transform (CWT) The Continuous Wavelet Transform (CWT) is an effective tool for analyzing guided wave signals. It has choices of basis function to match a defect signal. CWT is a powerful tool that decomposes signals concerning their frequency components. The

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decomposition of a signal S(t) is carried out by finding the signal correlation with the mother wavelet wðtÞ: 1 WTw ða; bÞ ¼ pffiffiffi a

Z1 SðtÞw 1





 tb dt a

ð1Þ

where w ðtÞ indicates the complex conjugate of the mother wavelet wðtÞ, a is the dilation parameter (scale) and b is the translation. Noting the above equation, the larger values of the wavelet coefficients WT is produced when there is more similarity between the signal and the mother wavelet. Exciting Rayleigh surface waves in rail tracks, it can be observed that the first biggest wave packet called the incident wave received close to the excitation point looks very similar to a tone-burst signal. Toneburst signals are sine waves modulated by windows such as the Hamming window:   xt SðtÞ ¼ sinðxt þ hÞ 0:08 þ 0:46 1  cos N

ð2Þ

where t, x, h and N are the time, circular central frequency, phase, and the number of cycles, respectively. Given this, a library of tone-burst signals with different values for N and h of zero and p were designed. Different values for N address the slightly different measurement of the incident wave each time and two different values for h addresses any reflection from a defect whose phase is 180 different from the incident wave. To be more accurate in choosing the best mother wavelet from the library, a computational loop is introduced to pick a wave packet from the library with maximum similarity with the incident wave. This similarity is determined by calculating the absolute cross-correlation between the incident wave and each wave packet in the library. It is worthy of mention that for defect detection purposes, a wave packet with 180 difference in phase with the original choice is selected.

3 Experiment The sample used for the experiment is a standard rail track used in the Hong Kong railway system. The dimensions and material properties are demonstrated in Fig. 1 and Table 1; respectively. The head of the rail track was initially polished so that any rust or dirt that may affect laser-ultrasonic signals were removed. After that, using an angle grinder an artificial crack was created on the head in a perpendicular direction to the main axes of the sample as shown in Fig. 1. The Rayleigh surface wave velocity for this sample is 3008 m/s. In Fig. 2 the experimental setup used for generating and receiving Rayleigh surface waves is depicted. For the excitation Nd: YAG laser (SLIII-EX, Continuum ElectroOptics, Inc.) with a wavelength of 532 nm and a pulse duration of 5 ns was used. To make the excitation signal narrowband, a slit mask is used to convert the point source of the laser to a line arrayed pattern (LAP). This way a narrowband Rayleigh wave at a

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selected central frequency in the sample can be generated [12]. To record Rayleigh wave signals a 3D-SLDV (PSV-500-3D-M) is used which sends data to an offline computer.

Fig. 1. The rail track used for experiment with a surface defect made by angle grinder

Fig. 2. The fully non-contact laser-ultrasonic system used in the experiment Table 1. Material properties of the rail track used in the experiment Modulus of elasticity E (Gpa) Density q (kg/m3) Poisson’s ratio 212 7799 0.28

4 Results and Discussion The vital step in analyzing the results with CWT is to pick the best mother wavelet. A line-scan that was previously done on an intact rail, helped to have a precise design of possible mother wavelets. For a signal to be analyzed, absolute cross-correlation between the incident wave and each member of the library is calculated. The member that generates the highest absolute cross-correlation is selected as the mother wavelet. It must be noted that the collected data using 3D-SLDV are in x, y, and z directions. However, as they look almost similar and signals in X-direction have better signal to noise ratio, only data in the x-direction is presented here. Knowing the velocity of the Rayleigh wave and location of the defect and receiver point, the expected time in which the defect can be seen is calculated. For demonstrating the performance of the proposed

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method, two signals from the same sample rail track measured at different locations were considered (Fig. 3a and Fig. 3c).

Fig. 3. Time temporal waveforms and their suggested mother wavelets: a) 1st signal, b) absolute cross-correlation between its incident wave the wavelet library, c) 2nd signal and d) absolute cross-correlation between its incident wave and the wavelet library

These two signals showed slightly different incident wave and they were a good choice for comparison purposes. For both signals, the excitation point was the same. The receiving points were between the excitation point and the defect. The receiving point for the first signal was 70 mm away from the excitation point and 160 mm away from the defect. As a result, the incident wave and the reflection from the defect were expected to be seen at 0.02 ms and 0.13 ms; respectively. For the second signal that was recorded 160 mm away from the excitation point and 70 mm away from the receiving point, a similar calculation for the time of flight can be done. In that case, the incident wave can be pinpointed at 0.53 ms. Similarly, the reflection from the defect can be observed at 0.99 ms. The incident waves in both signals look very similar. However, a small difference in their shapes creates different suggestions in using a proper mother wavelet. For the first signal, because the 8 cycles tone-burst signal has greater absolute cross-correlation with the incident wave it is suggested to be used as a mother wavelet. However, the same calculation for the second signals recommends using the 10-cycle tone-burst signal. To verify these recommendations, CWT of both signals using suggested mother wavelets were calculated and illustrated in Fig. 4 and Fig. 5. For better comparison purpose, CWT using other mother wavelets were also

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Fig. 4. Time-frequency plots of the 1st signal in Fig. 3a using different mother wavelets: a) Morlet, b) 6-cycle tone-burst, c) 8 cycle tone-burst and d) 10 cycle tone-burst

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shown in these figures. In Fig. 5, in which for the first signal, using 8 cycle tone-burst was recommended, it can be seen that the defect location thanks to generating greater wavelet coefficients is visible. The defect can also be detected using the 10-cycle

Fig. 5. Time-frequency plots of the 2nd signal in Fig. 3c using different mother wavelets: a) Morlet, b) 6-cycle tone-burst, c) 8 cycle tone-burst and d) 10 cycle tone-burst

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tone-burst signal. Nonetheless, because it has less similarity with the mother wavelet, it generates smaller wavelet coefficients. Meanwhile, it is observed that the Morlet wavelet and the 6-cycle tone-burst are less efficient in pinpointing the reflection from the defect. Besides, as was mentioned earlier, unlike other guided wave signals, lasergenerated signals can slightly differ in each time of measurement. As a result, in the second signal, the incident wave is different than the one in the first signal. Therefore, another mother wavelet is suggested for time-frequency analysis. This time as Fig. 5 illustrates, the 10-cycle tone-burst generates greater wavelet coefficients. Although the 8-cycle tone-burst signal can also be used, it will not produce the best result.

5 Conclusion In this study, an efficient method of time-frequency analysis based on Continuous Wavelet Transform (CWT) was proposed to deal with Rayleigh surface waves used for Rail Track inspection. The proposed method was verified with signals obtained from a fully-non-contact laser-ultrasonic transduction system. Laser-generated guided waves, in general, have lower signal to noise ratio and extracting defect-related information from such signals must be carried out in the best possible way. Meanwhile, seeing these signals vary in each time of measurement, suggesting using a single mother wavelet cannot be the best idea. Therefore, in this paper, a library of tone-burst signals was tailor-made so that a mother wavelet with a maximum resemblance with the incident Rayleigh wave is chosen for the analysis. The maximum resemblance is determined by calculating the absolute cross-correlation. Using the suggested approach, the best results for timefrequency plots were generated in which the reflection from the defect was robustly pinpointed. Acknowledgement. The work described in this paper is fully supported by a grant from City University of Hong Kong (Project No. 7005120) and a grant from the Innovation and Technology Commission (ITC) (Project No. ITS-205-18FX) of the Government of the Hong Kong Special Administrative Region (HKSAR), China. Any opinions, findings, conclusions, or recommendations expressed in this material (or by members of the project team) do not reflect the views of the Government of the HKSAR, ITC, or Panel of the Assessors for the Innovation and Technology Support Programme of the Innovation and Technology Fund.

References 1. Srivastava, A., Bartoli, I., Salamone, S., di Scalea, F.L.: Higher harmonic generation in nonlinear waveguides of arbitrary cross-section. J. Acoust. Soc. Am. 127(5), 2790–2796 (2010) 2. Masurkar, F., Tse, P.: Theoretical and experimental evaluation of the health status of a 1018 steel I-beam using nonlinear Rayleigh waves: application to evaluating localized plastic damage due to impact loading. Ultrasonics 108, 106036 (2020)

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3. Rostami, J., Safizadeh, M.: A case study of application of guided waves for detecting corrosion in pipelines. In: AIP Conference Proceedings, vol. 1433, pp. 439–442. American Institute of Physics (2012) 4. Rostami, J., Chen, J., Tse, P.: A signal processing approach with a smooth empirical mode decomposition to reveal hidden trace of corrosion in highly contaminated guided wave signals for concrete-covered pipes. Sens. (Switzerland) 17(2), 302 (2017) 5. Rostami, J., Tse, P., Fang, Z.: Sparse and dispersion-based matching pursuit for minimizing the dispersion effect occurring when using guided wave for pipe inspection. Mater. (Basel) 10(6), 622 (2017) 6. Rostami, J., Tse, P., Yuan, M.: Detection of broken wires in elevator wire ropes with ultrasonic guided waves and tone-burst wavelet. Struct. Health Monit. 19(2), 481–494 (2020) 7. Kim, N., Sohn, H., Han, S.: Rail inspection using noncontact laser ultrasonics. J. Korean Soc. Nondestr. Test. 32(6), 696–702 (2012) 8. Andhale, Y., Masurkar, F., Yelve, N.: Localization of damages in plain and riveted aluminium specimens using lamb waves. Int. J. Acoust. Vibr. 24(1), 150–165 (2019) 9. Tse, P., Rostami, J.: Matching pursuit with novel dispersive dictionary for mode separation in guided wave signals obtained from pipes. In: AIP Conference Proceedings, vol. 2102, no. 1, p. 050010. AIP Publishing LLC (2019) 10. Choi, S., Nam, T., Jhang, K., Kim, C.: Frequency response of narrowband surface waves generated by laser beams spatially modulated with a line-arrayed slit mask. J. Korean Phys. Soc. 60(1), 26–30 (2012) 11. Chen, J., Rostami, J., Tse, P.: The design of a novel mother wavelet that is tailor-made for continuous wavelet transform in extracting defect-related features from reflected guided wave signals. Measurement 110, 176–191 (2017) 12. Kim, D., Cho, Y., Lee, J.: Assessment of wall-thinning in carbon steel pipe by using lasergenerated guided wave. Nuclear Eng. Technol. 42(5), 546–551 (2010)

Experimental Evaluation of Nonlinear Wave/Damage Interaction for Delamination Detection in Laminated Composites Xixi Li(&), Eric Monteiro, Mikhail Guskov, Marc Rebillat, and Nazih Mechbal Laboratoire PIMM, Arts et Métiers Institute of Technology, CNRS, CNAM, HESAM Université, 151 Boulevard de L’Hopital, 75013 Paris, France [email protected]

Abstract. Structural health monitoring (SHM) deals with the early detection of structural damages to prevent catastrophic failures and is expected to provide major improvements with respect to safety and maintenance costs. With the increasing development of aeronautic industry, composite materials are being more and more widely used. In this case, the SHM of composite structure is crucial, especially the monitoring of local delamination of plies in composite materials. This paper presents an investigation on the detection of delamination type damage in carbon fiber reinforced polymer (CFRP) composite plates based on nonlinear acoustic effects. The LASER shock wave technique is used to generate realistic delamination in the composite plates. A damage index (DI) is proposed in this paper based on total harmonic distortion (THD) to evaluate the nonlinear acoustic effects induced by delamination. Experiments are conducted on four plates containing different sizes of delamination, including one undamaged plate for reference. Results show that acoustic nonlinearities are generated due to the presence of a realistic delamination damage, and the proposed DI is appropriate to evaluate the influence of the delamination size on the nonlinear acoustic effects under different excitation amplitudes. Keywords: Structural healthy monitoring (SHM) lamb wave
Damage index


Delamination
Nonlinear

1 Introduction One of the most important issues in engineering is the monitoring and the early detection of structural damages to prevent catastrophic failures. This process is referred to as Structural Health Monitoring (SHM) and its implementation is expected to provide considerable improvements with respect to safety and maintenance costs [1]. In order to test and validate delamination detection algorithms for SHM, experimental investigations are mandatory, and particularly physical supports are firstly needed. In this paper, a new method named Laser Shock Wave Technique (LSWT) [2] is used to generate calibrated delamination damages in composites samples. With this method, only delamination-type damage is generated inside the specimen and its through-thickness location and size of the damage can be well controlled. Furthermore, © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 190–199, 2021. https://doi.org/10.1007/978-3-030-64594-6_20

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this technique generates a realistic delamination damage compared to traditional damage generation technique, such as Teflon insert. Various SHM algorithms for damage detection in a SHM context have been investigated. Among them the wave propagation-based method has become the most commonly used method for its convenience and low cost. Specifically, Lamb waves are widely used in beam- and plate-like structures [3–5]. Compared to linear techniques, nonlinear Lamb waves are more sensitive to smaller, even barely visible damages, for example, delamination in composite structures [6]. Generally, the nonlinear phenomena are induced due to various types of physical mechanisms. In this paper, the mechanism named contact acoustic nonlinearity (CAN) [7] is focused. When the incident wave passes through the damage, the damage interfaces tend to move towards each other under compressive pressure, and opposite each other under tensile pressure [7]. This may lead to the contact between damage interfaces, and then higher harmonics can be generated due to this contact interaction [8–10]. Several studies focused on the higher harmonics generated by CAN in composite plates or beams containing delamination damage. Soleimanpour et al. [9] studied the potential of a baselinefree SHM techniques based on higher harmonics resulting from the interaction of guided wave with a delamination. Sohn et al. [11] explored the feasibility of using a non-contact guided wave imaging system to detect hidden delamination in multi-layer composites. Despite these studies, there is still no study performing the SHM algorithm for the detection of a realistic delamination-type damage in composite plates. It can be observed that all the above-mentioned experimental studies were performed on structures containing artificial delaminations or cracks, which might cause errors in applications when realistic delamination is present. In this study, composite plates containing realistic delaminations fabricated by laser shock were investigated. These calibrated delaminations are able to imitate physically the real delamination existed in a composite structure. The objective of this study is thus to investigate the physical mechanism of the generation of the nonlinear phenomena, i.e. the super-harmonics due to the presence of the delamination damage in a composite plate, and to evaluate quantitively the influences of delamination existence on acoustic nonlinear properties of the composite plates. To achieve this, experiments of composite plates containing fabricated delamination damage generated by laser shock are conducted. Then a damage index is proposed based on total harmonic distortion (THD) to evaluate the effect of both the second and the third harmonics to the total nonlinear acoustic effects. In this study, composed of 4 sections, the proposed damage index definition is described in Sect. 2. In Sect. 3 the configuration and results from experimental study is presented. Section 4 depicts the conclusion of this study and the future work in the next.

2 Damage Index Definition In this study, the nonlinear Lamb waves are used for the delamination detection. For those nonlinear waves that are induced by delamination, not only the second harmonic, but also other higher order harmonics exist such as the third harmonic. However, in most studies only the effect of second harmonic is taken into account. Therefore, a damage index to evaluate the energy of multiple higher harmonics is necessary.

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Total harmonic distortion (THD) is a common value used to measure the harmonic distortion present in a signal in audio system. It is defined as the ratio of the sum of the powers of all harmonic components to the power of the fundamental frequency: qffiffiffiffiffiffiffiffiffiffiffiffiffi PN 2ffi A ð1Þ i¼2 i THD ¼ ; A1 where A1 indicates the amplitude of the fundamental frequency, and Ai represents the amplitude of the ith order harmonic. Here, we set N = 3, indicating that only the energy of the second and third harmonic are calculated since the PZT sensor used in the experimental system is not sensitive enough to high frequency components. Then this THD becomes THD ¼

pffiffiffiffiffiffiffiffiffiffiffi 2 2 A2 þ A3 A1

ð2Þ

where A2 and A3 represent the amplitudes of the second and third harmonics respectively. Thus, the THD value in this study can be used to evaluate the total energy of the second and third harmonics with respect to the energy of the fundamental frequency. To evaluate quantitively the influence of the delamination damage in the specimens on the acoustic nonlinearity, a damage index (DI) is proposed based on the relative THD of damaged plates with respect to the healthy plate, which can be defined as DI ¼

THDdamage THDhealthy THDhealthy

;

ð3Þ

where THDdamage and THDhealthy are the THD values of damaged plates and the undamaged plate respectively. This DI evaluates the effect of delamination existence on the acoustic nonlinearities of the system.

3 Experiments for Nonlinear Wave/Damage Interaction Characterization 3.1

Description of the SHM System

The study is conducted using a SHM system shown in Fig. 1(a) developed by the author’s team. This system is composed of five components including the signal generator, amplifier, data acquisition system, multiplexer and the composite specimen equipped with two PZT disks. The specimen is suspended to the workbench, close to a stress-free boundary conditions to prevent the specimen from interacting with other supports. A schematic of the SHM experimental system is shown Fig. 1(b). The specimens used here for testing are composite plates. These plates are made of carbon fiber reinforced polymer (CFRP) material consisting of 16 plies with the stacking sequence of [0°/90°]8 and a dimension of 315 mm  100 mm  2.24 mm. Three damaged plates containing different sizes of delamination are used for damage detection, and a healthy plate is used for reference.

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The pitch-catch method is used for the delamination damage detection. Each composite plate is equipped with two PZT disks (diameter 25 mm, thickness 0.5 mm; NCE51 material, provided by NOLIAC), as shown in Fig. 2. The PZT disks are permanently bonded to the surface of the composite plate. One PZT is used as transmitter and the other is used as signal receiver. The transmitter PZT1 is 50 mm away from the left edge and the receiver PZT2 is 45 mm from the right edge, ensuring a wave propagation distance of 220 mm. When conducting experiments, input signals are launched by the signal generator and amplified by the amplifier. Then the multiplexer sends one signal to the transmitter and one signal to the data acquisition system. The transmitter converts the electrical signal to mechanical vibration to induce Lamb waves inside the plate. The waves propagate through the damage region inside the plate and arrive at PZT2. Then PZT2 transforms the elastic waves to electrical signals, and the signals are collected by the data acquisition system. The input signal is a five-cycle sine wave modulated by a sine window with the center frequency of 52 kHz. The measuring sampling frequency is 1 MHz, and the record length is 0.01 s to ensure the resolution of the received signals in frequency domain. The amplitude is 10 V from peak to zero.

Fig. 1. (a) Experimental system; (b) schematic of the experimental system.

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Fig. 2. Specimen: composite plate equipped with two PZTs

3.2

Damaged Specimens – Delamination Calibrated by Laser Shock

The damaged plate contains a circular delamination damage at the center. Damage was introduced into samples in a calibrated way using laser shock wave technique. The specimen was subjected to a symmetrical laser impact of two laser beams. This resulted in a nearly circular delamination of 7 mm diameter at midplane of the composite plate [12]. Figure 3 shows an example of the C-scan image of delamination damage calibrated by laser shock. In this example, the size and the in-depth position of this delamination is precisely determined by controlling the energy and time delay of the laser beams [13]. Four plates are considered here in this study, as shown in Fig. 4: the undamaged plate, the plate containing one laser impact that introduces the delamination-type damage at the diameter of 7 mm in the midplane of the plate, the plate containing two laser impact representing a delamination of approximately 14 mm created at midplane and the plate containing three laser impact representing a delamination of approximately 21 mm at midplane.

Fig. 3. C-scan of delamination damage calibrated by laser shock [13]

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Fig. 4. Schematic representing the four plates, each equipped with 2 PZTs [12]

3.3

Experimental Results and Discussion

In this section, a five-cycle sine wave modulated by a sine window was used as the excitation signal. The results were shown in both time domain and frequency domain. Figure 5 shows the comparison of the responses in frequency domain for the undamaged plate and the damaged plate containing two laser impacts under the excitation of the modulated wave excitation. From this figure, except for the peak at fundamental frequency, there are two perceptible peaks corresponding to the second and third harmonics (104 kHz and 156 kHz) respectively. In the insets of Fig. 5, the harmonics are given for comparison; the amplitudes of both the second and the third harmonics from the spectrum of the two-impact plate are larger than those from the undamaged plate. It can be concluded that the acoustic nonlinearities are enhanced by the presence of the delamination impacts. This can also be proved by showing the frequency spectrum of the residual signal between the two-impact damaged plate and the undamaged plate shown in Fig. 6. Note that the frequency amplitudes are normalized to the maximum value. Therefore, the super-harmonics, especially the second and third harmonics, provide a sensitive tool for the detection of laser impact delamination in composite plates. Then the effects of delamination size and excitation amplitude on the acoustic nonlinearity are studied and presented in the following. Figure 7 displays the variation of relative THD based DI against the number of delamination impacts under the excitation amplitude of 10 V and 50 V. From this figure, it can be observed that the DI increases monotonically with the number of

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delamination impacts regardless of the excitation amplitude. For the one-impact plate, the relative THD values of the two cases with different excitation amplitudes are close. However, for the two-impact and three-impact plates, the THD values of the case under excitation amplitude of 10 V is larger than the THD values under excitation at 50 V. This indicated that the proposed THD based DI is remarkably sensitive to the size of the delamination, but less sensitive to the excitation amplitudes. Figure 8 presents the variation of relative THD based DI against the excitation amplitude on different specimens. The excitation amplitude varies from 10 V to 50 V at the step of 10 V. From this figure, it can be observed that the DI displays rather flat trends with some small variations with increasing excitation amplitudes. For the twoimpact and the three-impact specimens, the two curves show a slightly decreasing trend of the Dis, while for the one-impact specimen, the DI curve is almost flat. This variation trend might be due to the nonlinearities of the system. In conclusion, compared to the influence of the damage size, the excitation amplitude has much less effects on the proposed DI.

Fig. 5. Frequency domain responses of signals obtained from undamaged plate and damaged plate containing two impacts

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Fig. 6. Frequency spectrum of residual signal between the two-impact damaged plate and the undamaged plate subject to modulated wave excitation

Fig. 7. Variation of DI based on relative THD against the number of delamination impacts for different excitation amplitudes

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Fig. 8. Variation of DI based on relative THD against excitation amplitude on different specimens

4 Conclusion and Future Work In this study, the acoustic nonlinearities due to the presence of damage was investigated, and a damage index based on the total harmonic distortion was proposed and validated experimentally. The experiments were conducted under the excitation of a five-cycle sine wave modulated by a sine window at 52 kHz. From the experimental results, it can be concluded that the presence of delamination damage enhances the nonlinearities of the Lamb waves propagating in the composite plate. A new DI based on relative THD values is proposed and used for the evaluation of the acoustic nonlinearities. Results show that the proposed DI is remarkably sensitive to the size of the delamination, but less sensitive to the excitation amplitudes. In future studies, the finite element simulations will be conducted and the effects of excitation amplitude and damage size on the severity of the nonlinearities are going to be investigated numerically. Different damage indices will be generated and evaluated as indicators of the severity of the damage. Next, the results of experiments and simulations will be applied to the SHM process.

References 1. Giurgiutiu, V.: Structural health monitoring of aerospace composites (2015) 2. Ghrib, M., et al.: Generation of controlled delaminations in composites using symmetrical laser shock configuration. Compos. Struct. 171, 286–297 (2017) 3. Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 365(1851), 303–315 (2007)

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4. Rose, J.L.: A vision of ultrasonic guided wave inspection potential. In: JProceedings of the 7th ASME NDE Tropical Conference - 2001, pp. 1–22 (2001) 5. Su, Z., Hong, M.: 13 - nonlinear ultrasonics for health monitoring of aerospace structures using active sparse sensor networks. In: Yuan, F.-G. (ed.) Structural Health Monitoring (SHM) in Aerospace Structures, pp. 353–392. Woodhead Publishing (2016) 6. Yelve, N.P., Mitra, M., Mujumdar, P.M.: Spectral damage index for estimation of breathing crack depth in an aluminum plate using nonlinear Lamb wave. Struct. Control Heal. Monit. 21(5), 833–846 (2014) 7. Broda, D., Staszewski, W.J., Martowicz, A., Uhl, T., Silberschmidt, V.V.: Modelling of nonlinear crack-wave interactions for damage detection based on ultrasound - A review. J. Sound Vib. 333(4), 1097–1118 (2014) 8. Yang, Y., Ng, C.T., Kotousov, A., Sohn, H., Lim, H.J.: Second harmonic generation at fatigue cracks by low-frequency lamb waves: experimental and numerical studies. Mech. Syst. Signal Process. 99, 760–773 (2018) 9. Soleimanpour, R., Ng, C.-T., Wang, C.H.: Higher harmonic generation of guided waves at delaminations in laminated composite beams. Struct. Heal. Monit. An Int. J. 16(4), 400–417 (2017) 10. He, S., Ng, C.T.: Modelling and analysis of nonlinear guided waves interaction at a breathing crack using time-domain spectral finite element method. Smart Mater. Struct. 26 (8), 85002 (2017) 11. Sohn, H., et al.: Delamination detection in composites through guided wave field image processing. Compos. Sci. Technol. 71(9), 1250–1256 (2011) 12. Ghrib, M.: Structural health monitoring of composite structures: LASER shock delamination generation and machine learning-based quantification. L’École Nationale Supérieure d’Arts et Métiers (2017) 13. Ghrib, M., et al.: Generation of controlled delaminations in composites using symmetrical laser shock configuration. Compos. Struct. 171, 286–297 (2017)

Modelling of the Shear Horizontal Waves High-Order Harmonics Generation Using Local Interaction Simulation Approach Mariusz Osika, Rafal Radecki, Aleksandra Ziaja-Sujdak, and Wieslaw J. Staszewski(B) AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland [email protected]

Abstract. In the last few years, researchers have paid more and more attention to Shear Horizontal (SH) waves propagation characteristics as new approach used for damage detection. In particular, the fundamental SH0 mode is interesting due to its non-dispersive characteristics and single-mode existence in a certain range of frequency. These features offer promising applicability for developing a new Structural Health Monitoring technique. In order to examine damage detection features of the SH0, it is necessary to first investigate it via numerical simulations. Thus, in this paper, a new modelling approach is developed, based on the Local Interaction Simulation Approach (LISA), which allows to selectively simulate the propagation of SH waves. Both linear and nonlinear material definitions are taken into consideration to investigate propagation features of the aforementioned waves. In the latter case, the Landau-Lifshitz model and the Green-Lagrange strain-displacement relation is used. Furthermore, a local type of nonlinearity, such as a crack, is introduced to the model as well. The high-order harmonics generation is investigated for various cases, depending on the particular presence of the nonlinearity source. Based on the simulation results, the influence of propagation distance on the magnitude of high-order harmonics is evaluated and a comparative analysis is carried out in order to distinguish the sources of the nonlinearity. Presented results demonstrate that LISA is a sufficient tool for the SH-wavefield analysis. Keywords: Shear horizontal wave propagation · Guided waves horizontal - local interaction simulation approach · Nonlinear · Structural health monitoring

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Introduction

It is known that even though the manufacturing process is constantly improved, the manufacturing defects, material degradation and in operational damages are still common. Thus, the maintenance of engineering structures requires reliable c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 200–209, 2021. https://doi.org/10.1007/978-3-030-64594-6_21

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methods for material evaluation, which enable precise and early detection of damages. In the past decades, various methods of Structural Health Monitoring (SHM) based on elastic waves propagation phenomenon in solids were proposed and implemented [11]. In particular, methods based on ultrasonic guided waves (UGWs), which propagate in widespread thin-walled structures are of interest. UGWs are in general multi-modal and dispersive, what make their analysis difficult, especially in high range of frequency-thickness product. Consequently, researchers focus on nonlinear effects in guided waves propagation. One of them is the higher-order harmonics generation, which is exhibited by the presence of wave components at integer multiples of the excitation frequency [1]. This phenomenon causes distortion of propagating wave, because a part of energy from the excitation frequency is shifted to the higher frequency bands. Nonlinear SHM techniques based on UGWs require extensive investigation of elastic wave propagation to understand the higher harmonic generation phenomenon. It is well known that sources of the higher harmonics can be categorized into two main groups: global type nonlinearities (i.e. material nonlinearities) and local type nonlinearities (e.g. fatigue crack, plastic zones of material). As analytical analysis is difficult, it is crucial to develop numerical tools for the effective and efficient simulations of wave propagation in nonlinear media and its interaction with local defects. The Local Interaction Simulation Approach (LISA) is of particular interest, because of its numerical properties and parallel computation algorithm. So far, numerous works have been presented about Lamb waves simulation using LISA in semi-infinity medium [2,8,10]. However, there is no research on the modelling of linear and nonlinear Shear Horizontal (SH) waves propagation using LISA for semi-infinity medium. In this paper, a new approach to model the propagation of SH guided waves based on LISA for semi-infinity medium is proposed. Presented models are described by two Lagrangian coordinates and one displacement component. To investigate the propagation features of the aforementioned guided waves, both linear and nonlinear material definitions are considered. The Landau-Lifshitz model of hyper-elastic material and the Green-Lagrange strain tensor definition are used. Moreover, a local type of nonlinearity, as a crack, is introduced in the numerical models. The higher-order harmonics generation phenomenon is investigated for various case-studies, depending on the source of the nonlinearity. Based on the simulation results, the influence of propagation distance on the magnitude of the higher-order harmonics is evaluated. Finally, a comparative analysis is conducted to distinguish and indicate the sources of the nonlinear effects.

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Numerical Modelling of SH Guided Waves Propagation

General discrete formulas for the LISA can be derived from the elastodynamic wave equation, namely Navier’s equation ˜ + ∇σ = ρW, ¨ F

(1)

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˜ = [F˜x F˜y F˜z ]T and W = [u v w]T are stress tensor, body force where σ, F vector and displacement vector field, respectively. Components of σ are related with elements of W by constitutive relations - in general nonlinear. In this ˜ = [0 0 F˜z ]T and z-invariant i.e. ∂ = 0 are assumed, work W = [0 0 w]T , F ∂z which means that medium is fully defined by two Lagrangian coordinates x, y and one displacement component w. As a result, only SH wave propagation can be considered and simulated in semi-infinity elastic medium. Thus, only one equation describing displacement in z axis direction is obtained, which is related to particle motion of SH waves ∂σyz ∂σxz + = ρw. ¨ F˜z + ∂x ∂y 2.1

(2)

LISA Definition for SH Guided Waves Propagation

The LISA is an extension of a Finite Difference (FD) method and was first proposed for wave propagation modelling in [2]. Using this simulation technique, geometrical models of the considered structures are discretized into regular grid of rectangular cells. The material properties are assumed to be constant within cells, but may differ among them. LISA can be used for wave propagation in complex media that are heterogeneous, anisotropic and nonlinear with sharp material parameters (i.e. acoustic impedance) changes. Because the explicit central difference formula is used for the time domain discretization, the LISA computation algorithm is well suited for parallel computations. To derivate LISA iterative equations initially, each cell is considered as discontinuous and the force equations are derived for each cell. Then, the displacement and stress continuity conditions are applied, to obtain the energy transfer from cell to cell. For more details see [9]. In the presented work Eq. (2) is used for derivation and the continuity of stress components σxz and σyz is assumed. 2.2

Nonlinear Material Model

As indicated in the introduction, material nonlinearities can cause higher harmonic wave generation. It is known from the literature [6,13] that a part of energy of propagating SH wave in nonlinear material is shifted to the third harmonic. Research of this physical effect for SH waves is still in its infancy and the underlying mechanism is not yet fully established. Therefore, it is important to develop simulation tool for modelling of SH wave propagation in nonlinear medium. Several mathematical models, in form of constitutive equations, were proposed to characterize general material nonlinearity [7]. One of them was introduced by Landau and Lifshitz in [5]. For the purpose of this work, it was adopted with fourth order expansion, to characterize the nonlinear medium. Appropriate extension to the fourth order of energy density U (εG L ) for this model is given by U (εG L ) =

λ 2 A C I + μI2 + I3 + BI1 I2 + I13 + EI1 I3 + FI12 I2 + GI22 + HI14 , (3) 2 1 3 3

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where λ, μ are the Lam´e constants, A, B, C are the third order elastic constants (TOEC) and E, F, G, H are the fourth order elastic constants (FOEC) [3]. The scalars I1 , I2 and I3 denote first, second and third invariants of the GreenLagrange strain tensor εG L , which is defined as follow εG L =

1 (H + HT + HT H), 2

(4)

where H is gradient of displacement field of particles. In order to obtain wave equation in nonlinear form, the components of second Piola-Kirchoff stress tensor should be calculated as follow σij =

∂U (εG L ) . ∂(εGL )ij

(5)

where, i, j ∈ {x, y, z}. Presented above geometrical relations for strains and nonlinear definition of stress components are substituted into Eq. (2). The nonlinear wave equation obtained this way, was implemented in numerical model based on the LISA. 2.3

Local Defect - Fatigue Crack

The second source of nonlinear phenomenon considered in this work is the shear stick-slip movement of fatigue crack surfaces. Due to the adopted assumption, described at the beginning of this section, only the shear movement of the crack surfaces (perpendicular to the SH wave propagation direction) can be modelled. In this case, the faces of the crack interact mechanically by the friction force Ff , which results from the contact between asperities under a normal force (pressure). In the LISA models, the Coulomb friction formulation with a stick-slip behaviour [9] is implemented with arbitrary assumed distribution of the pressure acting on the crack surfaces. To model stick-slip motion phenomenon of the crack interfaces using LISA, additional nodes are introduced in locus of grid, where the crack is assumed. These nodes are grouped in pairs and are considered as independent. For each of the nodes, a classic iteration equation derived for the LISA is considered. For (k−) (k−) = node pairs, which create the surface of the crack, constrains: wt+1 − wt (k+) (k+) are imposed for the stick motion. Superscripts indicate number wt+1 − wt and distinction (side) of crack nodes, subscripts describe actual and previous time step. In order to implement these constrains and determine crack nodes dis(k−) (k+) (k−) (k+) = −F˜r , the Lagrange placement wt+1 , wt+1 and reaction body forces F˜r multipliers method is used. During the calculation procedure, value of module of (k−) |, obtained in described manner, are compared with reaction body forces |F˜r (k) body force equivalent (F˜sf )max of the maximum static friction force for each (k) (k−) (k) node pairs (Fsf )max . If |F˜r | > (F˜sf )max crack nodes move independently i.e. slip motion. Furthermore, an opposite external body force equivalent F˜kf of kinematic friction force Fkf , acts on each of these nodes, which direction and sense depend on vector of relative velocity of the considered nodes.

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Numerical Studies

The results obtained from the numerical simulations based on SH-LISA are presented in this section. In carried out simulation studies only one component of particles displacement vector is considered, which corresponds to SH waves. Based on the obtained results, the influence of the nonlinear material and crackwave interaction on the generated higher-order harmonics is analysed. Three scenarios are considered namely a presence of material nonlinearity, crack and their combination. 3.1

Model Description

SH wave propagation in nonlinear medium and interaction with a local defect modelling are investigated for an aluminium plate, with cross-sectional dimensions - 2 mm thick and 1000 mm long. In order to build numerical models, geometrical model of cross-section of thin aluminium plate is discretized into regular grid of square cells. The grid dimensions are set to Δx = Δy = 0.1 mm, leading to 20 elements through the thickness of considered construction element. The time step for the simulations is set as 10 ns to maintain the numerical stability. All material properties of the simulated medium are assumed as follows: Young’s modulus E = 68.9 GPa, Poisson’s ratio ν = 0.33 and density ρ = 2700 kg/m3 . The nonlinear behaviour of the material is obtained by defining TOECs: A = −195.9 GPa, B = −118.3 GPa, C = −3.5 GPa and FOECs: E = 81.7 GPa, F = 165.2 GPa, G = 228.4 GPa, H = −25.1 GPa. The Landau’s TOECs and FOECs values have been calculated on basis of the data presented in [12]. The crack is introduced at the location 333 mm from the left side of the plate. The static and kinetic friction coefficients for aluminium are set to μs = 0.5 and μk = 0.4, respectively. Six depths of the fatigue crack are investigated as the percentage of the plate thickness (from 0 to 25% with a step of every 5%). For simulation models containing the fatigue crack, a distribution of compressive residual stress σxx (y) is assumed over the crack surfaces (along y axis direction). These stresses may result from manufacturing process, namely cold rolling. Based on [4], a symmetrical distribution of σxx (y) with respect to the centre of the plate thickness is assumed. For y ∈ 0, 1 mm, the residual stresses are described by σxx (y) = αy + β, where α = 20 MPa/mm, β = −10 MPa. These assumptions allow the determination of the body forces equivalents of maximum static and kinetic friction forces. A fifteen-cycled sine signal, enveloped by the Hanning window, at the centre frequency of 200 kHz is used as displacement excitation signal. It is uniformly distributed over the thickness of the plate and assigned to the left side of the plate. Due to the choice of thickness of the plate and the frequency of the excitation signal, the only SH mode excited in the structure is the SH0 mode. Its non-dispersive characteristics significantly simplifies the data analysis and interpretation of numerical results. Finally, in all simulation models, the measure-

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ments points are selected on the upper surface of the plate and the responses are collected every 20 mm over the entire length of the model. 3.2

Case 1 - Nonlinear Material

In the first scenario only the non-linear material is considered. The excitation frequency can be chosen arbitrary and the third harmonic should be present since all points on dispersion curves of SH guided waves are internally resonant with third harmonics. Results of the simulation presented in Fig. 1 are captured at three measurement points: 120 mm, 420 mm 720 mm from the excitation position. In the time domain only one non-dispersive wave packet is visible (for each point) that corresponds to primary non-dispersive SH0 mode. In the frequency domain, one can observe generation of the third harmonic. The amplitude of this higher-order component increases with distance as shown Fig. 1, what indicates that the cumulative effect is observed for the assumed values of the Landau’s constants.

Fig. 1. Numerical results obtained from SH-LISA simulations in time (top) and frequency (bottom) domain, with only nonlinear material considered.

3.3

Case 2 - Fatigue Crack Modelling

For the second numerical case a local nonlinear source, namely a shear movement of crack surfaces excited due to the interaction with SH wave, is simulated. In the SH-LISA numerical model Coulomb friction formulation of stick-slip behaviour of crack is implemented. Results of the simulation presented in Fig. 2 are captured at measurement point 720 mm from the excitation position. The results in the time and frequency domains are given for six different depths of damage. In

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Fig. 2. Numerical results obtained at the distance of 720 mm from SH-LISA simulations presented in time (top) and frequency (bottom) domain, with only fatigue crack considered.

the time domain, one wave package, which corresponds to SH0 mode, is clearly visible for each depth of the crack and differences between them are very small. The odd higher harmonics, caused by stick-slip movement of crack are very well visible in frequency domain. Moreover the amplitudes of the higher harmonics increase along with the crack depth.

Fig. 3. Numerical results obtained at the distance of 720 mm from SH-LISA simulations presented in time (top) and frequency (bottom) domain, when both nonlinear material and fatigue crack are considered.

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Case 3 - Nonlinear Material and Fatigue Crack

For the numerical model with crack and nonlinear material, the simulation results are similar to those presented in the previous Subsect. 3.3. Results of the simulation presented in Fig. 3 are captured at measurement point located 720 mm from the excitation position. Similarly, only one wave packet corresponding to SH0 mode is visible in time domain and odd higher harmonics in frequency domain. For presented amplitude spectras, a noticeable increase of the amplitudes for the third harmonic appear, when compared to the previous numerical case. This is especially visible for 5% depth of the crack. For the crack depths greater than 10% of the plate thickness, the distinction of the influence of both nonlinearities i.e. nonlinear material and fatigue crack on third amplitude is difficult. To compare the impact of the both considered nonlinear sources on the generation and amplitude of the third harmonic, the amplitude of third harmonics in the domain of propagation distance are presented on Fig. 4. It is clear to observe that in the case, where only the nonlinear material is the source of the higher harmonics’ generation, a cumulative effect over the propagation distance of the third harmonic SH0 mode is apparent. It is a proof of the satisfied synchronism conditions between the first and third harmonic. However, when the only source of nonlinear behaviour is the fatigue crack, one can observe that after the crackwave interaction, the amplitude of the generated third harmonic remains almost constant over the propagation distance. Finally, when both nonlinear sources are present in the defined numerical model, an in-phase superposition of the two third harmonics from two sources is obtained. The disturbance of the third harmonic amplitude in the vicinity of the fatigue crack in the last two cases is due to the mixing of the incident and crack-reflected wave packages, as observed in [10].

Fig. 4. Variation of the 3rd harmonics magnitudes over the propagation distance from the models with the combination of the presence of sources such as: linear material definition; nonlinear material definition; and fatigue crack.

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Conclusions

In this paper, a new modelling tool for SH wave propagation under LISA framework was established. The proposed approach (SH-LISA) has enabled model tests and the numerical simulations of nonlinear SH wave propagation and its interaction with local defect. In particular the third harmonic generation phenomenon caused by nonlinear material and slip-stick motion of surfaces of fatigue crack was explored. Three numerical scenarios considering the presence of one type of nonlinear sources or their combination were studied. Also the influence of the crack depth on the magnitude of the third harmonic was investigated. The obtained results show, as expected the cumulative effect for the third harmonic generation for SH wave due to the presence of the nonlinear material definition. An in-phase superposition of the third harmonic generated due to the nonlinear material definition and the third harmonic generated from the crack-wave interaction was observed, similarly to the results of the Lamb wave S0 mode presented by Radecki et al. in [10]. Moreover, the nonlinear material has less effect on the generation of the third harmonic than the modelled local defect. The presented results indicate that the SH0 mode can be used for damage detection such as fatigue cracks. However, a shear movement of such crack has to be initiated. Acknowledgements. The work presented in this paper was performed within the scope of the research project UMO-2018/30/Q/ST8/00571 financed by the Polish National Science Centre.

References 1. Broda, D., Staszewski, W., Martowicz, A., Uhl, T., Silberschmidt, V.: Modelling of nonlinear crack-wave interactions for damage detection based on ultrasound–a review. J. Sound Vib. 333(4), 1097–1118 (2014) 2. Delsanto, P., Schechter, R., Chaskelis, H., Mignogna, R., Kline, R.: Connection machine simulation of ultrasonic wave propagation in materials. II: the twodimensional case. Wave Motion 20(4), 295–314 (1994) 3. Hamilton, M.F., Ilinskii, Y.A., Zabolotskaya, E.A.: Separation of compressibility and shear deformation in the elastic energy density (l). J. Acoust. Soc. Am. 116(1), 41–44 (2004) 4. Hattori, N., Matsumoto, R., Utsunomiya, H.: Residual stress distribution through thickness in cold-rolled aluminum sheet. Key Eng. Mater. 622–623, 1000–1007 (2014) 5. Landau, L.D., Lifshitz, E.M.: Theory of Elasticity. Course of Theoretical Physics. Pergamon Press (1989) 6. Mozhaev, V.: A new type of surface acoustic waves in solids due to nonlinear elasticity. Phys. Lett. A 139(7), 333–337 (1989) 7. Norris, A.N.: Finite-amplitude waves in solids. In: Hamilton, M.F., Blackstock, D.T. (eds.) Nonlinear Acoustics, pp. 263–277. Academic Press, San Diego (1998) 8. Packo, P., Bielak, T., Spencer, A., Staszewski, W., Uhl, T., Worden, K.: Lamb wave propagation modelling and simulation using parallel processing architecture and graphical cards. Smart Mater. Struct. 21 (2012)

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9. Packo, P., Radecki, R., Leamy, M.J., Uhl, T., Staszewski, W.J.: Nonlinear ultrasonic and vibro-acoustical techniques for nondestructive evaluation. In: Kundu, T. (ed.) Nonlinear Ultrasonic and Vibro-Acoustical Techniques for Nondestructive Evaluation, pp. 103–137. Springer, Cham (2019) 10. Radecki, R., Su, Z., Packo, P., Staszewski, W.: Modelling nonlinearity of guided ultrasonic waves in fatigued materials using a nonlinear local interaction simulation approach and a spring model. Ultrasonics 84 (2017) 11. Stepinski, T., Uhl, T., Staszewski, W.: Advanced Structural Damage Detection: From Theory to Engineering Applications. Wiley (2013) 12. Wang, H., Li, M.: Ab initio calculations of second-, third-, and fourth-order elastic constants for single crystals. Phys. Rev. B 79, 224102 (2009) 13. Zarembo, L.K., Krasil’nikov, V.A.: Nonlinear phenomena in the propagation of elastic waves in solids. Phys. Usp. 13(6), 778–797 (1971)

Real Time Monitoring of Built Infrastructure

Hygrometric Moisture Measurements Based on Embedded Sensors to Determine the Mass of Moisture in Porous Building Materials and Layered Structures Christoph Strangfeld(B)

and Tim Klewe

Bundesanstalt f¨ ur Materialforschung und -pr¨ ufung, 12205 Berlin, Germany [email protected]

Abstract. Subfloors are layered structures, consisting largely of porous building materials, such as screed. They are often suffering damage from tap water leakage, which is a typical problem in buildings, and which has largely contributed to repair costs of almost 3 billion Euro in 2018 alone in Germany. In this context, especially mould plays a role, which is both destroying the structure and posing severe health risks. To determine the damaging effects of moisture, it is necessary to know the respective processes occurring in building materials, especially to quantify the amount of moisture and its progress in the material. In this study, humidity sensors are used to derive the material moisture experimentally. Capacitive sensors recording the relative humidity are embedded into the screed and in the insulation materials such as expanded polystyrene, extruded polystyrene, perlite and glass wool. For the application in screed, the sensors need to be shielded against the aggressive alkaline materials. To ensure an appropriate exchange with the environment, a permeable membrane is requested. Different membrane materials have been investigated regarding their robustness and their permeability. In the first experimental setup, two humidity sensor arrays with seven individual sensors are embedded in homogeneous screed samples. The measured corresponding relative humidity of the screed is converted to the material moisture based on the approach of Hillerborg. In a second experimental setup, a layered structure of a complete subfloor is built in a box of 0.8 m times 0.8 m. The humidity sensors are positioned in the different insulation materials of various thicknesses. By adding water, leakage damage is simulated and its progress and effect is investigated experimentally. The investigations point at the question if the observed moisture is able to generate damage such as mould. The moisture and corresponding humidity values are discussed. It will be shown that this low-cost hygrometric approach can be used easily for moisture monitoring of screed and insulation materials as well. c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 213–225, 2021. https://doi.org/10.1007/978-3-030-64594-6_22

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Introduction

An increased moisture level in building materials puts building structures at great risk. Materials might deteriorate and lose crucial properties such as stiffness, capability of providing thermal isolation, etc. Biochemical reactions on the surface of moist building materials might lead to the growth of poisonous mould. If not recognised by the users, its spores can cause serious illnesses [1]. In Europe, 16% of the population is living in moist and damp buildings [2]. The early detection of increased material moisture and the subsequent countermeasures against the corresponding damage can save structures from becoming uninhabitable and maintain human health[3]. In Germany, almost 3 billion Euro were paid off by insurance companies for pipe leakage damage in 2018. Most water pipes are installed in walls and ground shafts or below the floor construction. Often, a leakage is not immediately visible. If no evaporation is given, the humidity is increased significantly and mould as well as other damage occur. In most cases, these defects are recognised only if serious damage is already present, followed by unnecessarily increased repair costs. Figure 1 illustrates a modern floor construction and the possible locations for water penetration. In case of damage, often several ingress points of leakage water exist, and different layers of the floor can be penetrated. For flooding events or water leakage above the floor, water can be trapped under the floor cover or penetrate the screed if the isolation layer contains flaws. Damaged underfloor heating can also cause unwanted water ingress inside the screed layer, like shown in Fig. 1. A damaged insulation layer is often caused by leaking water supplies or again by flawed isolations (PE-foil). Unfortunately, there are no non-destructive methods so far that provide a reliable localisation and quantification of leakage water for all possible locations [4,5]. In practice, drill cores must be taken to find leakage water and to investigate the moisture content of the extracted materials with the Darr-Method [6]. Additional to that, drilled holes can be used to insert probes for relative humidity measurements like described in [7] and conducted in [8,9]. This measurement approach is the so called corresponding relative humidity (CRH). Several studies tried to link the CRH to the sorption isotherm of concrete and screed [10–12]. Furthermore, the isotherm’s hysteresis and moisture transport are based on measuring the CRH in experiment [13]. However, this can only be performed in the first screed layer to avoid an influence of the underlying insulation. If a large area is investigated, dozens of cores are extracted. This destroys the intact floor construction in both damaged and undamaged areas, which causes additional costs. Other methods to investigate destructively extracted screed samples are the carbid method [14] and the assessment of the corresponding relative humidity [15][16]. Both methods are usually performed to evaluate the readiness for installation

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Fig. 1. Sketch of a floor construction

of floor coverings but can also be used to control moisture contents after an occurred damage. A non-destructive way is described in [17] by measuring the moisture vapor emission rate of the subfloor with the use of anhydrous calcium chloride. However, this method only captures moisture in the surface area and do not allow monitoring. In this study, a non-destructive investigation method for leakage water in building floors is proposed. Relative humidity sensors are used to measure the moisture content. Two approaches are investigated: 1) embedment of the sensors directly in screed and 2) installation of the sensors in the insulation layer. With both approaches, the moisture content can be monitored with a high sensitivity.

2

Experimental Setup

To describe and quantify moisture in building materials, different definitions exist. In general, the moisture transport is a two-phase flow consisting of liquid water and water vapour. These two quantities are in equilibrium with each other under steady state conditions. Because of this, either the gaseous or the liquid phase can be measured for moisture quantification. Regarding the gaseous phase, the relative humidity in % RH is used. For the liquid phase, the moisture is given as the weight of the cumulated liquid water relative to the material dry weight, called mass percent (wt.-%). This definition is used for the homogeneous screed sample. For the insulation layer inside the heterogeneous layered structure, the moisture is quantified as the volume of the cumulated liquid water relative to the volume of the considered material volume, called volume percent (vol.-%). The relative humidity and the material moisture can be converted to each other using the sorption isotherm [18]. Figure 2 shows the two arrays of sensors before casting the screed samples. Every array holds seven relative humidity sensors with a spacing of 6 mm. As shown, the arrays are mounted with an offset to each other by 3 mm. The formwork of the screed sample has a height of 35 mm. Consequently, the three topmost sensors will measure the ambient relative humidity above the sample surface. The sensors are protected against the fresh screed by a membrane. To measure the corresponding relative humidity of the screed, this membrane must

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allow an interaction of the air volume inside the casing with the air volume in the pore system. Therefore, the membrane must be permeable to enable the transport of water vapour. Furthermore, the membrane must withstand the high alkaline environment of the screed, must block micro-particles of the fresh and fluent screed, and should prevent liquid water penetration. Nevertheless, the response time of the humidity sensor behind the membrane should be short enough for the specific measurement purpose.

Fig. 2. Sensor arrays for monitoring of the corresponding relative humidity of screed

Figure 3 shows the setup of the layered floor structure in which different insulation materials are examined. The surrounding box with a cross section of 0.8 m times 0.8 m is made of plexiglass; the bottom plate is made of polytetrafluoroethylene (PTFE). The top layer is the floor cover, which is placed on a screed plate. Underneath, the screed is insulated by means of a polyethylene (PE) foil. The next layer consists of varying insulation materials. In the experiments, expanded polystyrene (EP), extruded polystyrene (XP), perlite (PL) and glass wool (GW) is used with different thicknesses between 20 mm and 100 mm. Below this layer, a further PE foil as insulation is installed, followed by a plate made of washed concrete that should resemble the load-bearing structure in buildings. The lowest layer is made of expanded polystyrene and has the function to lift the entire setup. The distance between the floor cover and the PTFE bottom plate was planned to be at least 300 mm to avoid any influence from the bearing wooden pallet on other measurement techniques such as radar and neutron probe [19]. Figure 4 shows two additional photographs of the setup. Three single humidity sensors are placed in the extruded polystyrene insulation. The sensors have a red casing with an open slot (without any membranes). The holes in the insulation material have a diameter of 30 mm. Two sensors are positioned in the middle of the panel, the third one is directly located between two panel

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edges. It is expected that the moisture transport occurs faster at these edges. All three holes are subsequently sealed with waterproof tape. After placing the PE foil and the screed plate on the insulation layer, the moisture damage is simulated by pouring tab water into the sample. Depending on the supposed damage scenario, water is added in the screed layer or in the insulation layer. For the experiment, water was always added in the afternoon and measurements were taken the next morning. During this time period, the humidity values were recorded every 10 min. Every setup had at least 12 h to settle to ensure that the measured humidity was close to an equilibrium state and thus representative for the chosen damage scenario.

3

Results and Discussion

The screed layer or the insulation layer can both be penetrated by leakage water individually. This way, both cases are investigated separately based on embedded humidity sensors. However, first the casing and the required filter membranes for the embedded sensors are optimised in Sect. 3.1. Then, these sensors are embedded into fresh screed. The recorded data and the conversion to the material moisture is shown in Sect. 3.2. In a last step, the insulation layer is equipped with humidity sensors as well and water is added to the structure. The resulting moisture and humidity levels of the different materials is documented in Sect. 3.3.

Fig. 3. Experimental setup to simulate moisture damage in layered structures

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Fig. 4. Experimental setup; a: positioning of the humidity sensors, b: simulation of moisture damage

3.1

Investigation of Suitable Filter Membranes for Embedment in Screed

In previous studies, different materials were tested as filter membranes [20,21]. It turned out that PTFE filters show a highly hydrophobic behaviour. Water droplets remain on the surface and do not penetrate the filter membrane whereas water vapour can pass the filter. Furthermore, PTFE filter are assumed to be inert and robust against alkaline attack. PTFE filters with four different modal pore sizes of 1 µm, 10 µm, 50 µm, and 100 µm were tested to determine their water vapour permeability. The higher the modal pore diameter, the higher the water vapour exchange rate. The porosity Φ increases with the modal pore size, which leads to an even higher permeability. For testing in a climate chamber, two arrays of seven humidity sensors are equipped with the different filters. For each modal pore diameter, three humidity sensors are equipped with the same filter. Figure 5 illustrates the evolution of the measured relative humidity for a stepwise humidity rise from 28% RH to 90% RH. At an ambient temperature of 23 ◦ C, this corresponds to a partial water vapour pressure of 785 Pa or 2523 Pa, respectively. As expected, the filter membrane with the smallest modal pore diameter requires the longest time to reach the target humidity of 90% RH. Whereas the three filters with 10 µm, 50 µm, and 100 µm are close to each other, the delay significantly deviates for 1 µm. To quantify this, the response time t* to reach an 88% RH level is calculated. It is t*(1 µm) = 507 min, t*(10 µm) = 304 min, t*(50 µm) = 252 min, t*(100 µm) = 212 min. Based on these results, porous hydrophobic PTFE filter membranes with a modal pore size of 10 µm yield the best trade-off between water vapour permeability and liquid water resistance. In conclusion, the PTFE filters provide enough protection of the sensors from contact with the fresh cement paste during concreting. No liquid water and no micro-particles reach the sensor electronics. In cement based porous materials, moisture transport occurs over days and weeks. The response time of a few hours is sufficient for real time monitoring. On the contrary, fast humidity changes can-

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Fig. 5. Evolution of the measured humidity due to a humidity rise in dependence of the used filter membrane.

not be recorded. In case of moisture damage in layered floor structures, leakage water may penetrate the structure in minutes. Here, the filter membranes would cause a delay in recording the moisture variation. Based on the presented results, the casings with the selected membranes were manufactured, and the assembled humidity sensors were embedded in the fresh screed mixture. 3.2

Humidity and Moisture Measurements in Screed

The corresponding relative humidity was measured with the two sensor arrays in Fig. 2. The screed plates were casted and then stored in a climate chamber for hydration and evaporation. The measured humidity values will be shown until an equilibrium has reached. The screed plates in this equilibrium state were used for the moisture measurements in Sect. 3.3. A re-wetting of the plates was not conducted yet to simulate a flooding or water leakage from above the floor, like introduced in Sect. 1. This final step is planned at the end of the project. However, the changing moisture content during hydration and evaporation is suitable to evaluate the method in terms of moisture monitoring inside screed. Figure 6 shows the recorded values over time and over the sample depth of 35 mm. On the top surface, the humidity is fixed to 50% RH as set in the climate chamber. The measurement value at 35 mm depth, where no sensor was located, equals the humidity measured at 26 mm depth. Unfortunately, the humidity sensor closest to the bottom at 30 mm depth was broken after the embedment. Within the first 96 h of hydration, all sensors remain in or close to saturation. From then on, the moisture within the sample starts to evaporate from the top

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Fig. 6. Evolution of the corresponding relative humidity in the screed sample

Fig. 7. Calculated material moisture distribution [22]

surface. After approximately 600 h, the entire sample reached the equilibrium moisture content of around 50% RH as set in the climate chamber. Thus, the mixing water from the casting is hydrated in the cement matrix or evaporated.

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The pore volume distribution of the screed plates was measured based on mercury intrusion porosimetry and gas adsorption. Based on this additional geometrical information about the pore system, the measured humidity can be transferred into a moisture content in wt.-% based on the approach of Hillerborg [22]. In this approach, the variation of the surface tension due to the curvature of the water layer at the pore fringe is incorporated by the Kelvin equation [23]. This enables the computation of the pore saturation for every pore size and humidity level. In case of desorption, the moisture content is the summation of the capillary adsorbed water and the inkbottle water. The latter is entrapped water in larger pores which are connected to the pore system via smaller and still fully saturated pores. However, the conversion of the humidity into moisture content can only be done when the sensor has left saturation [22]. The sensor at 26 mm depth has left saturation after 268 h. Figure 7 presents the calculated moisture content over time and sample depth. In the area of high humidity, the highest moisture content of more than 2 wt.-% is present. In equilibrium after 600 h, the moisture is around 0.4 wt.-%. These results demonstrate that embedded humidity sensors are very sensitive to moisture variations in the surrounding screed. In case of leakage water ingress, these sensors can monitor the resulting increase in moisture and humidity in a nondestructive way. 3.3

Humidity and Moisture Measurements in the Insulation Layer

Besides the investigation of embedded sensors in screed, further sensors were installed in the insulation layer as shown in Fig. 4a). To simulate a moisture damage, a certain amount of water was poured into the layered structure followed by a minimum waiting time of 12 h. Figure 8 shows the evolution of the humidity for the four investigated materials. The respective starting humidity differs due to a varying ambient humidity between the different measurement

Fig. 8. Evolution of the humidity in the insulation material after adding water

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days. Nevertheless, the four materials show different sorption behaviours. The humidity increases continuously for all materials and tends asymptotic towards the equilibrium state. After 12 h, all materials reached a humidity of at least 85% RH, which would already support hazardous mould formation on the surface. This observation of high humidity conditions is confirmed in Table 1. For all materials and all amounts of water addition, the humidity reaches values between 83% RH and 100% RH. The equilibrium state was never reached within the measurement time; thus, the final humidity values are expected to be even higher. In this setup, the water addition of 0.5 l corresponds to approximately 0.8 lm−2 . However, even this small amount leads to high humidity values and a high risk of damage. Table 1. Overview of the resulting humidity values in the insulation material Insulation type

Material thickness

Water addition

Moisture

Humidity after 12 h

Humidity at test end

EP EP EP EP

20 mm 50 mm 70 mm 100 mm

0.5 l 2l 2l 2l

3.9 6.3 4.5 3.1

86.8% 97.5% 95.3% 81.8%

RH RH RH RH

94.1% RH 98.5% RH 100% RH 89.3% RH

GW GW GW

20 mm 60 mm 100 mm

0.5 l 1.5 l 4l

3.9 vol.-% 79.6% RH 3.9 vol.-% 87.5% RH 6.3 vol.-% 93.2% RH

83.8% RH 90.4% RH 95.4% RH

PL PL PL

20 mm 60 mm 100 mm

0.5 l 1.5 l 4l

3.9 vol.-% 86% RH 3.9 vol.-% 85.1% RH 6.3 vol.-% 96% RH

89.6% RH 94.5% RH 98.9% RH

XP XP XP XP

20 mm 50 mm 70 mm 100 mm

0.75 l 2l 2l 2l

5.9 6.3 4.5 3.1

88.3% 93.7% 94.3% 99.2%

vol.-% vol.-% vol.-% vol.-%

vol.-% vol.-% vol.-% vol.-%

58.2% 89.8% 87.3% 96.8%

RH RH RH RH

RH RH RH RH

The fast increase of humidity within the first hours and the final high humidity values clearly demonstrate the ability of moisture monitoring within the insulation material. Simple threshold values of e.g. 80% RH would indicate small amounts of leakage water within a few hours. Thus, hygrometric measurements appear very feasible for moisture monitoring of layered floor structures.

4

Conclusion

An experimental setup was designed for the investigation of a methodology that allows the determination of the risk of water-induced damage in layered floor constructions. The setup was designed in a way that the modular composition

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of varying setups could be reproduced. Hygrometic measurements were carried out to monitor the moisture contents of different materials after a simulated pipe leakage. Therefore, humidity sensors were embedded directly in the fresh screed mixture or were installed in the insulation material. For achieving the best results, different filter membranes made of PTFE were tested before to find an optimum relation between fast water vapour transport and sufficient sensor protection. The shown membranes shows a high resistance against liquid water and micro particles of the cement paste during casting. Furthermore, the water vapor exchange rate is sufficient for material’s moisture monitoring. This is a promising approach for embedded humidity sensors in future. Two damage scenarios, leakage water penetration of the screed and of the insulation material were considered. In both cases, the measured relative humidity was clearly corresponding with the moisture variations. For the screed, the entire humidity gradient was measured and converted into material moisture. This gives a detailed overview of the moisture distribution within the material. After the humidity left saturation sensors at around 2.3 wt.-%, a high sensitivity to material moisture changes was observed. The equilibrium state of 50% RH was reached at approximately 0.4 wt.-%. This is a novel approach of measuring and monitoring material’s moisture. Furthermore, the measurement of the corresponding relative humidity and its conversion does not require any calibration to material properties. Therefore, this approach is feasible for existing structures as well. For the insulation materials, already small water additions of 0.8 lm−2 led to high humidity values, which pose a high risk of mould growth and other moisture induced damage. In almost all cases, an increase of humidity above 80% RH was observed within the first 10 h. The investigated hygrometric method is very sensitive towards moisture variations within the insulation and shows a fast response time. Damage in real situations usually contain significantly higher quantities of water compared to the simulated scenarios. Therefore, the proposed method is ideally suited for non-destructive moisture monitoring in case of an occuring damage, which is usually recognised too late. As presented, embedded humidity sensors have a very high sensitivity for small moisture variations. This can be used to monitor the material moisture and to define thresholds for the risk of damage, especially the formation of mould. The described hygrometric measurements would be ideally suited for moisture monitoring in several building materials. Such systems could help to recognise pipe leakage already at a very early state in order to reduce the costs of repair significantly.

References 1. Brasche, S., Heinz, E., Hartmann, T., Richter, W., Bischof, W.: Vorkommen, Ursachen und gesundheitliche Aspekte von Feuchtesch¨ aden in Wohnungen. Bundesgesundheitsblatt-Gesundheitsforschung-Gesundheitsschutz 46(8), 683–693 (2003). https://doi.org/10.1007/s00103-003-0647-9

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2. Fraunhofer-Institut f¨ ur Bauphysik, Gesundes Wohnen: ohne Feuchte und Schimmel, November 2016 3. Umweltbundesamt, Leitfaden zur Vorbeugung, Untersuchung, Bewertung und Sanierung von Schimmelpilzwachstum in Innenr¨ aumen, Umweltbundesamt (2002) 4. Sch¨ urger, U.: Feuchtemessung zur Beurteilung eines Schimmelpilzrisikos, Bewertung erh¨ ohter Feuchtegehalte, in: 39. Aachener Bausachverst¨ andigentage, vol. 97, pp. 1–10 (2014) 5. Kruschwitz, S., Nagel, S., Strangfeld, C., W¨ ostmann, J., Borchardt, K.: Eignung zerst¨ orungsfreier Messtechnik f¨ ur die Bestimmung der Belegreife von Estrichen. Bautechnik 95(4), 265–274 (2017). https://doi.org/10.1002/bate.201700051 6. DIN, EN ISO 12570:2000: W¨ arme- und feuchtetechnisches Verhalten von Baustoffen und Bauprodukten - Bestimmung des Feuchtegehaltes durch Trocknen bei erh¨ ohter Temperatur, Deutsche Norm, German Standard 7. ASTM International, Standard test method for determing relative humidity in concrete floor slabs using in situ probes, F2170-16b 8. Parrott, L.J.: Moisture profiles in drying concrete. Adv. Cement Res. 1(3), 164–170 (1988). https://doi.org/10.1680/1988.1.3.164 9. Vinkler, M., V´ıtek, J.L.: Drying concrete: experimental and numerical modeling. J. Mater. Civil Eng. 28(9), 1–8 (2016). https://doi.org/10.1061/(ASCE)MT.19435533.0001577 10. Maekawa, K., Chaube, R., Kishi, T.: Modelling of concrete performance, E & FN Spon (1999) 11. Espinosa, R.M., Franke, L.: Inkbottle pore-method: prediction of hygroscopic water content in hardened cement paste at variable climatic conditions. Cement Concrete Res. 36(10), pp. 1954–1968 (2006). https://doi.org/10.1016/j.cemconres.2006.06. 011 12. Ishida, T., Maekawa, K., Kishi, T.: Enhanced modeling of moisture equilibrium and transport in cementitious materials under arbitrary temperature and relative humidity history. Cement Concrete Res. 37(4), 565–578 (2007). https://doi.org/ 10.1016/j.cemconres.2006.11.015 13. ˚ Ahs, M.S.: Sorption scanning curves for hardened cementitious materials. Constr. Build. Mater. 22(11), 2228–2234 (2008). https://doi.org/10.1016/j.conbuildmat. 2007.08.009 14. Radtke, F.: The carbide method!, Presentation (2016) 15. Technische Kommission Bauklebstoffe, TKB-report 2: Readiness for installation of floor coverings and moisture, Merkblatt TKB-B 2, Industrieverband Klebstoffe e. V., Juli 2013 16. DIN, ISO 12571:2013: W¨ arme- und feuchtetechnisches Verhalten von Baustoffen und Bauprodukten - Bestimmung der hygroskopischen Sorptionseigenschaften, Deutsche Norm, German Standard 17. ASTM International, Standard test method for measuring moisture vapor emission rate of concrete subfloor using anhydrous calcium chloride, F1869-16b 18. Kumar, A., Ketel, S., Vance, K., Oey, T., Neithalath, N., Sant, G.: Water vapor sorption in cementitious materials-measurement, modeling and interpretation. Transp. Porous Media 103(1), 69–98 (2014). https://doi.org/10.1007/s11242-0140288-5 19. Klewe, T., Strangfeld, C., Ritzer, T., Kruschwitz, S.: Zerst¨ orungsfreie Lokalisierung von Fl¨ ussigwasser in Fußb¨ oden durch Kombination von Radar und Neutronensonde, in: 10. Kolloquium Industrieb¨ oden, Esslingen, Germany, pp. 179–185 (2020)

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20. Strangfeld, C., Johann, S., M¨ uller, M., Bartholmai, M.: Moisture measurements by means of RFID sensor systems in screed and concrete. In: 8th European Workshop on Structural Health Monitoring, Bilbao, Spain, pp. 1–10 (2016) 21. Strangfeld, C., Johann, S., Bartholmai, M.: Smart RFID sensors embedded in building structures for early damage detection and long-term monitoring. Sensors 19(24), 1–18 (2019). https://doi.org/10.3390/s19245514 22. Strangfeld, C., Kruschwitz, S.: Monitoring of the absolute water content in porous materials based on embedded humidity sensors. Constr. Build. Mater. 177, 511– 521 (2018). https://doi.org/10.1016/j.conbuildmat.2018.05.044 23. Hillerborg, A.: A modified absorption theory. Cement Concrete Res. 15(5), 809– 816 (1985). https://doi.org/10.1016/0008-8846(85)90147-4

Continuous Static and Dynamic Strain Measurements on Civil Infrastructures: Case Study on One Pier of the Millau Viaduct Cartiaux François-Baptiste1(&), Le Corvec Véronique1, Cachot Emmanuel2, Vayssade Thierry2, and Servant Claude3 1

3

OSMOS Group SA, Paris, France [email protected] 2 CEVM, Millau, France Eiffage Infrastructures, Vélizy, France

Abstract. An experimental Structural Health Monitoring System has been set on one pier of the Millau Viaduct end of 2018. The results of more than one year of continuous strain measurements at a high sampling rate are released, focusing on different Data Analysis features which have been used on the large amount of raw data collected. The Monitoring System is a set of 16 Optical Strand strain sensors synchronized by a Data Acquisition System which enables real-time high-sampled measurements. All raw measurements are gathered on a distant cloud and available through the internet. Three different Data Analysis features are discussed, which give results on various phenomena from the same strain measurements, considering different time scales: Firstly, averaged measurements are combined in order to get an estimation of the displacement of the pier top under the effect temperature variations, secondly the high-rate sampled measurements are used to assess the effects of the usual traffic on the bridge pier and detect vehicles which effect in terms of strain is the most significant. Finally, the data gathered during periods of strong winds is used for Dynamic Identification of the vibration modes of the pier by the mean of an Eigensystem Realization Algorithm. Keywords: Civil infrastructure
Bridge monitoring
Fiber optic sensor Real-time monitoring
Data analysis
Dynamic identification




1 Introduction and Motivation The field of Structural Health Monitoring (SHM) of built infrastructure is expanding nowadays, with very active academic research and an increasing number of industrial applications. However, the usual way of practicing SHM is to dedicate a specific sensor solution to a specific issue, like for example a set of accelerometers on a bridge for the identification of real natural vibration modes, and independently some displacement sensors on the abutments for the assessment of temperature effects [1]. A more holistic approach can be implemented, in which either different types of measurements are combined together in order to assess correlations and anomalies by © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 226–235, 2021. https://doi.org/10.1007/978-3-030-64594-6_23

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data fusion, or several different types of information are deduced from one single sensor system. These two approaches are not exclusive one from each other and their combination in one global synchronized Monitoring System is now available as a value proposal by industrial actors of the field like OSMOS Group. This paper will focus on the second approach, i.e. getting different types of information from one single sensor system. The example chosen is the monitoring of strain on the concrete shafts of one pier of the Millau Viaduct in France. The motivation is to show that different topics can be addressed by using different dedicated data analysis tools on the same measurement data set, which is only strain measurement. This measurement is performed by Optical Strands long basis strain gages with fiber optic technology as described below.

2 Optical Strand Technology The Optical Strand system is based on high-precision sensors that measure deformations between two points with micrometric resolution. The optical-waveguide technology has been harnessed to allow measurements of structural changes. The OSMOS measuring system is based on the principle of intensity modulation with analog attenuation measurement, which was selected following an examination of all fiberoptic techniques of detecting changes in shape and position. This technique provides extremely stable and reliable solutions with an optimized price/performance ratio and minimized requirements for electronic and mechanical components.

Fig. 1. Schematic view of braided fiber optics (left) and light flux in the Optical Strand (right)

The Optical Strand comprises three braided optical fibers (Fig. 1). Any change of length (tension or compression) of the pre-tensioned sensor causes a proportional attenuation of the light in the optical fiber according to the micro bending principle (Fig. 1). The Optical Strand is the active part of the measuring system. Each end of the optical strand is in a splice-box that is used to mount the optical strand. One end of the optical strand is connected (spliced) to an optical link cable, which transports the measured information to an opto-electronic converter. At the other end of the optical strand, splicing together two fibers forms an “optical shortcut”. Connecting the sensors to a normal fiber-optic cable allows transporting the information of the measurement signal over long distances without conversion or amplification of the optical signal. The optical strand is insensitive to electromagnetic noise, works without any additional electrical energy, and therefore reaches a very high level of reliability and safety during its operation.

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The main advantage of these sensors is that they operate without dead time and are synchronized. The dynamic events can thus be monitored with a measurement frequency of up to 100 Hz, which makes it possible to carry out a continuous recording and to detect dynamic phenomena such as vehicle passages on bridges, earthquakes, shocks, etc. If no dynamic event occurs, a measurement point is created each one to 60 min. A static measurement is thus obtained, representative of the behavior of the structure over the long term. Many different applications of SHM can take advantage of the continuity of the high-sampled strain measurement by Optical Strands, among them strain assessment during static and dynamic load tests, bridge weigh-in-motion (Fig. 2) and fatigue assessment [2].

Fig. 2. Typical results of the application of strain measurements by Optical Strands for WeighIn-Motion. The example is from another bridge than the Millau Viaduct.

3 Case Study of the Millau Viaduct 3.1

General Description

The Millau Viaduct over the river Tarn in southern France is an iconic highway bridge which was completed in December 2004. The eight-span cable-stayed deck is 2460 m long and 27.75 m wide. It consists in an orthotropic steel box girder sustained on each span by 11 pairs of axial cables. The cables are anchored in steel pylons which transfer the descent of loads to the concrete piers. The bridge deck is one single girder without expansion joints except at both abutments: the variations of the joint openings reach

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significant values under the effects of the temperature through daily and yearly cycles, up to 0.3 m for daily cycles and 0.8 m along the year [1]. A continuous strain monitoring by Optical Strands is performed since November 2018 on one of the 7 piers: pier P7 which is the shortest one with a height of 77.6 m below the bridge deck (Fig. 3). This pier has been chosen because it is the most sensitive to the effects of the temperature: its location near to one end of the deck induces high displacements at the top of the pier and its short height makes it less flexible than the other piers, inducing significant stresses under the effects of the temperature variations.

Fig. 3. General drawing of the Millau Viaduct. Pier P7 at south end (right on the figure) is monitored by 16 Optical Strands.

3.2

Monitoring System

The Monitoring System of the pier P7 consists in the following items: 16 Optical Strands strain sensors with 2 m length. The Optical Strands are installed vertically on the sides of each one of the two pier shafts, at two different levels. The first level is 6 m above the ground and the second level is 30 m above the ground. 4 Optical Strands are set for each level and each shaft, in order to have a comprehensive measurement of the strain variations in the cross-section of the shaft. 2 Temperature sensors set on the north shaft: one for the temperature inside the hollow shaft and the other one for the ambient external temperature. 2 Expert Data Acquisition Systems (EDAS) which enable the synchronization of all Optical Strands and the data acquisition at a high sampling rate up to 100 Hz (for practical issues the sampling rate is configured at 50 Hz here). The EDAS first apply a filter to the measurement in order to recognize if the strain variations are significant on a short duration of a few seconds, due to traffic or wind. If they are, the whole record at 50 Hz sampling rate is automatically sent to the OSMOS cloud and is available on a web interface within a few seconds. If there is no significant strain variation, only one measurement every 10 min is transmitted to the cloud. This 10 min period can be configured up to one measurement every second. The monitoring system is operating since November 16, 2018. Its initial aim was the assessment of the effects of temperature variations on both shafts of the pier, but it immediately appeared that it was sensitive enough in order to accurately record the effects of traffic and wind as well (Fig. 4).

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Fig. 4. View of 2 Optical Strands set on the pier P7.

3.3

Reverse Modelling of the Pier

In addition to the data acquisition on field, a reverse modelling of the pier is performed by OSMOS in order get an estimation of quantities which are not directly measured: deflections at any point of the shafts in 3 directions and internal forces in the shafts. Such a reverse modelling has already been used in other cases like the concrete pylon of a cable-stayed bridge [3]. The model is called reverse because its inputs are the measurements taken on field and the outputs are the estimated loads, deflections and internal forces. This is different from classical design models which will use loads as an input data. This is also different from model parameter identification: the properties of geometry, material and bearing conditions are taken as assumptions and the loads are deduced from them. We notice that in the case of a load test, the combination of loads and measurements can be used for parameter identification. For each one of the two shafts of the pier, 8 strain measurements are available, which means that the loads to be applied on the shaft have to be expressed on an algebraic basis of 8 typical loads in order to ensure the reversibility of the model. Thus, the estimation of deflections and forces is performed by assuming that all loads are applied at the top of the shaft, which is five different typical loads. Three additional typical loads take into account the fact that the four strain measurements on one single cross-section of the shaft may not define a perfectly plane strain diagram (warping) and that the axial force deduced from the strain may be different at 6 m and 30 m height (Fig. 5). Once this basis of typical loads is defined, the elastic linear modelling of both two shafts as cantilever beams enables to compute the 8 theoretical measurements induced by each one of the 8 typical loads. This 8 by 8 matrix is reversed in order to get loads from the measurements. Then any result of the model can be deduced from the estimated loads, such as deflections at any point along the shaft in any direction or internal forces.

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Fig. 5. The 8 typical loads applied to the model, for one of the two shafts. The dashed lines show the two levels where the Optical Strands are set.

The results obtained from this reverse modelling are only estimations, as it does not pretend to give fully accurate values of each one of the numerous possible results from a limited number of real measurement points. However, the deflection estimations appear consistent with the order of magnitude of the displacement of the deck under thermal effects. Even with uncertainties on the exact values, the results of the model are accurate enough to qualify properly at least the direction of the displacement, directly deduced from the curvature of the shaft as given by the strain difference from one side to the opposite one. It is also mentioned that all the results provided are relative to deflections due to the bending of the shafts only, excluding effects of the flexibility of the foundation. 3.4

Effects of the Temperature Variations

The continuous monitoring of the pier P7 since November 16, 2018 with one measurement every 10 min enables a very precise assessment of the effects of the variations of the temperature on the concrete shafts. Strain cycles under the effects of the heat have ranges up to 0.20‰ each day, which is consistent with the external temperature variations. The North face of each shaft has a strain evolution which follows the natural dilatation of the material whereas the South face is constraint by the global bending of the pier, which follows the movements of the deck at the top. The reverse model of the shafts estimated cycles of longitudinal displacement of the top of the pier with a range up to 200 mm for daily evolutions and 500 mm for seasonal ones. Both shafts have the same top displacement even if they are independent in the model (Fig. 6).

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Fig. 6. Screenshot of the pier P7 with bending to the South under the effect of the heat. The arrows on the left part of the figure show the strain measurements on each one of the 16 Optical Strands. The curvature in direction South is consistent on both shafts.

3.5

Effects of Traffic Loads

Traffic loads are automatically recorded by the monitoring system as far as the strain variations they induce is over a defined threshold within a short period of time. In the case of the pier P7, the threshold has been set to a strain range of 0.0075‰ within 8 s. This configuration enables the automatic recording of around 20 passages every working day. The strain range under the effect of the heaviest traffic loads does not exceed 0.018‰, which is 10 times less than the effects of the temperature. Thus, the stress in the concrete pier under the effect of traffic is very low. Top displacements of the shafts have also been estimated under the effect of traffic loads. The heaviest vehicle recorded since November 16, 2018 induced a longitudinal displacement of 3 mm in direction South and North with a total range of 6 mm. In addition, transverse displacements have a range of 2 mm and vertical displacements, due to the global bending of the pylon which supports the cables, also have a range of 2 mm. Statistics on the effects of the traffic loads are performed by counting the induced strain cycles. Once they are sorted by amplitude for each day of the monitoring period, the stability of the response of the structure to the live loads can be checked. This statistical analysis enables to recognize significant outliers in the case of strong wind in the area of the Millau viaduct: the number of recorded strain cycles for a typical

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windy day is over 2000 instead of 20 and their range is up to 0.032‰ instead of the maximal value of 0.018‰ under traffic effects (Fig. 7).

Fig. 7. Statistical analysis of recorded strain cycles under the effects of live loads and wind.

3.6

Effects of Strong Winds: Dynamic Identification

The continuous strain measurements during periods of strong wind are used to perform a dynamic identification of the natural vibration modes of the pier. This identification first computes abstract modes on the basis of the 16 measurements by the mean of an Eigensystem Realization Algorithm. The chosen algorithm relies on a state-space system identification where the state-space model is obtained from a factorization of a Hankel matrix of Markov Parameters describing the correlations between all different measurements [4]. Table 1. Vibration modes identified from continuous strain measurements on the pier P7 under the effects of strong winds Frequency (Hz) Mode type Comments 0.26 Longitudinal 0.29 Longitudinal 0.33 Longitudinal Main longitudinal mode for pier P7 0.38 Longitudinal and transverse 0.42 Transverse 0.43 Longitudinal 0.45 Transverse Main transverse mode for pier P7 0.48 Transverse

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In a second step the identified abstract modes are interpreted through the reverse modelling in order to estimate the mode shapes. This estimation is not perfect in terms of deflections as they are not directly measured, but the type of each mode is accurately determined: longitudinal, transverse, vertical or combined (Fig. 8). From the analysis of different samples of 10 min continuous strain variations during the strong wind periods, 10 different modes were identified (Table 1) which are consistent with the design values of the natural frequencies of the structure and with values obtained from the initial dynamic testing in 2004 [5]. This development shows that Dynamic Identification can be performed from strain measurements instead of acceleration measurements, given that their sampling rate is high enough in order to perform a vibration analysis up to relevant frequency values. In the case of the Millau Viaduct, due to the large size and low stiffness of the structure, the main vibration modes have very low frequencies, thus the 50 Hz sampling rate is more than enough.

Fig. 8. Dynamic Identification: main longitudinal bending mode at the frequency of 0.33 Hz. The arrows on the left part of the figure show the abstract strain measurements which correspond to this mode according to the Eigensystem Realization Algorithm.

In addition to the Dynamic Identification, strain amplitudes and estimations of the deflection are also assessed in the case of strong wind events, with extreme values obtained during the storm of December 13, 2019. Up to 100 km/h winds induced strain cycles with an amplitude of 0.032‰ and an estimated cyclic displacement at the top of the pier of 12 mm in both longitudinal and transverse directions.

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4 Conclusion The experiment conducted on the Pier P7 of the Millau Viaduct, with 16 long basis Optical Strand strain sensors and a continuous recording at a high sampling rate for more than one full year, shows that different types of information useful for a long-term Structural Health Monitoring can be obtained from the same strain measurement datasets with dedicated data analysis tools: – Slow evolutions under the effects of the temperature variations can be assessed in terms of daily and seasonal strain cycles in the concrete shaft but also in terms of displacement estimated at the top of the pier, given a set of assumptions about the structure in order to perform a simplified reverse modelling, – The effects of the heavy traffic are also quantified in terms of number of strain cycles and their amplitude, which enables a continuous assessment of the level of traffic on the bridge and the detection of exceptional loads, – The strain cycles recorded during periods of strong wind allow a Dynamic Identification of the natural frequencies and mode shapes of the pier, to be compared with design values. A further development would be to combine these results with information obtained from other types of sensors like accelerometers for the vibrations or even topographical surveys for the displacements: in this last case the continuous record of the strain measurements can fill the gaps between successive surveys and be used for real-time anomaly detection.

References 1. Servant, C., Cachot, E., Vayssade, T., Virlogeux, M., Lancon, H., Hajar, Z.: The Millau viaduct: ten years of structural monitoring. Struct. Eng. Int. 4(2015), 375–380 (2015) 2. Cartiaux, F.B., Koutsonika, S., Andrikopoulos, G., Pelletier, P.M.: Bridge monitoring & assessment via optical strands. In: 4th Joint International Symposium on Deformation Monitoring, Athens (2019) 3. Cartiaux, F.B., Andrikopoulos, G., Pelletier, P.M.: Monitoring of bridges by optical strands. In: 11th International Workshop on Structural Health Monitoring, Stanford, vol. 1, pp. 252– 259 (2017) 4. Bulut, Y., Unal, B., Bernal, D.: Performance evaluation of system identification algorithms for structural health monitoring. In: 5th International Conference on Earthquake Engineering and Seismology, Ankara (2019) 5. Buonomo, M., de Ville de Goyet, V., Lothaire, A., Grillaud, G., Servant, C., Virlogeux, M.: Les essais de chargements statiques et dynamiques du viaduc de Millau. In: Office des Techniques d’Utilisation de l’Acier (OTUA) CS/DC-06.88 (2006)

Gradient-Boosting Applied for Proactive Maintenance System in a Railway Bridge David García-Sánchez1,2(&) , Francisco Iglesias2, Jesus Diez1, Iñaki Piñero1, Ana Fernández-Navamuel1,3,4, Diego Zamora Sánchez1, and José Carlos Jiménez-Fernandez1 1

2

TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico y Tecnológico de Bizkaia Astondo bidea Edificio 700, 48160 Derio, Spain [email protected] University of Cantabria, Avda. de los Castros, s/n, 39005 Santander, Spain 3 Basque Center for Applied Mathematics (BCAM), Bilbao, Spain 4 University of the Basque Country (UPV/EHU), Leioa, Spain

Abstract. This article contributes in the research direction of the application of Machine Learning techniques in bridge safety assessment and it lays basis to further improve the accuracy of safety assessment including analysis of real data. The communication puts forward the process and model of scale measured points correlation of bridge monitoring system on the frequency domain as a tactic to control the influence of a railway device (crossing) located on the top deck of a railway bridge. The process and model are put forward mainly for the characteristics of the damage detection for long-term assessment, going from an intensive multi-sensor monitoring system to a softer one. Finally, a GradientBoosting multi-regressor method has been developed to be easily implemented in a warning system that provides predictive skills to the current preventive maintenance strategy. The method is validated by simulating the undamaged and abnormal scenarios with Monte Carlo method. Keywords: Gradient-boosting


Correlation
Multi-sensor
Bridge

1 Introduction The objective of this paper is to present a damage detection tool for a Railway Bridge by the passage of actual circulations over a crossing [1] in its trace. To this end, the acceleration data obtained by monitoring has been used and techniques to recognize statistical patterns have been used, which allow to solve a simple classification problem between two possible states: the damaged one and the non-damaged one. The bridge on which the study is being developed has an important uniqueness: a crossing on its trace which carries a risk of amplifying the dynamic response of the bridge to a malfunction or adjustment of such device. A field work phase has been carried out where 8 triaxial accelerometers have been placed along the structure in variable positions. The collected data were processed in order to be able to recognize patterns of behavior and, once © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 236–244, 2021. https://doi.org/10.1007/978-3-030-64594-6_24

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these patterns were recognized, a statistical model based on Gradient Boosting was developed. 1.1

Description of the Structure

The bridge is located in Bilbao (Spain). It was built in 2002 and is made of reinforced and pre-tensioned concrete. It consists of 3 continuous units. The first one has 5 spans (22.0 m + 3  24.5 m + 24.0 m) that connect to a double arch over the Nervion River (second unit) and a final 12 meters’ span connecting to a nearby tunnel (third unit). Finally, it is necessary to mention that the fixed point against horizontal forces (longitudinal and transverse) is located in the center of the arch length. The main features of the bridge are shown below:

Fig. 1. Description of the structure and crossing location (Bolueta bridge, Bilbao).

The bridge under study fits into a vertical and horizontal agreement curve (Fig. 1). This bridge has an unconventional structural design and also has a crossing located on the center of the arch. Because of this, it is of great interest to study the dynamic behaviour of the structure at the passage of trains. 1.2

Objective

The measurement step was structured in 3 different phases, where the accelerometers were relocated at different positions of the bridge with the idea of being able to know the vibrations experienced by all parts of the deck and their correlation with the vibrations in the area of the crossing. In each phase 8 accelerometers were placed, out of which 2 of them did not vary their position (fixed accelerometers). These accelerometers were placed in the crossing area of the deck.

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Fig. 2. Scheme for the placement of accelerometers and measurement phases.

The rest (6) varied their position (moving accelerometers) depending on the measurement phase. In addition, temperature and humidity sensors were available. The following image (Fig. 2) shows the exact location of the sensors and the different phases are indicated by colors. 1.3

Monitoring System

The complete monitoring system consisted of 8 accelerometers (triaxial), 2 clinometers (biaxial) and temperature (°C) and humidity (%). Two data loggers with 16 input channels were used for data measurement. Solar panels were installed as a power supply complement as a power equipment. The collected data was sent to a central server via a 4G router. The generated data files were sorted into packages of 10-min windows considering the railway’s operating hours. A total of 195 measurements were made in synchronization with the trains’ passing times. Each measurement recorded acceleration data on each of the axes (X, Y, Z) for 600 s. The 195 measurements were divided into 3 campaigns leaving 2 accelerometers fixed and repositioning the rest, as detailed in Fig. 2. The sampling rate was 100 Hz, i.e. 60000 discrete samples of the accelerations experienced by the bridge were recorded in each one.

2 Preprocessing At this step different filters and smoothing techniques were applied until the main frequencies of the structure were obtained. Due to the excessive volume of starting data, a statistical study was necessary to reduce the amount of information to work with and ensure the robustness of subsequent models. 2.1

Sensor Selection and Data Cleansing

A first study of the measurements discarded those accelerometers whose measurement was not correct [2] or had drift to the extent. This resulted in each measurement of each phase (X, Y, Z) of each accelerometer based on time.

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Because measurements were acquired according to the bridge’s usage schedule, results were obtained with a background noise interrupted by impulses introduced by the passage of vehicles (in this case trains) along the structure. The amplitude and duration of these pulses (peaks) varied depending on the position of the accelerometer on the bridge and the type of measurement it performs (X, Y, Z). 2.2

Sample Selection

To obtain the natural frequencies of the structure, the behaviour of the bridge subject to free vibrations was studied, i.e. the bridge’s response once the load ceases (when the train has left the bridge). This free vibration response practically corresponds to the signal section from the local maximum (peak) to the damping region together with the background noise. Once the peak detection algorithm was applied, it was able to select the sections on which to analyse the free oscillations and the main frequencies. 2.3

Frequency Domain: Fourier Analysis

Once the samples of interest are available, the main frequencies of the structure associated with that excitation are calculated. The outputs obtained at this pre-processing stage are the first 3 frequencies for each accelerometer in each of the 195 measurements. The code used allowed to extract the first 3 frequencies of the structure, although for physical simplicity of calculation we worked only with the first frequency. Due to the rigidity of such structures and the frequency of excitation of the loads, the vibration mode that will appear, almost exclusively, is the first one. Because the approach of the study is of the EMA (Experimental Modal Analysis) [3, 4], the approximation of the statistical study to the first mode of vibration is structurally justified. In the field of maintenance of structures, less precision is usually needed as far as the numerical solution is concerned. But another factor must be taken into account: the mode of excitation of the structure. Depending on the type of loads that produce the excitation, the bridge response will be associated with different vibration modes. For the present case of study, the loads that produce the recorded signal are loads associated with the nominal operating regime of the bridge (passing trains) and, therefore, the excitation mode of that structure shall be almost exclusively the first mode of vibration as explained in [5]. For this reason, the analysis carried out in this work is based on EMA.

3 Statistical Study 3.1

Introduction

At this stage we address the extraction of series to further develop the statistical model. The statistical study allows to take advantage of the information obtained in the monitoring process to evaluate the status of the structure. According to the approach set

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out in [6], the matrix of correlations (or covariance) is proposed as the starting point for developing a global diagnostic tool on the state of the structure to find out what are the measurements in the variable positions that better correlate with measurements in fixed positions. In this way, only measures with the highest correlation coefficient R are selected for the development of the predictive model. 3.2

Linear Correlation Analysis

During the monitoring process, a sample of data was obtained that corresponds to the undamaged state of the structure. In this type of studies, it is advisable to normalize the data, especially when working with sensors of different nature. In this case no such standardization was necessary as the sensors are of the same type. In addition, each component of the processed data worked independently, i.e. the 3 axes of accelerations (X, Y, Z) were not related to each other. The procedure therefore involves calculating the variance and covariance values of the variable accelerometers with respect to the fixed ones, resulting in covariance matrices for each component (X, Y, Z) of each main frequency (1st, 2nd, 3rd) of the 3 measurement phases for each fixed accelerometer. The procedure follows the guidelines set out in [6] where expressions of the type are: ð1Þ

ðA7Xi  E ðA7X ÞÞ2 =ðM  1Þ

ð2Þ

ðA1Xi  EðA1X ÞÞðA7Xi  E ðA7X ÞÞ=ðM  1Þ

ð3Þ

rA7XA7X ¼ rA1XA7X ¼

XM i¼1

XM

ðA1Xi  E ðA1X ÞÞ2 =ðM  1Þ

rA1XA1X ¼

i¼1

XM i¼1

 Mr ¼

rA1XA1X rA1XA7X

rA1XA7X rA7XA7X

 ð4Þ

r ¼ r2A1XA7X =rA1XA1X rA7XA7X

ð5Þ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi R ¼ rA1XA7X = rA1XA1X rA7XA7X

ð6Þ

Where Eqs. (1), (2) and (3) are the parameters that make up the Eq. Covariance Matrix (4). The above equations show an example of the covariance matrix that we obtained. This matrix studies the correlation between A1 (variable accelerometer) with A7 (fixed accelerometer), particularly the X component. This results in a r and R (Eqs. (5) and (6)) value for this component. The same could be done with the Y and Z axis data. Similarly, the r and R parameters can be obtained for each variable accelerometer component that collect the degree of correlation between that variable accelerometer and the fixed accelerometer. As two fixed accelerometers (A7 and A8) are available, it will proceed similarly with the other accelerometer (A8) and its components (A8X, A8Y, A8Z).

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Finally, as a result of the pre-processing and statistical study, the initial sample of 195 records is reduced to 20 (Fig. 3).

A7

100%

A8

80% 60% 40% 20% 0% A1X A1Y A1Z A2X A2Y A2Z A3X A3Y A3Z A4X A4Y A4Z A5X A5Y A5Z A6X A6Y A6Z

Fig. 3. Correlation (indicator R) over A7 and A8.

4 Predictive Model 4.1

Introduction

In this point, the design of statistical models that allow the use of the available information of the monitoring process and the statistical study to provide the infrastructure manager with a predictive tool for bridge damage due to the influence of the crossing is developed. According to this approach, a classification algorithm between two states (damaged and undamaged) has been configured to assess the state of the structure globally and in real time. This procedure is based on unsupervised machine learning techniques, with only data available for the undamaged state of the structure. The confidence level has been set at 75%. These points will be used to train and validate the tool. In short, using only the first frequency obtained in the pre-processing phase, we will not have the required precision, but it is justified due to the mode of excitation of the structure. Table 1 and Fig. 4 show the accelerometers that will be used for training our tool. Table 1. Correlation summary A7 A8 A7 A8 A1X 0.861 0.752 A3X A1Y 0.866 0.805 A3Y A1Z 0.797 A3Z 0.752 A4X A6X 0.857 A4Y 0.814 A6Y 0.855 A4Z 0.821 0.835 A6Z

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A1X

A1X

A1Y

A1Y

A1Z

A4Y

A3Z

A4Z

A4Z

A6X A6Y

Fig. 4. Inputs-Outputs for predictive modelling

4.2

Generation of Additional Synthetic Data Using Monte Carlo (Synthetic Benchmarking)

Because only 20 quality samples are available for the development of the SHM statistical model, additional data simulation has been carried out using Monte Carlo [7] analysis techniques in order to design a more robust model. The tool is designed using the free Jupyter Notebook “Colaboratory” environment. The Collaborative tool, using Python language, allows you to work with almost any regression method applied to multi-response systems. This results in a total of 100 samples for each accelerometer that will be divided into two blocks: • Training phase: It will collect 70% of the samples obtained and serve to train our system. • Validation phase: It covers the remaining 30% of samples and will be used to check how reliable is the model obtained in the previous phase. 4.3

Multioutput Regressor: Gradient Boosting

The goal is to get a tool able to predict the response at any point of the bridge from the fixed accelerometers placed and compare with the actual measurement. This will trigger the alarm when the expected correlation (Damage Index) is lost. The Gradient Boosting regression method [8] will be used in this work. This method is based on decision trees and groups their weak prediction models into a more robust joint system. It uses an algorithm that seeks to minimize gradient in iterations. Once the validation sample is available, the procedure to follow is similar to that performed with the data in the original sample. The resulting frequency values in fixed accelerometers (prediction values) will be calculated using the already trained Machine Learning tool. These obtained values will be compared with the values calculated by simulation, that is, the reference model. Synthetic alarm values are generated to test the tool. To compare the results and check the level of accuracy of the tool to be implemented, the value obtained in the prediction of that tool and the actual value calculated

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above are graphically displayed. Control line displayed shows the variation of the response (s, 2s, 3s). The overall results are shown in Fig. 5. (A7X)

10

PredicƟons (A7X)

8

(A7Y)

PredicƟons (A7Y)

(A8X)

PredicƟons (A8X)

6

8

4 6

2 0

4 (A7Z)

PredicƟons (A7Z)

10

2.5

8

2.45

6

2.4

4

2.35 7

(A8Y)

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20

6.75

15

6.5

10

6.25

5

6

0

(A8Z)

PredicƟons (A8Z)

Fig. 5. Global results.

It can be seen that reference and validation models exhibit similar behaviours, although there are differences due to sample size and that random sampling does not perfectly fit the actual behaviour of the structure, although adapts considerably.

5 Conclusions The enormous technological advances made in recent decades in the fields of instrumentation and damage detection have made SHM a mature area in terms of monitoring, conveying the challenge to the correct management of the large amounts of data available and the creation of smart structures.

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The main advantages of using SHM-based methodologies lie in their versatility and flexibility to adapt to any system or structure, regardless of the level of complexity. In addition, the increasing availability of data makes it easier to build robust statistical models that make these algorithms more reliable. The study demonstrates the usefulness of managing available historical data derived from periodic monitoring processes to monitor the evolution of the behaviour of certain critical elements in structures. Acknowledgements. The work presented here has received funding from Horizon 2020, the EU’s Framework Programme for Research and Innovation, under grant agreement number 690660 (Project: RAGTIME), and also under grant agreement number 769373 (Project: FORESEE).

References 1. Wei, Z., Núnez, A., Liu, X., Dollevoet, R., Li, Z.: Multi-criteria evaluation of wheel/rail degradation at railway crossings. Tribol. Int. 144, Article number 106107 (2020) 2. García-Sánchez, D.: Statistical control and regression models. A practical approach in bridge control. Ph.D. thesis, University of Cantabria (2016) 3. Conte, J.P., Astroza, R., Benzoni, G., Feltrin, G., Loh, K.J.: Experimental Vibration Analysis for Civil Structures (2018) 4. Haro, J.S.: Development of theory on impacts. Simplified method of calculation of impacts in structures. Ph.D. thesis, University of Cantabria (2017) 5. Zhou, J., Yang, J., Wu, D.: A method for Analysis Linear Correlation for Multi-Sensor of Bridge Monitoring System (2009) 6. Weia, X., Russell, J., Živanović, S., Toby Mottram, J.: Measured dynamic properties for FRP footbridges and their critical comparison against structures made of conventional construction materials. Composite Structures 223, Article number 110956 (2019) 7. Ellam, L., Girolami, M., Pavliotis, G.A., Wilson, A.: Stochastic modelling of urban structure. Proc. R. Soc. A: Math. Phys. Eng. Sci. 474(2213), Article number 20170700 (2018) 8. Gascon, M., Kumar, N., Ghosh, R.: Predicting power plant equipment life using machine learning. J. Energy Resour. Technol. Trans. ASME 142(7), Article number 072101 (2020)

Vibration-Based SHM Strategy for a Real Time Alert System with Damage Location and Quantification Ana Fernández-Navamuel1,2(&), Diego Zamora-Sánchez1, Tomás Varona-Poncela1, Carlos Jiménez-Fernández1, Jesús Díez-Hernández1, David García-Sánchez1, and David Pardo2,3 1 TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Científico y Tecnológico de Bizkaia Astondo bidea, Edificio 700, 48160 Derio, Spain [email protected] 2 University of the Basque Country (UPV/EHU), Bilbao, Spain 3 Basque Center for Applied Mathematics (BCAM), and Ikerbasque, Bilbao, Spain

Abstract. We present a simple and fully automatable vibration-based Structural Health Monitoring (SHM) alert system. The proposed method consists in applying an Automated Frequency Domain Decomposition (AFDD) algorithm to obtain the eigenfrequencies and mode shapes in real time from acceleration measurements, allowing to provide a diagnosis based on a Support Vector Machine algorithm trained with a database of the modal properties in undamaged and damaged scenarios accounting for temperature variability. The result is an alert system for controlling the correct performance of the structure in real time with a simple but efficient approach. Once the alert is triggered, the undamaged mode shapes (which could be previously stored in a database of modal parameters classified by temperature) and the current (damaged) mode shapes, can provide guidance for further application of Finite Element Model Updating (FEMU) techniques. The method is trained and validated with simulations from a FE model that is calibrated employing a genetic algorithm with real data from a short-term vibration measurement campaign on a truss railway bridge in Alicante (Spain). Keywords: Structural health monitoring maintenance
Machine learning


Structural dynamics
Bridge

1 Introduction Structural Health Monitoring (SHM) is a multi-disciplinary procedure for the evaluation and control of the state of real structures [1]. With the latest improvements in the areas of instrumentation, data acquisition and transmission, a door has been opened in this field thanks to the possibility of disposing of large amounts of data without excessive costs [2, 3]. Hence, research interests in this area are now posed on the

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development of powerful algorithms that can receive and interpret these data and transform them into useful information to help managers in decision making [1]. In this sense, one of the most broadly developed methodologies in the field of civil engineering is known as vibration based SHM [3]. This discipline builds on the existing relation between the modal response of a structure and its physical properties such as mass or stiffness [3, 4]. Since the presence of structural damage directly affects the physical properties, it will then induce changes in the modal response of the considered structure [4]. There are two main challenges for modal identification in real practice. First, precision in the properties must be enough to track the structural response [5]. Second, the selected method should be suitable for automation to display results close to realtime and provide continuous assessment [5, 6]. In bridges, where it is not economic to perform Experimental Modal Analysis (EMA) with known loads (shakers, drop weights, etc.), Operational Modal Analysis (OMA) techniques are the standard for vibration-based SHM [5, 7]. One of the most commonly employed OMA methodologies is the Frequency Domain Decomposition (FDD), introduced in [8]. This method has the advantage of being easily automatable [9, 10]. There exist several works in the literature attempting to reach this goal, including the standard Automated Frequency Domain Decomposition (AFDD) [11] and automatic applications of the Stochastic Subspace Identification method (SSI) [10, 12]. In the field of SHM, the implementation of damage detection algorithms using the modal response can be addressed from two main perspectives: model-based and datadriven [3]. Model-based approaches mostly rely on Finite Element Model Updating (FEMU), which minimizes the discrepancy between the experimental and the numerical responses by modifying certain parameters in physics-based models [13]. Its main drawback is that it implies solving several direct problems to achieve convergence, which prevents its use from real-time applications [1, 3]. Data-driven methods exploit the potential in the data acquired during monitoring campaigns to characterize the possible states of the structure and solve classification problems by associating labels to different scenarios [1]. In addition, once these algorithms have been trained and validated, they can provide fast predictions based on the learned information, thus addressing real-time applications [14]. An important fact when working with real fullscale structures is the effect of operational and environmental conditions in the measured data, which can mask the presence of certain damages and hinder the performance of the algorithms [3]. Hence, dealing with the effect of a changing environment is a key issue in the field, and there exist several works employing different techniques, such as Principal Component Analysis or regression models [15, 16]. In addition, experimental data under possible damage scenarios is rarely available in real practice [1]. This lack of information makes the training of these algorithms an Unsupervised Learning task (only information about the undamaged condition is used), leading to achieve only the first Level in the Rytter’s scale of damage states [17]. This work addresses the development of a data-driven damage detection algorithm for the evaluation of the state of a steel truss bridge in Alicante (Spain) accounting for

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the effect of varying environmental conditions. We use the acceleration measurements acquired after train passage recorded during a short-term monitoring experience to extract the modal properties of the structure (representing the baseline condition) and use them to calibrate a FE model of the bridge with a genetic algorithm. Finally, we use this model to simulate ambient vibration records at different temperatures and damage scenarios to train a Support Vector Machine (SVM) algorithm for damage detection. We validate the methodology with a testing database that contains new scenarios.

2 Methodology 2.1

Description of the Structure

The structure under study is a steel bridge with a Pratt truss scheme, which serves as a passage over the gulch known as Barranco de Aguas for Line 1 of the FGV TRAM Network in Alicante (Spain). The bridge has a total length of approximately 106 m, consisting of two sections: one hyperstatic with two spans of 42.00 m each and another isostatic span of 21.12 m length, being this last one the chosen to be monitored. This span was chosen because of a fatigue study performed to the previous old bridge, which was then replaced for the current bridge in 2018 with same overall structural scheme (see images in Fig. 1 for the old and new structure).

Fig. 1. Old and new bridges over Barranco de Aguas, with trusses of identical scheme and dimensions, but different structural elements

According to the real geometry and material properties of the bridge, a parametric finite element model was built in ANSYS APDL with beam type elements (BEAM188) for the truss steel profiles and shell type elements (SHELL181) for the composite slab and concrete low walls. Despite the span is assumed to be isostatic, it is observed that the analytical response fits better with the measured modes by adding some extra longitudinal restraint given by adjustable elastic springs (LINK180), which are finally added for calibration. Figure 2 shows the FE model built in ANSYS APDL.

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Fig. 2. Detail of the 3D model built in ANSYS APDL for the analyzed span

2.2

Modal Identification

In this work, a short-term monitoring period was carried out to record acceleration measurements after train passage on the bridge. The instrumentation system included triaxial accelerometers with different specifications. We employed mainly accelerometers with ±2 g of full scale for the registration of free vibration measurements occurring once the train has passed the viaduct. Ten accelerometers are placed on the bottom chord (five on each side of the bridge: every two panels), as shown in Fig. 3. In this experience, the duration of the measurements was limited by the sensitivity of the accelerometers. Thus, a high sampling rate (600 Hz) was employed to compensate the shorten in duration.

Fig. 3. Scheme and photos showing sensor placement

Since the objective of this work was to provide a close to real-time assessment tool, we use a MATLAB implementation of the already introduced standard Automated Frequency Decomposition (AFDD) to obtain the eigenfrequencies and mode shapes. The methodology is fully explained in [8, 10]. We use the obtained eigenfrequencies and mode shapes as the input to an updating step to calibrate an initial FE model based on design specifications that will be used for the training and validation of the damage detection algorithm. However, an intermediate step is required before entering the calibration process, since the mode shape vectors are hardy manageable [18]. The Modal Assurance Criterion (MAC) is used as damage sensitive feature to update the FE model [19]. It measures the discrepancy between the corresponding numerical and experimental mode shapes, and can be written as: 2 T /exp
/num MAC ¼

/exp 2 k/num k2

ð1Þ

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where /exp and /num stand for the experimental and numerical mode shapes, respectively. In a preliminary analysis, it was found that the second mode (torsion) was close to the first mode (first bending mode), generating convergence problems in the model updating algorithm due to permutation of modes. Therefore, only bending modes are considered in this work, computing the first and third mode shapes only (see the chosen modes in Fig. 4 and Fig. 5). Given the existence of symmetry, only the data of one side of the bridge (five sensors) is processed. The FE model is correspondingly restrained to avoid the appearance of undesirable modes. The calibration algorithm uses four features for updating (the first two bending natural frequencies and the corresponding MAC values).

Fig. 4. Detail of the first and third experimental 3D mode shapes (bending modes) obtained with MACEC

Fig. 5. Detail of the corresponding experimental 2D mode shapes obtained with AFDD

2.3

Machine Learning Algorithm for Novelty Detection (SVM)

In this work we propose the implementation of a damage detection tool to identify the presence of abnormal behavior under different environmental conditions based on the dynamic response of the structure. The proposed algorithm is known as Support Vector Machine (SVM), commonly used to solve both classification and regression problems [20, 21].

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The methodology consists in five main steps: (a) modeling of the effect of temperature, (b) generation of the synthetic damage scenarios and (c) configuration of the SVM algorithm. a) Modelling the Effect of Temperature Given the structural characteristics of the bridge under study together with its location, the main source of variability during normal service is temperature. Hence, we will account for this phenomenon using the model presented in [22]. This model relates the elastic modulus of steel with temperature and is shown in Fig. 6. The curve can be approximated by the following polynomial of second order: Esteel ðT Þ ¼ 3:51
105  T 2  3:89
103  T þ 2:03

ð2Þ

Fig. 6. Steel elastic modulus evolution with temperature [22]

We employ this function to generate a set of models whose response is representative of the undamaged state of the bridge under different ambient temperatures. b) Generation of the Synthetic Damage Scenarios Similar to the temperature effect, we must somehow modify the parameters in the calibrated FE to account for the presence of damage. These synthetic damage regions are found to be enough to give robustness to the algorithm so that it can classify new data and assess the presence of damage. We generate damage distinguishing three different regions along the bridge length, each of them represented by the value of the elastic modulus of the elements belonging to that region. Damage at a certain zone is simulated through a reduction of the undamaged elastic modulus values. Hence, the damage levels are represented by the admissible range of variation with respect to the reference value. In this work, we considered four reference values according to seasonal average temperatures, to apply the reduction factors due to damage. Table 1 shows the corresponding ranges.

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Table 1. Damage labels for classification Damage level (Undamaged) Slight Intermediate Hard

Range of values (%) Class label Temperature variability only 1 (70–80] 2 (55–70] 3 (25–50] 4

With these assumptions, we generate several scenarios corresponding to damage of different severity at different locations and extract the synthetic response of the model in the form of acceleration signals that can then be processed through AFDD to provide the modal properties (natural frequencies and mode shapes). The undamaged condition is swept with the temperature model. Each damage scenario starts from an undamaged model corresponding to certain temperature from which elastic modulus of the elements associated to the damaged region are reduced. Once these models are created, synthetic acceleration signals are generated by applying a white noise excitation and obtaining the responses at the same places where the sensors were located in the real structure. The AFDD algorithm is used to process these time series to extract natural frequencies and mode shapes as if it were real experimental input. The final damage sensitive features to feed the SVM algorithm are the first two first bending eigenfrequencies and MAC values. c) Configuration of the SVM Algorithm SVM is a data-driven algorithm to solve classification and regression problems [20]. It learns relationships in the data and finds the optimal space to separate the data among the different classes [21]. This approach results to be very versatile since it can fit many different types of discriminant functions from which the hypothetic classification space is formed, including linear, neural network or radial basis models [23]. Particularly, this algorithm can be understood as an optimization problem in the sample space with the following formulation [23]: min b0 ; b

kbk22

subject to

yi xTi b þ b0




ð3Þ  1 8i ¼ 1; . . .; n;

where xi is a feature vector in the sample space, b is the slope of a normal to the optimal separating hyperplane, b0 is an intercept, and yi is a class variable. SVM technique is usually applied to solve two-class classification problems (the class variable is then binary and can take values of either +1 or −1), although it can also be extended to multi-class problems [23]. In our case, we aim at classifying between four possible severities of damage, being undamaged, slight damage, intermediate damage and hard damage.

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The configuration of the algorithm implies two main stages: training and validation. Therefore, we generate a synthetic training dataset to feed the algorithm during training and correspondingly a testing dataset to validate the algorithm and check its performance. The training set contains labelled information (damage sensitive features and corresponding class variable value) for the SVM algorithm to learn from that information and find its parameter values. Specifically, this dataset contains several scenarios associated to each of the classes to be identified in order to adequately characterize each group. It is important to mention that, although damage at differentiated regions of the bridge is generated, the class assignation only depends on the severity of the induced damaged, that is, damage at two different regions with the same severity correspond to the same class. Hence, the class variable is an integer value in the range [0, 3], where 0 corresponds to undamaged, and 3 corresponds to hard damage. The next step is validation. We feed the algorithm with new scenarios (unseen by the algorithm during training) whose class variable is known. The trained SVM will receive these new cases and predict the class to which they correspond, based on the training. We calculate the performance of the algorithm comparing the predictions on the SVM and the real structural condition associated to each element in the validation dataset: Performanceð%Þ ¼

number of correct predictions  100 size of testing dataset

ð4Þ

3 Results In this section, we present the results obtained from the application of the SVM algorithm following the steps presented in the previous section. In order to show the results graphically, we applied Principal Component Analysis (PCA) to reduce the dimensions of the feature space to 2D and thus make it representable. Figure 7 shows the results including both training and validation datasets, where the colored regions correspond to each class to be classified (from left to right, increasing severity of damage, indicated by the class variable number). The performance of the algorithm is calculated for the validation dataset, providing an 87% according to Eq. (4) that is indeed a good result. In addition, we observe that mainly for the hard damage scenario, the different regions where damage exist are easily distinguished. Hence, for strong damages, the algorithm also informs on the possible location of this damage, suggesting further inspections in that region. The effect of temperature variations was considered as undamaged scenarios. It also induces certain variability in the different damage scenarios, but it does not generate conflict in the assignation of the damage severity.

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Fig. 7. 2D representation of the SVM results with four damage classes

4 Conclusions and Future Work This work addresses the implementation of a damage detection tool based on a machine learning algorithm known as Support Vector Machine, using the modal response of a truss bridge. The work starts from the calibration of a FE model of the bridge by submitting it to an updating procedure that minimizes the discrepancy between the experimental and the numerical responses. These responses are obtained by applying the AFDD algorithm to acceleration measurements. Next, the calibrated FE model is employed to generate multiple scenarios to train and validate the data-driven algorithm. We account for the environmental variability by relating the elastic modulus of the steel elements of the bridge with ambient temperature, thus allowing to sweep a wide range of undamaged service conditions. The presence of damage is represented by localized reductions of the elastic modulus depending on the degree of severity of damage. 4 levels of damage are considered for the SVM algorithm to solve the classification problem, being undamaged, slight damage, intermediate damage and hard damage. Based on these assumptions, the training and validation datasets are synthetically generated and fed to the SVM algorithm to model the classifier. Results show that the algorithm generates predictions with a performance close to 90%, thus suggesting its implementation for evaluating new data in future monitoring processes. Since SVM rapidly detects abnormal behavior, it can be used as a preliminary alert system to activate a more complex damage identification technique, such as FE model updating. In this strategy, once the alert is triggered by the SVM, the undamaged and current mode shapes can be used to perform an updating procedure based on the GA algorithm of the calibration step. That is, the alert system controls the correct performance of the structure in real time and, if novelty is detected, an exhaustive damage identification (not real-time) is conducted through model-based techniques.

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Acknowledgements. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project). This paper reflects only the author’s views. The European Commission and INEA are not responsible for any use that may be made of the information contained therein.

References 1. Farrar, C.R.: Structural Health Monitoring: A Machine Learning Perspective (2013) 2. Huston, D.: Structural Sensing, Health Monitoring and Performance Evaluation. Taylor & Francis (2011) 3. Brownjohn, J.M.W., de Stefano, A., Xu, Y.L., Wenzel, H., Aktan, A.E.: Vibration-based monitoring of civil infrastructure: challenges and successes. J. Civil Struct. Health Monit. 1 (3–4), 79–95 (2011). https://doi.org/10.1007/s13349-011-0009-5 4. Soe, M.T.: Vibration-Based Finite Element Model Updating and Structural Damage Identification, May 2013 5. Brincker, R., Ventura, C.: Introduction to Operational Modal Analysis (2015) 6. Reynders, E., Houbrechts, J., De Roeck, G.: Fully automated (operational) modal analysis. Mech. Syst. Signal Process. 29, 228–250 (2012). https://doi.org/10.1016/j.ymssp.2012.01. 007 7. Carden, E.P., Fanning, P.: Vibration based condition monitoring: a review. Struct. Health Monit. 3(4), 355–377 (2004). https://doi.org/10.1177/1475921704047500 8. Brincker, R., Zhang, L., Andersen, P.: Modal identification of output-only systems using frequency domain decomposition. Smart Mater. Struct. 10(3), 441 (2001). https://doi.org/10. 1088/0964-1726/10/3/303 9. Brownjohn, J.M.W., Magalhaes, F., Caetano, E., Cunha, A.: Ambient vibration re-testing and operational modal analysis of the Humber Bridge. Eng. Struct. 32(8), 2003–2018 (2010). https://doi.org/10.1016/j.engstruct.2010.02.034 10. Magalhães, F., Cunha, Á.: Explaining operational modal analysis with data from an arch bridge. Mech. Syst. Signal Process. 25(5), 1431–1450 (2011). https://doi.org/10.1016/j. ymssp.2010.08.001 11. Brincker, R., Andersen, P., Niels-Jorgen, J.: Automated Frequency Domain Decomposition for Operational Modal Analysis 12. Magalhães, F., Cunha, Á., Caetano, E.: Online automatic identification of the modal parameters of a long span arch bridge. Mech. Syst. Signal Process. 23(2), 316–329 (2009). https://doi.org/10.1016/j.ymssp.2008.05.003 13. Friswell, M.I., Mottershead, J.E.: Finite Element Model Updating in Structural Dynamics, vol. 38. Springer, Dordrecht (1995). https://doi.org/10.1007/978-94-015-8508-8 14. Vitola, J., Tibaduiza, D., Anaya, M., Pozo, F.: Structural damage detection and classification based on machine learning algorithms. In: 8th European Workshop on Structural Health Monitoring, pp. 5–8, July 2016 15. Mujica, L.E., Rodellar, J., Fernández, A., Güemes, A.: Q-statistic and t2-statistic pca-based measures for damage assessment in structures. Struct. Health Monit. 10(5), 539–553 (2011). https://doi.org/10.1177/1475921710388972 16. Ding, Y., Li, A.: Assessment of bridge expansion joints using long-term displacement measurement under changing environmental conditions. Front. Architect. Civil Eng. China 5 (3), 374–380 (2011). https://doi.org/10.1007/s11709-011-0122-x 17. Rytter, A.: Vibrational Based Inspection of Civil Engineering Structures (1993)

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18. Bakdi, A., Kouadri, A., Mekhilef, S.: A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones. Renew. Sustain. Energy Rev. 103(January 2018), 546–555 (2019). https://doi.org/10.1016/j.rser. 2019.01.013 19. Chen, H.P.: Structural Health Monitoring of Large Civil Engineering Structures. Wiley Blac. Wiley Black (2018). https://doi.org/10.1002/ejoc.201200111 20. Worden, K., Manson, G.: The application of machine learning to structural health monitoring. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 365(1851), 515–537 (2007). https://doi.org/10.1098/rsta.2006.1938 21. HoThu, H., Mita, A.: Damage detection method using support vector machine and first three natural frequencies for shear structures. Open J. Civil Eng. 03(02), 104–112 (2013). https:// doi.org/10.4236/ojce.2013.32012 22. Meruane, V., Heylen, W.: Structural damage assessment under varying temperature conditions. Struct. Health Monit. 11(3), 345–357 (2012). https://doi.org/10.1177/ 1475921711419995 23. Sen, D., Nagarajaiah, S.: Data-driven approach to structural health monitoring using statistical learning algorithms, pp. 295–305 (2018). https://doi.org/10.1007/978-3-31968646-2_13

Slab Vibration Model Coupled with Pier Structure on Continuous Girder Bridge Shohei Kinoshita1(&), Shigeru Kasai1(&), Murtuza Petladwala1(&), and Hideaki Takaku2(&) 1

Data Science Research Laboratories, NEC Corporation, Kawasaki City, Japan {kinoshita_jz,kasaishigeru,murtuza}@nec.com 2 East Nippon Expressway Company Limited, Sendai City, Japan [email protected]

Abstract. This paper proposes slab vibration model coupled with pier structure on continuous girder bridge. Vibration mode properties are expected as promising indicators in bridge health monitoring. However, there are various kinds of bridge structures depending on geographical conditions. Especially in the case of a continuous girder where an intermediate support is elastically supported by a portal pier with steel beam, vibration coupling with pier structure should be considered for modal analysis of slab vibration. Previous research of slab vibration mode without pier vibration coupling could not see whether extracted modes are physical modes or not. We proposed a slab vibration continuum model on 2-continuous-span girder with a single degree of freedom kinematic system at pier position, and estimated the vibration modes. RC-slab vibration modes were experimentally investigated on a real expressway bridge, which has 2-continuous-span steel girder where the intermediate support is elastically supported by a portal pier with steel beam. As a result, coupling vibration mode which deflected at pier position was estimated theoretically from proposed model. In experiment, the mode was observed by analyzing slab acceleration response measured at 14 points in the bridge. It is concluded that the proposed slab vibration model is effective for modal analysis, and it can be useful for bridge structural health monitoring. Keywords: Slab vibration model
Vibration mode bridge
Pier structure
Vibration coupling


Continuous girder

1 Introduction In the last decades a significant portion of reports in Structural Health Monitoring (SHM) is studied upon the vibration-based methods [1–3]. Vibration mode properties are expected as promising indicators in bridge SHM. Especially modal shapes are good features because they reflect local damage of the structure [4]. For bridge SHM, slab is one of the most important structures in bridge because it suffers daily traffic load and weather effect like rain and snow. Especially slabs on continuous girder were constructed in many expressways and are aging in Japan. It is important in SHM of slab on continuous girder to understand characteristics of slab vibration mode. Related to slab (deck) vibration mode on continuous girder, several models are studied. Marchesiello © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 256–265, 2021. https://doi.org/10.1007/978-3-030-64594-6_26

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(1999) reported about dynamics of multi-span continuous straight bridges (decks) subject to moving vehicle excitation with modelling bridges as a multi-span continuous isotropic plate [5]. Gorman (2000) investigated bridge deck free vibration by imposing a set of rectangular plate forced vibration problem and constraining constants appearing in the solutions so that boundary conditions are satisfied [6]. Rezaiguia (2009, 2012) studied multi-span continuous bridge deck (slab) based on modal method with decomposing mode shape into two admissive functions for bridge axis and bridge transverse axis [7, 8]. Fisli (2019) modeled the bridge deck as a multi-span orthotropic rectangular plate in his work on the bridge deck response under moving truck loading [9]. Guebailia (2019) described the dynamic behavior of the path surface slab with modelling it as an orthotropic thin plate with multi-spans [10]. Those previous researches treat intermediate support in bridge axis as simple support or knife-edge support. However, there are various kinds of bridge structures depending on geographical conditions, which affect bridge vibration characteristics. For example, in the case of a continuous girder where an intermediate support is elastically supported by a portal pier with steel beam, slab vibration can couple with pier structure vibration. The vibration coupling should be considered for modal analysis of slab vibration. Although the intermediate pier of slab vibration on continuous girder is often modeled as simple support, without considering pier vibration coupling it could not see whether extracted modes are physical modes or not. For instance, when we estimate an actual physical mode is not physical mode, we will miss the change of the mode even when the structural properties change. Eventually fault decision could be made in structural health monitoring. Report about slab vibration modelling with pier structure accompanied by experimental validation in real bridge is limited to the best of authors’ knowledge. This paper focuses on slab vibration modelling considering the coupling with pier structure on continuous girder bridge by numerical study and experimental validation in real bridge. Our approach is thought to be more useful in terms of computational resource when compared with common finite element modelling method. Slab vibration continuum model on 2-continuous-span girder is proposed with a single degree of freedom kinematic system at pier position. Modal shape about bridge axis is studied from fundamental differential equation about bending of uniform beam. At intermediate support, boundary condition is given in the way that the shear force of beam is equal to input force for mass in the kinematic system. Numerical example is studied with assuming bridge parameters. To validate the effectiveness of our slab vibration model, slab vibration modes are extracted in a real expressway bridge in Japanese. 14 accelerometers were deployed in the bottom side of the slab. Numerical example from our model matched well with experimental result. This paper is organized as follows. Section 2 explains the slab vibration model and numerical example; Sect. 3 explains the experimental validation in a real bridge including setup and mode shape and evaluation of the model; Sect. 4 concludes the work in this paper.

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2 Slab Vibration Model Bridge slab on continuous girder is modeled with focusing on the bridge axis without finite element modeling. We assume that there is a kinematic system in intermediate support as the effect of pier structure. Mode shape is determined with semi-analytical approach. Numerical example is studied with using parameters about the real bridge in following section. 2.1

Bridge Axis Slab Vibration Model with Kinematic System

Bridge slab is generally modeled as a rectangular thin plate. The mode shape function is expressed as the product of admissible two functions. One is an eigenfunction of multi-span continuous beam in bridge axis, the other is another eigenfunction of a single span beam in bridge transverse axis. Both eigenfunctions satisfy the boundary conditions. In order to simplify the effect of intermediate support, we propose a model focusing on bridge axis behavior as multi-span continuous beam. Multi-span continuous Euler Bernoulli beam with one degree of freedom kinematic system is shown in Fig. 1. To simplify the discussion, a kinematic system with mass component Mp and spring component Kp is considered as the effect of pier structure. In a uniform Euler Bernoulli beam, equation of motion about deflection W(x,t) is given by EI

@ 4 W ðx; tÞ @ 2 W ðx; tÞ þ qA ¼0 @x4 @t2

ð1Þ

where E is Young’s modulus, I is moment of inertia of cross-section, q is density, A is cross sectional area of the beam. With variable separation method, we assume deflection W(x,t) as product of shape function Y ð xÞ and time function ejxt , which is written as W ðx; tÞ ¼ Y ð xÞ
ejxt

ð2Þ

where j is imaginary unit, x is angular frequency.

Fig. 1. Bridge axis slab vibration model with a single degree of freedom kinematic system

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2.2

259

Determination of Mode Shape

To determine the mode shape of multi-span continuous beam, we need to extract eigenfunctions. We assume that deflection in the r th span is expressed in Eq. (3). Wr ðxr ; tÞ ¼ Yr ðxr Þ
ejxi t

ð3Þ

where Yr ðxr Þ is shape function for the bridge axis. Shape function of i th mode vibration in the r th span is expressed in Eq. (4). Yr;i ðxr Þ ¼ Cr;i;1 sinðki xr Þ þ Cr;i;2 cosðki xr Þ þ Cr;i;3 sinhðki xr Þ þ Cr;i;4 coshðki xr Þ

ð4Þ

where ki is eigenvalue and has relation with angular frequency xi in Eq. (5). rffiffiffiffiffiffiffiffiffiffiffi x2 qA ki ¼ 4 i EI

ð5Þ

Cr;i;1 , Cr;i;2 , Cr;i;3 , Cr;i;4 is constants decided from boundary condition at each ends. The boundary conditions are listed as follows: Vertical deflection is equal to zero at the ends   Yr;1 ðx1 Þx1 ¼0 ¼ 0; Yr;2 ðx2 Þx2 ¼0 ¼ 0 ð6Þ Bending moments are equal to zero at the ends   d 2 Yr;1 ðx1 Þ d 2 Yr;2 ðx2 Þ ¼ 0; ¼0 dx21 x1 ¼0 dx22 x2 ¼0

ð7Þ

Vertical deflection, the slope and bending moments at the intermediate support are continuous   Yr;1 ðx1 Þx1 ¼l1 ¼ Yr;2 ðx2 Þx2 ¼l2

ð8Þ

  dYr;1 ðx1 Þ dYr;2 ðx2 Þ ¼ dx1 x1 ¼l1 dx2 x2 ¼l2

ð9Þ

  d 2 Yr;1 ðx1 Þ d 2 Yr;2 ðx2 Þ ¼ dx21 x1 ¼l1 dx22 x2 ¼l2

ð10Þ

Shear force of beam is equal to input force for mass in the kinetic system at the intermediate support [11]. As the equation of movement of the mass, we assume

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 @ 3 W1 ðx1 ; tÞ V ðx1 ; tÞjx1 ¼l1 ¼ EI  @x3 x1 ¼l1  2  @ W1 ðx1 ; tÞ ¼ Mp þ Kp
W1 ðx1 ; tÞx1 ¼l1  2 @t

ð11Þ

x1 ¼l1

Constants of the kinematic system of intermediate support is approximated from static bending rigidity and mass of the beam. Assuming that effect of bending rigidity is static bending, Kp is given by Kp ¼

48Ep Ip l3p

ð12Þ

where Ep is Young’s modulus, Ip is moment of inertia, lp is length of pier. Mass constant Mp is approximated by product of the density and volume of pier structure. Substituting the expression (3), (4) and (5) into all the boundary and continuity conditions (6) to (11), after some manipulations and simplifications, we obtain linear simultaneous equations on Cr;i;1 , Cr;i;2 , Cr;i;3 , Cr;i;4 . For non-trivial solutions of the system, the determination of the coefficients matrix about the simultaneous equations equals to zero. Angular frequency xi at eigenvalue ki is extracted by Graphical method in the way the determination gets lower than pre-defined threshold with xi . For each xi which satisfies the above condition, mode shapes are calculated. 2.3

Numerical Example

To validate the effectiveness of slab vibration model, an example is presented: a continuous 2-span multi-girder bridge slab with span length l1 = 39.1 m, l2 = 24.3 m. The parameters are listed in Table 1a, 1b for slab on 2-continuous span girder, pier beam respectively. Length, width and thickness of slab on 2-continuous span girder are decided from a real bridge shown in following section. Mass density and Young’s modulus are decided as equivalent values as composite of concrete slab and steel girder based on general design specification [12]. Length, width and thickness of intermediate pier beam are decided from a real pier beam shown in following section. Material is modeled as concrete, assuming that steel pier beam is filled with concrete.

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Table 1. Parameters of numerical example

(b) Pier Beam

(a) Slab on Multi-Span Bridge Item Length Width Thickness Young’s modulus Mass density

Condition =39.1m, =24.3m 9.25 m 0.23 m 4.0 x 1011 N/m2 3.3 x 103 kg/m3

Item Length ( ) Width ( ) Height ( ) Young’s modulus ( Mass density ( )

)

Condition 35.5 m 3.1 m 2.9 m 2.5 x 1010 N/m2 2.4 x 103 kg/m3

Three estimated mode shapes are shown Fig. 2a, 2b and 2c in an ascending order of eigenvalue. Amplitude was normalized in the way that the maximum absolute value becomes 1. In mode A of Fig. 2a, it deflects in whole span of the beam including P1 point. This shape is due to a kinematic system at P1, and is estimated as coupling mode with pier structure. In mode B of Fig. 2b, there are 1st deflection shapes in both spans. In mode C of Fig. 2c, there are 2nd deflection shape and 1st deflection shape in A1–P1 span and P1–P2 span, respectively.

(a) mode A

(b) mode B

(c) mode C

Fig. 2. Mode shapes of slab model

3 Experimental Validation at Real Bridge 3.1

Experimental Site

The experimental test was conducted at a real bridge shown in Fig. 3 to validate the effectiveness of the slab vibration model by comparing above numerical example and real slab vibration mode. The bridge stands on the river, and is located in an expressway of Eastern Japan. It has 2 continuous-span steel girder whose intermediate support is supported by a portal pier with steel beam.

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(a) Bridge exterior

(b) P1 pier (portal pier with steel beam)

Fig. 3. Experimental site: an expressway bridge in Eastern Japan

Fig. 4. Bridge structure and sensor position (a) top view (b) side view of down line (c) view on portal pier

Fig. 5. Mode extraction flow (a) Acceleration waveform (b) Spectrum of free vibration region

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Setup and Analysis Flow

The bridge structure and sensor positions are shown in Fig. 4a, 4b and 4c, as top, side and portal pier view respectively. There are two traffic directions (up line and down line) with similar structure in the bridge. Each direction is separated in structure and isolated each other about vibration. Acceleration was measured in down line where traffic direction is from left to right in Fig. 4b. 14 accelerometers were placed in a row at bottom-side of driving lane of the bridge concrete slab, represented by circlednumbers in Fig. 4a. There are 7 accelerometers evenly spaced in each span, based on the assumption that we could observe up to 3rd deflection shape in each span. Mode extraction flow is shown in Fig. 5. We focus on free vibration after a large truck passes the bridge for the mode extraction, assuming that free vibration caused by large vehicle is composed of bridge vibration mode characteristics. The flow consists of mainly 4 steps. In first step, free vibration region is selected from the acceleration data acquired when a large truck passed the bridge. The free vibration region is defined as it starts from time-stamp of the rear axle peak value, which is shown in dashed-line rectangle in Fig. 5a. In second step, free vibration region in all accelerometers were transformed to frequency domain by Fourier transformation. In third step, peaks are selected from frequency spectrum amplitude. Finally, shapes are extracted from relative amplitude and phase among all accelerometers at peak frequencies. In order to give a statistical significance to the results, acceleration data of 40 large trucks are measured in normal traffic and modes are extracted from each data. Table 2 shows condition on data acquisition and signal processing with related to Fast Fourier Transformation (FFT). Table 2. Conditions on data acquisition and signal processing Item Data sampling Frequency Sensor Sensitivity FFT Sample Number FFT Window function Frequency Resolution in Spectrum

3.3

Condition 5120 Hz 0.001 V/(m/s2) 16,384 points Hanning 0.3125 Hz

Result

Three modes were extracted. Typical mode shapes were shown in Fig. 6a,6b and 6c in an ascending order of frequency. The amplitudes of each accelerometer were plotted in dot with x axis and y axis as accelerometer position and amplitude of each position respectively. Amplitudes were normalized in the way that the norm about the amplitude of all accelerometers becomes 1. As the result of modal extraction, at 3 Hz it deflected in all positions including P1 point. At 13 Hz, there were 1st deflection shapes in both A1–P1 span and P1–P2 span although the amplitude in former was larger than that of later. At 17 Hz, there 2nd deflection shape and 1st deflection shape in A1–P1 span and P1–P2 span respectively though the amplitude in later was larger than that of former. In some shapes at 13 Hz and 17 Hz, phases varied among accelerometers.

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(a) 3 Hz

(b) 13 Hz

(c)17 Hz

Fig. 6. Extracted mode shape

Slab vibration model was investigated by comparing the shapes of numerical example in Fig. 2 with experimental results in Fig. 6, with using Modal Assurance Criteria (MAC) [13]. MAC is similarity indicator between two mode shapes, and it is one when two shapes are same, and zero when two shapes are orthogonal. Average MACs were obtained from all experimental shapes in 3 Hz, 13 Hz, and 17 Hz, for slab vibration model in mode A, B and C, respectively. As a result, in all shapes we got over 0.85 in MAC, which meant that all numerically-estimated shapes matched the experimentally-extracted shapes well. Especially mode A in Fig. 2a, which is estimated as pier coupling vibration due to pier structure, was experimentally validated with 0.98 in MAC. This suggested that slab vibration coupled with pier structure is successfully modeled. One of the reason why MACs in shape B and C are lower than that of A is phase variance among accelerometers (Table 3). Table 3. Mode shape MAC between numerical example and experimental result Numerical example Fig. 2a, mode A Fig. 2b, mode B Fig. 2c, mode C

Experimental result Fig. 6a, 3 Hz Fig. 6b, 13 Hz Fig. 6c, 17 Hz

Average MAC 0.98 0.86 0.85

4 Conclusion Slab vibration model considering pier structure was proposed. Pier structure was modeled as a single degree of freedom kinematic system. In numerical study, general deflection shapes and the shape which deflects in intermediate support were estimated. The effectiveness of the proposed model has been studied on a real bridge slab mode shapes. Numerical example matched well with experimental results. This paper mainly focused on bridge axis behavior of the slab on 2-continuous girder. In future we plan to expand the proposed model to bridge transverse axis, and multi-span girder more than 2. Additionally it is expected to obtain more accurate slab vibration model by experimentally investigating vibration characteristics of real pier beam.

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References 1. Doebling, S.W., Farrar, C.R., Prime, M.B.: A summary review of vibration-based damage identification methods. Shock Vibr. 30(2), 91–105 (1998) 2. Farrar, C.R., Cornwell, P.J., Doubling, S.W., Prime, M.B.: Structural Health Monitoring Studies of the Alamosa Canyon and I-40 Bridges, Los Alamos Laboratory, LA-13635-MS (2000) 3. Fan, W., Qiao, P.: Vibration-based damage identification methods: a review and comparative study. Struct. Health Monitoring 10(1), 83–29 (2010) 4. Casas, J.R., Moughty, J.J.: Bridge damage detection based on vibration data: past and new development. Front. Built Environ. 3(2017), Article 4. www.frontiersin.rog 5. Marchesiello, S., Fasana, A., Garibaldi, L., Piombo, B.A.D.: Dynamics of multi-span continuous straight bridges subject to multi-degrees of freedom moving vehicle excitation. J. Sound Vibr. 224(3), 541–561 (1999) 6. Gorman, D.J., Garibaldi, L.: A highly accurate and efficient analytical approach to bridge deck free vibration and analysis. Sound Vibr. 7, 399–412 (2000) 7. Rezaiguia, A., Laefer, D.F.: Semi-analytical determination of natural frequencies and mode shapes of multi-span bridge decks. J. Sound Vibr. 328, 291–300 (2009) 8. Rezaiguia, A., Fisli, Y., Ellagoune, S., Laefer, D.F., Ouelaa, N.: Extension of a semianalytical approach to determine natural frequencies and mode shapes of a multi-span orthotropic bridge deck. Struct. Eng. Mech. 43(1), 71–87 (2012) 9. Fisli, Y., Rezaiguia, A., Guenfoud, S., Laefer, D.F.: Dynamic response of a multi-span, orthotropic bridge deck under moving truck loading with tandem axles. Diagnostyka 20(4), 37–48 (2019) 10. Guebailia, M., Ouelaa, N.: The evaluation of the dynamic response of the moving exciter due to the irregularities of the slab, Computational Methods and Experimental Testing In Mechanical Engineering, Lecture Notes in Mechanical Engineering (2019) 11. Rao, S.S.: Vibration of Continuous Systems. Wiley, Hoboken (2007) 12. Japan Road Association: Specifications for highway bridges part I common (2014) 13. Allemang, R.J.: The modal assurance criterion - twenty years of use and abuse. Sound Vibr. 37(8), 14–21 (2003)

Towards Monitoring of Concrete Structures with Embedded Ultrasound Sensors and Coda Waves – First Results of DFG for CoDA Niklas Epple(B) , Daniel Fontoura Barroso , and Ernst Niederleithinger Bundesanstalt f¨ ur Materialforschung und -pr¨ ufung (BAM), Berlin, Germany [email protected]

Abstract. Due to the importance of reinforced concrete structures for modern society, damage assessment during the entire life-cycle of such structures has become a special interest in non-destructive testing. Using embedded ultrasound sensors in combination with other measurement methods, numerical modeling and self-made data collectors, tailored specifically for monitoring tasks, the German research group DFG FOR CoDA aims to investigate and develop novel methods for damage detection and rapid model updating in reinforced concrete structures. In the first stage of the project, besides the development of custom-built, lowcost data collectors, ultrasonic transducers are embedded in a large, reinforced concrete specimen on a BAM test site near Berlin. In this experiment, the influence of changing environmental conditions (mainly temperature) on the ultrasound signal is investigated using coda-wave interferometry. The results show a correlation between changes in temperature and ultrasonic velocity. Such changes must be taken into consideration in a long-term monitoring setup to distinguish between reversible and permanent changes. By correcting the data using a linear relation between concrete temperature and velocity change to remove the seasonal trends and by low-pass filtering the data to remove daily variations can remove most of the temperature influence on the ultrasound measurements. Keywords: Ultrasound · Coda wave interferometry · Embedded sensors · Non-destructive testing · Structural health monitoring

1

Introduction

Concrete is one of the most used construction materials on earth. Its durability, compressive strength, and many more favorable attributes make it the perfect material for a wide range of applications. Especially in infrastructure, safety monitoring and damage assessment are crucial during the life-cycle of a concrete structure. Early damage detection helps to increase the lifetime of those structures, delaying expensive reconstruction. Some of the most used tools for c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 266–275, 2021. https://doi.org/10.1007/978-3-030-64594-6_27

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structural health assessment are Ultrasound (US) measurements. In recent years, a new technique called Coda Wave Interferometry (CWI) originally developed in seismology has been adapted in NDT for US data evaluation [1]. Unlike standard techniques based solely on the first arrival, it utilizes the entire waveform, especially the latter part of the recording, the so-called coda. This allows sensing of areas beyond the direct path and detection of small changes in the medium. One of the major challenges with coda wave monitoring is the distinction between the influence of environmental variations and permanent damages on the measured signal, the localization of these damages, and the integration of these results into digital bridge models and static computations by architects and civil engineers. To solve these problems, the German research foundation has funded a research group uniting experts from Technical University Munich (TUM), Ruhr University Bochum (RUB), Bochum University of Applied Science (HSB) and the German Federal Institute for Materials Research and Testing (BAM). Combining Civil Engineering, Material Science, Nondestructive Testing, and wave propagation expertise, the group aims at characterizing damage indicators found in coda wave measurements, combined with a wide range of state of the art measurement techniques to assess and monitor reinforced concrete structures. In this paper, we will present the first results from BAM within this project. We will describe a large-scale experiment investigating the relationship between temperature and ultrasound velocity changes.

2

Theory of Coda Wave Interferometry

When measuring with ultrasonic waves in scattering media only a part of the energy is transmitted from source to receiver on the direct path. Cavities and material changes induce scattering, which results in significant parts of the signal arriving at the receiver after the first arrival. This scattering process, while looking like unwanted random noise, is actually highly repeatable and the signal does not change if material parameters and sensor coupling remain constant. The multiply scattered part of ultrasonic measurements is called coda. Coda waves have not only propagated on the direct path between source and receiver but sensed a wider area and spent more time in the medium. Therefore they are susceptible to subtle changes in a larger area. While classical first break picking would not show any changes, using Coda Wave Interferometry (CWI) small velocity changes can be detected. The evaluation of velocity changes with CWI is described extensively in [1–4], and [5]. We refer to these publications for an exact mathematical description and will only describe the basic principles here. The calculation of velocity changes (dv/v) with CWI relies on the maximization of the correlation coefficient (CC) between a perturbed and unperturbed waveform on a time interval T. The unperturbed waveform is compared to either a time-shifted version of the perturbed signal (on a short time window) or a stretched version of large parts or the entire perturbed waveform. If the CC is close to a value of one, the signals are considered to be similar or almost identical. The time shift (dt/t) or stretching

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factor (α) resulting in a CC closest to one can then be linked to a velocity change (dt/t = −dv/v or α = −dv/v). In this work, we will use this stretching technique (see [2]) for the entire recording. The reference measurement (unperturbed signal) is kept fixed for the entire experiment, so the results can be directly linked to the zero state. A computationally more expensive technique proposed in [6] using shifting references has not been applied for the experiments presented in this paper but might be used in the future for permanent monitoring of structures. The resulting quantities of CWI are the correlation coefficient on the one hand and the relative velocity change (in percent) on the other hand. Most of the evaluation is based on velocity changes.

3 3.1

Experiments US Sensors for Embedding into Concrete

In previous research at BAM, piezoelectric ultrasonic transducers - designed for embedding into concrete - have been developed in cooperation with Acoustic Control Systems, Ltd. (ACS, Moscow, Russia). Unlike externally attached sensors, these transducers have an almost omnidirectional radiation pattern in concrete at a center frequency of 60 kHz. At this frequency, relatively large distances between source and receiver can be covered at a good resolution. Their ability to act as both source and receiver makes them a perfect tool for long term monitoring. A detailed description of the sensors can be found in [7]. 3.2

“All Inclusive” Specimen

When monitoring large scale reinforced concrete structures, environmental changes influence the ultrasound signal just as damages do. It is known that temperature [8] and moisture [9] are influencing the wave propagation. When performing a long-term monitoring experiment outside the lab, these factors need to be considered to not accidentally misinterpret a sudden drop in temperature as a damaging event. For the evaluation of the influence of environmental aspects on the specimen, particularly on the coda waves, we have equipped an over ten-year-old specimen at BAM TTS Horstwalde south of Berlin with 19 ultrasound sensors and 5 temperature sensors. All sensors were embedded in boreholes in mid-October 2019. The 19 US sensors are installed at a depth of 40 cm. The temperature sensors (NTC Thermistors) are installed directly below sensors 10,12,15,17 and 19. The sensor layout is depicted in Fig. 2. The temperature sensors are calibrated for a temperature range of −10 ◦ C to 35 ◦ C and provide precise measurements in this range. Therefore, they are perfectly suited for monitoring subtle temperature changes induced by daily and seasonal temperature variations. After mounting the sensors, the boreholes were refilled with grout. The specimen is covered by a tarp to minimize the influence of moisture on the signal and thus allows the determination solely of the temperature dependencies. Solely for the purpose of temperature monitoring a smaller amount of

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Fig. 1. Transducer ACS SO807 [7]

Fig. 2. “All Inclusive” specimen at BAM TTS Horstwalde. The positions of the embedded Ultrasound transducers are indicated by X, the positions where a thermistor has been embedded additionally are indicated by O.

sensors would have sufficed, but as the specimen is supposed to be used for damage localization in a future experiment we decided to equip it with a full sensor network right away. In mid-November 2019 a data acquisition device similar to the one used in [10] was installed for hourly measurements of all 342 Sensor combinations. The measurements are set to run for at least one year without any loading or destruction so that a yearly cycle of environmental variations can be investigated.

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We record 10000 samples per measurement at an acquisition speed of 2 MHz. All data is uploaded to an online database accessible for all members of the research group hourly.

4

Results

All ultrasound measurements are preprocessed before CWI velocity change calculations. This includes the removal of any offset, the suppression of crosstalk at the beginning of the signal, bandpass filtering between 10 kHz and 100 kHz for the suppression of low- and high-frequency noise and the removal of the pretrigger samples. 4.1

Large Scale Model “All Inclusive”

The measurements at the All-Inclusive specimen at BAM TTS Horstwalde started on Nov.15 2019. In this work, we present the measurement results until the end of May 2020. Unfortunately, there were several power blackouts at the measuring station during this period. While this would have been a minor problem with the new data collection device developed within the research group [11] and [12], which restarts automatically and continues the measurements, the Laptop and NI system need to be restarted manually. Unfortunately, either Christmas holidays or the COVID 19 pandemic prevented a quick restart so several days of data are missing (see Fig. 3(a), January 2020). A comparison of the temperature measurements within the specimen with data recorded by a station operated by the German weather service (DWD) in the village of Baruth (≈ 8 km away from the specimen location) showed the necessity of interior temperature measurements. While large outside temperature changes are recognized within

(a)

(b)

Fig. 3. (Negative CWI Velocity change (blue) and concrete temperature change (orange) from November to the end of May (a) and for April (b), recorded in the center of the specimen.

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the specimen with some delay, small fluctuations do not diffuse inside the concrete. As we want to investigate the influence of temperature on the ultrasound signal, an analysis solely based on environmental temperatures would be certain to produce meaningless results. To investigate this temperature dependency, we compare the temperature measurements within the specimen with the CWI velocity change calculated with a fixed reference for a period from November 2019 to the end of May 2020 and all 342 source-receiver combinations. An analysis of the measurements for sensor combinations with long distances (> 1 m) shows a decrease of correlation caused by noise. Furthermore, some sensors seem to have coupling problems, causing energy loss when they are used as sources. Embedding sensors in an existing structure most likely increases the number of sensors with bad coupling, as proper bonding of new and old cement paste can not be entirely ensured. Figures 3(a) and 3(b) show the negative velocity change compared to the temperature for US Transducer combination ‘S11-R9’ and temperature sensor ‘T1’. The numbers correspond to the numbers in Fig. 2. The sensors are located in the middle of the specimen to reduce the influence of boundary reflections. The authors chose to display the negative velocity change to transform the indirect proportionality between temperature change and velocity change into a direct proportionality. The changes in velocity and temperature have a similar trend. Especially after the long blackout in the beginning of January, both graphs show a stunning accordance. In the zoomed plot Fig. 3(b) from April’s data the daily variations in a day-night cycle are visible in concrete temperature and velocity change. The apparent discrepancies for the early measurements in November and December were first interpreted as an instrument drift but as it is not continuing in the later measurements we attribute it to the continuous drying of the mortar used for refilling the boreholes. A general decrease in correlation until early January supports this hypothesis. The similarity of the temperature and velocity curves, especially from January onwards indicates a linear relationship between temperature and Ultrasound velocity change. Figure 4(a) shows the negative velocity change plotted over the temperature change for the data from January to the end of May. The general linear trend is obvious, but linear regression analysis for limited measurement periods in either cold or warm concrete results in a slightly different gradient compared to the regression for the entire data. An analysis of this gradient for all source-receiver combinations with acceptable signals shows a rate of velocity change between 0.03%K −1 and 0.05%K −1 with some outliers. Generally, a linear trend of decreasing velocity with increasing temperature is visible in the data, but needs to be analyzed over a long time to get a stable estimation of the gradient. As in an infrastructure monitoring tasks, temperature-induced differences are not of interest, since they are rarely resulting in permanent changes or damages, we want to remove those effects from the recorded data. With the gradient calculated by linear regression analysis, the long term trends can be removed (Fig. 5(a), blue curve). Comparing the original, uncorrected, data to the corrected

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(a)

(b)

Fig. 4. (a) Linear relation between concrete temperature and negative velocity change for the same sensor pair used in Fig. 3. Gradients are calculated for different periods. (b) The gradient of the linear relation for all source-receiver pairs with acceptable signal quality for the warm, cold, and the entire period.

curve, the seasonal differences between winter and spring/summer are mostly eliminated, while the low-frequency daily variations remain in the data. The coda signal used for this analysis senses a larger area, not only the direct path between source and receiver. Therefore, an analysis of the coda will contain information from the near-surface areas just like it contains information from the center. The temperature measurements, however, are taken in the center of the specimen only. Therefore, rapid daily variations affect the ultrasound signal stronger than the core temperature. This can be observed in Fig. 3(b). The temperature peaks and troughs are slightly delayed compared to the US results. This behavior is dependent on the position of sensors and the dimensions of the specimen. Therefore, to generalize monitoring for various geometries and setups, these influences should be removed just like the seasonal variations. As the negative CWI velocity change is a time series, a frequency analysis can help with this problem (Fig. 5(b)). This plot shows the frequency content of the velocity changes from January to May 2020. As a Fourier analysis requires equally spaced data, the data missing due to power outages was linearly interpolated. Besides the low-frequency variations with way less than one cycle per day, one can see peaks at one, two, and three cycles per day (cpd). While the one cpd peak is indicating the daily temperature variations, the higher frequency peaks are more difficult to interpret. They may be related to differences in heating and cooling rate for example, but need to be investigated more thoroughly in the future. As these variations are cyclic, they are not permanent. So for damage detection, we can remove them from the data by low-pass filtering. For filtering we used a fourth-order Butterworth low-pass filter with a cutoff frequency of 10 days. The data needed to be interpolated in the intervals the measurement system was malfunctioning. Therefore, we refrained from using higher cutoff frequencies, as we did not want to imply we have reliable information on cyclic behavior for this

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interpolated timespan. The filtered velocity change (black curve in Fig. 5(a)) still has some variations of approximately 0.1%, but the frequent daily variations are removed.

(a)

(b)

Fig. 5. (a) Negative velocity change from January to May as original data (orange), after removal of the linear temperature and velocity relation (blue) and after low pass filtering the corrected signal (black). (b) Frequency content of the velocity change from January to May.

5

Discussion and Outlook

The influence of temperature change on long term ultrasound measurements has been demonstrated. In the data we can see a decrease of velocity with an increase in temperature. This is expected, as material densities decrease with increasing temperature, and therefore, the wavespeed decreases. In the first months of measurement in the presented experiments, we assume that solidification of the mortar used for refilling the boreholes influenced the results. Therefore, when installing sensors in existing structures we propose to analyze the results of the first three months with caution, but further analysis of this topic is necessary. We calculated a rate of velocity change between 0.03%K −1 and 0.05%K −1 . This is in good agreement with Niederleithinger and Wunderlich [8] who calculated a rate of change of 0.05%K −1 in a temperature range between 0 ◦ C and 50 ◦ C in laboratory experiments. Earlier investigations - all with external sensors - have given values of 0.16%K −1 [1] and 0.33%K −1 [13]. Especially the last result was calculated for measurements with higher temperatures where material changes cannot be excluded. Using the linear relation between velocity change and temperature change can remove the seasonal variations, while short therm fluctuations remain. Ultrasound measurements using coda waves are affected by changes not only on the direct path between source and receiver. Temperature changes - when measured in the core of the specimen - are less sensitive to daily variations compared to the CWI results, as the temperature change requires time to diffuse into the core

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of the specimen. This is supported by the data presented in this experiment. For monitoring and damage detection in infrastructure, we want to remove those high-frequency daily variations from the data just like the low-frequency seasonal variations. This is achieved by a frequency analysis followed by lowpass filtering of the data. The resulting smoothed curve still indicates some long term variations, but the velocity change is on a corridor below 0.1% change. With this smoothed curve, reduced from most environmental effects, the detectability of permanent damaging events should be increased. With the dense sensor network in the presented specimen, this hypothesis will be tested in a future experiment after a full annual cycle has been recorded with the present setup. Furthermore, the remaining small changes will be investigated in the future work of the research group to eliminate as many uncertainties as possible on the way towards ultrasound monitoring of reinforced concrete structures. Acknowledgements. We would like to thank the German Research Foundation (DFG) for funding project FOR 2825. Furthermore, we would like to thank all project partners from TU Munich, Rhur University Bochum, and Bochum University of Applied Sciences.

References 1. Plan`es, T., Larose, E.: A review of ultrasonic coda wave interferometry in concrete. Cement Concrete Res. 53, 248–255 (2013) 2. Lobkis, O.I., Weaver, R.L.: Coda-wave interferometry in finite solids: recovery of p-to-s conversion rates in an elastodynamic billiard. Phys. Rev. Lett. 90(25), 4 (2003) 3. Poupinet, G., Ellsworth, W.L., Frechet, J.: Monitoring velocity variations in the crust using earthquake doublets: an application to the calaveras fault, California. J. Geophys. Res.: Solid Earth 89(B7), 5719–5731 (1984) 4. Roberts, P.: Development of the active doublet method for monitoring small changes in crustal properties. Seismol. Res. Lett 62(1), 36–37 (1991) 5. Snieder, R., Grˆet, A., Douma, H., Scales, J.: Coda wave interferometry for estimating nonlinear behavior in seismic velocity. Science 295(5563), 2253–2255 (2002) 6. Niederleithinger, E., Wang, X., Herbrand, M., M¨ uller, M.: Processing ultrasonic data by coda wave interferometry to monitor load tests of concrete beams. Sensors (Basel, Switzerland) 18(6) (2018) 7. Niederleithinger, E., Wolf, J., Mielentz, F., Wiggenhauser, H., Pirskawetz, S.: Embedded ultrasonic transducers for active and passive concrete monitoring. Sensors (Switzerland) 15(5), 9756–9772 (2015) 8. Niederleithinger, E., Wunderlich, C.: Influence of small temperature variations on the ultrasonic velocity in concrete. In: AIP Conference Proceedings, vol. 1511, pp. 390–397 (2013) 9. Ohdaira, E., Masuzawa, N.: Water content and its effect on ultrasound propagation in concrete - the possibility of NDE. Ultrasonics 38(1), 546–552 (2000) 10. Wang, X., Chakraborty, J., Bassil, A., Niederleithinger, E.: Detection of multiple cracks in four-point bending tests using the coda wave interferometry method. Sensors (Switzerland) 20(7), 1986 (2020)

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11. Barroso Fontoura, D., Epple, N., Niederleithinger, E.: Portable Low-Cost Ultrasound Measurement Device for Concrete Monitoring. Manuscript in preparation (2020) 12. Knopp, F., Mielentz, F., Bernstein, T.: Ultraschall-messsystem f¨ ur die langzeit¨ uberwachung von betonkonstruktionen. In: DGZfP Jahrestagung 2019. Friedrichshafen, Germany (2019). https://www.ndt.net/article/dgzfp2019/ papers/P11.pdf 13. Niederleithinger, E.: Seismic methods applied to ultrasonic testing in civil engineering. Habilitationsschrift, Rheinisch-Westf¨ alische Technische Hochschule Aachen, Aachen (2017)

Ultrasonic Wave Scattering at Liquid-Solid Interface by a Phased Array Sensor Using Distributed Point Source Method (DPSM) Apuroop Sai Vempati1 and Rais Ahmad2(&) 1

Department of Mechanical Engineering, California State University, Northridge, Los Angeles, CA 91330, USA [email protected] 2 Department of Civil Engineering and Construction Management, California State University, Northridge, Los Angeles, CA 91330, USA [email protected]

Abstract. Ultrasonic Phased Array technology has seen significant development in recent years. A phased array sensor generates stronger ultrasonic beam and facilitates beam steering without physically moving the sensor probe by controlling the excitation of the sensor’s piezo-electric elements. This allows faster and wider ultrasonic scanning compared to conventional sensors. The major challenge for ultrasonic beam focusing and beam steering by a phased array transducer is to control the excitation frequency. In this paper an efficient excitation frequency algorithm has been developed using Distributed Point Source Method (DPSM) to generate stronger, focused ultrasonic beam. DPSM is a mesh free semi analytical technique that has been used for solving a variety of engineering problems such as ultrasonic wave propagation in different mediums. Ultrasonic field calculation in two semi-infinite mediums like liquid-liquid and liquid-solid with regular ultrasonic sensors have already been calculated using DPSM. In this paper, a new approach of modeling liquid-solid interface by a phased array sensor using DPSM has been developed. For a phased array sensor, the angle and the strength of the acoustic beam depend on the number, placement and activation frequency of the point sources. An excitation algorithm for the point sources has been developed to generate strong focused acoustic beams. The time phasing is also used to calculate the acoustic field for a case where a phased array transducer is placed in a semi-infinite liquid which continues onto a semi-infinite solid medium. Keywords: Phased array sensor phase
Ultrasonic field


DPSM
Ultrasonic transducer
Time

1 Introduction Phased array ultrasonic technology has seen significant development for the last decade. Phased array sensors allow faster acoustic scanning compared to a conventional sensor. It produces stronger focused acoustic beam and facilitates the steering of the probe without physically moving the sensor. Distributed Point Source Method (DPSM) © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 276–285, 2021. https://doi.org/10.1007/978-3-030-64594-6_28

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is a method to calculate the ultrasonic field in different mediums. DPSM is a mesh free semi analytical technique that has been developed to model acoustic field in liquid and solid mediums and also its interface. This technique has already been used to model ultrasonic fields in homogeneous and multi-layered fluid structures. The method is extended to model the ultrasonic fields generated in both fluid and solid media near a fluid-solid interface when the transducer is placed in the fluid medium. In this research, phased array transducers are modeled to generated focused acoustic beams to propagate through liquid-solid interface using DPSM. A methodology is developed to calculate retarded time for excitation of point sources to produce a steerable beam with low number of grating beams and high steering ability. A good understanding of how ultrasonic waves, produced from phased array sensors, propagate through homogenous liquid has been well established and applied to the cases when only a single fluid medium is present. However very few studies have been carried out to study the factors effecting calculation of time phasing. In this research, we investigated the acoustic wave propagation phenomenon at the solid-fluid interface and in solid medium, when the transducer is time phased introducing steering ability of producing bulk wave in solid medium. Propagation of elastic waves through homogeneous, elastic media has been well established and applied to the cases when the transducer is immersed in fluid and the beam reflects and refracts at the solid-liquid interface. However, very few studies have been carried out on time phased transducer placed in fluid media. No one investigated yet the wave propagation in solids when the waves are generated by finite size phased array ultrasonic transducers as done in typical NDE testing. This unsolved problem is to be studied.

2 Phased Array Transducer Researchers are using phased transducers to develop improved and efficient ultrasonic inspection techniques for material inspection. Wooh and Shi [1], Azar and Wooh [2,3], studied the steering and focusing behavior of phased array transducers to investigate integrity of concrete structures. Mahaut et al. [4] developed phased matrix array probe to inspect coarse grain components in stainless steel. Yang et al. [5] developed Minimum Redundancy Linear Array Method to design sparse linear arrays. Kažys et al. [6,7], Jasiūnienė et al. [8] and Gavrilov [9] used phased array in medical application like internal surgery. Ahmad et al. [10] first developed a technique based on Distributed Point Source Method (DPSM), to model phased array transducers by discretizing the sensor surfaces with point sources and used optimized excitation sequence of the point sources to generate and steer strong focused beam in homogeneous fluid medium. The technique was developed for square sensors only.

3 DPSM for Modeling Phase Array Transducers Placko and Kundu [11] first conceptualized Distributed Point Source Method (DPSM) for modeling magnetic and acoustic sensors. In DPSM, the active surface of the transducer is discretized into an array of point sources. Each point source transmits a

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signal. The total signal is the superposition of the signals transmitted by each point source. For a phased array ultrasonic sensor Ahmad et al. [10] first derived the pressure field and the velocity vector are: pð xÞ ¼ v 1 ð xÞ ¼ ¼ v 2 ð xÞ ¼ ¼ v 3 ð xÞ ¼ ¼

XN m¼1

pm ðrm Þ ¼

  exp ikf rm  ixðt  Dtm Þ A m¼1 m rm

XN

ð1Þ

XN

v ðr Þ m¼1 1m m  Am x1m expðikf rm Þ ikf m¼1 ixq rm2

XN

1  rm

 exp ðix ðt  Dtm ÞÞ

ð2Þ

exp ðix ðt  Dtm ÞÞ

ð3Þ

XN

v ðr Þ m¼1 2m m  Am x2m expðikf rm Þ ikf m¼1 ixq rm2

XN

1  rm



XN

v ðr Þ m¼1 3m m  Am x3m expðikf rm Þ ikf m¼1 ixq rm2

XN



1 rm

 exp ðix ðt  Dtm ÞÞ ¼ v0

ð4Þ

The time lag Dtn can be obtained from the following equation, so that all signals arrive at point P at the same time: Dtn ¼

Dp Pmax  Pn ¼ c c

ð5Þ

Where, c is the velocity of the acoustic wave in the fluid medium; Pmax is the distance of the farthest point source from the target point and Pn is the distance of nth point source, respectively from point P. If the coordinates of target and concerned point source are (xt, yt, zt) and (xi, yi, zi) then Pn can be expressed as Pn ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð xt  xi Þ 2 þ ð yt  yi Þ 2 þ ð z t  z i Þ 2

ð6Þ

whereas, Pmax can be determined by the maximum Pn, so if the wave propagation starts at time 1 s the farthest point would have zero Dtn and for the rest of them the time phase increases as we go closer to the target point source. Velocity of the M target points distributed on the transducer face due to point sources distributed just below the transducer surface at a distance, can be written in the matrix form as: VS ¼ MSS AS

ð7Þ

Where the explanation of Vs, Mss and As are given in Kundu [12]. For a general set of target points located on any surface, the pressure and velocity due to the transducer sources can be written as:

Ultrasonic Wave Scattering at Liquid-Solid Interface

PRT ¼ QTS AS ; VT ¼ MTS AS

279

ð8Þ

Fig. 1. Conceptual pulsing sequence for point sources and time phase determination

For each nth point source Dtn is calculated and added to a base time t to avoid negative time. The harmonic time dependence of velocity and pressure fields in the fluid medium are expressed as separate matrices. The activation time for each point source is tn and the matrix TS is a N x N diagonal matrix with nth diagonal element being exp(−ixtn). ð9Þ

VS ¼ MSS TS AS 2 6 where; TS ¼ 6 4

expðixt1 Þ

0

0

..

0

0

0 .

0

3 7 7 5

ð10Þ

expðixtN Þ

Assuming that time phased active point sources create time phased passive sources let tm be the time at which mth passive point source on either liquid or solid side (of the interface) is activated. Both liquid (A1*) and solid (A1) passive sources are activated at the same time i.e., have the same tm for the mth passive point source as they are interface point sources offset into fluid and liquid medium to avoid singularity at the interface, but unlike the liquid passive sources the solid passive sources have three components x1, x2 and x3 (x, y and z). We assume that the time phasing affects only the x3 (z) component of the solid medium passive source. The harmonic time dependence can be multiplied to individual source strength matrices A1 and A1* as matrices T1 and T1*. 2 6 where; T1 ¼ 6 4

expðixt1 Þ

0

0

..

0

0

0 .

0 expðixtM Þ

3 7 7 5

ð11Þ

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4 Numerical Results and Discussion All numerical results are generated using Matlab based DPSM code developed based on the formulations presented in chapter 5. A 2.2 MHz transducer with area of 4 mm2 was considered. Transducer frequency and dimensions are based on possible future fabrications of phased array transducers. The material properties for aluminum and water (liquid and solid half space) used in numerical investigations are given in Table 1. Table 1. Properties of water and aluminum P-Wave speed in water (cf) Density of water (qf) P-wave speed in aluminum (cp) S-wave speed in aluminum (cs) Density of aluminum (qs) First Lamé constant of aluminum (k) Second Lamé constant of aluminum (l) Poisson’s ration of aluminum (m) Rayleigh wave speed in aluminum (cr) Rayleigh critical angle (hc)

4.1

= = = = = = = = = =

1.48 km/sec 1 g/cc 6.5 km/s 3.13 km/s 2.7 g/cc 55.56 GPa 24.95 GPa 0.345 2.923 km/s 30.4192 deg

Number of Point Sources

The problem geometry considered here has finite size transducer (square or circular with surface area of 4 mm2). The solid half-space dimensions in the in-plane and outof-plane directions are much greater than transducer side (or diameter). The ultrasonic field is computed on a plane that bisects the transducer and the solid half-space and thus forms a plane of symmetry of the problem geometry. One hundred (100) point-sources are distributed behind the square transducer face to model the transducer, and additional point sources are placed along the fluid-solid interface as shown in Fig. 2. On each side of the fluid-solid interface, 99 point-sources are distributed on the central plane. Sources are placed in the illuminated region of the interface and also well beyond the illuminated region. A total of 297 point-sources are then necessary on each side of the fluid-solid interface to model the problem geometry with three planes of point sources. Increasing the number of point sources to five planes of sources did not significantly improve the computed ultrasonic field in the central plane. The number of point sources necessary for proper convergence of the DPSM technique has been discussed in detail in Placko and Kundu [12]. The convergence of the problem solution has been also tested by increasing the number of point sources in the in-plane direction and at the transducer face. When the spacing between two neighboring point sources is less than one-third wave length, then the problem is always found to converge. Further increasing the number of point sources did not change the computed results significantly. For the results presented in this dissertation, the distance between two neighboring point sources has been kept at wavelength/p. Thus, the results presented here are

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well converged. Different coordinates and spatial distribution of point sources along the interface are shown in Fig. 2.

Fig. 2. Spatial distribution of active (red) and passive (blue) point sources when transducer is inclined at (a) 5°, (b) 15°, and (c) 30.42°.

4.2

Ultrasonic Field Computation

Results presented here show ultrasonic pressures inside the liquid half-space and in the solid half-space. Note that all stresses (S11, S33, and S31) are expressed in the x1-x3 coordinate system as shown in Fig. 3 (b), (c) and (d).1. It has been checked and verified that the compatibility conditions are satisfied across the fluid-solid interface. In the following figures, a side-scale bar is provided to give an idea of the magnitudes of the ultrasonic fields in the contour plots. Note that the scale bars are not identical in all figures. Ultrasonic fields are computed for a number of inclination angles of transducer (0º, 15º, and 30.42º angles) and for phased transducer a number of angles of incidence (5º, 15º, and 30.42º) measured from the normal to the liquid-solid interface. Results are presented in Figs. 3, 4 and 5. Plots are generated for the projected length of 40 mm on the x1 axis and 20 mm for x3 axis. Liquid half-space and solid half-space are each 10 mm in the x3 direction.

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Fig. 3. Field generated by a 2.2 MHz non-phased ultrasonic beam produced by a transducer inclined at 0° angle measured counter clockwise from x1-axis. Plots show (a) the pressure field in liquid half-space, (b) normal stress along x1-axis (S11), (c) normal stress along the x3-axis (S33), and (d) shear stress (S31).

The incident angle 30.42º is the critical angle (Rayleigh angle). The following figures show the ultrasonic field in aluminum and water at 2.2 MHz frequency. It should be noted that the applicability of the method includes a wide range of ultrasonic frequencies. A 2.2 MHz transducer was considered in the model as it is closest to existing sensors. Fluid pressure fields and contour plots of normal stresses (S11 and S33) as well as shear stress (S31) in the solid are plotted. The S11 and S33 are considered to be P-waves and S31 the S-wave. The solid half-space and liquid halfspace are plotted separately as the magnitude of incident beam and refracted vary a lot. The plotted coordinates are shifted in the direction beam is steered to accommodate for refracted wave. For phased transducer both layer-wise offset and point-wise offset are plotted. Figure 3 (a) shows the pressure field produced by the phased array sensor at an angle of 0° in the liquid half space. Figs. 3(a), (b) and (c) show the ultrasonic fields in the solid medium (aluminum) at 2.2 MHz frequency, for 0° inclination of the acoustic beam produced by the phased sensor. Fig. 4 (a) shows the pressure field produced by the phased array sensor at an angle of 15° in the liquid half space. Figs. 4(a), (b) and (c) show the ultrasonic fields in the solid medium (aluminum) at 2.2 MHz frequency, for 15° inclination of the acoustic beam produced by the phased sensor. In Figs. 3 and 4, the angles of incidence is less than the critical angle. When the focused beam angle of incidence is less than the critical angle, some energy is transmitted into the solid

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half-space. The wave speed in aluminum (6.5 km/sec) is much higher than that in water (1.48 km/sec). Therefore, when the ultrasonic beam hits the interface, it is expected that the transmitted field should propagate in aluminum with a higher transmission angle than the angle of incidence in water. In most cases above we observe a weak Rayleigh wave near the liquid-solid interface. This is due to some of the grating lobes hitting the interface at critical angle. It can also be observed that the magnitude of surface wave is lower than refracted beam as it’s a result of incident grating lobes.

Fig. 4. Field generated by a 2.2 MHz non-phased ultrasonic beam produced by a transducer inclined at 15° angle measured counter clockwise from x1-axis. Plots show (a) the pressure field in liquid half-space, (b) normal stress along x1-axis (S11), (c) normal stress along the x3-axis (S33), and (d) shear stress (S31).

Figure 5(a) shows the pressure field produced by the phased array sensor at an angle of 30.42° in the liquid half space. Figs. 5(a), (b) and (c) show the ultrasonic fields in the solid medium (aluminum) at 2.2 MHz frequency, for 30.42° inclination of the acoustic beam produced by the phased sensor. As mentioned in Table 1, 30.42° angle is the critical angle to produce Rayleigh wave along the fluid – solid (water – aluminum) interface. Figures 5(b–c) show the ultrasonic fields in aluminum and water at 2.2 MHz frequency, for critical angle 30.42º of incidence. The leaky waves in water are clearly visible in Fig. 5(b). The Rayleigh waves can be seen along the liquid-solid interface in Figs. (c) and (d). Note that the fluid pressure in the liquid half-space clearly shows a higher level of energy at the corner location.

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Fig. 5. Field generated by a 2.2 MHz non-phased ultrasonic beam striking the interface at 30.42° critical angle measured from x3-axis measured counter clockwise from x1-axis. Plots show (a) the pressure field in liquid half-space, (b) normal stress along x1-axis (S11), (c) normal stress along the x3-axis (S33), and (d) shear stress (S31).

5 Conclusion The effect of time phasing for interaction between a bounded ultrasonic beam and a liquid-solid interface is studied theoretically. The pressure field in the fluid half-space and the stress fields in the solid half-space due to time phased transducer are plotted using DPSM. The ultrasonic bounded beam generated by a transducer of finite-size is modeled to generate the Leaky Rayleigh wave at the fluid-solid interface. Results presented here show how phased transducers producing a focused beam inclined at the critical angle generate leaky guided waves at the interface. In the ultrasonic field surface plots presented here the transmission, reflection, and different interactions of the ultrasonic beams in the fluid and the solid can be observed visually. This study is important for analyzing and understanding the propagation of ultrasonic waves used for NDE weld inspections, and to study the effect of time phased sonic/ultrasonic transducers on beam steering ability. Acknowledgement. This research is partially funded by RSCA Grant from California State University, Northridge.

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References 1. Wooh, S.C., Shi, Y.: A design strategy for phased arrays. In: Thompson, D.O., Chimenti, D. E. (eds.) Review of Progress in Quantitative Nondestructive Evaluation, vol. 18A, pp. 1061– 1068. Plenum Press, New York (1999) 2. Azar, L., Wooh, S.C.: A novel ultrasonic phased arrays for the nondestructive evaluation of concrete structures. In: Thompson, D.O., Chimenti, D.E. (eds.) Review of Progress in Quantitive Nondestructive Evaluation, vol. 18B, pp. 2153–2160. Plenum Press, New York (1999) 3. Azar, L., Wooh, S.C.: Phase steering and focusing behavior of ultrasound in cementitious materials. In: Thompson, D.O., Chimenti, D.E. (eds.) Review of Progress in Quantitive Nondestructive Evaluation, vol. 18B, pp. 2161–2167. Plenum Press, New York (1999) 4. Mahaut, S., Godefroit, J.-L., Roy, O., Cattiaux, G.: Application of phased array techniques to coarse grain components inspection. Ultrasonics 42, 791–796 (2004) 5. Yang, P., Chen, B.: Shi, Ke-Ren, “A novel method to design sparse linear arrays for ultrasonic phased array.” Ultrasonics 44, e717–e721 (2006) 6. Kažys, R., Jakevičius, L., Mažeika, L.: Beamforming by means of 2D phased ultrasonic arrays. ULTRAGARSAS 1(29), 12–15 (1998). ISSN 1392–2114 7. Kažys, R., Kairiūkštis, L.: Investigation of focusing possibilities of convex and cylindrical phased arrays. ULTRAGARSAS (ULTRASOUND) 64(4), 46–51 (2008). ISSN 1392–2114 8. Jasiūnienė, E., Kažys, R., Mažeika, L.: Simulations of ultrasonic fields of radial ultrasonic array. ULTRAGARSAS (ULTRASOUND) 62(2), 44–50 (2007). ISSN 1392–2114 9. Gavrilov, L.R., Hand, J.W.: Two-dimensional phased arrays for surgery: movement of a single focus. Acoust. Phys. 46(4), 390–399 (2000). Translated from AkusticheskiÏ Zhurnal 46(4), 456–466 (2000). 10. Ahmad, R., Kundu, T., Placko, D.: Modeling of Phased Array Transducers. J. Acoust. Soc. Am. 117, 1762–1776 (2005) 11. Placko, D., Kundu, T.: A Theoretical study of magnetic and ultrasonic sensors: dependence of magnetic potential and acoustic pressure on the sensor geometry, advanced NDE for structural and biological health monitoring. In: Kundu, T. (ed.) SPIE's 6th Annual International Symposium on NDE for Health Monitoring and Diagnostics, 4–8 March 2001, Newport Beach, California (2001) 12. Placko, D., Kundu, T.: DPSM for Modeling Engineering Problems. in Ch. 3 Ultrasonic Modeling Fluid Media and Ch. 4. Advanced Applications od Distributed Point Source Method – Ultrasonic Field Modeling in Solid Media

Multi-type Sensor Placement for Structural Health Monitoring of Tied-Arch Bridges Bartlomiej Blachowski(&) , Andrzej Swiercz , Mariusz Ostrowski , Piotr Tauzowski , and Lukasz Janowski Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5b, 02-106 Warsaw, Poland [email protected]

Abstract. Performance of any Structural Health Monitoring (SHM) system strongly depends on a set of sensors which are distributed over the structure under investigation. Optimal deployment of sensors on large scale structures such as tied-arch bridges is quite a challenging problem. Moreover, deployment of a sensor network consisting of different types of sensors (accelerometers, inclinometers or strain gauges) over a large scale bridge renders the task of optimization even more demanding. In the present study, a conventional sensor placement method for distribution of a homogenous sensor network is expanded to the heterogeneous case. First, the basic equations governing the estimation error will be recalled. Then, the Fisher information matrix is assembled using normalized translational and rotational mode shapes. Finally, a computational procedure is proposed which allows optimal sensor positions to be selected among thousands candidate locations. The effectiveness of the proposed strategy is demonstrated using a realistic example of a tied-arch bridge located in Poland. Keywords: Optimal sensor placement
Structural health monitoring Tied-arch bridges
Multi-type sensor network




1 Introduction Performance of any Structural Health Monitoring (SHM) system strongly depends on the set of sensors distributed over the structure under investigation [1–3]. Especially, deployment of such sensors on large scale structures such as tied-arch bridges constitutes a challenging problem [4, 5]. Displacements of the monitored structure under operational loads are most frequently utilized for its condition assessment. Direct displacement measurements can be conducted in various ways among which referencebased methods such as linear variable differential transducer (LVDT), laser Doppler vibrometer (LDV) or vision-based systems are representative examples. However, in the case of bridges the above mentioned methods are sometimes problematic and not easy to implement. For this reason, besides reference-based displacement transducers often other types of sensors are used, which do not require reference points, such as: strain gauges [6, 7], accelerometers [9–11] and inclinometers [5]. These sensors can still provide information about displacements, but with the aid of sophisticated numerical integration and filtering techniques. Moreover, deployment of sensor © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 286–297, 2021. https://doi.org/10.1007/978-3-030-64594-6_29

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network constituting of different types of sensors over a large scale bridge makes the task of optimization even more demanding [12]. Recently, the challenges and advances in the field of sensor placement problems dedicated to the SHM area, are reviewed in the excellent papers [14, 15]. In the present study, a previously proposed method for distribution of homogenous sensor network [13] is expanded to the heterogeneous case. Effective computational procedure is proposed for selection of the optimal sensor positions among thousands candidate locations. The effectiveness of the proposed strategy is demonstrated on a realistic example of a tied-arch bridge located in Poland.

2 Response Estimation Error for Multi-type Sensor Arrangement Before solving the optimal sensor placement problem, appropriate metrics have to be defined. One of the most frequently used is the determinant of the Fisher information matrix. The basic information about this metric and formulation of the sensor placement as a discrete optimization problem is briefly recalled in this section. We start the derivation of the methodology for multi-type sensor placement with the equations that represent the response vector of the structure 2

U

3

6 7 yðtÞ ¼ 4 W 5gðtÞ C

ð1Þ

where y 2 Rnd þ nr þ ne , U 2 Rnd nm , W 2 Rnr nm , C 2 Rne nm and g 2 Rnm . Indices nd ; nr ; ne and nm denote the number of translational DOFs of the FE model, the number of rotational DOFs, the number of finite elements at which strains can be evaluated and the number of modes, respectively. The overall vector yðtÞ consists of nc candidate sensor locations, where nc ¼ nd þ nr þ ne . Equation (1) describes structural response in the absence of measurement noise. In reality, however, signals from different sensors are corrupted by different level of noise and the corresponding equations take the following form 2

3 2 3 wd ðtÞ Us 6 7 6 7 ys ðtÞ ¼ 4 Ws 5gðtÞ þ 4 wr ðtÞ 5 Cs

ð2Þ

we ðtÞ

where ys 2 Rns , Us 2 R~nd nm , Ws 2 R~nr nm , Cs 2 R~ne nm and wd 2 R~nd , wr 2 R~nr . Matrices Us ; Ws contain selected components of the modal matrix U associated with translational and rotational DOFs, respectively. Cs is a strain-displacement transformation matrix. Vectors wd ; wr and we represent measurement errors of displacement, inclination and strain sensors, respectively. Index ns is the overall number of multi-type sensors and index ~nd is reduced number of displacement sensors.

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Assuming that the number of sensors is equal to or greater than the number of modal coordinates to be identified, i.e., ns  nm , we can determine the least square estimate of modal coordinates using Eq. (2), namely 0  ~gðtÞ ¼ B @ UTs

2 311 Us  7C  T T 6 Cs 4 Ws 5A Us Cs

WTs

2

Us

3þ

 6 7 CTs ys ðtÞ ¼ 4 Ws 5 ys ðtÞ Cs

WTs

ð3Þ

where matrix UTs Us is called the Fisher information matrix (FIM) and constitutes an essential part of Eq. (3). Using Eq. (3), an estimate of the total structural response (measured and unmeasured quantities) can be determined as follows 2

U

3

2

U

32

Us

3þ

6 76 7 6 7 ~yðtÞ ¼ 4 W 5~gðtÞ ¼ 4 W 54 Ws 5 ys ðtÞ C C Cs

ð4Þ

After basic manipulations, the difference between the actual structural response obtained from Eq. (1) and its estimate from Eq. (4) allows to determine the estimation error 2

32 3 þ 2 3 U Us wd ðtÞ 6 76 7 6 7 eðtÞ ¼ ~yðtÞ  yðtÞ ¼ 4 W 54 Ws 5 4 wr ðtÞ 5 C

Cs

ð5Þ

w e ðt Þ

and the covariance matrix of the estimation error takes the following form n

E eðtÞeðtÞT

o

3 3 þ 82 > < w d ðt Þ  7 6 76 7 6 ¼ 4 W 54 Ws 5 E 4 wr ðtÞ 5 wd ðtÞT > : C we ðtÞ Cs 2

U

32

Us

wr ðtÞT

90 2 3 2 3 þ 1 T Us U > =  B 6 76 7 C @ 4 W 54 W s 5 A we ðtÞT > ; Cs C

ð6Þ of the measurement noise have zero mean hAssuming that the icomponents  nh T E w d ðt ÞT w r ðt ÞT w e ðt ÞT ¼ 0 and uncorrelated covariance matrix E wd ðtÞT h i   the covariance wr ðtÞT we ðtÞT T wd ðtÞT wr ðtÞT we ðtÞT g ¼ diag r2d I nd ; r2r I nr ; r2e I ne matrix from Eq. (6) takes the following form n

E eðtÞeðtÞT

o

3þ 8 2 > < rd I nd 6 76 7 ¼ 4 W 54 Ws 5 0 > : Cs C 0 2

U

32

Us

0 2 rr I nr 0

9 02 3 2 3 þ 1T U Us 0 > = B6 7 6 7 C 0 @4 W 5 4 W s 5 A > ; 2 C Cs re I ne

ð7Þ

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where r2d ; r2r and r2e denote the noise variances specific to the displacement, inclination and strain sensors, respectively. From the formula (7) it can be easily observed that by selection of proper measurement locations we can influence the overall estimation error. To compare two different sensor configurations, certain metrics should be defined first. Two such metrics are frequently used in the literature. The first one is based on the trace of the covariance matrix (7) r2e;avg ¼

 n o 1 trace E eðtÞeðtÞT ns

ð8Þ

Formula (8) gives information about average estimation error. The second metric is related to the maximal element on the diagonal of the covariance matrix n  n o o r2e;max ¼ max diag E eðtÞeðtÞT

ð9Þ

and corresponds to the component of the response vector with the largest estimation error for a given sensor configuration. The above metrics (Eqs. 8 and 9) can be incorporated into the optimization process for finding the best achievable sensor locations.

Fig. 1. Number of degrees of freedom used in different types of finite elements.

Depending on the type of the elements used in the discretization of the structure, different types of quantities can be present in the numerical model of the structure. In the case of spatial finite elements, these quantities can represent translational and rotational degrees of freedom. In Fig. 1 two most frequently used shell and brick elements and corresponding DOFs have been shown.

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3 Numerical Example The algorithm discussed in Sect. 2 for the optimal sensor placement problem is now illustrated using a numerical model of a real bridge located in central Poland. For this example we assume two types of sensors: displacement sensors and inclinometers with possible alignment with 3 global axes of the structure: X–direction (longitudinal), Y– direction (transversal) and Z–direction (vertical). Firstly, the overview of the structure and its numerical model are presented, then results of modal analysis are shown, and finally, the sensor arrangements under various criteria are discussed. 3.1

Numerical Model

The presented multi-type sensor placing technique was tested using a numerical model of the tied-arch railway bridge. The simply supported structure is 75-m long with boxsectioned main girders transversally connected by lateral bracings (arch part) and cross beams linking horizontal girders. The railway track is placed on the railway ballast supported by concrete slab arranged on the cross-beams. The general view of the modelled structural parts is presented in Fig. 2. The main parts of the steel structure were modeled using shell elements, the hangers and the railway track using beam elements and the concrete slab and the railway ballast by means of volume elements. In all, the numerical model was composed of 68 195 finite elements and 71 804 nodes.

Fig. 2. Modelled structural parts of the railway bridge using Abaqus software.

3.2

Modes Selection

The created numerical model was applied for computation of the first 100 mode shapes, natural frequencies and effective masses. Figure 3 presents selected mode shapes with

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the highest contribution to the total effective mass in the longitudinal (X), transversal (Y) and vertical (Z) directions. The applied color map refers to the vertical modal displacements (Z–direction, red – positive and blue – negative displacement). The natural frequencies and the mode numbers corresponding to the presented mode shapes are summarized in Table 1. The explicit bending modes are illustrated in Figs. 3b, 3e, 3f, 3j and the torsional in Figs. 3a, 3d, 3h, however most of them are of mixed type. The omitted mode shapes are related to local vibrations, mostly of hangers. Table 1. Determined frequencies corresponding to the mode shapes shown in Fig. 3. Mode no. 1 2 3 4 5 31

Frequency [Hz] 0.95 1.84 2.17 2.56 3.15 5.13

Note in Fig. 3 (a) (b) (c) (d) (e) (f)

Mode no. 34 35 38 39 48 49

Frequency [Hz] 5.19 5.23 6.75 6.93 7.61 9.40

Notation in Fig. 3 (g) (h) (i) (j) (k) (l)

Mode no. 50 71 72 74 94 96

Frequency [Hz] 9.51 11.63 11.65 11.73 14.88 16.89

Note in Fig. 3 (m) (n) (o) (p) (q) (r)

In Fig. 4, the modal effective masses aggredated for translational (Fig. 4a) and rotational (Fig. 4b) DOFs are presented. The total modal effective mass for all computed modes (i.e. 100) was 90.4%, 89.7% and 89.4% of the structural mass in the X–, Y– and Z– translational directions, respectively. The numerical model was utilized for computation of the modal submatrices U and W. The crucial point for the sensor placement algorithm is the selection of modes. Depending on the application, the modes of interest can be addressed to: a) Monitoring of the general behavior of structures, b) Assessment of vertical displacements caused by (mainly) vertical in plane bending deformations of the bridge, c) Monitoring of torsional and out-of-plane vibration, e.g., for monitoring of flexible structures, d) Evaluation of the vertical displacement of the structure under operational applied loadings, e) Assessment of bending and torsional vibrations used for monitoring of structural response perturbations and damage detection.

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

(m)

(n)

(o)

(p)

(q)

(r)

Fig. 3. Selected mode shapes of the bridge; the corresponding frequencies are listed in Table 1.

The analyses were performed focusing on the following mode sets: • Set A containing 11 modes grouping modes with the highest values of the modal effective masses for the translational and rotational DOFs; selected modes no.: 1, 2, 3, 4, 5, 31, 34, 35, 39, 50 and 94. • Set B containing 12 modes grouping the bending modes with the highest values of the modal effective masses in Z-direction (translation) and Y-direction (rotation); selected modes no.: 2, 5, 31, 34, 35, 39, 50, 70, 71, 72, 74 and 96. • Set C containing 7 modes grouping the torsional modes with the highest values of the modal effective masses in Y-direction (translation) and Z-direction (rotation); selected modes no.: 1, 3, 4, 38, 48, 49 and 94. • Set D containing 5 mode shapes as proposed in [12] for response reconstruction of operational loading; selected modes no.: 2, 5, 31, 39 and 50.

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• Set E containing 12 mode shapes as proposed in [12] (extended previous mode set); selected modes no.: 1, 2, 3, 5, 26, 31, 39, 48, 50, 59, 60 and 75. (a)

(b)

Fig. 4. Aggregated effective masses corresponding to (a) translational and (b) rotational DOFs computed for the first 100 modes.

In Subsect. 3.3 there are considered two variants for the optimal sensor placement problem: (i) in the original form by application of Effective Independence approach and (ii) in the modified one by weighting the modal submatrices U and W. 3.3

Sensor Placement Results

Recall, two types of sensors are used: displacement sensors and inclinometers to be placed in some locations and aligned longitudinally, transversally or vertically. The zones for the possible sensor locations are limited to the horizontal girders (bottom flanges) and the arch girders (top flanges) as marked by the red dots in Figs. 5 and 6. The number of possible locations is 954 for each type of sensor and for each direction. The results obtained for the mode sets A–E by using the classical EFI approach are presented in Fig. 5. The displacement sensor locations and their directions are marked by the single-arrowed blue lines, whereas the arrangements of the inclinometers are denoted by the double-arrowed green lines. Generally, the number of the displacement sensors is much higher than inclinometers. Thus, the applied algorithm preferably selects the displacements sensors over inclinometers.

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In order to differentiate the composition of sensor number of each group (e.g., due to sensor costs), the weighting of modal submatrices U and W can be applied by ~ computed as follow ~ and W replacing them in Eq. (7) with normalized submatrices U ~F ¼ U

U ~ F ¼ W or U ~2 ¼ W ~2 ¼ U ; W ;W kU kF kW kF kU k2 kW k2

where the symbols k
kF and k
k2 denote the Frobenius and the 2-norm, respectively. ~ 2 are normalized vector-wise. ~ 2 and W Note that the submatrices U

(a)

(b)

(c)

(d)

(e)

Fig. 5. Multi-type sensor locations obtained for mode sets: A–E (original formulation).

The sensor locations determined for the same mode sets A–E using the modified EFI algorithm are presented in Fig. 6. The left column shows the outcomes computed with normalization by the Frobenius norm, whereas the right column – by the 2-norm. The displacement sensor locations and their directions are marked in the same fashion as in the previous example. The applied modified EFI algorithm delivers a comparable number of sensors of both types.

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Fig. 6. Multi-type sensor locations obtained for mode sets: A–E (weighting formulation).

4 Conclusions The present work investigates the problem of optimal placement of multi-type sensors. Starting from the estimation theory, it has been shown that the estimation error of the overall measurement system depends on the Fisher information matrix (FIM). Similarly as in the conventional Effective Independence method, it has been shown that maximization of the determinant of the FIM reduces the estimation error. However, contrary to single-type sensor placement problem, in the case of multiple-type sensors one has to take into account that the measured quantities are in different physical units and contaminated by different measurement errors. For this reason special normalization is

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required. Finally, the effectiveness of the proposed methodology has been demonstrated on the example of a real-scale arch bridge represented by thousands of candidate locations and one hundred of mode shapes. Acknowledgements. The authors gratefully acknowledge the support of the National Science Centre, Poland, granted under grant agreements 2018/31/B/ST8/03152 and 2017/26/D/ST8/ 00883, and of the National Centre for Research and Development, Poland, granted in the framework of the PBS programme (PBS3/B9/36/2015).

References 1. Kammer, D.C., Tinker, M.L.: Optimal placement of triaxial accelerometers for modal vibration tests. Mech. Syst. Sig. Process. 8(1), 29–41 (2004) 2. Papadimitriou, C., Lombaert, G.: The effect of prediction error correlation on optimal sensor placement in structural dynamics. Mech. Syst. Sig. Process. 28, 105–127 (2012). https://doi. org/10.1016/j.ymssp.2011.05.019 3. Mahjoubi, S., Barhemat, R., Bao, Y.: Optimal placement of triaxial accelerometers using hypotrochoid spiral optimization algorithm for automated monitoring of high-rise buildings. Autom. Constr. 118, 103273 (2020) 4. An, Y., Chatzi, E., Sim, S., Laflamme, S., Blachowski, B., Ou, J.: Recent progress and future trends on damage identification methods for bridge structures. Struct. Control Health Monit. 26(10), e2416-1-30 (2019). https://doi.org/10.1002/stc.2416 5. Olaszek, P., Wyczalek, I., Sala, D., Kokot, M., Swiercz, A.: Monitoring of the static and dynamic displacements of railway bridges with the use of inertial sensors. Sensors 20(10), 2767-1-24 (2020). https://doi.org/10.3390/s20102767 6. Swiercz, A., Kolakowski, P., Holnicki-Szulc, J.: Identification of damage using low frequency harmonics in trusses and beams. In: Proceedings of ISMA2006: International Conference on Noise and Vibration Engineering, vol. 4, pp. 2137-2144 (2006) 7. Kolakowski, P., Sekula, K., Swiercz, A.: A concept of long-term monitoring of a railway truss bridge excited by trains. In: Proceedings of the 4th European Workshop on Structural Health Monitoring, pp. 175–182 (2008) 8. Blachowski, B., Swiercz, A., Jankowski, L.: Virtual Distortion Method based optimal sensor placement for damage identification. In: ISMA 2018/USD 2018, International Conference on Noise and Vibration Engineering/International Conference on Uncertainty in Structural Dynamics, pp. 3815–3824 (2018) 9. Blachowski, B., Swiercz, A., Pnevmatikos, N., Experimental verification of damage location techniques for frame structures assembled using bolted connections. In: COMPDYN 2015 5th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, pp. 2588–2599 (2015) 10. Blachowski, B., Swiercz, A., Gutkiewicz, P., Szelazek, J., Gutkowski, W.: Structural damage detectability using modal and ultrasonic approaches. Measurement 85, 210–221 (2016) 11. Blachowski, B.: Modal sensitivity based sensor placement for damage identification under sparsity constraint. Periodica Polytechnica Civil Eng. 63(2), 432–445 (2019) 12. Blachowski, B., Swiercz, A., Ostrowski, M., Tauzowski, P., Olaszek, P., Jankowski, L.: Convex relaxation for efficient sensor layout optimization in large‐scale structures subjected to moving loads. Computer-Aided Civil Infrastructure Eng. 1–16 (2020). https://doi.org/10. 1111/mice.12553

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13. Blachowski, B., Ostrowski, M., Tauzowski, P., Swiercz, A., Jankowski, L.: Sensor placement for structural damage identification by means of topology optimization. AIP Conf. Proc. 2239(1), 020002-1-11 (2020). https://doi.org/10.1063/5.0007817 14. Ostachowicz, W., Soman, R., Malinowski, P.: Optimization of sensor placement for structural health monitoring: a review. Struct. Health Monit. 18(3), 963–988 (2019). https:// doi.org/10.1177/1475921719825601 15. Tan, Y., Zhang, L.: Computational methodologies for optimal sensor placement in structural health monitoring: a review. Struct. Health Monit. 19(4), 1287–1308 (2020). https://doi.org/ 10.1177/1475921719877579

Detection of Earthquake-Induced Damage in Building Structures Using Earthquake Response Data Punit Kumar(&), Ankur Gautam(&), and Suparno Mukhopadhyay(&) Indian Institute of Technology, Kanpur, Kanpur 208016, UP, India {patelpk,suparno}@iitk.ac.in, [email protected]

Abstract. Under strong ground shaking, buildings may suffer damages leading to strength and/or stiffness degradation. The estimates of such damages may be obtained by identifying parameters, defining a nonlinear model of the building behavior, from the measured building vibration responses under seismic excitation. There are several techniques, like nonlinear system identification and finite element model updating, which may be used in detecting such damages. However, these methods are typically computationally expensive, and often have associated convergence issues. In this work, a fast damage detection technique is developed for detecting damages in buildings under seismic excitations. The method uses the vibration responses of the building recorded during the seismic event, along with the measured ground motion. This measured data is used to estimate the Park and Ang damage index, representative of the level of damage in the building. The ductility demand and hysteretic energy dissipation, necessary in defining this index, are directly estimated from the measured data, bypassing the need of structural parameter identification, making the damage detection computationally faster. The method is illustrated using experimental data from a laboratory scale four story reinforced concrete frame, progressively damaged through shake table tests, with the inter-story hysteretic behavior modeled using the Bouc-Wen model. Keywords: Seismic damage detection
Bouc-Wen model
Ductility demand
Hysteretic energy
Park-Ang damage index
Shake table tests

1 Introduction When a structure is subjected to strong earthquakes, it may suffer some damages. The damage may be visible or invisible. Visible damage can be cured, when possible, using appropriate retrofitting strategies. But if the damage caused in a structure after an earthquake is invisible, and no retrofitting is adopted, then it is possible that owing to further increase in damage the structure collapses even under weak earthquakes like aftershocks. So, there is a strong need to develop fast easily applicable methods to detect such damages. In vibration based structural health monitoring, the detection of such damages is typically done by using the measured vibration responses of the structure during the earthquake, employing techniques like system identification and finite element model © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 298–305, 2021. https://doi.org/10.1007/978-3-030-64594-6_30

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updating. However, since the structural behavior during strong damaging earthquakes is typically nonlinear due to strength and/or stiffness degradation, one needs to engage into nonlinear system identification techniques to identify the structural parameters using earthquake response data. Some typical nonlinear system identification methods which are suitable for this purpose include the methods based on the Kalman filter (Yang et al. [6], Chatzi and Smyth [1]). However, these techniques often have convergence issues and are computationally expensive. To overcome the challenges posed by nonlinear system identification techniques for seismic damage monitoring, an alternative quick damage detection technique is developed here for application in reinforced concrete (RC) structures. This technique uses measured acceleration responses of the structure along with the measured ground motion to estimate the ductility demand and hysteretic energy dissipation of the structure during the earthquake. These estimates are then used to calculate the Park and Ang damage index (Park and Ang [5]), a widely popular index representative of the level of damage in RC structures. This direct evaluation of damage-index from the measured acceleration response and the ground motion data, circumventing the need of nonlinear structural identification, makes the proposed approach computationally faster. The proposed method is presented in the next section, and is then illustrated using shake table tests on a four storied RC frame progressively damaged using ground motions of gradually increasing intensities.

2 Proposed Methodology Complex nonlinear behavior of structures, exhibiting varying types of hysteresis, can be modeled via the Bouc-Wen model of hysteresis (Foliente [2], Chatzi and Smyth [1]), and this model has naturally found applicability in modeling the nonlinear seismic behavior of structures (Goda et al. [3]). For an N-storied building, modeled as an Ndegree of freedom system, the inter-story restoring forces under earthquake motion is modeled here using the Bouc-Wen model. The equation of motion in terms of the interstory displacements can then be written as: M€ u þ Cu_ þ Kau þ KðI  aÞz ¼ m€ ug

ð1Þ

where u = {u1 u2 … uN}T denotes the vector of inter-story displacements with ui being the ith inter-story displacement; €ug is the ground acceleration; z = {z1 z2 … zN}T is the vector of hysteretic displacements; a is a diagonal matrix with any ith diagonal entry, a i, being the ratio of post-yield to initial stiffness of the ith story; I is the identity matrix; and M, C and K are the mass, damping and stiffness matrices, as given in Eq. (2): 2

m1 6 m M¼6 4 2 .. .

0

0

m2 .. .

0 .. .

3


7


7 5;




2

c1 6 0 C¼6 4 .. .

c2

0

c2 .. .

c3 .. .

3


7


7 5;




2

k1 6 0 K¼6 4 .. .

k2

0

k2 .. .

k3 .. .

3


7


7 5




ð2Þ

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with mi being the mass of the ith floor, and ci and ki being the damping and stiffness, respectively, of the ith story; and m = {m1 m2 … mN}T. As in case of a single degree of freedom system (Goda et al. [3]), a simultaneous differential equation, involving the hysteretic energy (e) and the shape, strength degradation, stiffness degradation and pinching parameters, relates the hysteretic displacement for any ith story, zi, to the inter-story displacement ui. Now, let the yield displacement of any ith story be denoted by uyi. Then, dividing each equation in Eq. (1) by the yield displacement of the corresponding story, we get: M€ l þ Cl_ þ Kal þ KðI  aÞlz ¼ PðtÞ

ð3Þ

where l = {l1 l2 … lN}T, any li = ui/uyi, l z = {lz1 lz2 … lzN}T, any lzi = zi/uyi, P(t) = {P1(t) P2(t) … PN(t)}, and any Pi (t) = – mi €ug /uyi. We assume that the post-yield to initial stiffness ratios and the yield displacements are known for each story, for example, from a pushover analysis using an initial finite element model. We also assume that all the floor accelerations and the base motion are measured. Then the Newmark-beta integration scheme can be used to estimate l_ and l €, while balancing Eq. (3) would give an estimate at any time instant from the known l of the normalized restoring force vector (f s ) at any time instant tk as: _ k Þ f s ¼ K1 ½Pðtk Þ  M€ lðtk Þ  Clðt

ð4Þ

Noting that the normalized restoring force for the ith story, fsi, is related to li and lzi as: fsi ¼ ai li þ ð1  ai Þlzi

ð5Þ

we can estimate the normalized hysteretic energy dissipation at story i up to time tk as: Z eni ¼

tk

Z l_ i fsi dt  ai

0

tk

l_ i li dt

ð6Þ

0

The maximum hysteretic energy dissipation, enmax ;i , can then be computed by taking tk to be the total duration of the response. The ductility demand of the ith story, lmax;i , can be computed as the maximum absolute value of li . Finally, the Park and Ang damage index can be estimated for any ith story as: DPA;i ¼

lmax;i lcapacity;i

þ

dE enmax ;i lcapacity;i

ð7Þ

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where lcapacity;i is the ductility capacity of the story under monotonic loading (which can be estimated based on an initial finite element model) and dE is a coefficient for cycling loading effects on overall damage severity (which is typically available from past experimental studies). Generally, a value of DPA up to 0.2 indicates slight damage, between 0.2 to 0.5 indicates moderate damage, between 0.5 to 1.0 indicates severe damage, and DPA  1.0 indicates collapse damage state. Since the computations involved in Eqs. (4) to (7) are based only on the past and current data, the method can be used to continuously update the damage index with the arrival of new data at each successive time instants. Hence, the method has the potential to be applicable in a near real-time condition. However, there will be some time delay due to the computational time required to process the new data at each time instant.

3 Shake Table Experimental Application To illustrate the proposed methodology, a suite of shake table experiments was performed on a laboratory scale four story reinforced concrete frame (Fig. 1). The floor plan of the model was 535 mm X 750 mm. The height of each story was 440 mm. Beam and column cross-sections were 35 mm X 60 mm and 50 mm X 35 mm, respectively. Thickness of each slab was 60 mm. Fe500 (yield strength 500 N/mm2) steel bars of diameter 3 mm, 4 mm and 6 mm were used as reinforcements in the model. After placing the bars in the formwork, casting was done in a single day storywise. Renderoc RG micro-concrete and sand, in 60%–40% ratio, were mixed to prepare the mortar, using water to mixture ratio of 0.16. The mean 7 day compressive strength of the concrete mix, tested on 70.7 mm cube samples, was found to be 24.40 N/mm2 . Pushover analysis was done in SAP2000 to get a (= 0.20) and uy (= 2.8 mm) for all the stories. The ductility capacity was assumed to be 6, and dE was taken as 0.10 based on previous studies on concrete structures [5]. The shake table tests were performed on the eighth day from casting using the 1.2 m  1.8 m platform uniaxial servo-hydraulic shaking table facility, with 40 kN maximum payload capacity, at the Structural Engineering Laboratory of IIT Kanpur, India. The building was instrumented with 10 strain gauge based accelerometers from Honeywell (https://www.honeywell.com/), located at the floor levels and base, with two accelerometers per floor, as shown in Fig. 1. The data was recorded using a 24-bit Data Acquisition System from National Instruments (https://www.ni.com/en-in.html), at a sampling frequency of 2000 Hz. The tests were performed using the ground motion recorded at the Chihaya station during the Kobe 1995 Earthquake, after time scaling, and after multiplication with different scale factors to have successively stronger peak ground accelerations (PGA), ranging from 0.1 g to 2.4 g. Between each test with the scaled ground motion, a white noise base excitation, of 0.05 g PGA, was also applied to estimate the gradually damaged modal parameters using linear system identification.

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The measured acceleration data from the white noise tests were used for modal identification using the Eigensystem Realization Algorithm [4]. Fig. 2 shows the variation of the identified damping ratio and frequency for the first mode against increasing intensities of the applied Kobe earthquake ground motions. As expected, the frequency decreases as the frame gets progressively damaged with increasing intensity motions. It is also observed that the damping ratio increases with damage. This is possibly due to increased energy dissipation from local crack opening/closing, and increased friction between damaged concrete.

Fig. 1. Four story RC frame used in shake table experiments (red circles denote accelerometer locations)

After each white noise test, the identified modes from the white noise test data are used to estimate the current story stiffness based on the associated eigenvalue problem, as well as the story damping coefficients. These estimated values are then used, along with the recorded accelerations in the subsequent earthquake test, to estimate the Park and Ang damage index after that earthquake test using the proposed method. Figure 3(a) shows the gradual increase of the estimated damage index with the increase in the

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intensity of the applied earthquake motions, along with some pertinent visual observations of damage that were noticed at certain PGAs. It can be seen that the appearance of the first hairline cracks at Floors 1 and 2 at PGA 0.4 g, the damage index of Floor 2 shows a slight jump. Similarly, with the appearance of damage at the joints of Floor 3 at the PGA of 0.9 g, the corresponding damage index of Floor 3 shows a jump. At 1.6 g PGA, all the joints of Floors 2, 3 and 4 showed damages, with the corresponding damage indices increasing significantly. The damages at Floors 3 and 4 observed during this earthquake test with 1.6 g PGA are shown in Figs. 3(b) and 3(c). From these results it is evident that the damage indices estimated from the measured acceleration data can capture the occurrences of the actual damages in the structure with reasonable accuracy.

Fig. 2. Variation of identified: (a) damping ratio and (b) natural frequency, of first mode, with increasing intensity (PGA) of applied Kobe earthquake motions

To illustrate the sensitivity of the estimated damage indices to a and uy , the variation of the damage indices of Floor 2, with increasing PGA of the applied motions, is shown in Fig. 4 for different values of a and uy . It is evident that while a does not affect the final estimated damage index significantly, the effect of uy is significant and increases with damage. Hence, the uncertainty in the estimated damage index due to an uncertainty in the estimated uy should also be accounted for, to obtain reliable estimates of the damage.

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(a)

(b)

(c)

Fig. 3. (a) Variation of damage index with intensity (PGA) of applied ground motions, (b) visual observation of damage at Floor 3, and (c) visual observation of damage at Floor 4

Fig. 4. Variation of damage-index of second floor for different: (a) uy and (b) a, with increasing intensity (PGA) of applied Kobe earthquake motions

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4 Conclusion In this paper, a fast damage detection technique is developed for RC structures, which bypasses the need of nonlinear system identification. The proposed methodology is illustrated using shake table tests on a four story RC frame which is progressively damaged by using ground motions of gradually increasing intensities. The first modal frequency and the damping ratio, identified from intermediate white noise tests, are found to decrease and increase, respectively, with increasing intensity motions as the frame gets progressively damaged. The increment in damping is possibly due to the increased energy dissipation from local crack opening/closing, and the increased friction between damaged concrete. It is observed that the proposed approach can detect the damages at different stories through jumps in the computed corresponding Park and Ang damage indices. It is also observed that the effect of uy , the yield displacement, on the estimated damage index is significant and increases with damage.

References 1. Chatzi, E.N., Smyth, A.W.: The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing. Struct. Control Health Monitoring 16, 99–123 (2009) 2. Foliente, G.C.: Hysteresis modeling of wood joints and structural systems. J. Struct. Eng. 121, 1013–1022 (1995) 3. Goda, K., Hong, H.P., Lee, C.S.: Probabilistic characteristics of seismic ductility demand of SDOF systems with Bouc-Wen hysteretic behavior. J. Earthquake Eng. 13, 600–622 (2009) 4. Juang, J.N., Pappa, R.S.: An eigensystem realization algorithm for modal parameter identification and model reduction. J. Guidance Control Dyn. 8, 620–627 (1985) 5. Park, Y.J., Ang, A.H.S.: Mechanistic seismic damage model for reinforced concrete. J. Earthquake Eng. 111, 722–739 (1985) 6. Yang, J.N., Lin, S., Huang, H., Zhou, L.: An adaptive extended Kalman filter for structural damage identification. Struct. Control Health Monit. 13, 849–867 (2006)

Assessment of CNC Machine-Induced Vibrations on an Industrial Inter-story Floor Chiara Bedon(&)

, Enrico Bergamo and Salvatore Noé

, Marco Fasan

,

Department of Engineering and Architecture, University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy [email protected]

Abstract. The prevention of unfavourable machine-induced vibrations represents a crucial issue for the design of industrial facilities. A special attention is required for the structural assessment of the load-bearing members, that should be optimally designed with the support of specific input parameters. The characterization of the expected vibration sources, together with a reliable structural model, is in fact a key step for the early design stage. In this paper, a case-study eyewear factory is investigated. Its layout takes the form of a two-span, two-story precast concrete frame. The lack of customer/ designer communication resulted in various non-isolated Computer Numerical Control (CNC) vertical machinery centers mounted on the inter-story floor. Accordingly, the floor started to suffer for severe resonance issues. This research study focuses on the dynamic investigation of the structure. An efficient, coupled experimental-numerical approach is presented and validated for early predictive studies. Based on field experiments on the floor, but also on the machinery components, the most unfavourable conditions are first detected and characterized with the support of accelerometers and video-tracking displacement acquisitions. The experimental outcomes are then further assessed with Finite Element (FE) numerical models, giving evidence of the accurate predictability of resonance issues. Keywords: Computer Numerical Control (CNC) vertical machinery center
Machine-induced vibrations
Precast concrete floor
Field experiments
Finite element (FE) numerical analysis

1 Introduction As known, numerical methods and tools can offer a strong support to structural designers. A multitude of combinations of loads can be efficiently assessed in the early design stage of buildings and infrastructural systems, so as to prevent potential unfavourable operational conditions. This is also the case of vibrational issues, where simplified or more refined approaches can be taken into account to ensure appropriate levels of human comfort [1, 2]. In the case of machine-induced vibrations, however, even more attention and expertise is required, given that the knowledge of the input vibrational source (and its © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 306–315, 2021. https://doi.org/10.1007/978-3-030-64594-6_31

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dynamic interaction with the structure) represents a key influencing parameter for the design of the load-bearing members. A combination of machinery and structural parameters should be than further explored. This paper investigates a case-study industrial building in Italy, that hosts an eyewear factory [3]. The production of optical glasses is based on various CNC vertical machinery centers (Fig. 1). At the time of the structural design, no appropriate communication was spent on the final destination of use of the building. This resulted in various non-isolated machines directly mounted on the inter-story floor, with severe resonance issues and consequent management troubles.

Fig. 1. General view of the examined inter-story floor, with CNC machines and equipment.

Learning from the past experience, this research study focuses on the dynamic investigation of the most severe operational conditions for the inter-story floor, and thus on the characterization of the input machinery configurations of technical interest. The novel concept involves a coupled analysis of the machinery and structural components. Once the structure itself is properly described in its key dynamic parameters, with the support of Finite Element (FE) numerical analyses, the final result takes the form of a reliable an efficient experimental-numerical design procedure that should be generally used to prevent similar unfavourable conditions.

2 Case-Study System 2.1

Building

The examined factory is located in Italy, and has been constructed in 2019. All the load-bearing components are designed in accordance with the National regulations for the design of seismic-resistant structures [4, 5]. The two-story, two-span, precast concrete has 13m of elevation, with plan dimensions of 67  30 m (Fig. 2). The structural concept includes a grid of plinth-

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restrained, square columns (80  80 cm the cross-section) and a series of longitudinal precast beams, that are used to support the inter-story floor and the roof (wing-shaped members). Based on earlier observations and client requirements, the study was focused on the single-span region emphasized in Fig. 2 (11  14.9 m its size).

Fig. 2. Plan view of the inter-story floor (dimensions in m).

2.2

Inter-story Floor

The floor in Fig. 2 consists of a series of adjacent, unconnected double tee modular units and a continuous, cast-in-situ concrete slab on their top, that ensures the structural continuity of the diaphragm. The nominal resistance classes for the concrete mixtures in use are Rck = 67 MPa for the precast members and Rck = 30 MPa for the slab. Depending on their final position in the floor (Fig. 2), the double tee elements have cross-section features agreeing with Fig. 3. The nominal height is set in h = 0.8 m, while the width B is generally equal to 2.34 m, 2.50 m or 2.55 m (Fig. 3). The distance of the webs (b = 1.3 m) and the thickness of the top cap (hcap = 0.05 m) are kept fix.

Fig. 3. Nominal cross-section of the double tee modular elements for the inter-story floor (dimensions in m).

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The modular units are characterized by high slenderness, given that they are simply supported over a span L = 14.62 m. At the ends, tee beams offers a 0.2 m wide cantilever base support. Along the span, moreover, the modular elements are characterized by an upward bow (0.04 m = L/365 the maximum amplitude at mid-span). Accordingly, the cast-in-situ slab has a nominal thickness hslab comprised between 0.11 m and 0.15 m (Fig. 2). The total mass of a single module with slab (with B = 2.5 m) is Mmodule = 23500 kg = 25.9 ton. 2.3

CNC Machines

The critical region emphasized in Fig. 2 and detailed in Fig. 4 is composed of five modules (B = var) and hosts three non-isolated machines. Among others, the floor region includes an OKUMA - GENOS M560 – V-e machinery tool [6] with a total mass (MOKUMA = 7700 kg, with Mspindle = 400 kg) that roughly corresponds to 1/3rd the weight of the supporting module. Additional sustained masses are represented by the MATSUURA and BRIDGEPORT machines (4500 kg and 2700 kg) and their equipment (150 kg/machine), that are variably positioned on the floor (Fig. 4).

Fig. 4. Plan view of the CNC machines on the floor region object of study (dimensions in m).

3 Experimental Study A series of field dynamic experiments was carried out on the inter-story floor in October 2019, to assess its dynamic performance under the operative CNC machines. The on-site tests, more in detail, were performed after that the OKUMA machinery center revealed some severe vibrational issues for the factory. 3.1

Analysis of the OKUMA Machinery Center

At the time of building construction, eight rigid supports were used to install the OKUMA center (Fig. 5a). Under the typical operational conditions for the eyewear

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factory object of study, the spindle in Fig. 5b is subjected to cyclic vertical displacements and accelerations that are directly transferred to the supporting floor.

(b)

Vertical acceleration (m/s2)

(a) 6 4 2 0 -2 -4

Experimental

-6 0

0.3

Synthetized

0.6

0.9

Time (s) (c)

(d)

Fig. 5. OKUMA machinery center: (a) detail of rigid supports and (b) moving spindle, with (c) example of the video-tracking acquisition of the spindle displacements and (d) corresponding cyclic acceleration under the worst operational condition

During the field experiments, careful attention was thus paid to capture the key features of the input vibrational source. Such a goal was achieved with the support of a digital tri-axial accelerometers (ADXL355 type [7]) that was mounted on the moving

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spindle, as well as from coupled video-tracking acquisition techniques (Fig. 5c), based on the Tracker software [8]. Among the various working programs of the OKUMA machine, the worst operational condition was taken into account for the inter-story floor. The corresponding acceleration-time history is shown in Fig. 5d. 3.2

Field Measurements on the Inter-story Floor

In parallel, additional experimental records were collected for the inter-story floor, with a digital tri-axial accelerometer (ADXL355 type) that was variably positioned on the floor region object of study (s#n labels in Fig. 6), under different OKUMA working programs. For the most severe condition (Fig. 5d), the positions s#4 and s#5 were taken into account for major field measurements. Additional records were also collected, for comparative purposes, on the floor with the OKUMA at rest (under the effects of in-place jumps by a technician in s#4).

Fig. 6. Reference setup for the field dynamic experiments (plan view, with dimensions in m).

From the collected experimental records, the time-acceleration and the corresponding Power Spectral Density (PSD) functions were thus assessed. An example is proposed in Fig. 7, with evidence of the first five seconds of measurements, as obtained from the sensor s#4. From the comparative plots, in particular, it is possible to clearly detect the first two fundamental vibration frequencies of the floor, in the order of f1,exp = 7.4 H and f2,exp = 9.4 Hz respectively. Higher vibration modes can be also perceived in the range from 12 Hz to 40 Hz, especially in Fig. 7a. As far as the OKUMA machine operates as in Fig. 5d, more pronounced resonance effects can be

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perceived for the lowest natural vibration frequencies of the floor (Fig. 7b). For the control point s#4, the vertical acceleration peak was measured in 0.26 m/s2. 5x10-3

1º

6x10-5

4x10

1º

2º

PSD ((m/s2)2/Hz)

PSD ((m/s2)2/Hz)

8x10-5

-5

2x10-5

0

4x10

-3

3x10-3

2º

2x10-3 10-3 0

0

10

20

30

40

50

60

Frequency (Hz) (a)

0

10

20

30

40

50

60

Frequency (Hz) (b)

Fig. 7. Experimental Power Spectral Density (PSD) for the floor with (a) resting or (b) working OKUMA machine (s#4 data corresponding to 5s of acquisition).

4 Finite Element Numerical Analysis The analysis was carried out in ABAQUS [9], based on a geometrically simplified but accurate FE model, that was validated to capture the vibrational response of the interstory floor. Based on preliminary sensitivity analyses on full-size FE floors, the selected region in Fig. 2 was only described, with appropriate boundaries. 4.1

Solving Approach

S4R shell elements were used for the double tee modules (Fig. 8). In the case of the webs, 0.1m high shell elements with average thickness were used (Fig. 8a), while the slab was described with S4R elements and offset (Fig. 8b). A variable shell thickness was used along the span (0.2m long segments), to account for the slab geometry. The upward bow of precast modules was disregarded, and a distributed tie constraint was used between the precast and cast-in-situ shell elements (Fig. 8). The floor region of interest was thus assembled through adjacent precast modules and a continuous slab, but also including the supporting beams and columns (Fig. 9). 3D solid elements were used for them, to account for their geometry (and thus restraint effect for the floor). Each precast web was thus locally restrained to the beams (shell-tosolid constraint). Additional boundaries were defined along the edges of the slab, due to continuity. Finally, two linear constitutive laws were used for the concrete materials, with mean nominal values for the dynamic modulus of elasticity [1].

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(a)

(b)

Fig. 8. Modelling of the precast modular elements: (a) cross-section (example for B = 2.5 m) and (b) side view (dimensions in m).

Fig. 9. Layout of the FE model for the area of investigation of the inter-story floor (ABAQUS).

Regarding the machines, a mixed approach was taken into account. A series of distributed masses was first used to reproduce the actual position of the sustained weights (i.e., with the m terms in Fig. 6 distributed on their area of influence). In the case of the OKUMA machine, see Fig. 9, an additional lumped term (Mspindle) with a rigid link was used for the spindle movements. In this manner, the analysis was focused both on the prediction of the natural vibration modes of the floor (that can be affected by the masses of the machines at rest), as well as on the additional dynamic effects deriving from the operative OKUMA center.

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Results

A linear “frequency” analysis was first carried out on the floor with the sustained masses only. The lower fundamental modes are proposed in Fig. 10, and are associated to a global plate bending deformation of the floor. Worth of interest is the good correlation of experimental-numerical frequencies (Figs. 7, 10). Moreover, the total machinery mass was found responsible of a −6% variation for them (f1,empty = 7.81 Hz, f2,empty = 9.98 Hz), thus resulting in even more pronounced vibrational issues.

f1= 7.37Hz

f2= 9.41Hz

Vertical acceleration (m/s2)

Fig. 10. Numerical vibration modes of the floor with CNC machines at rest (ABAQUS). 0.6 2° mode 1°mode

0.3

0

-0.3

|max(s#4)| TEST

ξexp=9%

max(s#1, s#4)

-0.6 6

8

10

12

Frequency (Hz) (a)

(b)

Fig. 11. SSD analysis of the floor with working OKUMA (ABAQUS): (a) vertical acceleration at s#1 and s#4 control points and (b) selected deformed shape (f = 7.37 Hz)

A linear Steady State Dynamics (SSD) analysis was then carried out, with the operative OKUMA machine. Based on Fig. 7b, the analysis was set in the range of 2– 10 Hz, including the input vibration modes of Fig. 10 (and further extended up to the 20th mode). An experimentally derived modal damping was used (nexp = 9%, thus markedly higher than the conventional n = 2–3% for concrete floors [2]), with the input spindle acceleration from Fig. 5d. Additional damping sources (i.e., machines, etc.) were disregarded. Worth of interest in Fig. 11 is that the floor with the active OKUMA clearly suffers for marked resonance troubles, especially in the range f1–f2, but also for higher frequencies. The acceleration peak was calculated in the order of 0.3 m/s2

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(max. s#4 and s#1), with a close agreement with the field records (Fig. 11a). Non-null s#2 and s#3 records were also numerically predicted, as an effect of the intrinsic flexibility of the supporting precast beams, thus confirming the role of refined FE models. The maximum dynamic deflection was predicted in 0.08 mm (max. s#4 and s#1). Compared to a static equivalent vertical force for the OKUMA spindle, the SSD model resulted in a dynamic amplification factor DAF = +3.1. The velocity peak, finally, was predicted in 6−7 mm/s (max. s#4 and s#1), thus remarkably smaller than the limit value of 20mm/s recommended in [10]. As far as the same coupled experimental-numerical approach is taken into account, accordingly, predictive analyses for general structures can help designers to prevent (or minimize) possible severe vibrational issues. This management issues could focus on the structural detailing under coupled machinery-induced excitations.

5 Conclusions The prediction of machine-induced vibrations is a key step for the early design stage. However, the description of the vibration source, as well as the reliability of the available structural models, can be challenging. In this paper, a case-study eyewear factory was investigated, with a focus on the vibration issues of its floor, where nonisolated CNC machines were mounted. An experimental characterization of the machinery activity was presented. The reliable FE analysis of the floor was thus carried out with realistic input excitations. The good match of field and numerical predictions confirmed the added value of coupled experimental-numerical predictive studies, so as to prevent (or mitigate) possible machine-induced vibrational issues.

References 1. Feldmann, M., Heinemeyer, C., Butz, C., et. al. Design of floor structures for human induced vibrations. Technical report EUR 24084 EN (2009). https://doi.org/10.2788/4640 2. Bachmann, H., Ammann, W.: Vibrations in structures induced by man and machines. IABSE, Zurich, Switzerland (1987). ISBN 3-85748-052-X 3. Bergamo, E., Fasan, M., Bedon, C.: Efficiency of coupled experimental-numerical predictive analyses for inter-story floors under non-isolated machine-induced vibrations. Actuators 9 (3), 87 (2020). https://doi.org/10.3390/act9030087 4. DM 17/01/2018: Norme Tecniche per le Costruzioni (NTC2018) (2018) 5. Circolare n.7 del 21/01/2019: Istruzioni per l'applicazione dell' ``Aggiornamento delle Norme Tecniche per le Costruzioni'' (2019) 6. OKUMA. https://www.okuma.eu/it/ 7. Bedon, C., Bergamo, E., Izzi, M., Noé, S.: Prototyping and validation of MEMS accelerometers for structural health monitoring-the case study of the Pietratagliata cablestayed bridge. J. Sens. Actuator Netw. 7(3), 30 (2018). https://doi.org/10.3390/jsan7030030 8. Tracker-Video Analysis and Modeling Tool. https://physlets.org/tracker 9. Simulia: ABAQUS Computer Software, Providence, RI, US (2020) 10. UNI9916: Criteri di misura e valutazione degli effetti delle vibrazioni sugli edifice. Ente Nazionale Italiano di Unificazione (UNI), Milano, Italy (2014)

Continuous Dynamic Monitoring System of Foz Tua Arch Dam: Installation and First Results Sérgio Pereira1(&), Filipe Magalhães1, Jorge Gomes2, Álvaro Cunha1, José Paixão3, and José Lemos2 1

Construct, ViBest, Faculty of Engineering, University of Porto, Porto, Portugal [email protected] 2 Concrete Dams Department, National Laboratory for Civil Engineering, Lisbon, Portugal 3 Dams Department, EDP - Energias de Portugal, Porto, Portugal

Abstract. The Foz Tua hydroelectric development is located in the north of Portugal at the mouth of the Tua river, a tributary of the Douro river, and is equipped with 270 MW of power capacity, making it a very important asset in the country’s energy production capacity. Its reservoir is accomplished through a 108 m high concrete arch dam whose construction was concluded in 2017. The arch dam has been equipped with a vibration-based structural health monitoring system, which is composed by a set of accelerometers that were radially disposed over the two upper visit galleries. The accelerometers are connected to a set of digitizers distributed in the dam, being the synchronization of the data assured by GPS. This paper describes the addressed monitoring system, as well as the results obtained during the first months of operation, such as the characterization of accelerations (maximum and effective values) and the automatic identification of the dam modal properties. Additionally, the influence of operational conditions on modal properties is preliminarily studied, namely the effect of reservoir water level variation on the tracked natural frequencies. Keywords: Dam engineering
Operational modal analysis monitoring
Operational and environmental effects


Structural health

1 Introduction Foz Tua hydroelectric power plant is one of the most recent plants in Portugal, adding an important contribution to the hydroelectric sector. In order to assess Foz Tua arch dam structural health and the effect of exceptional events in its behaviour through the studying of the dam’s dynamic properties and their evolution over time, continuous dynamic monitoring of the dam is being carried out by ViBest-FEUP and LNEC, which takes into account the variation of ambient and operational conditions. Integrated monitoring systems considering real-time data directly obtained from structures, such as the one implemented in Foz Tua dam, are very important to the long-term management of large civil infrastructures [1]. Many vibration-based systems © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 316–326, 2021. https://doi.org/10.1007/978-3-030-64594-6_32

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have already been successfully implemented in very different types of structures in the past, such as bridges [2], wind turbines [3], buildings [4], stadia roofs [5] or bell-towers [6]. Such vibration-based health monitoring systems rely on operational modal analysis to continuously identify modal properties, which can be used as monitoring features to evaluate the structure’s health condition evolution over time. In the case of dams, experimental modal analysis based on forced vibration test has been extensively used in the past and is still being used nowadays [7], to identify the most relevant dynamic parameters of these massive structures with the main purpose of establishing correlations with numerical predictions or in some cases developing the updating of finite element models. Furthermore, there are already a couple of examples of vibration-based health monitoring systems which have been successfully implemented in dams with geometrical characteristics similar to those of Foz Tua dam [8, 9] , and a good match was achieved when comparing the application of both experimental and operational modal analysis to a concrete dam [10]. In this sense, since state-of-art applications indicate the suitability of vibrationbased health monitoring systems to dams, good results are expected to be achieved with the continuous dynamic monitoring of Foz Tua dam. Therefore, this paper presents a brief description of the dynamic monitoring system installed in this dam and the results obtained during the first months of continuous dynamic monitoring, including the characterization of vibration levels, modal identification and the tracking of modal properties.

2 Instrumented Dam and Monitoring System The Foz Tua hydroelectric development is located in the north of Portugal at the mouth of Tua river, a tributary of the Douro river. The power plant is equipped with 270 MW of power capacity, making it a very important asset in the country’s energy production capacity. Its 27 km long reservoir, with 106 Mio. Cubic meters of capacity, is accomplished through a 108 m high concrete arch dam whose construction was concluded by the end of 2016. The complex is equipped with a pumping system that allows recovering water from downstream, contributing to optimize the hydroelectricity production management of the Douro basin. The structure corresponds to a concrete double-curvature arch dam, embedded in a narrow valley zone. The 275 m long arch (crest length) is composed of 18 concrete blocks, separated by vertical contraction joints, and includes visit galleries at six different levels, besides the general gallery for drainage. The full storage level is at 172 m. Figure 1a) shows a picture of the dam and its reservoir dated November 2016, before the start of the exploration period. To ensure a good characterization of the dynamic behaviour of the dam, the arch has been equipped with a vibration-based structural health monitoring system. This equipment is composed by a set of 12 accelerometers that were radially disposed over the two upper visit galleries, 4 in the upper visit gallery (GV1), being half of them on each side of the dam spillway gates, and the other 8 accelerometers in the second visit gallery (GV2). All the accelerometers are connected to a set of digitizers, and the

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synchronization of the data is assured by GPS. The system is continuously recording data with a sampling rate of 50 Hz, producing a data file every 30 min. Figure 1b) characterizes the position of the accelerometers, marked in blue in a scheme of the dam. There is one more accelerometer on the left side of the structure, following the dam geometry, which is not entirely symmetric. Foz Tua dam continuous dynamic monitoring system is configured to record time series of accelerations with a duration of 30 min at all instrumented points, which is an adequate duration for the application of the techniques used in its treatment, even though this can be adjusted if necessary. The recorded samples are pre-processed, which includes the elimination of offsets, the application of an eighth-order low-pass Butterworth filter and re-sampling with a frequency of 25 Hz. After this step, the automatic processing performed evaluates each 30 min sample individually in order to obtain modal parameters and features characterizing the vibration levels. Every day 48 values are obtained for each parameter. In the next section, the dam’s vibration levels are evaluated and its modal properties are identified, with its evolution being tracked over time.

a)

b)

Fig. 1. Foz Tua arch dam: a) aerial view [11]; b) position of accelerometers marked with blue dots.

3 Continuous Dynamic Monitoring 3.1

Characterization of Vibration Levels

During the monitoring period the dam has been subjected only to ambient vibration, that is, no excitation was intentionally induced in the structure with the purpose of performing modal identification. In this sense, the vibrations in the structure were caused solely by the conditions in the surrounding natural environment, such as the wind, small seismicity or nearby road traffic, among others, and by the normal operation of the hydroelectric plant underlying the dam. In the specific case of the power plant, besides everyday human activities, the main source of excitation would come from the energy production circuit, therefore it is expected to find the turbine rotation

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frequency (185.5 r.p.m.) as a predominant frequency in the measured time series. For each time series of 30 min, the maximum acceleration measured by each sensor and the root mean square (RMS) of its accelerations during this period were respectively identified and calculated, allowing to characterize the intensity of the measured vibrations. Though this processing was performed for the entire monitoring period, the representation of its evolution over time, presented in Fig. 2, comprehends just two months between January 20th and March 20th, in order achieve a clearer figure, which can be more easily analysed. Each of the 12 measuring channels is represented by a different colour and short periods with no data refer to situations of system failure or maintenance.

Fig. 2. Maxima and RMS accelerations measured between 20/01/2018 and 20/03/2018.

The top and the bottom parts of Fig. 2 complement each other. While the information about the maxima accelerations measured by each sensor allows to verify the suitability of the monitoring system to the studied application and to check for abnormal activity around the sensors or any malfunction, the root mean square values give the analyst a more precise view of the general vibration level of the structure during each recorded period. The analysis of the evolution of the RMS presented in Fig. 2 suggests that the operating conditions can be divided into three different stages: a) From January 20th to the end of January (left part of the figure) medium to high vibration levels are obtained, with clear and constant differentiation of the accelerations measured on each sensor, indicating an uninterrupted operation of the energy production system. This type of excitation may create obstacles to successfully perform automated modal tracking [12];

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b) From February 3th to March 5th (central part of the figure) much lower vibration levels are recorded along with sparsely distributed higher accelerations, suggesting pure ambient vibration as the main source of structural excitation, occasionally complemented with excitation from the operation of the energy production system; c) Around March 13th (right part of the figure) very high vibration levels are continuously measured during short periods. This massive source of energy probably corresponds to the opening of the dam’s spillway gates, during periods of intense rains and consequent flood. A zoom on the transition from the operating conditions described in a) to the conditions described in b) is presented in Fig. 3. It becomes clear that the vibration levels vary largely depending on the operating situation. The RMS of the measured accelerations decreases from a few micro g to 10 times smaller intensities when the turbines stop operating. Additionally, the dominance of the excitation coming from the energy production system becomes obvious when considering that each sensor registers an almost constant acceleration intensity during consecutive days, implying the same vibration source is present.

Fig. 3. Maxima and RMS accelerations measured between 25/01/2018 and 10/02/2018.

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Automatic Operational Modal Analysis and Effect of Operating Conditions

From the time series of accelerations, modal parameters estimates can be obtained, using state of the art identification methods. Since different analysis stages require different approaches, when starting to analyse a set of data, it is important to apply simpler methods that provide preliminary results, solid enough to allow preparing other deeper and more detailed analysis. An example of such straightforward methods consists of the application of the singular value decomposition to each time series of acceleration. The assembly of the sample first singular values allows the construction of a colour map such as the one presented in Fig. 4, where colours are a function of intensity, with warm colours (red) associated with higher values. The red zones indicate the existence of more energy in the frequency bands to which they are associated, thus providing approximate

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estimates of the natural frequencies of the structure and the harmonics present in the excitation motivated by the operation of the energy production turbines. Figure 4 is related to the first 10 days of continuous dynamic monitoring, from 08/12/2017 to 18/12/2017. There are two evident horizontal red alignments, very close to each other, between 2 and 4 Hz, which probably correspond to the dam’s first two vibration modes, and two more red alignments between 4 and 6 Hz, potentially indicating the third and fourth vibration modes. A few other alignments seem to exist between 6 and 10 Hz, however, they present considerable frequency variations during the analysed period, not being possible with this preliminary analysis to distinctly identify how many vibration modes would be hidden in this blurred interval of frequencies. Additionally, very well-defined dark red horizontal lines appear occasionally between the first two alignments and near the top of the figure, probably corresponding to the turbine rotation frequency (185.5 r.p.m. – 3.09 Hz) and its harmonic (9.27 Hz).

Fig. 4. Colour map with frequency evolution between 08/12/2017 and 18/12/2017.

After the first analysis, modal identification was performed using the Covariancedriven Stochastic Subspace Identification (SSI-Cov) method. The first five modes of vibration were identified from time-series recorded on December 8th, the first day of monitoring, and natural frequencies, damping values and modal configurations were obtained. A two-dimensional representation of the modal configurations is presented in Fig. 5. The modal ordinates obtained from the modal identification are represented with blue dots and the full modal configurations were obtained using interpolations. Only sensors between number 5 and number 11 (see Fig. 1) were used in this representation to facilitate the distinction between symmetrical and anti-symmetrical shapes, and all the obtained configurations are clear and unequivocal. The first and fourth modes are anti-symmetrical and the second, third, and fifth are symmetrical modes of vibration.

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To continuously track the structure’s modal properties, the SSI-Cov method was combined with a routine based on cluster analysis that automates its application. This approach and its theoretical background are described in [13]. Natural frequencies, damping ratios and mode shapes were obtained for the first five modes, during a period of over three months. Frequency (f) and damping ratios (d) means and standard deviations of the five tracked modes are resumed in Table 1, along with the description of their mode shape, presented before. The identified natural frequencies fit in the intervals defined through the evaluation of the colour map, demonstrating the relevance of this simple tool in preliminary analyses. While the natural frequency for the first four modes present standard deviation values between 0.029 and 0.081 Hz, for the fifth mode it presents a standard deviation of almost 0.3 Hz, indicating significant oscillations of this mode during the analysed period. Additionally, the damping ratios of the five modes present mean values between 1.1 and 1.5%, which is common for this type of structure, and standard deviations between 0.3 and 0.4%. The identification of other modes, even though possible, is conditioned by the number of sensors available in the same horizontal alignment (GV2), making it harder to correctly identify the mode shape of superior mode orders. Mode 1

Mode 2

Mode 4

Mode 5

Mode 3

Fig. 5. Modal configuration of the first five modes of Foz Tua arch dam. Table 1. Modal parameters for Foz Tua dam first five modes. Mode 1 2 3 4 5

fmean [Hz] fstd [Hz] 3.02 0.029 3.25 0.036 4.19 0.043 5.55 0.081 6.49 0.288

dmean [%] dstd [%] 1.12 0.410 1.38 0.433 1.45 0.322 1.52 0.317 1.25 0.405

Description Antisymmetric Symmetric Symmetric Antisymmetric Symmetric

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Nevertheless, it is easier to understand the evolution of modal parameters when graphically portrayed. Therefore, the temporal evolution of the first five vibration modes natural frequencies is characterized in Fig. 6 using 6-h moving averages. With the moving average, a visually clean figure is obtained without losing accuracy in the characterization of natural frequencies’ fluctuations. Small variations are distinguishable in the first three modes, and considerable ones occur with the fourth mode, explaining its 0.081 Hz standard deviation value. However, the highest variations are presented by the fifth mode, whose natural frequency value varies inside a range of about 1 Hz. Though very different variations of frequency occur between modes, in terms of variation amplitude, it is worth noticing that the shape of the evolution representation is consistent across modes, indicating the same physical phenomena is provoking these frequency variations in the five modes. In this sense, a four-day zoom in the frequency evolution of the five modes is presented in Fig. 7a), side by side with the evolution of the level of water (Fig. 7b)) in the dam reservoir (in meters above sea level), during the same four days. Focusing on the fifth mode, which presents the larger variations, the inverse relation between natural frequency and reservoir water level becomes clear: when the water level rises the value of natural frequency decreases, and vice-versa. Therefore, the relation between these two variables was studied. A strong quadratic relation, with a determination coefficient of almost 0.99, was found between reservoir water level and the fifth mode natural frequency (see Fig. 8). This result is consistent with the relations that were found between reservoir water level and the natural frequencies of Baixo Sabor arch dam [9], but in the present case, smaller water level variations are needed to provoke higher frequency fluctuations. Especially in the case of the fifth mode, which decreased by about 13%, from 6.67 Hz to 5.78 Hz in just 18 h.

Fig. 6. Time evolution of 6-h moving average of natural frequencies.

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a)

b)

Fig. 7. a) 4-day zoom of natural frequencies evolution; b) 4-day zoom of water level evolution.

Fig. 8. Relation between water level and the fifth mode frequency.

4 Conclusion and Future Developments The dynamic monitoring system installed in Foz Tua arch dam was described and the main results obtained during the first few months of monitoring were presented. The processing of the time series of accelerations continuously recorded during this period and the good results obtained demonstrate that the dynamic monitoring system is functioning properly and detecting vibrations under much different operating conditions. The referred operating conditions present diverse acceleration levels and the monitoring system has been able to characterize all of them. The accelerations measured when the electricity production system is operating are about ten times greater than those measured when the dam is submitted only to environmental excitation. Periods in which the turbines are operating are easily identified on colour maps of singular values since the rotation frequency and its harmonics are represented by thin but well-defined red lines. The results of modal identification show a close relation

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between the water level in the reservoir and the vibration frequencies values, in agreement with results obtained with a similar application. It is worth noticing the enormous frequency variations of the fifth vibration mode during the studied period when the reservoir water level varied less than 4 m. In the future, the statistical relations developed between water level and natural frequencies will be used to minimize the effect of the first on the second, thus creating features sensible to small fluctuations, which may be used to detect abnormal structural behaviour. Acknowledgments. This work was financially supported by: Base Funding - UIDB/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções - funded by national funds through the FCT/MCTES (PIDDAC); PTDC/ECM-EST/0805/2014|16761 – DAM_AGE Advanced Online Dynamic Structural Health Monitoring of Concrete Dams, funded by FEDER funds through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT - Fundação para a Ciência e a Tecnologia. The authors would like also to acknowledge all the collaboration and support provided by EDP Produção.

References 1. Gomes, J.P., Palma, J., Magalhães, F., Pereira, S., Monteiro, G., Silva Matos, D.: Seismic monitoring system of baixo sabor scheme for structural dynamic behaviour monitoring and risk management. In: 26th International Congress on Large Dams (2018) 2. Magalhães, F., Cunha, A., Caetano, E.: Vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection. Mech. Syst. Sig. Process. 28, 212– 228 (2012) 3. Oliveira, G., Magalhães, F., Cunha, Á., Caetano, E.: Development and implementation of a continuous dynamic monitoring system in a wind turbine. J. Civ. Struct. Health Monit. 6(3), 343–353 (2016) 4. Rainieri, C., Fabbrocino, G., Manfredi, G., Dolce, M.: Robust output-only modal identification and monitoring of buildings in the presence of dynamic interactions for rapid post-earthquake emergency management. Eng. Struct. 34, 436–446 (2012) 5. Martins, N., Caetano, E., Diord, S., Magalhães, F., Cunha, T.: Dynamic monitoring of a stadium suspension roof: Wind and temperature influence on modal parameters and structural response. Eng. Struct. 59, 80–94 (2014) 6. Ubertini, F., Comanducci, G., Cavalagli, N.: Vibration-based structural health monitoring of a historic bell-tower using output-only measurements and multivariate statistical analysis. Struct. Health Monit. 15(4), 438–457 (2016) 7. Gomes, J.P., Lemos, J.V.: Characterization of the dynamic behavior of a concrete arch dam by means of forced vibration tests and numerical models. Earthq. Eng. Struct. Dynam. 49(7), 679–694 (2020) 8. Mendes, P.: Observação e Análise do Comportamento Dinâmico de Barragens de Betão. Faculdade de Engenharia da Universidade do Porto, Portuguese (2010) 9. Pereira, S., Magalhães, F., Gomes, J.P., Cunha, Á., Lemos, J.V.: Dynamic monitoring of a concrete arch dam during the first filling of the reservoir. Eng. Struct. 174, 548–560 (2018)

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10. Gomes, J., Pereira, S., Magalhães, F., Lemos, J.V., Cunha, Á.: Input-Output vs Output-only modal identification of Baixo Sabor concrete arch dam. In: 9th European Workshop on Structural Health Monitoring, Manchester, United Kingdom (2018) 11. EDP. Energias de Portugal (2018). https://www.a-nossa-energia.edp.pt/centros_produtores/ 12. Pereira, S., Reynders, E., Magalhães, F., Cunha, Á., Gomes, J.P.: The role of modal parameters uncertainty estimation in automated modal identification, modal tracking and data normalization. Eng. Struct. 224, 111208 (2020) 13. Magalhães, F., Cunha, A., Caetano, E.: Online automatic identification of the modal parameters of a long span arch bridge. Mech. Syst. Sig. Process. 23(2), 316–329 (2009)

Compressive Sensing and On-Board Data Recovery for Vibration–Based SHM Matteo Zauli1(B) , Federica Zonzini1 , Nicola Testoni1 , Alessandro Marzani3 , and Luca De Marchi2 1

Advanced Research Center on Electronic Systems (ARCES), University of Bologna, Bologna, Italy {matteo.zauli7,federica.zonzini,nicola.testoni}@unibo.it 2 Department of Electronical, Electronic and Information Engineering (DEI), University of Bologna, Bologna, Italy [email protected] 3 Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Bologna, Italy [email protected]

Abstract. A primary challenge in the design of reliable and long–lasting Structural Health Monitoring (SHM) systems consists in ensuring real– time functionalities through cost–effective solutions. As such, energy– aware architectures demand the joint optimization of data sampling rates, on–board storage requirements, and communication data payloads. These requirements became particularly crucial with the development of mesoscale SHM systems, where the periodic gathering of signals from increasingly denser sensor networks made the data management task a primary issue. In the specific context of vibration–based SHM, where structural responses exhibit peculiar spectral profiles characterized by a sparse frequency content concentrated around the natural frequencies, the Compressive Sensing theory inspired compelling approaches for data collection and gathering to central processing units. The current work combines such advanced sub–Nyquist sampling procedures with a low-cost/low-power miniaturized Smart Sensor Network targeted on the extraction of vibration signals. The network is constituted by several recording nodes equipped with MEMS accelerometers and microcontrollers which are arranged in clusters, and microprocessors-based cluster heads in charge of data decompression and feature extraction for the characterization of the structural integrity. Keywords: Compressive sensing · Vibration analysis Health Monitoring · Smart sensor network

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Industrial facilities and aging structure size is one of the most relevant parameters to be taken into account when designing integrity assessment networks; at c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 327–334, 2021. https://doi.org/10.1007/978-3-030-64594-6_33

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the same time, the sensor density must be compatible with fine–grained damage localization [10]. Additionally, a crucial point in the deployment of effective Structural Health Monitoring (SHM) systems concerns the real–time and long–lasting functionality of the underlying sensor network. Indeed, the wide adoption of full–scale monitoring solutions is hampered by factors such as the amount of data to be acquired, the necessity to avoid network congestion through smart data management and the limited power budget to enable autonomous functionalities. To account for these constraints, a new SHM paradigm inspired by the Compressed Sensing (CS) theory is investigated in this paper. CS allows to implement cost-effective and energy-aware solutions capable to extend the sensors’ life– cycle. In particular, CS jointly optimizes data sampling rates, on–board storage requirements, and communication data payloads. Focusing on the vibration– based SHM field, structures in dynamic regime present characteristic frequency patterns [9] where the total structural energy is concentrated in few and highly localized harmonic components. The sparse profile of the recorded signals in the frequency domain makes CS strategies applicable and appealing. Once vibration waveforms have been recovered on the basis of the selected CS implementation, the classical modal analysis procedures can be applied for modal parameter estimation, including the extraction of natural frequencies of vibration and the corresponding modal shapes [2]. In scientific literature, successful on–board vibration diagnostics has already been reported. Among the significant works, a customized version of the Imote2 sensor platform was presented in [14], proving to be effective for the on–line structural assessment of long–span structures. Similarly, authors in [7] employed the Narada wireless sensor as a prototyping board for acceleration compression in the framework of bridge assessment. Moreover, an hardware–oriented alternative was presented in [6], which deals with a distributed and on–line adapted compression approach to better capture the non–stationary response of real use– cases. In this paper, we exploit the sensor network presented in [11] to implement CS strategies in vibration monitoring. The remainder of the paper is organized as follows. The basics of CS are recalled in Sect. 2 to provide a theoretical introduction. Then, the core elements of an had–hoc designed SHM architecture are presented in Sect. 3, hence discussing how the data compression and reconstruction tasks could be efficiently distributed in presence of a clustered network topology. The distinguishing features of the hardware and software components are coherently detailed. Section 4 follows, which presents the experimental validation, whilst conclusions are drawn at the end.

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Compressive Sensing

The applicability of CS strategies relies on the tight interweaving between (i ) the sparsity condition of the class of signal to be compressed and the (ii ) incoherence property1 of the sparse representation basis with respect to the sensing operator. The former aspect relates to the fact that real–world signals possess an inherently sparse nature in a preferential representation domain. This is particularly the case of vibration data that exhibit a peculiar spectral trend characterised by a small number of non–vanishing coefficients c in the Fourier domain, their magnitude retrieving most of the total structural information [6]. If this hypothesis is verified in a given sparsity basis Ψ ∈ RN ×N , a perfect recovery of the sparse waveform is possible from a reduced number of signal observations, provided that a sensing matrix A ∈ RM ×N can be found which is maximally incoherent w.r.t. Ψ [4]. A is a rectangular matrix with N > M and its effect is to shrink the total signal length into an M –dimensional vector. In the seminal work by Cand´es [3], it has been proven that sensing matrix populated by normally–distributed random values satisfy the incoherence property with respect to most orthonormal bases. Hence, these matrices were chosen in this work. In algebraic form, let’s define with s ∈ RN ×1 a generic vibration waveform, which is supposed to be sparse in the Discrete Cosine Transform (DCT) basis Ψ , so that s = Ψc (1) Its compressed form s˜ ∈ RM ×1 can be derived by resorting to a matrix–vector multiplication as s˜ = As (2) Conversely, the inverse CS procedure can be considered as a convex optimization problem which firstly tries to minimize the error on the projected coefficient estimates cˆ satisfying a prescribed fitness function. To address this task, several strategies are available, which differs in terms of computational complexity and consistency of the recovered information. Among these, the Orthogonal Matching Pursuit algorithm [8], which provides an iterative greedy solution, was selected in the present work for its hardware–oriented structure. Finally, the recovered signal sˆ is computed by plugging cˆ into Eq. (1).

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An HW/SW architecture for SHM has been designed, in which the entailed coding and decoding procedures are implemented directly on the sensors’ boards, i.e. in strict proximity where the structural information is actually sensed. As 1

Incoherence expresses to what extent two different basis are orthogonal one to the other. A good measure of incoherence is provided by the scalar product: the lower the values, the higher the two basis are orthogonal.

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Fig. 1. The proposed HW/SW architecture for modal analysis. From left to the right, the compression/decompression stages are allocated to the PSNs and the CHNs, respectively. Then, the pure modal analysis is performed, firstly identifying cluster–related modal shapes Φj and then merging the local estimates into a full-scale modal shape matrix Φ. Finally, the current health status can be derived by monitoring trends in MAC values over time.

schematically depicted in Fig. 1, a decentralized sensor network topology is proposed, which involves multiple Peripheral Sensor Nodes (PSN) directly deployed on the structure and arranged in clusters, each of them being coordinated by a corresponding Cluster Head Node (CHN). The advantages of such a hierarchical approach are the following: (i ) the computational complexity is minimized, a condition which is ensured by the inherent cluster parallelism, i.e. the capability to retrieve local information prior than returning the full–scale structural parameters; (ii ) communication congestion is avoided while the local channel availability is increased, i.e. the sensor density Nj per cluster is significantly lower than the cumulative one; (iii ) the available hardware and software resources of the network are optimally allocated, subdividing the computational task between the PSNs and their master CHN. As a main result, it is possible to reduce the latency in providing a final structural assessment and the power budget of the whole system, thanks to the combination of the streaming data processing and the limited amount of data which is exchanged through the network (i.e. only sensors’ pre–processed modal features are required to be transmitted rather than the entire signal waveform). As far as the algorithmic part is concerned, the proposed approach workflow involves the cascade of the compression/decompression stages, on top of which the pure modal identification process is stacked. Once the sensor–to– cluster assignment has been defined, the following tasks need to be accomplished: 1. Compression: each PSN acquires and on–board compresses vibration data, eventually returning si ;

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2. Decompression: upon receiving the compressed signals s˜i,j , i ∈ {1, . . . , Nj }, every CHN recovers the original time series from all its mastered PSNs and arranges them into a column matrix; ˆ j = [ˆ s1,j . . . sˆNj ,j ] the column matrix of recon3. Local Modal Analysis: given S structed signals, the matrix Φj = [Φ1,j . . . ΦP,j ] of cluster–dependent modal shape vectors Φp,j , p ∈ {1, . . . , P } is extracted by resorting to one of the available modal identification method. In particular, the Frequency Domain Decomposition method [2] is adopted in this work due to its favourable trade– off between the accuracy of the retrieved modal shape coordinates and the limited computational complexity of the underlying algorithm; 4. Global Modal Analysis: the full–scale modal shape matrix Φ = [Φ1 . . . ΦP ] is computed by merging together local modal shape estimations Φj as prescribed by the PoSER [13] approach; 5. Diagnosis: the Modal Assurance Criterion [5] is computed between the currently estimated Φ and the reference modal shape matrix Φref , which is thought to be representative of the structural healthy status. The final structural integrity characterization, here intended as a simple presence or absence of defects, is eventually obtained by identifying MAC reductions in equally– indexed modal shapes curves below a damage threshold θ = 90%.

4 4.1

Experimental Validation Materials

The pinned–pinned steel beam schematically drawn in Fig. 2 was instrumented with a double chain of six PSNs, each of them being orchestrated by a purposely devoted CHN. Worthy to be noticed, the two clusters are overlapped in correspondence of their terminal position (position ’PA’), as requested by the PoSER algorithm. From an electronic standpoint, the Smart Sensor Network (SSN) developed within the Intelligent Sensor Systems Lab of the University of Bologna is employed, which constitute of light–weight (less than 4 g), small footprint (23 mm × 30 mm) and low-power (41 mA are absorbed in continuous mode from a V voltage reference) sensor nodes. Each PSN is equipped with an STM32F303 microcontroller unit (MCU) which and a 6 Degree–of–freedom (DoF) Microelectro-mechanical System (MEMS) inertial measurement unit providing both linear and angular vibration data. On the other hand, the CHN features a more powerful MCU with extended storage capabilities and embedded digital signal processing functionalities enabling for the execution of more burdensome operations. PSNs are connected in a wired daisy chain fashion by means of a Sensor Area Network (SAN) bus exploiting data–over–power communication. The complete hardware and software description of the network can be found in [11,12]. Concerning the entailed CS operations, both the sensing matrix A and the DCT sparsity basis Ψ are pre–charged into the PSN/CHN node memory at the network startup since they do not need to be refreshed during the real–time monitoring phase.

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Fig. 2. Experimental testbed and related sensor installation plan, comprising two clusters (C1 and C2, respectively) of six accelerometers. An inset depicting the practical connection of three PSNs is also enclosed.

4.2

Methods

A sampling rate of 200 Hz was selected to be compliant with the expected vibration behavior of the structure, whose three most energetic frequencies were predicted to lie below 50 Hz basing on closed analytic formulae (see [13]). Coherently, the parameter P was set equal to three. Acceleration data along the z axis were acquired repeatedly over time windows of 75 s (i.e. each time series consisted of 15 000 samples), which were framed into data segment of N = 512 measurement values in order to be compliant with the available processing/storage capabilities of the nodes while ensuring a sufficient frequency resolution in the modal identification process. On the other hand, the number M of samples surviving after the compression stage was fixed by the compression factor CR = N/M , a quantity which was varied in the interval [2:10]. Experiments were designed to leave the beam vibrating under ground borne vibration, so as to mimic the typical vibration conditions of operational excitation conditions. In order to quantify the accuracy of the CS–framework, MAC index [1] was used to measure the level of modal correlation between CS–processed and uncompressed modal shape estimates. The target was to provide MAC values above the threshold θ = 90%, because such value is compatible with the subsequent assessment of the structural integrity [5]. 4.3

Results

MAC values are reported in the left panel of Fig. 3, superimposed to the benchmark level of 90% in order to better track the quality of the identified structural information. Beside, the three vertical charts in the right side of Fig. 3 refer to

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the different modal components, each of them including exemplary full–scale reconstructed modal shapes for a selection of compression factors.

Fig. 3. Left: trend in MAC values between CS–reconstructed and compression–free full–scale modal shapes as a function of the compression ratio, superimposed to the chosen accuracy level indicated by the black- dotted line at 90%. Red–rumbled, green– rounded and blue-squared line styles refer to the first, second and third modal shape, respectively. Right: reconstructed full–scale modal shapes for a representative selection of CR values.

It is worth noting that an abrupt reduction in modal fitting occurs for compression factors exceeding CR = 4. It is also evident that the performance is heavily dependent on the selected mode shape. In fact, if the MAC trend affecting the second modal shape (green-rounded line) is just slowly decreasing with increasing CRs, the behaviour associated with the first modal shape (red-rumbled curve) shows a more pronounced decreasing behaviour. A more peculiar pattern characterises the MAC associated to the third modal shape (blue–squared line). This phenomenon can be attributed to the fact that the energy of the third mode is very weak w.r.t. the other two. Indeed, in this case, the random effects associated to the sampling phase and to the superimposed noise generate the oscillations in the estimated MAC values. On the contrary, MAC percentages for low CRs (i.e. CR ∈ [2; 4]) are always above 90% for all the inspected modal shapes, apart from the limit case CR = 4 were the modal fitting of the first and third mode slightly falls nearby 87%. An additional proof of the obtained outcomes is provided by the good level of

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superposition in the corresponding modal shape curves depicted in left–hand side of Fig. 3.

5

Conclusion

The present paper investigates the suitability of a hierarchical SHM architecture combining hardware–oriented CS strategies with conventional modal analysis procedures for the long–term and cost–effective vibration diagnostics of aging structures. An experimental campaign conducted on a pinned–pinned steel beam revealed that vibration data can be compressed up to one fourth of the total time length while attaining a modal shape reconstruction accuracy of 90% or more.

References 1. Allemang, R.J.: The modal assurance criterion-twenty years of use and abuse. Sound Vib. 37(8), 14–23 (2003) 2. Brincker, R., Zhang, L., Andersen, P.: Modal identification of output-only systems using frequency domain decomposition. Smart Mater. Struct. 10(3), 441 (2001) 3. Candes, E.J., et al.: The restricted isometry property and its implications for compressed sensing. C. R. Math. 346(9–10), 589–592 (2008) 4. Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006) 5. Ibrahim, R.A.: Handbook of Structural Life Assessment. Wiley, Hoboken (2017) 6. Klis, R., Chatzi, E.N.: Vibration monitoring via spectro-temporal compressive sensing for wireless sensor networks. Struct. Infrastruct. Eng. 13(1), 195–209 (2017) 7. O’Connor, S.M., Lynch, J.P., Gilbert, A.C.: Compressed sensing embedded in an operational wireless sensor network to achieve energy efficiency in long-term monitoring applications. Smart Mater. Struct. 23(8), 085014 (2014) 8. Orovi´c, I., Papi´c, V., Ioana, C., Li, X., Stankovi´c, S.: Compressive sensing in signal processing: algorithms and transform domain formulations. Math. Prob. Eng. 2016 (2016) 9. Paz, M., Kim, Y.H., et al.: Structural Dynamics. Springer, Heidelberg (1991) 10. Sony, S., Laventure, S., Sadhu, A.: A literature review of next-generation smart sensing technology in structural health monitoring. Struct. Control Health Monit. 26(3), e2321 (2019) 11. Testoni, N., Aguzzi, C., Arditi, V., Zonzini, F., De Marchi, L., Marzani, A., Cinotti, T.S.: A sensor network with embedded data processing and data-to-cloud capabilities for vibration-based real-time SHM. J. Sens. (2018) 12. Testoni, N., Zonzini, F., Marzani, A., Scarponi, V., De Marchi, L.: A tilt sensor node embedding a data-fusion algorithm for vibration-based SHM. Electronics 8(1), 45 (2019) 13. Zonzini, F., Girolami, A., De Marchi, L., Marzani, A., Brunelli, D.: Cluster-based vibration analysis of structures with graph signal processing. IEEE Trans. Ind. Electron. (2020) 14. Zou, Z., Bao, Y., Li, H., Spencer, B.F., Ou, J.: Embedding compressive sensingbased data loss recovery algorithm into wireless smart sensors for structural health monitoring. IEEE Sens. J. 15(2), 797–808 (2014)

A Novel Time-Frequency Distribution for Real-Time Monitoring of Civil Infrastructures Said Quqa(&)

, Giacomo Bernagozzi , Luca Landi and Pier Paolo Diotallevi

,

DICAM, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy [email protected]

Abstract. Real-time structural health monitoring (SHM) acquires countless importance when applied to large-scale civil infrastructures, where the damage should be managed immediately to avoid both economic and human loss. Recent studies in the field of real-time identification of bridges generally assume linear time-varying (LTV) structural models, justified on the grounds that continuously varying traffic load may slightly change the structural behavior over time. Time-varying load also involves non-stationary input excitation, which cannot be modeled as Gaussian white noise, as in the traditional outputonly identification methods, and may be characterized by time-varying frequency spectrum which could affect the effectiveness of commonly used identification algorithms. In this paper, the Modal Assurance Distribution (MAD) is employed for the dynamic identification of LTV structures. Based upon the instantaneous operating deflection shapes (ODSs) evaluated through the wavelet packet decomposition, the MAD represents the instantaneous ODS similarity between narrow-band signal components, highlighting the presence of timevarying modal responses. Compared to the most used traditional time-frequency representations (TFRs), representing the distribution of energy through the timefrequency plane, the MAD enables a clearer reading of the modal responses, facilitating their extraction for real-time damage identification. The practical application to a benchmark structure shows the potential of the MAD as a novel TFR which could give rise to a new family of system and damage identification methods. Keywords: Modal identification
Time-varying system Multivariate analysis
Time-frequency representation


Modal assurance


1 Introduction Among the algorithms traditionally employed for modal identification, the frequency domain decomposition (FDD) is certainly one of the most implemented for the integrity monitoring of civil structures [1]. Furthermore, recent variants have improved robustness and efficacy to the method, making it one of the best-known algorithms in the field of structural health monitoring (SHM). In particular, the enhanced frequency domain decomposition (EFDD), consists of extracting the peaks of the singular values © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 335–345, 2021. https://doi.org/10.1007/978-3-030-64594-6_34

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computed from spectral density functions using the modal assurance criterion (MAC) [2]. Signal components that generate similar operational deflection shapes (ODSs) are thereby identified and associated with the same mode to evaluate natural frequencies and modal shapes. The wide use of FDD and EFDD has taken hold assuming that the structural response is recorded during stationary excitation consisting of a Gaussian white noise process. This assumption is valid for most Operational Modal Analysis (OMA) applications to civil structures, i.e. in which only the structural response is recorded without knowing the exciting input [3]. If the excitation is not strictly stationary (e.g., in the case of bridges with passing vehicles), it is usually sufficient to increase the duration of the recording to improve the results of the algorithm. However, for some types of structures (e.g., moving turbines, flexible bridges under strongly non-stationary actions, and buildings during seismic events), the stationarity assumption may generate misleading results in the evaluation of structural conditions. For such cases, several structural and damage identification methods have been proposed in the last decades [4, 5]. In this context, time-frequency domain techniques are generally preferred, which describe the energy distribution of the analyzed signal in a two-dimensional representation (i.e., a time-frequency representation – TFR). The short-time Fourier transform (STFT) and the wavelet transform (WT) are among the most used to obtain spectrograms (in time and frequency) and scalograms (in time and scale) respectively [6]. These techniques are usually applied to one-dimensional (or univariate) signals, producing however practical issues in the evaluation of modal shapes since recordings collected at different locations may generate different TFRs. With the introduction of the empirical mode decomposition (EMD), and its noiseassisted variant (i.e., the ensemble empirical mode decomposition – EEMD) [7] the Hilbert spectrum has also become an excellent tool for the evaluation of the structural damage, giving rise to the Hilbert-Huang transform (HHT). However, the main issues regarding EMD and the related variants are the well-known mode mixing problem and the inability to identify vanishing components. These issues make the method impractical in the case of strongly non-stationary excitation. The authors of the present work recently presented the Modal Assurance Distribution (MAD) [8] as a further step of signal processing to obtain a particularly convenient time-frequency representation of multivariate signals. This procedure exploits the concept used in the EFDD of selecting the modal responses as the signal components with similar ODSs, extending it into the time-frequency domain. This method demonstrated to be highly effective in identifying modal parameters of linear timevarying (LTV) systems in the presence of signal disturbances, crossing modes, and vanishing components. The procedure was tested on frame buildings subjected to ambient [8] and earthquake excitation [9]. Moreover, a simplified variant based on a clustered filter bank (CFB) for wireless smart sensor networks was also proposed [10]. In this paper, the MAD is employed in a novel monitoring approach proposed for infrastructures subjected to nonstationary excitation and evolving structural conditions. This approach is tested on a prestressed concrete bridge previously studied in [10]. This work shows how the MAD can provide users with effective and intuitive representation of the modal components contained in the structural response and is particularly suitable for the extraction of instantaneous modal parameters. These parameters are employed in

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the Application part for the assessment of structural integrity using a flexibility-based approach, showing how damage localization can be achieved almost in real time.

2 Monitoring Approach for Civil Infrastructures In this section, first, a brief theoretical background on the MAD is provided. Then, the outline of a monitoring procedure suitable for infrastructures subjected to nonstationary excitation, such as bridges and viaducts under traffic load, is given, together with an intuitive approach for the visualization of modal features in multivariate structural responses that allows the users to identify modal components in a twodimensional representation. 2.1

Modal Assurance Distribution

The first step of the procedure presented in [8] and used in this paper to obtain the MAD of a multivariate signal consists of a preliminary decomposition of each recording collected on the structure using a time-frequency (or time-scale) domain transform. From now on, all the transforms mentioned in this study are performed in the time-scale domain. However, it will be mentioned as “time-frequency” domain to highlight its intuitive physical interpretation, since the conversion from scale to frequency and vice versa is straightforward. In particular, in this work, the wavelet packet transform (WPT) is employed to obtain a TFR that consists of the starting point for the formation of the MAD. Assuming a transformation level of n, each signal is decomposed into 2n subbands, i.e., signal components with limited frequency ranges. Let n Di 2 R2 s be the matrix of wavelet coefficients thus obtained, where s ¼ T=2n and T is the length of the original signal. The vector uk ½t 2 Rr1 formed by the elements of the TFR Di corresponding to the time instant t and frequency subband k for all the recording locations i ¼ 1; . . .; r can be interpreted as an instantaneous operational deflection shape (ODS) of the analyzed structure. In particular, the normalized elements of uk ½t over the location r can be written as: ui;k ½t ¼

d i;k ½t d r;k ½t

ð1Þ

where d i;k ½t is the t-th sample of the k-th wavelet component of the structural response collected at location i. The similarity between adjacent instantaneous ODSs (namely, uk and uk þ 1 ), is then tested using the MAC. Evaluating the MAC for each time instant t gives rise to a two-dimensional distribution, i.e., the MAD, the elements of which can be obtained as:
T
2
u ½tu
k þ 1 ½t k  T  mk ½t ¼  T uk ½tuk ½t uk þ 1 ½tuk þ 1 ½t

ð2Þ

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Since each signal recorded on the structure consists of the structural response (assuming the modal superposition) plus recording noise, the time-frequency areas in which modal responses are present will be characterized by persistent values in the MAD close to 1, while the noise of the remaining areas will generate random similarities between ODSs, producing MAD with random (beta-distributed) values in the range of 0 to 1. 2.2

Outline of the Monitoring Procedure

The introduction of low-cost MEMS sensors in SHM applications has enabled the managers of structures and infrastructures to deploy dense sensor setups for long-term monitoring, in order to detect and, in some cases, localize damage since the earliest stages. The compromise to be accepted to obtain a much lower cost compared to traditional piezoelectric sensors is generally related to the limited sensitivity offered by MEMS technologies [11]. Thereby, in some cases, ambient excitation may not be enough to reach the minimum vibration level suitable for low-cost devices. The monitoring procedure proposed in this paper is aimed at the identification of structural modal parameters when the signal quality is high enough to enable modal decomposition using the MAD, i.e., when the high-valued areas of the distribution prevail over the random similarities generated by noise. The MAD can thus be employed to identify vanishing signal components with high signal-to-noise (SNR) ratio that may manifest at the occurrence of particular events, such as passing vehicles or considerable wind excitation on civil infrastructures. In particular, a mask n matrix S 2 I2 s able to select only the wavelet components d i;k ½t that produce MAD values higher than a user-defined threshold h can be generated as follows:  sk;t ¼

1 0

if mk1 ½t  h otherwise

W

mk ½t  h

ð3Þ

where sk;t are the elements of S. Such a matrix can be employed to select the  i ¼ Di
S, where
represents the point-wise product, that correspond to elements D areas with high SNR.  k ½t, formed by the A clustering procedure can thus be applied to group the vectors u  i , i ¼ 1; . . .; r, into sets corresponding to different modes. To this ðk; tÞ-th elements of D aim, a density-based spatial clustering of applications with noise (DBSCAN) [12] approach is employed in this work, that is able to track the slight evolution of modal shapes over time which may generate non-convex clusters in the r-dimensional space due to short-term modifications produced by operational effects or damage (if the inspection window is sufficiently long to catch different damage scenarios). The j-th modal response of the signal collected at location i can thus be extracted  j , which represent the applying the inverse wavelet packet transform (IWPT) to D i  i classified in the j-th cluster. It should be noted that the reconstructed elements of D modal response may have zero intervals due to the form of the masking matrix.

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However, in the non-zero intervals, the instantaneous modal shapes and natural frequencies can be evaluated using the Hilbert transform since each extracted response should be mono-component (i.e., should have a single dominant frequency). The instantaneous modal parameters thus identified can be employed in a near-realtime damage identification procedure. In this work, the percentage displacement variation of the uniform load line (ULL, i.e., the deflection shape under a unitary load vector) is used as a damage sensitive feature (DSF). In particular, an instantaneous approximation of the structural flexibility matrix can be evaluated as F½t ¼

Xp j¼1

1 / ½t/Tj ½t x2j j

ð4Þ

where /j ½t is the instantaneous modal shape evaluated using the set of components associated with the j-th cluster. Mass-normalized modal vectors should be used in (4), however, assuming a uniform distribution of masses along with the axis of the structure, a proportional approximation of the flexibility matrix can be calculated [13]. Assuming that a unit load vector p is applied to the structure, the corresponding instantaneous displacement vector can be calculated as u½t ¼ F½tp

ð5Þ

The instantaneous variation of u½t with respect to a baseline condition evaluated at the beginning of the monitoring process is then evaluated, generating an instantaneous damage index for each instrumented location. 2.3

Visualization of Modal Features

Although “automatization” is a strong inclination in recent SHM applications, human intervention is still essential both in the preliminary phase, when the monitoring process is designed and the operative algorithms are selected, and during decision making. Each point of the MAD is associated with an instantaneous vector uk ½t which can be interpreted as an instantaneous ODS. This information can be exploited to obtain an intuitive color distribution in the time-frequency plane representative of the different modal responses forming the multivariate signal. A criterion to obtain such a representation may involve the selection of three recording channels, say the r-th, g-th, and b-th, and associating the amplitude of the wavelet coefficients d i;k ½t, i ¼ r; g; b, to color layers red, green, and blue to generate a color figure. Another approach, more suitable for densely instrumented structures, may involve the sum of amplitudes at multiple locations in the determination of a single color layer. In the following section of this paper, an example of the second approach is given for the experimental case study analyzed herein.

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3 Application In this section, the monitoring procedure presented above is applied using the acceleration data recorded on the S101 Bridge during an experimental campaign conducted by the Vienna Consulting Engineers (VCE) and the University of Tokyo in 2008 [14–16]. The benchmark is a post-tensioned concrete bridge, schematized in Fig. 1, with continuous slab supported by two pairs of piers. A three-day experimental campaign was carried out, inducing a progressive settlement of north-western pier. In this paper, five 330-s data sequences collected in different damage conditions are analyzed, organized as a single sequence with a total duration of 1650 s. The first part of this signal refers to the “undamaged” structure, collected before lowering the column, and is used to evaluate the parameters of the baseline configuration. The second interval was collected upon cutting the base of the column, while the remaining three were acquired after lowering the column by 1, 2, and 2.7 cm.

Fig. 1. Scheme of the case study and sensor deployment (Dimensions in cm), adapted from [14].

Vibration data were collected using a BRIMOS network consisting of 15 threedirectional FBA-23 force balance accelerometers from Kinemetrics [16] and a 16-bit analog-to-digital converter (ADC), with a sampling frequency of 500 Hz. In this application, each signal has been downsampled at 50 Hz and the FejérKorovkin 22 wavelet function was used to obtain the preliminary TFR for each recording channel, with a decomposition level 8, thus generating 256 subbands. The MAD is then evaluated considering the multivariate sequence consisting of all the 15 recording channels. A noise-assisted procedure was applied considering an ensemble of 10 trials with noise components having a standard deviation of 30% with respect to that of the collected signal (see [8] for more details on the noise-assisted procedure and related parameters). A masking procedure is then performed, selecting the elements with MAD values higher than h ¼ 0:5. In Fig. 2, all the elements selected are represented in a time-frequency plane.

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Fig. 2. Clustering process of instantaneous operational deflection shapes.

The ODSs associated with each point of the distribution were then normalized to have a unitary norm and a DBSCAN approach was applied to group the ODSs having Euclidean distance less than 0.1. Moreover, only clusters containing more than 10 points were considered for further analyses. Colors in Fig. 2 represent different clusters, while grey points are classified as outliers since they do not comply with the aforementioned conditions. A separate signal component for each cluster is therefore reconstructed through the application of the IWPT to the related subset of wavelet coefficients. The Hilbert transform is thus employed to evaluate the instantaneous frequency of each component extracted. In Fig. 3, the identified values are reported and compared with the results of the HHT, evaluated on the signal collected by sensor 6, employing the EEMD algorithm for the extraction of the intrinsic mode functions (IMFs). Here, a considerable advantage in using the procedure proposed is noticeable since a clear mode mixing problem is observable in the results of the HHT. According to the results reported in [14], the MAD-based algorithm is able to correctly identify the frequencies of the first two modes (first flexural and first torsional, i.e., violet and blue lines in Fig. 3, respectively) for all the damage conditions. On the other hand, the instantaneous frequencies of IMFs are strongly variable (gray lines in Fig. 3), and a single IMF is extracted in the frequency range between 4 and 6 Hz.

Frequency [Hz]

15 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 IMFs

10 5 0

0

330

660

990

1320

1650

Time [s]

Fig. 3. Instantaneous frequencies identified using the procedure proposed and the EEMD.

The instantaneous amplitudes of the modal components reconstructed using the IWPT on the coefficients contained in the first two clusters are then evaluated and

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reported in Fig. 4. A baseline configuration (U) and four inspection instants (A, B, C, and D) are selected, one within each damage scenario, to simulate a periodic evaluation of the structural integrity. In particular, these instants are chosen when modal parameters are available for both the identified modes, in order to conduct a flexibility-based procedure employing the first two modal responses. For the baseline and each inspection instant, the instantaneous modal amplitudes have been used to evaluate a modal shape vector (through normalization) which is then used to estimate a matrix that is proportional to the flexibility matrix of the benchmark structure. The ULL is therefore evaluated and the percentage variation in the displacement calculated for each instrumented location with respect to the baseline condition U is reported in Fig. 5. An outlier in the displacement of sensor 11 (close to the settled pier) is already noticeable from condition A, where the column was cut. However, other values with comparable percentage variation are registered around sensors 1, 2, and 14 during the first inspection. As the lowering of the column increases (i.e., in conditions B, C, and D), the damage index becomes sharper around the settled pier, demonstrating to be useful information to localize the anomaly.

Modal amplitude 1

U

A

C

D

0 -1 0

330 U

Modal amplitude 2

B

1

660 A

Time [s]

990

B

1320 C

1650 D

1 0 -1 0

330

660

Time [s]

990

1320

1650

Fig. 4. Instantaneous modal amplitudes of the first two clusters; same colors used in Fig. 1.

displacement [%]

10 A B C D

8 6 4 2 0

1

2

3 4 5 6 7 8 9 10 11 12 13 Sensor

14 15

Fig. 5. Damage localization in the inspection configurations.

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As a final analysis of this study, the comparison between the novel TFR proposed in this paper and different spectrograms is shown in Fig. 6. In particular, in order to obtain the representation of Fig. 6(a), the wavelet coefficients calculated through the WPT of each recording channel were masked through the approach proposed in  i , i ¼ 1; . . .; 15, matrices. Three color layers, corresponding Sect. 2.2, thus obtaining D
P

P7



 15 j, G ¼
14 D to red, green, and blue, were defined as R ¼jD i¼8 i , and B ¼ i¼1 Di , respectively, and then normalized to generate a standard RGB model. The spectrograms are instead generated using the data collected by sensors 3, 6, and 15, individually. It can be noted that each spectrogram provides different information based on the location where data is collected. Moreover, the energy distributions of Figs. 6(b)–(d) do not enable the users to recognize different modal responses or notice if the dynamic behavior is changing. On the other hand, the color coding of the multivariate dataset shown in Fig. 6(a) allows discrimination between different modal responses since they are characterized

Fig. 6. Visualization of natural frequencies in the time-frequency plain: (a) masked wavelet coefficients, (b) spectrogram of sensor 3, (c) spectrogram of sensor 6, (d) spectrogram of sensor 15.

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by different colors. Moreover, an evolution of damage can be noticed around 660 s when the white area between 10 and 15 Hz becomes green, highlighting the reduction of energy in this frequency range for the signal collected by sensor 15, which is confirmed in the related spectrogram.

4 Conclusions In this paper, a novel monitoring procedure has been proposed using the modal assurance distribution to evaluate instantaneous modal parameters which can be employed for real-time damage identification. The results obtained analyzing the data collected on a full-scale experimental benchmark confirm the effectiveness of the procedure, which enables to automatically extract separate modal responses from multivariate data. The instantaneous parameters identified using the MAD-based procedure are also compared to those of a noise-assisted variant of the HHT, which is currently widely employed in SHM applications. In this study, however, HHT showed mode-mixing issues and the identification of non-physical modes. An intuitive visualization criterion of multivariate datasets is also outlined in this work, showing superior performances over well-known traditional time-frequency representations since it also enables the users to discern between different modal components and damage conditions using a particular color coding.

References 1. Brincker, R., Zhang, L., Andersen, P.: Modal identification of output-only systems using frequency domain decomposition. Smart Mater. Struct. 10, 441–445 (2001) 2. Jacobsen, N.-J., Andersen, P., Brincker, R.: Using enhanced frequency domain decomposition as a robust technique to harmonic excitation in operational modal analysis. In: Proceedings of ISMA2006 International Conference Noise Vibration Engineering, Heverlee, Belgium, pp. 3129–3140 (2006). 3. Brincker, R., Ventura, C.E.: Introduction to Operational Modal Analysis. Wiley, Chichester (2015) 4. Nagarajaiah, S., Basu, B.: Output only modal identification and structural damage detection using time frequency & wavelet techniques. Earthq. Eng. Eng. Vib. 8, 583–605 (2009) 5. Kijewski, T., Kareem, A.: Wavelet transforms for system identification in civil engineering. Comput. Civ. Infrastruct. Eng. 18, 339–355 (2003) 6. Vetterli, M., Kovačević, J.: Wavelets and Subband Coding, Prentice-Hall Inc (1995) 7. Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1, 1–41 (2009) 8. Quqa, S., Landi, L., Diotallevi, P.P.: Modal assurance distribution of multivariate signals for modal identification of time-varying dynamic systems. Mech. Syst. Sig. Process. 148, 107136 (2021) 9. Quqa, S., Landi, L., Diotallevi, P.P.: Seismic structural health monitoring using the modal assurance distribution. Earthq. Eng. Struct. Dyn. Under review (n.d.) 10. Quqa, S., Landi, L., Diotallevi, P.P.: Instantaneous modal identification under varying structural characteristics: a decentralized algorithm. Mech. Syst. Sig. Process. 142, 106750 (2020)

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11. Noel, A.B., Abdaoui, A., Elfouly, T., Ahmed, M.H., Badawy, A., Shehata, M.S.: Structural health monitoring using wireless sensor networks: a comprehensive survey. IEEE Commun. Surv. Tutor. 19, 1403–1423 (2017) 12. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of KDD Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, pp. 226–231 (1996) 13. Bernagozzi, G., Mukhopadhyay, S., Betti, R., Landi, L., Diotallevi, P.P.: Output-only damage detection in buildings using proportional modal flexibility-based deflections in unknown mass scenarios. Eng. Struct. 167, 549–566 (2018) 14. Vienna Consulting Engineers: Progressive damage test S101 - Flyover Reibersdorf (2009) 15. Siringoringo, D.M., Fujino, Y., Nagayama, T.: Dynamic characteristics of an overpass bridge in a full-scale destructive test. J. Eng. Mech. 139, 691–701 (2013) 16. Döhler, M., Hille, F., Mevel, L., Rücker, W.: Structural health monitoring with statistical methods during progressive damage test of S101 Bridge. Eng. Struct. 69, 183–193 (2014)

Nonlinear SHM Methods for High Sensitivity

Non Destructive Auscultation and Imaging of Damages by Distributed Sensor Array: Step Towards Passive SHM Under Real Conditions Lynda Chehami(B) , Emmanuel Moulin , and Marina Terzi Univ. Valenciennes, CNRS, UMR 8520 -IEMN- Institut d’Electronique de Micro´electronique et de Nanotechnologie, DOAE-D´epartement d’Opto-Acousto-Electronique, 59313 Valenciennes, France [email protected]

Abstract. The passive imaging based on the Green’s functions reconstruction from ambient noise correlation became a promising technique in structural health monitoring. Here, this approach is used to detect and locate linear defects (cracks, holes...) in thin reverberant plates with a small number of sensors. Correlation matrices before and after defect occurrence are estimated from friction noise. Based on a dispersive backpropagation algorithm in a thin plate, the differential matrix of correlations (before and after defect) is used for defect localization. This technique shows satisfactory results for linear defects, but refers to a measurement on a baseline healthy sample, which can be strongly affected by environmental conditions. In this context, an active baseline-free damage localization method that uses a repetitive pump-probe experiment, is proposed. A series of experiments are conducted in a thin aluminum plate using 7 PZTs sensors glued at known positions. One transducer generates a high frequency probe wave with central frequency 20 kHz, while a continuous low frequency pump of 1 Hz is produced by a shaker. A steel ball pressed against the plate to mimic a nonlinear defect is considered. The aim here is to produce solid-solid contact that will be modulated by the pump wave, as would be the case for instance in fatigue cracks. In order to enhance this effect, signals recorded at different times (corresponding to different loading states of the contact) are subtracted and back-propagated to locate the origin of the modulation. Keywords: Noise correlation

1

· Beamforming · Pump-probe

Introduction

Twenty years ago, Weaver and Lobkis proposed a new way to exploit and take benefit from noise sources. The authors have experimentally shown on an aluminum block that the cross-correlation of elastic noise between two receivers provide the transient response between the two receiver positions as if one of c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 349–358, 2021. https://doi.org/10.1007/978-3-030-64594-6_35

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them acted as an elastic transient source, interpreted as the Green’s function GF of the medium [1]. Actually, this property is not limited to thermal noise source but to any noise source distribution that generates an equipartitioned wavefield. In fact, in some situations it is desirable to avoid the need of active energy sources. So, the major interest of this result is the estimation of the GF without requiring any energy source. The main application of the Green’s function extraction from noise correlation function (NCF) is in seismology where Artman has demonstrated the universal interest [2]. Passive Green’s function estimation also offers promising applications in other domains ranging from ultrasound [3], underwater acoustics [4,5] to medical imaging [6]. In ultrasound applications, and particularly in structural health monitoring field, power consumption and complexity of the electronics might represent key issues for the standard methods used in active configurations (pitch-catch). An alternative way could then consist in taking benefit of ambient noise to reveal defects (cracks, holes, etc) by passively estimating the GF using the physical principle based on noise correlation approach. Only a few works are devoted to structural health monitoring. In this field, a pioneer work consisted to use the traffic excitation to recover resonant frequencies and modal damping of a bridge from noise cross-correlation [7]. Later, this approach have been applied to extract the Rayleigh-Lamb Green’s transient responses on plates [8,9]. The method is sufficiently sensitive to observe elastic signature of a plate defect on the crosscorrelation [8]. To localize a defect, an original method has been proposed [10] that is particularly efficient when noise sources distribution is not uniform. The possibility of detecting and localizing defects using ambient noise (friction noise, acoustic noise...) more or less uniformly generated on surface plates has been recently demonstrated in recent works: numerically [11] and experimentally [11– 13]. Some studies on the defect characterization are also derived based on the estimation of the far field pattern called scattering cross-section” [14]. A statistical approach was also proposed by Chehami et al. [15], which relates the similarity between the cross-correlation and the GF to the structural properties of the plate and the number of uncorrelated sources. Based on all these studies, suitable noise sources for passive imaging application should be uncorrelated, spatially stationary and with wide-band frequency contents in the ultrasound range. This technique shows satisfactory results for linear defects, but requires additional measurements on a baseline intact sample for comparison with the actual one. The matter is that material’s characteristics can slowly evolve due to aging, environmental effects, etc. In addition, measurements in the original state of a structure are frequently not available or not possible. The problem can be solved by using nonlinear methods based on strong acoustic nonlinearity induced by damage [16]. In this case, certain nonlinear criterion should be obtained instead of a simple difference in linear properties. In this context, an active baseline-free damage localization method that uses a repetitive pump-probe experiment, is proposed here. The paper is organized as follows. In Sect. 2, we study the passive defect localization tested for holes, massive inclusions or even cracks. In Sect. 3 the

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baseline free-method base on pump-probe experiment is proposed and tested in mimic defects similarly to a crack or delaminations. Results of localization and imaging are presented and followed by concluding remarks in Sect. 4.

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A reverberating plate with a set of transducers fixed on its surface and subject to ambient acoustic noise is considered. It has been shown in a recent work [11] that the differential correlation matrix, that is obtained from the subtraction of the correlation matrices with and without defect, is given by ΔC(t) = [ΔG(t) − ΔG(−t)] ⊗ f (t) + Δn(t),

(1)

where ΔG is the part of the Green’s function due to the defect and Δn the correlation residue (or reconstruction error), depending on the number and the distribution of noise sources. Function f (t) can be considered as a virtual excitation waveform and is given by  NS τa t Rn (τ ) dτ, (2) f (t) = 2Sρh −∞ with Ns is the number of sources, h the plate thickness, ρ the volume density, S the plate area, Rn (τ ) the autocorrelation of the noise source and τa the characteristic attenuation time constant of the plate. As shown in previous works [11,12], this differential correlation matrix can be used as the input of a defect localization algorithm, as though it was an actively acquired signal matrix. However, two conditions are necessary for efficient detection: first the noise should have non-negligible components in the useful frequency band (where wavelengths are of the same order of magnitude as the defect size) and second, the correlation residue should be small compared to ΔG(t) ⊗ f (t). The second condition means that the noise sources should be both uncorrelated and well distributed over the plate surface or at least, as will be explained later, spatially stationary between the acquisitions with and without defect. More recently, Chehami et al. have proposed a statical model which quantifies the accuracy of the GF reconstruction called “RNL” to “Relative Noise Level”:   5 η0 1 +π . (3) RNL(Ns ) = Ns 4 τa As a consequence, for accurate retrieval of a Green’s function, the reconstruction error must diminish when increasing the number of sources Ns , the damping time τa , and small plate area. One interesting result is that optimal reconstruction quality would be achieved for negligible attenuation (τa  η0 ). In that case RNL  (α − 1)/Ns , which implies the existence of a lower bound of the RNL equals to 5/4 Ns and 2/Ns for the rectangular and the chaotic plate, respectively.

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The principle of the imaging algorithm used here is based on in backpropagating each element of indexes (i, j) of the matrix ΔC according to the current pixel position (x, y). The back-propagation function (bpf) of ΔC is given by [11] N R ,NR ΔCij (ω) ej [di (x,y)+dj (x,y)] k(ω) , (4) bpf (x,y) (ω) = i=1,j=1(i=j)

where di (x, y) is the distance between the ith receiver and the pixel at position (x, y), k(ω) is the wave number of the Lamb mode A0 , ΔCij (ω) is the Fourier transform of ΔCij (t) and NR is the number of elements in the array (eight in this example). Then, by inverse Fourier transform, the pixel energy at position (x,y) is obtained by integrating the back-propagation function over time T0 (accordingly to the approximation of back-propagation in an infinite plate), i.e.,  E(x, y) =

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(5)

where T0 is typically the inverse of the bandwidth. The bpf function leads to a constructive sum and a maximum of the pixel intensity on the defect location [11]. As for pixels located elsewhere, the obtained intensity is made up of a summation of non-coherent contributions (including correlation residues Δn, reverberated wavepackets, grating lobes). To estimate the differential correlation matrix, the 8 receivers record the ambient noise during T = 60 s. The operation is performed one time without the defect and one time with the defect. Then the subtraction of the correlation matrices is performed. The noise is produced on the plate by rubbing continuously and manually the whole plate surface with a soft scrubbing pad. Material fatigue often appears in the form of local change of physical properties. Therefore we have tested three kinds of defects: Type of defect

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The beamformed images obtained with a massive inclusion and hole in the plate are displayed in Fig. 1. We observe that despite the fact that the inclusion is smaller than the hole, the spurious lobe level is higher for this last. This can be explained by the fact that the scattering strength of a hole is quite small for flexural waves.

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Fig. 1. Beamformed images obtained from ΔC resulting from frictional noise in dB scale. (a) a hole 15 mm of diameter, located at (1.025, 0.343) m (center of the hole). (b) a massive inclusion 8 mm diameter, located at (0.58, 0.348) m (center of the inclusion).

The next challenge is to detect a surface crack and a through-crack. Results for both cracks introduced above are presented in Fig. 2. As can be seen, the defects are successfully detected in both cases because the cracks strongly interact with the acoustic field. The quality of the image obtained with the throughcrack informs us about the satisfying reconstruction of the Green’s functions. However what is detected here is the strongest point of the defect which is likely the edge of the groove and the shape of the defect is not recovered. To achieve this, a complex inverse problem would have to be solved, which is completely beyond the scope of this paper.

Fig. 2. Beamformed images obtained from ΔC resulting from frictional noise in dB scale. (a) surface crack of 30 mm × 1 mm 2 mm depth, located at (0.82, 0.49) m (center of the crack). (b) a through-crack of 30 mm × 1 mm 3 mm depth, located at (0.86, 0.54) m (center of the crack).

The passive localization method presented here has been shown to be efficient and robust when considering differential mode.

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Baseline-Free Repetitive Probing Method

In order to extend the application range of the correlation technique, a concept of a baseline-free pump-probe method is proposed here in active way for a model defect mimicking the behavior of a real nonlinear one. In these series of experiments, the objective is to localize the origin of modulation due to a solid-solid contact (here it is a steel ball pressed against the surface of a plate) on a thin plate using ultrasound. 3.1

Experimental Procedure

An aluminum rectangular plate (1 m × 0.5 m × 3 mm) is horizontally suspended with elastic cords on a metallic support. A 1 cm diameter steel ball is pressed against the top surface of the plate with elastic steel ruler fixed on the support cage (see Fig. 3).

Fig. 3. (left) Pump-probe experimental setup. (right) Nonlinear defect “solid-solid” contact.

The ball plays the role of a nonlinear defect (i.e., fatigue crack) to be localized. In order to find out the defect position, a pump-probe experiment has been conducted. One PZT patch (glued to the plate to provide acoustical coupling) generates a high-frequency (HF) probe wave which is a 100 ms burst with 1 cycle of 5 Vpp sine wave at fprobe = 20 kHz. At the same time, a vibrating shaker rigidly attached to the plate and to the support cage produces a lowfrequency (LF) pump wave which is a continuous 3 Vpp sine wave at fpump = 1 Hz. LF shaker parameters are so as plate continuously performs vertical oscillations, in a such a way that the position of the ball and the plate-ball contact loading state (stress, displacement) slightly changes all the time. The reverberated signals are acquired at 7 PZT patches similar to the PZT emitter, using National Instrument acquisition card (500 kS/s, 8 channels). The signals are amplified at reception. The total length of the HF probe reverberated signals

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is 50 ms which is much less than one period of the pump wave. The acquisitions are repeated for N = 20 times while HF and LF stimulus are excited. An average time between 2 acquisitions is 50 ms. These different acquisitions correspond to different contact loading states and defect position. The idea of these different acquisitions is to collect as much data (information) as possible for different defect state without removing the defect. Elastic rubber cords attaching shaker to the support cage makes the shaker “invisible” in the resulting images (shaker-plate contact assumed to be negligible). 3.2

Signal Processing and Imaging Results

Let us denote by sji (j = 1...M ) the ith acquired signal at known receiver position rj , M is the number of receiving transducers. To enhance the effect of modulation, one should make subtractions between signals at different loading states and the average of all the acquisitions, i.e., Δsji (t)

=

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with n = 1...N acquisitions. We obtain then N × M subtractive matrices which will be the inputs for the beamforming algorithm described in Sect. 2 (see Eq. 4). The pixel energy is computed according to Eq. 5. Finally, by non coherent summation we get the final image result. The obtained results based on this repetitive probing method are shown on Fig. 4. The positions of transducers are indicated by crosses and that of the shaker and defect by, respectively, a circle and a square. The origin of coordinates is taken at the left lower corner of the plate. The spatial pixel resolution is Δx = Δy= 5 mm. One of the transducers array is used as emitter for each figure. On the obtained images we can see that it is possible to localize defect inside and outside the transducers area. In comparison with the results of a conventional back-propagation algorithm applied for baseline experiments (see L. Chehami et al. [12]), in the current images signal-noise-ratio (SNR) is lower: there are many artifacts. This can be interpreted by the lack of information about the modulation effects: in contrast to the array of emitters used for baseline experiments, in pump-probing experiments only one transducer plays role of emitter. Finally, we should mention that some problems can occur if during the experiment acoustic emission (squealing effects) appears.

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4

Conclusions

In this work, we have firstly presented an experimental validation of a passive technique for defect localization in metallic plates. We have successfully detected defects from noise generated by different sources between 10 kHz and 40 kHz. Using noise correlation approach, we have experimentally shown that the method is suitable and robust to detect holes, massive inclusions and even surface or through cracks in the plate. Second, we proposed a baseline-free repetitive pump-probe experimental procedure to detect a contact-like nonlinear defect. The localization of the origin of modulation based on adapted back-propagation algorithm has been described. Efficiency of the algorithm has been illustrated with localization of a 1 cm steel ball pressed against vertically oscillating aluminum plate. Imprecision of this algorithm is connected with an asymmetry of transducers geometry (only one of them plays the role of emitter), in contrary to conventional beamforming algorithm, where all the transducers are at the same time both emitters and receivers. Thus, in this configuration there are preferred directions, and a success of defect localization depends on mutual location of defect and transducers. These preliminary experiments are very promising for Structural Health Monitoring applications and are expected to be of

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considerable value in the future. Further work will focused on the improvement of the SNR images and adapting the current algorithm for passive baseline-free SHM, without need of ultrasonic emitter. Acknowledgment. This work has been supported by the French National Research Agency (ANR): Grant ANR2011-BS09-03901 (PASNI Project), and Grant ANR2017CE08-0013-01 (PANSCAN project).

References 1. Weaver, R., Lobkis, O.: On the emergence of the Green’s function in the correlations of a diffuse field: pulse-echo using thermal phonons. Ultrasonics 40, 435–439 (2002) 2. Artman, B.: A return to passive seismic imaging. Stanf. Explor. Proj. 111, 361–369 (2002) 3. Mirgorodskii, V.I., Gerasimov, V.V., Peshin, S.V.: Experimental studies of passive correlation tomography of incoherent acoustic sources in the megahertz frequency band. Acous. Phys. 52, 606–612 (2006) 4. Roux, P., Kuperman, W.A., NPAL Group: Extracting coherent wave fronts from acoustic ambient noise in the ocean. J. Acous. Soc. Am. 116, 1995–2003 (2004) 5. Sabra, K.G., Roux, P., Thode, M., D’Spain, G.L., Hodgkiss, W.S., Kuperman, W.A.: Using ocean ambient noise for array self-localization and selfsynchronization. IEEE J. Ocean. Eng. 30, 338–347 (2005) 6. Sabra, K.G., Conti, S., Roux, P., Kuperman, W.A.: Passive in vivo elastography from skeletal muscle noise. Appl. Phys. Lett. 90, 194101-1–194101-3 (2007) 7. Farrar, C.R., James, G.H.: System identification from ambient vibration measurements on a bridge. J. Sound. Vib. 205, 1–18 (1997) 8. Sabra, K.G., Srivastava, A., di Scalea, F.L., Bartoli, I., Rizzo, P., Conti, S.: Structural health monitoring by extraction of coherent guided waves from diffuse fields. J. Acous. Soc. Am. EL 123, 8–32 (2008) 9. Larose, E., Roux, P., Campillo, M.: Reconstruction of Rayleigh-Lamb dispersion spectrum based on noise obtained from an air-jet forcing. J. Acous. Soc. Am 122, 3437–3444 (2007) 10. Moulin, E., Abou Leyla, N., Assaad, J., Grondel, S.: Applicability of acoustic noise correlation for structural health monitoring in nondiffuse field conditions. Appl. Phys. Lett. 95, 094104–094104–3 (2009) 11. Chehami, L., Moulin, E., de Rosny, J., Prada, C., Bou Matar, O., Benmeddour, F., Assaad, J.: Detection and localization of a defect in a reverberant plate using acoustic field correlation. J. App. Phys. 115, 104901-1–104901-7 (2014) 12. Chehami, L., De Rosny, J., Prada, C., Moulin, E., Assaad, J.: Experimental study of passive defect localization in plates using ambient noise. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 62(8), 1544–1553 (2015) 13. Chehami, L., Moulin, E., de Rosny, J., Prada, C., Chatelet, E., Lacerra, G., Gryllias, K., Massi, F.: Nonlinear secondary noise sources for passive defect detection using ultrasound sensors. J. Sound Vib. 386, 283–294 (2017) 14. Chehami, L., Moulin, E., Assaad, J., de Rosny, J., Prada, C.: Scattering crosssection estimation using passive imaging in reverberating elastic plates: case study of rigid isotropic inclusion. In: 2018 IEEE International Ultrasonics Symposium (IUS). IEEE (2018)

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15. Chehami, L., Moulin, E., de Rosny, J., Prada, C.: Accuracy of Green’s function estimation from correlation of diffuse elastic waves on thin plates. J. Acoust. Soc. Am. 146(5), 3505–3511 (2019) 16. Novak, A., Bentahar, M., Tournat, V., El Guerjouma, R., Simon, L.: Nonlinear acoustic characterization of micro-damaged materials through higher harmonic resonance analysis. NDT & E Int. 45(1), 1–8 (2012)

Estimation of Deterioration Due to Corrosion in the RC Members Using Higher Harmonics Ahmet Serhan Kırlangıç(&) Department of Civil Engineering, Bahçeşehir University, Istanbul, Turkey [email protected]

Abstract. In general, vibration-based condition assessment techniques rely on monitoring the changes in the modal frequencies of a structure, which may not provide an objective diagnosis in case of missing prior information. Instead, tracking and measuring the higher harmonics of the natural frequencies caused by any damage in the dynamic response of the structure can be used for the damage quantification. This type of nonlinear vibration-based monitoring can be realized by performing higher-order spectral (HOS) analysis, which is widely popular for the inspection of machinery systems, whereas limited studies are available regarding the civil engineering structures. As a rare application, herein, a HOS based diagnostic technique is adapted to estimate the deterioration in corroded reinforced concrete members. The proposed diagnostic technique, which utilizes the higher order spectral analysis, is demonstrated on two laboratory-scale RC poles. The poles are first subjected to the impact vibration tests, and then the recorded transient signals are analyzed with the proposed technique to extract the diagnostic index which is a measure for the damage. The preliminary test results manage to discriminate the corroded pole from the intact one, whereas further study is planned to investigate the sensitivity of the diagnostic index with respect to the level of the damage. Keywords: Condition assessment
Reinforced concrete
Corrosion-induced damage
Nonlinear vibration
Higher-order spectral analysis

1 Introduction In general, damage due to corrosion in the reinforced concrete (RC) structures is assessed performing electrical resistivity tests which measure the lost mass in steel rebars (ASTM C876-15). The deterioration in the concrete itself as a by-product of corrosion is not concerned until it appears on the concrete surface in the forms of cracking or delamination. Such deterioration in the concrete is assessed mostly by using ultrasonic methods, such as pulse velocity, impact-echo, and ultrasonic surface wave techniques (ASTM C597-16; ASTM C1383-15; Kirlangic et al. 2016). Alternatively, an overall approach to diagnose the structural health of a reinforced concrete element as a whole composite material, instead of evaluating the reinforcement and the concrete separately, can be adapted by utilizing vibration-based methods. Traditionally vibration tests are performed to monitor the modal frequencies and the mode shapes to evaluate the overall health of the structural integrity. However, © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 359–366, 2021. https://doi.org/10.1007/978-3-030-64594-6_36

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interpretation of the detected minor shifts in such modal parameters may not always lead to objective conclusions (Farrar et al. 2004; Brownjohn et al. 2011; Das et al. 2016). Instead, monitoring the nonlinearity, which is correlated with the higher harmonics of the fundamental modes caused by a fault in the vibration signals can improve the diagnosis. The degree of such nonlinearity, and hence the significance of the fault, can be measured by performing higher order spectral (HOS) analysis. Particularly, in the field of condition monitoring for machinery, the HOS based damage/fault detection is already widespread. Previously the HOS analysis is mostly performed for monitoring the mechanical systems, such as turbine blades, shafts, rolling bearings etc. (Jeffries et al. 1998; Sinha 2007; Dong et al. 2015). Limited number of studies utilizing the HOS are available regarding the civil engineering structures; for instance, Gelman and Kirlangic (2020) inspected the driven concrete piles for damage detection, whereas Xiang and Tso (2002) examined the lab-scale concrete slabs specimens for flaw detection. This paper aims to discuss the potential of HOS analysis for estimation of corrosion-induced deterioration in the reinforced concrete members. The applicability of the technique is demonstrated on two lab-scale RC poles, one intact and one corroded. The discrimination of the corroded specimen from the healthy one is realized using the diagnostic features extracted from the vibration signals.

2 Signal Processing Method for Diagnosis The higher-order spectrum is defined as the higher-order moments of a random process x(t) (Chandran and Elgar 1994). The second order spectrum, simply power spectrum, contains no phase information, whereas the third-order spectrum (aka bispectrum), provides information on the skewness of a signal when decomposed over frequency, and thus detect phase coupling present in the amplitude and phase modulated signals (Hillis et al. 2006). The bispectrum is expressed as (Collis et al. 1998): Bðf1 ; f2 Þ ¼ E fX ðf1 ÞX ðf2 ÞX  ðf1 þ f2 Þg

ð1Þ

where X denotes the Fourier transformation, X* is the complex conjugate of X, and f1 and f2 are the frequencies. Bispectrum B presents a measure of the phase coupling between frequencies caused by their interaction with a nonlinear mechanism within the inspected structure (Hinich 1982). In the case of such nonlinearity, the bispectrum exposes the pairs of phase-coupled frequencies, which is beneficial to quantify the degree of nonlinearity. A linear system will not exhibit such phase coupling, and therefore will have a zero value for bispectrum. The complex valued bispectrum B can be normalized as (Collis et al. 1998): jBðf1 ; f2 Þj bðf1 ; f2 Þ ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n o n o E jX ðf1 ÞX ðf2 Þj2 E jX  ðf1 þ f2 Þj2

ð2Þ

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The normalized bispectrum (aka bicoherence) b varies between zero and one. A higher b value is measured as the damage, and hence the nonlinearity caused by it increases. Due to its direct representation of the damage, the bicoherence b can be employed as a diagnostic feature.

3 Experimental Work 3.1

RC Specimens and Vibration Test Setup

Two 90-cm-long reinforced concrete poles having 30-cm-diameter are examined; one of the poles is intact (C1) and the other one is corroded (C2) as shown in Fig. 1. Both have the same reinforcement; six 20M longitudinal rebars and 10M stirrups spaced with 20 cm interval. The concrete cover is 4 cm. C2 is corroded up to 10% mass loss by implementing the accelerated corrosion technique. Five longitudinal surfacebreaking cracks, of two extend all along the column, while other three are 30, 50 and 55 cm long are observed on C2. The longest two, which are the widest two at the same time (1.5 mm and 2 mm wide), coincide on the rebars along the column. The shorter three cracks are observed to be very fine. The vibration test setup, shown in Fig. 1, includes an instrumented impact hammer (sensitivity = 22 mv/kgf), a tri-axial accelerometer (sensitivity = 1000 mV/g), a signal conditioner, an analogue filter, and a 4-channel oscilloscope to monitor and acquire the data. The accelerometer is attached 10 cm down with respect to the top of the poles. The impact hammer is hit horizontally at the opposite face at the height same as the accelerometer’s to excite the flexural modes mainly. The impact tests are repeated 50 times for each column. The vibration signals recorded in three directions, namely horizontal 1 (H1), horizontal 2 (H2), and vertical (V) on both poles are shown in Fig. 2. The maximum amplitude of vibration is 30 g, which is attained via the direction H1.

Fig. 1. The test set-up and the RC poles.

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The Results of Diagnosis

Determination of the Resonance Frequencies and the Higher Harmonics. Three dominant resonance frequencies at 795 Hz, 825 Hz, and 1178 Hz are visible in the power spectral densities (PSDs) of the signals recorded for the intact pole (Fig. 3). The former two are identified as the frequencies of the flexural modes in the horizontal directions H1 and H2 respectively since both are the strongest frequencies in all three channels. The latter one, 1178 Hz, on the other hand, is apparent only in the vertical direction V, and therefore represent the axial vibration mode. In short, these three frequencies are considered for measuring the phase-coupling with their higher harmonics. On the other hand, for the corroded pole C2, it is found that the flexural frequencies decrease to 730 Hz and 755 Hz (Fig. 3), which are corresponding to 15.7% and 16.2% reduction in the flexure stiffness in two orthogonal horizontal directions

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compared to the intact pole. Regarding the axial resonance frequency, it is found to reduce to 922 Hz caused by 38.7% loss in the axial stiffness. In summary, the hammer stroke in the horizontal direction H1 enables to excite the orthogonal flexural modes greatly in all three channels of the receiver as well as the axial mode in the vertical direction V. Therefore, nine cases in total, three frequencies in the channels, are decided to be examined by performing the bispectral analysis. The PSDs given in Fig. 3 suggests that the higher harmonics is the strongest for the first flexural mode, 730 Hz, measured via the horizontal direction H1 in the corroded pole. In Fig. 4, the first three harmonics for this mode in C2, 730 Hz, 1460 Hz, and 2190 Hz, in the direction H1 are indicated. Diagnosis with the Bispectral Analysis. The vibration signals measured on the poles are processed with the bispectral analysis to extract the bicoherence b. The fundamental and the second order harmonics, f1 and f2, determined previously in the PSDs plots for each case, which are tabulated in Table 1, are substituted in Eqs. (1) and (2) successively to calculate the b values. A single bicoherence value is extracted by processing fifty signals. In the horizontal direction H1, the bicoherence value b(f1, f2) for the first flexural mode is found 0.977 for the intact pole C1, which increases to 0.984 for the deteriorated pole C2. Similarly, for the second flexural mode, C2 reveals a higher bicoherence value, 0.953, compered to C1, 0.876. Whereas, a higher difference between the bicoherence, which is equal to 0.184 is attained for the axial mode. In the horizontal direction H2, similar to the results obtained for the direction H1, the b values obtained from C1 are lower than the ones for C2. The bicoherence values from the undamaged and damaged poles are separated by 0.055, 0.055, and 0.223 for the first and the second flexural, and the axial modes respectively. In summary, in both horizontal channels (H1 and H2), the axial resonance provides the largest increase in the bicoherence, which is in agreement with the fact that the reduction in stiffness, and thus the deterioration, is highest in the axial direction (38.7%). Lastly, for the vertical direction V, the second flexural and the axial modes reveal a reduced bicoherence value for the corroded pole with respect to the healthy one, which contradicts to the expected diagnosis. Only in the first flexural mode an increase in the bicoherence is measured for the damaged pole. Because the hammer stroke is oriented horizontally, the amplitudes of the vibration acceleration for the vertical channel are in the order of 10 g, which is almost three times less than the amplitudes measured in the horizontal directions. The low impact energy, apparently, could not manage to excite the higher harmonics effectively, and thus reducing the credibility of the signals for this channel for the diagnostic analysis. As a summary, all the results of the bispectral analyses are tabulated in Table 1.

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Fig. 4. The higher harmonics of resonance frequency for C2.

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Table 1. The diagnostic features. Channel Resonance frequency H1 1st Bending: 795 Hz (C1) 730 Hz (C2) 2nd Bending: 820 Hz (C1) 750 Hz (C2) Axial: 1180 Hz (C1) 920 Hz (C2) H2 1st Bending: 795 Hz (C1) 730 Hz (C2) 2nd Bending: 820 Hz (C1) 750 Hz (C2) Axial: 1180 Hz (C1) 920 Hz(C2) V 1st Bending: 795 Hz (C1) 730 Hz (C2) 2nd Bending: 820 Hz (C1) 750 Hz (C2) Axial: 1180 Hz (C1) 920 Hz (C2)

C1

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0.977 0.984

0.876 0.953

0.785 0.970

0.859 0.914

0.894 0.949

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0.878 0.970

0.969 0.957

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4 Conclusions A pilot study is conducted on two small scale RC poles to demonstrate the diagnostic technique based on the bispectral analysis. The developed signal processing routine is performed for three resonance frequencies measured by three orthogonal channels in total. The diagnostic bicoherence feature is found higher for the corroded pole, and hence discriminating the damaged specimen from the undamaged one successfully for seven cases out of nine. The two cases of unsuccessful diagnosis are considered due to the low amplitude of vibration. The pilot study presented herein will be continued with multiple specimens with different levels of deterioration in order to perform a sensitivity investigation upon the exact loss in the stiffness of RC elements. Additionally, the excitation level is the main limitation for a reliable assessment. Therefore, the further investigations will focus on selection of the optimum excitation type and amplitude.

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Acknowledgments. The author thanks Professor Giovanni Cascante from the Department of Civil and Environmental Engineering at the University of Waterloo for providing the test equipment and the test specimens. This work was supported by The Scientific and Technological Research Council of Turkey (TUBİTAK) [Reintegration Grant, Project ID: 118C022].

References ASTM C597-16: Standard Test Method for Pulse Velocity Through Concrete. West Conshohocken, PA: ASTM International (2016) ASTM C876-15: Standard Test Method for Corrosion Potentials of Uncoated Reinforcing Steel in Concrete. West Conshohocken, PA: ASTM International (2015) ASTM C1383-15: Standard Test Method for Measuring the P-Wave Speed and the Thickness of Concrete Plates Using the Impact-Echo Method. West Conshohocken, PA: ASTM International (2015) Brownjohn, J.M.W., de Stefano, A., Xu, Y., Wenzel, H., Aktan, A.E.: Vibration-based monitoring of civil infrastructure: challenges and successes. J. Civil Struct. Health Monitor. 1 (3–4), 79–95 (2011) Chandran, V., Elgar, S.: A general procedure for the derivation of principal domains of higherorder spectra. IEEE Trans. Signal Process. 42(1), 229–233 (1994) Collis, W.B., White, P.R., Hammond, J.K.: High order spectra: the bispectrum and trispectrum. Mech. Syst. Signal Process. 12(3), 375–394 (1998) Das, S., Saha, P., Patro, S.K.: Vibration-based damage detection techniques used for health monitoring of structures: a review. J. Civil Struct. Health Monitor. 6(3), 477–507 (2016) Dong, G., Chen, J., Zhao, F.: A frequency-shifted bispectrum for rolling element bearing diagnosis. J. Sound Vibr. 339, 396–418 (2015) Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R., Czarnecki, J.J.: A review of structural health monitoring literature: 1996–2001. Los Alamos National Laboratory, Los Alamos (2004) Gelman, L., Kirlangic, A.S.: Novel vibration structural health monitoring technology for deep foundation piles by non-stationary higher order frequency response function. Struct. Control Health Monitor. 27, e2526 (2020). https://doi.org/10.1002/stc.2526 Hillis, A.J., Neild, S.A., Drinkwater, B.W., Wilcox, P.D.: Global crack detection using bispectral analysis. Proc. Roy. Soc. A: Math. Phys. Eng. Sci. 462(2069), 1515–1530 (2006) Hinich, M.J.: Testing for Gaussianity and linearity of a stationary series. J. Time Series Anal. 3 (3), 169–176 (1982) Jeffries, W.Q., Chambers, J.A., Infield, D.G.: Experience with bicoherence of electrical power for condition monitoring of wind turbine blades. IEE Proc. Vision Image Signal Process. 145(3), 141–148 (1998) Kirlangic, A.S., Cascante, G., Polak, M.: Damage assessment of concrete beams with defects using surface waves. ACI Mater. J. 113(1), 73–81 (2016) Sinha, J.K.: Higher order spectra for crack and misalignment identification in the shaft of a rotating machine. Struct. Health Monitor. 6(4), 325–334 (2007) Xiang, Y., Tso, S.K.: Detection and classification of flaws in concrete structure using bispectra and neural networks. NDT E Int. 35(1), 19–27 (2002)

A Damage Detection Method of Bridges Utilizing Vehicle Vibration Time History Signal Zhongru Yu1,2(&), Shuai Shao1,2,3, Guojun Deng1,2, and Zhixiang Zhou3 1

College of Civil Engineering, Chongqing Jiaotong University, Chongqing, China [email protected], [email protected], [email protected] 2 State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing, China 3 College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China [email protected]

Abstract. Traditional bridge health monitoring methods rely on a large number of sensors, but due to the high cost of sensors and its installation, and timeconsuming installation process, those methods are not suitable for the rapid and effective health monitoring of a large number of medium and small-span bridges. However, the method based on vehicle-bridge coupled vibration theory (VBI) to extract structural modal parameters to identify the damage from vehicle body response, such as frequency and mode shape, is often difficult to obtain accurately in practical applications. In order to solve this problem, a simple method is proposed in this paper to extract the transformation features related to structural damage from the acceleration response of passing vehicles to identify the damage. First, the transformation characteristics related to the bridge structure damage are extracted from a large number of vehicle body acceleration responses by combining Mel-frequency cepstral analysis with Teager energy operator in the signal undecoupled state, and the mathematical statistical model of these transformation characteristics is constructed. Then, the structural damage is identified by analyzing and comparing the statistical distribution of these transformation features. It over-comes the deficiency of uncertainty of single test result. The numerical simulation and test results show that this method can effectively identify the structural damage, and well reflect the degree of structural damage by taking the degree of trans-formation characteristic difference as the index. Keywords: Structure health monitoring
Damage detection
Vehicle-bridge interaction
Mel-frequency cepstral coefficients
Teager energy operator

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 367–377, 2021. https://doi.org/10.1007/978-3-030-64594-6_37

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1 Introduction In recent years, large-scale bridge structural health monitoring system obtained fast development. And it has gradually from the stage of academic research applied to practical engineering. However, the monitoring system and the technology today tend to be more focused on large, complex bridges and some key bridges. The health and safety of more and more small and medium span bridges can not be maintained timely and effectively. Conventional bridge health monitoring methods are often far from ideal monitoring results due to the limitation of input cost, vehicle mobility and personnel trait [1, 2]. Yang et al. [3] first proposed an indirect measurement method based on vehicle body response in 2004, simplified the vehicle into a sprung mass model, theoretically proved that the vehicle body response contained bridge frequency information and gave an approximate solution. Subsequently, researchers around the world have carried out a series of studies based on this idea [4–11]. Yang et al. [12–14] proposed to use the response of vehicle and bridge contact point instead of vehicle body response, avoiding the influence of vehicle's own frequency on the test signal. Moreover, Hilbert Transform were used to process the response of the contact point, and the Instantaneous Amplitude Squared (IAS) was obtained, which contains abundant structural damage information [13]. The effectiveness of the method is verified by numerical analysis, and it is shown that the method is insensitive to general environmental noise, vehicle damping and bridge damping. But in practice, the damage of the bridge may last for several years, and the time interval between each independent test and different tests may be long, making it impossible to judge the damage accurately and evaluate the state of the bridge timely. In order to overcome these shortcomings, this paper proposes a method for damage identification based on Mel-frequency Cepstrum (MFC) analysis. The time-history signal of vehicle acceleration was taken as the original data of the test, the frequency domain spectrum of the signal was obtained by Short-Time Fourier Transform (STFT) of the time-history of acceleration, and the damage signal feature was extracted by Melfrequency cepstrum analysis and Teager energy operator. Based on the analysis of the acceleration signal of each test, the characteristic difference index caused by the damage of the structure is calculated to determine the damage of the structure. The effectiveness of the method is verified by numerical simulation and laboratory experiment.

2 Methodology 2.1

Theoretical Vehicle-Bridge Interaction Model

A simplified mathematical model is used to theoretically analyze the VBI system [3], as shown in Fig. 1. A moving vehicle equipped with an acceleration sensor passes through a simply supported beam bridge of length L at a constant speed v, where the vehicle is simplified to a single degree of freedom (SDOF) sprung mass model, including a concentrated mass mv and a lightweight spring with a stiffness of kv . The

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bridge is considered as Euler-Bernoulli beam with smooth deck, and the section size and mass distribution are uniform along the length of the bridge. Without considering the influence of damping on the system, the dynamic equations of vehicle body and the bridge are written as follows mv €qv þ kv ðqv  ujx¼vt Þ ¼ 0

ð1Þ

mu + EIu '''' = f c (t )δ ( x − vt )

ð2Þ

The vertical displacement qv of the vehicle body is calculated from the static equilibrium position of the system; u is the vertical displacement of the bridge deck; m is the mass per unit length of the bridge; E is the elastic modulus of the material; and I is the second moment of area. €qv and €u represent the second order partial derivatives of the vertical displacement of the vehicle body and the bridge with respect to time t and position x respectively. u '''' represents the fourth-order differential of the bridge vertical displacement to the position x, d as Dirac function.

Fig. 1. Vehicle-bridge interaction model

The contact force between the vehicle and the bridge is expressed as the elastic force generated by the relative displacement between the vehicle and the bridge, as shown in Eq. (3). fc ðtÞ ¼ kv ðqv  ujx¼vt Þ

ð3Þ

It is considered that structural damage is caused by local stiffness reduction. In the numerical simulation, the damage is simulated by reducing the stiffness of the element, and the damage condition is shown as follows. 1) 2) 3) 4)

DC0: DC1: DC2: DC3:

Undamaged state (reference state) 10% reduction of stiffness at mid-span 30% reduction of stiffness at mid-span 50% reduction of stiffness at mid-span

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Mel-Frequency Cepstrum Analysis

The vehicle-bridge interaction system is a complex time-varying system. Based on the idea of Short-Time Fourier Tansform (STFT), the non-stationary process is regarded as the superposition of a series of short-time stationary signals. For the acceleration signal € qv ðtÞ in the whole time domain, the signal can be discretized by framing and windowing. In this paper, Hanning window function is used in signal preprocessing. Teager energy operator is a kind of nonlinear difference operator which can characterize the signal energy transformation and instantaneous energy value. After the original signal is processed by framing and windowing, it is discrete into i segment signal. For the discrete signal av;i ðtÞ, the Teager energy operator is defined as following.

2 av;i ðtÞ Q av;i ðtÞ ¼ a_ v;i ðtÞ  av;i ðtÞ€

ð4Þ


 Q av;i ðtÞ is the output of Teager, and av;i ðtÞ represents the ith segment of acceleration signal. In general, Mel-scale is developed to simulate the human auditory system, which is sensitive to frequencies between 2 kHz–5 kHz. The natural frequency of bridge is much lower, so the response range of bridge frequency is reconsidered and an adaptive formula is proposed to simulate the variation trend of Mel-scale [15]. As shown in Eqs. (5), (6) f m ¼ Mðf Þ ¼ 5 lnð1 þ Þ 5

ð5Þ

f ¼ M 1 ðmÞ ¼ 5ðem=5  1Þ

ð6Þ

Where, f is the Hertz-frequency and m is the Mel-frequency. The relationship between the adaptive Mel-scale and the Hertz-scale is shown in Fig. 2. According to Eqs. (5), (6), the Hertz-scale frequency after STFT can be converted to the Mel-scale frequency. Then the logarithmic energy spectrum is filtered by using a triangular filter bank with Mel-frequency distribution, and the output vector is transformed by Discrete Cosine Transform (DCT) to obtain the corresponding Melfrequency Cepstrum Coefficient (MFCC).

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Fig. 2. Transformation between Hertz-scale and Adapted Mel-scale

The damage index is expressed as the Euclidean distance of MFCC between the damage state and the undamaged state, where Mj0 represents the jth MFCC in the undamaged state. As shown in Eq. (7). vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX u n2 DI ¼ t ðMj0  Mjk Þ2

ð7Þ

j¼n1

Based on the above research ideas, the calculation flow chart of damage identification based on MFCC is shown in the Fig. 3.

Fig. 3. Calculation flow chart

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3 Results and Discussion 3.1

Numerical Simulation

The above finite element model is simulated in Ansys, and the configuration parameters of the vehicle and the bridge are set according to the parameters given in Table 1. When the vehicle body mass is 2000 kg, the driving speed is 10 km/h, and the beam is undamaged, the time history signal of the vehicle acceleration is shown in Fig. 4.

Table 1. System dependent parameter Vehicle Body mass 20 kN

parameters Vehicle speed 10 km/h

Spring stiffness 1e3 kN/m

Bridge parameters Span Elasticity length modulus 20 m 2.1e11 Pa

Second moment of area 0.0054 m4

Unit mass 1400 kg/m

After fully considering the time and frequency resolution of the signal, the whole signal is divided into 13 segments by setting the window function of the STFT, that is, each segment corresponds to a structural region. Each segment of the signal is processed by the Teager energy operator, and the Fourier Transform is performed. Then the transform results are passed through a Mel-frequency triangle filter bank and the DCT is carried out to obtain the MFCC of each signal. Finally, Eq. (7) is used to calculate the Euclidean distance of each signal transformation feature relative to the reference state, as shown in the Fig. 5.

Fig. 4. Acceleration time history signal in undamaged state

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Fig. 5. DI values under different damage conditions

From Fig. 5, it can be seen that the DI value defined by MFCC can sensitively identify the existence of structural stiffness reduction. Since there is no noise in the test environment, the DI value of the undamaged state as the reference state is 0. In addition, the signal is segmented to realize the correspondence between each signal and the actual structure region. It can be seen from the figure that the DI value appears a peak at the damage location. So, It can be considered that there is damage in this location. When the stiffness is reduced by 10% at mid-span, the DI value is close to 0.6. When the damage becomes more serious, the peak also increases with the aggravation of the damage degree. 3.2

Experimental Verification

Introduction to the Experimental. In order to verify the effectiveness of the method in the actual test environment, a preliminary laboratory test was carried out (Fig. 6).

Fig. 6. Site layout of test equipment

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Fig. 7. Data acquisition system

In the test, three rectangular steel pipes were spliced together and bolted to form a bridge deck. The length of the bridge is 2.5 m, the width is 12 cm, the height is 2 cm, and the thickness of the rectangular steel pipe is 1 mm. A car model that can control the speed is used to simulate the vehicle crossing the bridge. The car’s shelf has been modified to carry sensors and additional mass. The vertical acceleration was tested by two Donghua low-frequency piezoelectric acceleration sensors, and collected by DH5902N rugged data acquisition and analysis system. The field layout of the test device and the data acquisition system are shown in the Fig. 7 and Fig. 8. The corresponding parameters of the test model are shown in Table 2. Table 2. Laboratory experiment dependent parameters The length of the beam 2.5 m

The height of the beam 0.02 m

The width of the beam 0.12 m

Body weight 1.15 kg

Driving speed 0.5 m/s

Sensor sampling frequency 1000 Hz

In the process of testing, the undamaged state of the structure is first tested. The beam was tested for many times by the vehicle at a constant speed, and the data of the two sensors were processed on average. And finally, 20 independent vehicle acceleration time-history signals were collected. The mid-span of the test beam was slit to simulate structural damage, and other variables were kept unchanged in the following tests. 20 independent tests were carried out on the structure in this damaged state. Finally, 20 independent vehicle acceleration time-history signals were collected. The time history signal of the vehicle acceleration in the initial undamaged state is shown in Fig. 9.

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Fig. 8. Time-history signals of vehicle acceleration measured in the experiment

Result and Analysis. Similar to numerical analysis, the same method is used to process and analyze the test signal. The signal is divided into 13 segments in the time domain by framing, and each segment corresponds to a structural region. According to Eq. (7), the DI value of each signal segment is calculated, and the result is shown in Fig. 10. Due to the interference of motor vibration, road roughness and other noise signals in the actual test process, DI value under undamaged state is not 0, but fluctuates at a low level. The DI value in the mid-span damage state of the structure is calculated. It is obvious from the box diagram that the DI value calculated by different tests fluctuates in a range. Similar to the numerical simulation results, the peak occurs at the mid-span, and the DI value around the peak is also high, indicating that there is damage at and near the location. The experiment only tested for a single damage, and did not consider multiple damages or different degrees of damage. In the future, the experimental scheme will be adjusted to discuss more experimental variables.

Fig. 9. Box statistics of DI values

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4 Conclusion This paper consists of two parts. In the first part, a structure damage identification method based on MFCC is proposed. In the second part, the feasibility of the method is verified by numerical simulation and laboratory experiment. Some conclusions as follows. 1. The DI value based on MFCC is sensitive to damage and can effectively monitor the stiffness change of the structure. 2. Numerical simulation results show that the DI value has different responses to different degrees of damage, that is, the DI value reflects the severity of structural damage. 3. Through the analysis and statistics of a number of test data, the results show that the method has high stability and is not sensitive to the noise of the test environment. In this paper, a preliminary experiment is carried out to verify the effectiveness of the method, and some variables such as vehicle speed, body mass and other damages are not considered. In addition, although this method can accurately identify where the damage occurs, it also shows a high DI value for the undamaged area, which will affect the accurate location of local damage. For that, the author is carrying out relevant work and discussing more influencing factors, which is expected to comprehensively verify and improve the method.

References 1. Sun, L.M., Shang, Z.Q., Xia, Y.: Development and prospect of bridge structural health monitoring in the context of big data. China J. Highw. Transp. 32(11), 1–20 (2019) 2. Deng, L., He, W., Yu, Y.: Research progress in theory and applications of highway vehiclebridge conversion. China J. Highw. Transp. 31(7), 38–54 (2018) 3. Yang, Y.B., Lin, C.W., Yau, J.D.: Extracting bridge frequencies from the dynamic response of a passing vehicle. J. Sound Vibr. 272(3–5), 471–493 (2004) 4. Mcgetrick, P.J., Gonzlez, A., Obrien, E.J.: Theoretical investigation of the use of a moving vehicle to identify bridge dynamic parameters. Insight-Non-Destr. Test. Condition Monitor. 51(8), 433–438 (2009) 5. Yang, Y., Chang, K.: Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique. J. Sound Vibr. 322(4–5), 718–739 (2009) 6. Yang, Y.-B., Chen, W.-F., Yu, H.-W., et al.: Experimental study of a hand-drawn cart for measuring the bridge frequencies. Eng. Struct. 57, 222–231 (2013) 7. Malekjafarian, A., O'brien, E.J.: Application of output-only modal method in monitoring of bridges using an instrumented vehicle. In: Civil Engineering Research in Ireland, August, 2014, Belfast, UK (2014) 8. Siringoringo, D.M., Fujino, Y.: Estimating bridge fundamental frequency from vibration response of instrumented passing vehicle: Analytical and experimental study. Adv. Struct. Eng. 15(3), 417–433 (2012) 9. Yang, Y., Li, Y., Chang, K.C.: Constructing the mode shapes of a bridge from a passing vehicle: A theoretical study. Smart Struct. Syst. 13(5), 797–819 (2014)

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10. Malekjafarian, A., Obrien, E.J.: Identification of bridge mode shapes using short time frequency domain decomposition of the responses measured in a passing vehicle. Eng. Struct. 81, 386–397 (2014) 11. Li, Z., Au, F.T.K.: Damage detection of bridges using response of vehicle considering road surface roughness. Int. J. Struct. Stab. Dyn. 15(3), 1450057 (2015) 12. Yang, Y.B., Zhang, B., Qian, Y., et al.: Contact-point response for modal identification of bridges by a moving test vehicle. Int. J. Struct. Stab. Dyn. 18(5), 1850073 (2018) 13. Zhang, B., Yao, Q., Wu, Y., et al.: An effective means for damage detection of bridges using the contact-point response of a moving test vehicle. J. Sound Vibr. 419, 158–172 (2018) 14. Yang, Y.B., Zhang, B., Qian, Y., et al.: Further revelation on damage detection by IAS computed from the contact-point response of a moving vehicle. Int. J. Struct. Stab. Dyn. 18 (11), 1850137 (2018) 15. Mei, Q., Gül, M., Boay, M.: Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis. Mech. Syst. Signal Process. 119, 523–546 (2019)

Guided Wave Propagation and BreathingDebond Localization in a Composite Structure Shirsendu Sikdar(&), Wim Van Paepegem, and Mathias Kersemans Mechanics of Materials and Structures (UGent-MMS), Technologiepark 903, 9052 Zwijnaarde, Gent, Belgium [email protected]

Abstract. Carbon-fibre reinforced composite laminates are extensively used in aerospace, automotive, wind energy and marine engineering structures due to their light-weight advantage, high-energy absorption capability, fire resistance, high stiffness-to-weight ratios and construction flexibilities. This work is mainly focused on the analysis of nonlinear ultrasonic guided wave propagation and breathing-debond source localisation in a stiffened composite structure. In the process, the finite element method based 3D numerical simulations has been carried out on a stiffened composite structure using a preassigned network of piezoelectric transducers (PZT). From the analysis of the results, it is observed that the presence of plate-stiffener breathing-type debonds produces higherharmonics in the registered PZT signals. Based on the identified differential parameters in the higher-harmonics, the breathing-debond source locations are effectively identified by using a fast and efficient baseline-free SHM strategy that uses Fast-Fourier-Transform of the registered sensor signals from the target structure to detect single as well as multiple breathing-debond locations in the stiffened composite structure. Keywords: Guided wave
Geometric nonlinearity
Debond
Structural health monitoring
Piezoelectric transducer

1 Introduction Multi-layered carbon-fibre composites are widely used as a lightweight construction material for Aerospace, Automotive, Wind-turbine and Marine structures due to their major referred advantages like high in-plane strength and resistance against fire, moisture and noise [1–4]. Stiffened composite structures (SCSs) made of a prefabricated carbon-fibre laminate baseplate and epoxy-bonded stiffeners are often used for such lightweight construction. Breathing-debonds can occur at the stiffener-plate junctions, due to impact, fatigue, improper handling and ageing, which can lead to a catastrophic structural failure [4–6]. Therefore, an effective localisation of these hidden debond regions in the SCSs is important to avoid such sudden structural failures. Many researchers [4–9], presented studies on early-stage mitigation and localization of structural damage before the damage can propagate further and lead to failures. The ultrasonic guided wave propagation based non-destructive evaluation (NDE) and structural health monitoring (SHM) strategies have proven their damage detection © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 378–386, 2021. https://doi.org/10.1007/978-3-030-64594-6_38

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potential for metallic/composite structures. The guided wave propagation based SHM and NDE strategies offer long-distance monitoring potential with high-sensitivity against small hidden defects in the structures [5, 7–15]. These SHM techniques often involve the application of bonded piezoelectric transducers (PZTs) [5, 7, 9]. The ultrasonic guided wave propagation techniques detect the incipient damage based on selected nonlinear characteristics/phenomena (e.g., higher-harmonic generation [16], sub-harmonic generation [17], mixed frequency-response [18, 19] and nonlinear resonance [20]). It is well known that the nonlinear characteristics are highly sensitive against the presence of contact-type damage like- debond and fatigue-crack. Moreover, these nonlinear characteristics have less influence against the environmental impacts compared to the linear response characteristics. Initially, the nonlinear ultrasonic strategies were focused on the nonlinearity of bulk waves [21–23]. Recently, the studies are mainly focused on the nonlinearity of ultrasonic guided waves [24]. The nonlinear ultrasonic guided waves provide long-range SHM potential than the nonlinear ultrasonic bulk waves. It is demonstrated that the generation of higher harmonics (second or third order) can be used for the assessment of location and severity of incipient damages in structures as the generation of higher-harmonics involves various nonlinear phenomena [25]. The recent studies address the investigations on the higherharmonic generation due to contact nonlinearity [26] and nonlinear elasticity [16]. It is noticed that a limited number of works are available that discusses the nonlinearity in ultrasonic guided wave propagation due to the occurrence of breathing-type damage in composite structures, and there is a major scope of future research in this field. This paper presents an extensive study on ultrasonic guided wave propagation in SCSs with breathing -debonds at the plate-stiffener interface. A series of threedimensional (3D) finite element simulation of guided wave propagation in SCSs are presented in this paper. It is envisioned this research will help to understand the breathing-debond induced nonlinear guided wave generation and propagation phenomenon in SCSs that would guide the researchers to develop robust SHM frameworks/tools.

2 Numerical Simulation In the study, the finite element analysis based 3D numerical simulation of PZT actuator induced ultrasonic guided wave propagation in a SCS (450 mm  450 mm  1.8 mm) is carried out using the explicit and implicit codes in ABAQUS [27]. The explicit analysis was used to model the SCS and the implicit analysis was used to model the PZTs. The ‘standard-explicit co-simulation’ was assigned to link the implicit and explicit solutions [27]. In the SCS model, a debond (30 mm  30 mm) was modelled by untying the debond region nodes at the plate-stiffener interphase. The SCS model with a plate-stiffener debond and the selected PZT network is presented in Fig. 1.

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Fig. 1. Numerical model of the SCS with a plate-stiffener debond.

The 8-noded ‘C3D8I’ elements (0.5 mm  0.5 mm  0.2 mm) are assigned to model the SCS. The baseplate and stiffener in the SCS are made of the same carbonfibre reinforced composite laminate, and the assumed material properties of the laminate are given in Table 1. Table 1. Assumed material properties of the scs. E22 E11 (GPa) (GPa) Laminate 73.22 73.22 Adhesive 4.05 4.05 Material

E33 (GPa) 9.8 4.05

G12 (GPa) 4.14 1.445

G23 (GPa) 3.35 1.445

G13 m12 m13 m23 (GPa) 3.35 0.03 0.37 0.37 1.445 0.42 0.42 0.42

q (kg/m3) 1565 1150

The PZT (10 mm dia. and 0.4 mm thin discs) are modelled with ‘C3D8E’ elements (approximate size: 0.05 mm  0.05 mm  0.1 mm) that offer the electro-mechanical coupling behaviour. In the simulation, 1-PZT actuator and 8-number of PZT sensors are considered and their locations on the SCS are given in Table 2. Table 2. PZT positions on the SCS. PZT (No.) Actuator PZT 1 PZT 2 PZT 3 PZT4 PZT5 PZT 6 PZT 7 PZT8

X (mm) 125 325 100 5 5 100 325 445 445

Y (mm) 180 5 5 100 325 445 445 325 100

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A 100 kHz 7-cycle sine Hanning pulse was used as the input signal (as voltage). The input signal and its frequency spectrum are presented in Fig. 2.

Fig. 2. (a) 100 kHz input signal and (b) the frequency spectrum of the signal.

The PZT (NCE51) properties are considered as per the manufacturer, NOLIAC® (Kvistgård, Denmark), as given below: 3 2 131 8:7 90:4 0 0 0 6 0 131 90:4 0 0 0 7 7 6 6 0 0 121 0 0 0 7 7½GPa, 6 elastic stiffness: ½C ¼ 6 0 0 20:12 0 0 7 7 6 0 4 0 0 0 0 20:12 0 5 0 0 0 0 0 22:5 mass density: q = 7200 [kg/m3],

2

1945 piezoelectric permittivity constant: ½e ¼ 4 0 0

2

0 1945 0

3 0 0 5  8:85  1012 ½F=m, 1912

3 0 0 0 0 13:36 0 and coupling constant: ½e ¼ 4 0 0 0 13:36 0 0 5½C=m2  6:15 6:15 15:76 0 0 0 In the simulation, a stable time-step of 1e-7 is considered and the contact-acousticnonlinearity in the model is assigned by a frictionless surface-to-surface contact.

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3 Breathing-Debond Source Localisation Strategy A baseline-free SHM strategy is used that uses the FFT-coefficient (FFTC) magnitudes of the second harmonic (2H) signals corresponding to each sensor-to-sensor path in the SCS sensor-network. The debond probability indicator, ‘In(x, y) within the PZT network can be represented as: In ðx; yÞ ¼

n1 X n X

NDij w

ð1Þ

i¼1 j¼i þ 1

  u  Pij ðx; yÞ =ðu  1Þ ’ is a spatial distribution function [27] with vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi offi unR f2 2 u Þ df u f ðFFTCi þ FFTCjo is the nonlinear debond index. The positive values and NDij ¼ t 1nR f 2 2 where ‘w ¼



f1

ðFFTCi Þ df

debond indexes are calculated for each sensor pair where ‘f1’ and ‘f2’ are the lower and upper bands of the 2H signals, and ‘FFTCi’ and ‘FFTCj’ are the amplitude areas of the second harmonic FFTC of any PZT sensor-sensor pair (i, j), as in [27].

4 Results and Discussion 4.1

Analysis of Guided Wave Signals and Nonlinearity

Ultrasonic guided wave signals are obtained from the numerical simulation described in Sect. 2. The time-domain signals corresponding to PZT 3 and PZT 6 are presented in

Normalized amplitude

1.0 PZT3 PZT6

0.5

0.0

-0.5

-1.0 0

200

400 600 Time (µs)

800

1000

Fig. 3. Typical guided wave signals collected from PZT 3 and PZT 6 in the SCS.

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Fig. 3. It is noticed that the wave propagation in SCS at 100 kHz generates multiple guided wave modes. FFT of the collected time-domain signals reveals the nonlinear characteristics of the signals in terms of the prominent generation of higher-harmonics in the frequencydomain. A typical comparison of the signals in Fig. 3 are presented in Fig. 4, and the comparison shows an amplitude difference in the harmonics and higher-harmonics.

0.0016

H1

FFTC magnitudes

0.0014 0.0012

PZT3 PZT6

0.0010 0.0008 0.0006 0.0004

H2 H3

0.0002

H4

0.0000

100

200 300 Frequency (kHz)

400

500

Fig. 4. A typical comparison between the PZT 3 and PZT 6 (ref. Fig. 1) signals in frequencydomain.

The generation of higher-harmonics occurred due to the breathing phenomenon of the plate-stiffener debond in the SCS during the Actuator-induced ultrasonic guided wave propagation. In the process, the breathing-debond acted as an additional actuator that generates the higher-harmonic signals as appeared from the waveform plot in Fig. 5.

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Fig. 5. Waveform plots show the PZT Actuator induced guided wave propagation and the breathing-debond induced nonlinear wave propagation phenomenon at a later stage.

4.2

Localisation of Breathing-Debond

Analysis of experimental and numerical simulation signals The localisation strategy described in Sect. 3 is applied for the detection of breathingdebond locations in the SCS. In the process, on the 2H signals are considered as inputs to the debond source localization algorithm in MATLAB. The nonlinear debond index (NDX) map is presented in Fig. 6, which indicates the breathing-debond location with a higher NDX magnitude.

Fig. 6. Nonlinear debond index (NDX) map showing the predicted breathing-debond location.

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5 Conclusions Nonlinear breathing-debond response on elastic wave propagation was numerically investigated for a SCS. It is found that the presence of such debonds in SCS generates nonlinearity in terms of higher-harmonics in the signals and the higher-harmonic magnitudes increases with the decrease in the sensor-to-debond distance in the SCS. The applied nonlinear debond-response source localization strategy has shown its potential for breathing-debond source localization in SCS using the 2H signals. It is expected that this work will help in further understanding of the nonlinear influence on ultrasonic guided wave signals. Acknowledgement. This research work was supported by the Research Foundation-Flanders (FWO) Belgium under grant agreement no. FWO.3E0.2019.0102.01.

References 1. Peters, S.T.: Handbook of Composites. Chapman and Hall, Boca Raton (1998) 2. Gay, D., Hoa, S.V., Tsai, S.N.: Composite Materials: Design and Application. CRC, New York (2003) 3. Sikdar, S.: Multi-level nondestructive analysis of joint-debond effects in sandwich composite structure. Polym. Test. 1(80), 106149 (2019) 4. Sikdar, S., Mirgal, P., Banerjee, S., Ostachowicz, W.: Damage-induced acoustic emission source monitoring in a honeycomb sandwich composite structure. Compos. Part B: Eng. 158, 179–188 (2019) 5. Giurgiutiu, V., Zagrai, A.N., Bao, J.J.: Piezoelectric wafer embedded active sensors for aging aircraft structural health monitoring. Struct. Health Monitor. 1, 41–61 (2002) 6. Sikdar, S., Ostachowicz, W.: Ultrasonic guided lamb wave based debonding monitoring of advanced honeycomb sandwich composite structures. Strain 55(1), 12302 (2019) 7. Giurgiutiu, V.: Tuned lamb wave excitation and detection with piezoelectric wafer active sensors for structural health monitoring. J. Intell. Mater. Syst. Struct. 16, 291–305 (2005) 8. Hay, T.R., Wei, L., Rose, J.L.: Rapid inspection of composite skin-honeycomb core structures with ultrasonic guided waves. J. Compos. Mater. 37(10), 929–939 (2003) 9. Sikdar, S., Kundu, A., Jurek, M., Ostachowicz, W.: Nondestructive analysis of debonds in a composite structure under variable temperature conditions. Sensors 19(16), 3454 (2019) 10. Sikdar, S., Ostachowicz, W.: Nondestructive analysis of Core-junction and Joint-debond effects in advanced composite structure. Polym. Test. 73, 31–33 (2019) 11. Sikdar, S., Kudela, P., Ostachowicz, W.: Assessment of barely visible impact damage/debonding in a 3D-core sandwich composite structure. Comput. Assist. Methods Eng. and Sci. 24, 259–268 (2017) 12. Sikdar, S., Banerjee, S.: Study of guided wave propagation in a honeycomb composite sandwich plate in presence of a high-density core region using surface-bonded piezoelectric sensors. J. Vibr. Eng. Technol. 4(5), 431–438 (2015) 13. Yang, B., Xuan, F.Z., Xiang, Y., Li, D., Zhu, W., Tang, X., Xu, J., Yang, K., Luo, C.: Lamb wave-based structural health monitoring on composite bolted joints under tensile load. Materials 10(6), 652 (2017)

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14. Balasubramaniam, K.: Lamb-wave-based structural health monitoring technique for inaccessible regions in complex composite structures. Struct. Control Health Monitor. 21 (5), 817–832 (2014) 15. Lowe, M.J.S., Challis, R.E.C., Chan, W.: The Transmission of Lamb waves across adhesively bonded lap joints. J. Acoust. Soc. Am. 107(3), 1333–1345 (2000) 16. Bermes, C., Kim, J.Y., Qu, J., Jacobs, L.J.: Nonlinear lamb waves for the detection of material nonlinearity. Mech. Syst. Sig. Process. 22, 638–646 (2008) 17. Solodov, I., Wacker, J., Pfleiderer, K., Busse, G.: Nonlinear self-modulation and subharmonic acoustic spectroscopy for damage detection and location. Appl. Phys. Lett. 84, 5386–5388 (2004) 18. Aymerich, F., Staszewski, W.J.: Experimental study of impact-damage detection in composite laminates using a cross-modulation vibro-acoustic technique. Struct. Health Monitor. 9, 541–553 (2010) 19. Lim, H.J., Sohn, H.: Fatigue crack detection using structural nonlinearity reflected on linear ultrasonic features. J. Appl. Phy. 118, 244902 (2015) 20. Van Den Abeele, K.A., Carmeliet, J., Ten Cate, J.A., Johnson, P.A.: Nonlinear elastic wave spectroscopy (NEWS) techniques to discern material damage. Part II: Single-Mode Nonlinear Reson. Acoust. Spectrosc. Res. Nondestruct. Eval. 12, 31–42 (2000) 21. Cantrell, J.H., Yost, W.T.: Nonlinear ultrasonic characterization of fatigue microstructures. Int. J. Fatigue 23, S487–S490 (2001) 22. Barnard, D.J., Dace, G.E., Buck, O.: Acoustic harmonic generation due to thermal embrittlement of Inconel 718. J. Nondestr. Eval. 16, 67–75 (1997) 23. Biwa, S., Hiraiwa, S., Matsumoto, E.: Experimental and theoretical study of harmonic generation at contacting interface. Ultrasonics 44, 1319–1322 (2006) 24. Matlack, K.H., Kim, J.Y., Jacobs, L.J., Qu, J.: Review of second harmonic generation measurement techniques for material state determination in metals. J. Nondestr. Eval. 273, 1–23 (2015) 25. Jhang, K.Y.: Nonlinear ultrasonic techniques for non-destructive assessment of micro damage in material: a review. Int. J. Precis. Eng. Manuf. 10, 123–135 (2009) 26. Shen, Y., Giurgiutiu, V.: Predictive modeling of nonlinear wave propagation of structural health monitoring with piezoelectric wafer active sensor. J. Intell. Mater. Syst. Struct. 25, 506–520 (2014) 27. Sikdar, S., Paepegem, W.V., Ostachowicz, W., Kersemans, M.: Nonlinear debond response analysis in a smart composite structure using elastic wave propagation. Compos. Part B: Eng. 195, 179–188 (2020)

Towards the Next Generation of Performance Indicators Supported by SHM

Structural Health Monitoring (SHM) Goes to Space Aswin Haridas1(&), Carlos Miguel Giraldo2, and Holger Speckmann1 1

2

Testia GmbH, Cornelius-Edzard-Street 15, 28199 Bremen, Germany [email protected] Airbus Operations, S.L, Paseo John Lennon S/N, 28906 Getafe, Madrid, Spain

Abstract. In recent years, the possibility of exploring outer space has captivated interest among various stakeholders around the globe. Be it for space tourism, for unmanned or manned planetary explorations or for the health status assessment of satellites, new developments in asset monitoring systems are envisaged to ensure the robustness and reliability of these missions. Structural Health Monitoring (SHM) is one such technology that can bring us one step closer to this goal by asserting increased levels of safety and breaking down the overall mission costs. By using intelligent sensor networks for diagnosis and prognosis of the asset condition, SHM ensures the integrity of the assets at every step of the mission. However, implementing SHM solutions for space mission have not received much consideration due to complexities that arise from several factors including, environmental conditions, measurement reliability and unavailability of adequate standards. This article dwells deeper into understanding the capabilities of the currently available SHM sensor technologies under the influence of these factors. Following the analysis, remarks are made on promising technologies and the potential they behold in future space missions. Keywords: Structure health monitoring Future technologies


Outer space
Asset monitoring


1 Introduction With the rapid growth in the space exploration sector, prominent stakeholders including, NASA and ESA have recognized the importance of monitoring the critical assets for improved mission safety, reliability & affordability [1, 2]. Therefore, having Structural Health Monitoring (SHM) sensors and systems on board for condition monitoring, fault detection and prescribing recovery actions is a certainly a must for ensuring mission success. Over time, the data accumulated using these sensors and systems could also pave the way towards having reusable space assets, reducing the vehicle downtime, operating costs and the maintenance costs [3]. SHM was born from the conjunction of several techniques that share a common basis with Non-Destructive Testing (NDT). In fact, several NDT techniques can be converted into SHM techniques by integrating sensors and actuators into the structure (permanently installing the same) [4]. Some of the most commonly used SHM © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 389–399, 2021. https://doi.org/10.1007/978-3-030-64594-6_39

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technologies include, conventional strain gauges, fiber optic sensors and acoustic sensing techniques, among many others [4]. Since the fundamental working principles of each of these techniques are different from each other, their performance is highly dependent on the use case and the ambient conditions [5]. Therefore, when selecting appropriate SHM techniques for the space application, the performance of the sensors must be optimized based on the influence of the space environment on their measurement ability. For example, the chosen technology should grapple with ambient temperatures and vacuum, cosmic radiations and electromagnetic emissions, whilst maintaining high measurement accuracy and repeatability. Although there have been many studies to investigate the performance of SHM technologies for monitoring space assets, the majority of the research have looked into the performance of an individual SHM technology [1, 2, 6–11]. In this context, this paper investigates into the most commonly used SHM technologies and compares their performance based on a set of requirements. These requirements have been derived from several factors including, environmental conditions, measurement reliability, unavailability of adequate standards and the technology maturity. The analysis and evaluation presented in this paper is aimed at determining one (or more) promising technology (or technologies), which can be envisaged to revolutionize the space industry in the near future by setting very high standards for safety, reliability and robustness.

2 Currently Available SHM Technologies To identify the opportunities and benefits of the currently available SHM techniques, especially for space applications, a thorough understanding of the inherent definitions and capabilities offered by these technologies is necessary. Since the use of SHM for space application is fairly a recent innovation, additional developments are necessary, especially for characterizing the behavior of the sensors in various conditions offered by the space environment. Therefore, standards defining the minimum requirements for a SHM system, especially for space applications are nonexistent. However, since the system requirements for space applications can be comparable with (to an extent) the ones defined for fixed wing aircrafts, we have used the SAE ARP 6461 standards for our study [12]. This standard provides, “Guidelines for implementation of structural health monitoring on fixed wing aircrafts”, which could be relevant for applications including (not limited by), helicopters, space crafts and launchers. Essentially, a sensor used for SHM can be classified into active and passive sensors, based on the mechanism of data collection [5]. Consider that the structure to be monitored is equipped with sensors, whose state and physical parameters are evolving due to its interaction with the surrounding environment. Passive monitoring sensors merely monitor this evolution, without imparting any energy (internationally) into the structure under test. Active monitoring sensors monitors the parameters that evolve as a result of the external perturbation. In comparison to passive sensing technologies for SHM, active sensing techniques use methodologies to impart energies (internationally) into the structure to study its response to the input.

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General Architecture of SHM: Operation and Damage Monitoring

Based on the intended function of the SHM system, the inputs to the system and the outputs from the system, the measurement methods can be broadly classified into operation monitoring groups and the damage detection/monitoring groups [12]. Operation Monitoring Systems. Encompasses all the techniques that contribute to the evaluating the condition and utilization of the structure. These parameters can be derived from the damage tolerance and fatigue evaluation of the structure. The output of the techniques could be an advisory indication or the structure usage evaluation. In case of space applications, the sensor (or the group of sensors) should be able to provide a real time indication of the performance & condition of the structure at various launch phases. As necessitated by the requirements, some of the most widely used sensors in this category is detailed in the following section. Fiber Optic Sensors. By providing reliable links for telecommunication, an overall improved performance and increased bandwidth cost, optical fiber technologies have revolutionized the telecommunication industry [13]. Furthermore, the use of sensors based on fiber optic technology have gained momentum due to its higher sensitivity & form factor versatility [13–16]. Fiber optic sensors have since then revolutionized the sensing capabilities for applications such as, strain sensing, vibration monitoring, temperature measurements etc., to name a few. Based on the sensing technologies, fiber optic sensors can be classified into three categories, namely, single point sensors, multiplexed sensors and distributed sensors [14]. The working principle of each of these sensing technologies is illustrated in Fig. 1.

Fig. 1. The working principle of fiber optic sensors (Adapted from [14]).

Single point fiber optic sensors are essentially small and durable sensor units incorporated onto a high bandwidth optical fiber [14]. One of the most common fiber optic sensing technique, namely, Fiber Bragg Grating (FBG) is an example of this category. In comparison to the conventional sensing systems for monitoring the operational parameters, the working principle of FBG’s are fundamentally different. In simple terms, an FBG is manufactured by using a UV laser to modify a single-mode optical fiber. The resultant germanium-doped microstructures creates a periodic variation in the refractive index, which alters the optical properties of the coherent light

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source passing through the fiber. Depending on the grating parameters, the Bragg grating reflects a very narrow wavelength band, while all the other wavelengths are transmitted through the optical fiber. Since the reflected wavelength band is dependent on the grating period, any external parameters that influences the change in the grating period can be identified as a change (or shift) in the reflected wavelength. Using an instrument, which is known as an interrogator (i.e. the data acquisition device), the shift in the wavelength is recorded. Usually, for the sensor applications, these single point sensors are multiplexed and located at strategic locations along the fiber to create a series of connected units for a quasi-distributed measurement [14]. Alternatively, a distributed fiber sensing system detects the changes in scattered light along the length of the fiber and correlates the same with the local variation of the physical quantities (strains or temperature) [15–17]. In other words, the fiber inherently behaves as the sensing element. As per the mechanism of elastic and inelastic scattering in an optical fiber, the sensing techniques can be classified into, Rayleigh, Brillouin and Raman scattering techniques. Rayleigh scattering is a physical phenomenon caused due to non-propagating density fluctuations (implies, scattered power is proportional to the input power). On the other hand, Brillouin and Raman scattering are due to the inelastic physical phenomenon resulting in a degree of frequency shifts. Whilst Rayleigh and Brillouin scattering are being widely investigated for strain measurement applications, Raman scattering is being studied for temperature measurements. A detailed review of these techniques has been published by Xiaoyi Bao and Chen Liang [17]. Fiber optic sensing technologies have been applied for monitoring strains and temperatures of structures such as, bridges, dams, skyscrapers, aircraft structures etc. (not limited to). In addition, they have also been used for applications which require ultra-precision measurements (e.g. nanometer level deformations) [16–18]. In all of these applications, to ensure the accuracy, repeatability and stability of the measurements several factors play an important role. Parameters including, the choice of adhesive for mounting the fiber, the implementation strategy (externally mounted or embedded) and sensor orientation (important for composite structures) must be optimized [5, 19, 20]. In the context of SHM for space applications, fiber optic sensors (one or many) can be implemented for measuring the absolute parameters of interest with a minimal influence of EMI induced noise. In addition, these sensors are comparatively inexpensive and offer the possibility of reducing the overall weight, since the sensors can be multiplexed to measure over longer distanced. However, the lack of suitable standards and the need for discriminating the wavelength shift due to strains and temperature necessitate trials and field tests prior to implementing the same for space applications in the near future [13–18]. Strain Gauges, PT100 and Thermocouples. These sensors are most commonly used to measure the strains and temperatures on critical structures. Typically, a strain gauge consists of an insulating backing which supports a metallic foil pattern. Using a suitable adhesive, these sensors are bonded on to the structure under test (e.g. cyanoacrylate) [21]. When the structure deforms, the foil deforms, which changes its electrical resistance. Using a Wheatstone bridge, the change in resistance is measured which is then related to the strain (by a gauge factor).

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Alternatevely, a platinum resistance thermometer (PRT; most common type PT100) relates the change in resistance to the absolute value of temperature. For a PT100 sensor, a 1 °C temperature change will cause a 0.384 Ω change in resistance, so even a small error in measurement of the resistance (for example, the resistance of the wires leading to the sensor) can cause a large error in the measurement of the temperature [22]. Thermocouple, on the other hand, measures the voltage generated between two wire legs (made of different metals), which are welded together to create a junction. Thermocouples are used in many industrial markets including, power generation, oil/gas, pharmaceutical, biotech, cement, paper & pulp, etc. They are typically selected because of their low cost, high temperature limits, wide temperature ranges, and durable nature [22, 23]. Damage Monitoring Systems. Consists of all the direct measurement techniques for monitoring and detecting damages on structures. The capabilities of the techniques listed within this list are evaluated based on their ability to (detect and) localize the damages and characterize their physical sizes (also based on type of detectable damages, probability of detection, inspection area). The output is equivalent to an inspection report, which could also be an advisory indication (providing structural prognostics) that can contribute to improve repair or maintenance. Some of the most commonly used sensors in this category and their working principles are discussed in the following sections. Piezoelectric Wafer Active Sensors (PWAS). These sensors utilize the ultrasound wave theory to detect structural damages. Since an elastic ultrasound wave reflect & scatter upon encountering a defect (similar to the conventional NDT ultrasonics), the characteristics of the reflected wave can describe the location and size of the damage (e.g. time of flight, signal amplitude and signal frequency). In comparison to conventional NDT ultrasonics, due to the ability to strongly adhere the sensors onto the sample surface by an adhesive layer, the detection area using a PWAS is larger (waves propagate parallel to the surface too (which are called lamb waves [24]). A PWAS can be used as an active sensor or a passive sensor. In case of the former, the sensor is used to generate an elastic wave, while the latter uses the same to collect an elastic wave. In case of the active sensing modality, the structural response of the structure due to the imparted energy (via an elastic wave) is recorded and analyzed to assess the structural health. Alternatively, in the passive sensing modality, no energy is imparted (intentionally) into the structure and the elastic waves generated due to damage formation and propogation within the structure is collected and analyzed. Furthermore, based on the use case, these sensors can either be arranged in a pulseecho arrangement or a pitch-catch arrangement [25]. In a pulse-echo arrangement, a single sensor generates and receives the signals reflected from the defects (e.g. phased array [26]). Alternatively, in a pitch-catch arrangement, two separate sensors are used, one for generating the signal and the other for receiving the reflected signals. In all of these cases, signals from an undamaged, pristine structure is required for comparison [25, 26]. The accuracy and repeatability of detecting damages using a PWAS is dependent on several factors including, material properties, structure geometry and environmental conditions [26–29]. Even though these sensors have been demonstrated

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for monitoring the health of metallic and composite structures, a thorough assessment of the use cases are necessitated to determine the Probability of Detection (POD) [30]. Acoustic Emission (AE) Sensors. The phenomenon of AE was first observed by converting mechanical vibrations into an electric voltage by Forester [31]. Ever since then, AE has been used as a passive SHM technique, which captures the stimulated energy propagating through the structure due to a sudden change of material stress (using a global or local stimuli). The working principle of a typical AE sensor system is illustrated in Fig. 2. An AE transducer captures the acoustic signals, which is then amplified, converted and evaluated using a data acquisition system [32]. In general for load bearing structures, the sources of AE include, fatigue crack nucleation and growth, plastic deformation, creep, fracture, stress corrosion cracking and corrosion fatigue. Currently available AE sensors use piezoelectric actuators for collecting the signals, while the use of an optical fiber sensor have also been suggested [33, 34]. Even though this technique is highly mature (industrial applications from the early 60’s) and provides an early and rapid detection of flaws, defects and cracks (high sensitivity), the fact that it remains a passive technique along with the need for rigorous calibration has questioned its scope of application, especially in the aerospace sector [35].

Fig. 2. The working principle of Acoustic Emission (AE; Adapted from [33]).

2.2

Capability of Currently Available SHM Technologies- Decision Matrix

Prior to comparing the capabilities of the SHM technologies discussed, the requirements necessitated for space asset monitoring must be detailed. Based on prior experience together with inputs from the ArianeGroup, the set of requirements have been classified into four broad categories, namely, system parameters, condition monitoring parameters, damage monitoring parameters and environmental parameters. The influential factors in each of these categories and their desired state are listed in Table 1. In order to achieve a fair comparison between the techniques, a weighted decision matrix would be prepared. In order to do so, a ranking score would be assigned and each of the technologies would be ranked depending on its performance in comparison with the desired state.

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Table 1. Decision matrix definition Parameters

Desired

Decision matrix score

State

1

0.5

0

Total weight

Low

Low

Medium

High

Installation effort (cost)

Low

Low

Medium

High

Power consumption

Low

Low

Medium

High

Transmitting data to a remote location

Yes

Yes

No

Self-diagnostic ability

Yes

Yes

No

System parameters

Possibility of repair in accessible areas

Yes

Yes

Partially**

No

Measurement accuracy, repeatability,

High

High

Medium

Low

resolution & robustness Monitoring area

Large

Large

Sensor cost/ unit area

Low

Low

Medium

Small

Data acquisition time

Low

Low

Medium

High

Sensor application location*

Ext. & emb

Ext. & emb

Ext. & semi emb

Only ext

TRL level

High

TRL 8–9

TRL 6–7

TRL < 6

Strain measurement range

High

Large

Temperature measurement range

High

Large

Small

Distributed sensing capability

Yes

Yes

No

High

Condition monitoring parameters Small

Damage monitoring parameters Defect measurement sensitivity

High

Yes

Identifying the defect type

Yes

Yes

Yes w/o location

Resistance to cosmic radiation with high energy

High

Yes

No

Resistance to electromagnetic radiation

High

Yes

No

Usable in vacuum (10–10 and 10–11 Atm)

Yes

Yes

No

Usable in low gravity

Yes

Yes

High

No

Environmental parameters

*

No No

Ext. And emb. – External and embedded **Depends on whether the sensor is embedded and the use case

To identify the most suitable inspection technique (or techniques), the ranking score of each of the technologies is combined with a weighting factor (to prioritize) to evaluate its capability, shown in Table 2. The evaluation table above compares the performance of each of the sensor technology based on the requirements of the space industry. Based on the weighted sum, fiber optic sensors are observed to cater to the inspection requirements, whilst being immune (or partially; requires further tests) to ambient conditions. In comparison to the fiber optic sensors, AE, conventional strain & temperature sensors and PWAS sensors are heavier (including pheriperals), require a higher installation cost (per unit area) and effort and are not fully immune to the space environment (requires further tests to confirm).

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Key

a) Fiber optic sensors b) Strain gauges & thermocouples Parameters Weighing factor

a)

c) PWAS d) AE b) c)

d)

Total weight

5

1

0

0

0

Installation effort (cost)

3

0.5

0

0

0

Power consumption

3

0.5

0.5

0

0.5

Transmitting data to a remote location

4

1

1

1

1

Self-diagnostic ability

3

0

0

0

0

Possibility of repair in accessible areas** Measurement accuracy, repeatability, resolution & robustness * Monitoring area

4

0.5

1

1

1

5

1

1

1

1

2

0

0

1

1

Sensor cost/ unit area

2

1

0

0.5

0.5

Data acquisition time

2

1

0

1

1

Sensor application location*

3

1

0.5

0

0

TRL level

4

0.5

1

0.5

0.5

Strain measurement range Temperature measurement range Distributed sensing capability

5 5 3

1 1 1

1 1 0

0 0 0

0 0 0

Defect measurement sensitivity Identifying the defect type Resistance to cosmic radiation with high energy Resistance to electromagnetic radiation

5 5

0.5 0

0.5 0

1 1

1 1

5

1

0*

0*

0*

5

1

0

0

0

Usable in vacuum (10-10 and 10-11 Atm)

5

0*

0*

0*

0*

Usable in low gravity

5

Total * Requires further tests to confirm

0*

0*

0*

0*

53.5

32.5

30

31.5

** considering the popularity of embedding the sensors

3 Conclusion The motivation for this work was derived from the rapid growth in the space exploration section and the necessity for monitoring the health of critical structures, thereby, increasing reliability, safety and affordability. In this article, some of the most commonly used SHM techniques were classified based on the SAE ARP 6461 standards (“Guidelines for implementation of structural health monitoring on fixed wing aircrafts”), due to the unavailability of specific standards for implementing SHM for space applications. Further, these techniques were compared to each other based on a set of

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requirements, which were inputs derived from prior experiences and from prominent stakeholders in the space industry. These categories covered various aspects including, system parameter, operational parameters (condition and damage monitoring) and environmental parameters. By performing a weighted evaluation to determine the ability the SHM technologies to meet the requirements posed in each of the previously mentioned categories, fiber optic sensors were determined to be the most promising. Especially, considering the stringent requirements to minimize the total cost and the total weight, whilst ensuring immunity to the space environment. Even though the other sensor technologies considered, namely, conventional strain gauges, thermocouples, PWAS and AE are highly mature, they fail to be comparable with the capability of fiber optic sensors. Although, the research presented in this paper does not cover the complete scope of available sensor technologies, it is envisaged to facilitate future space missions, making them safer, reliable and dependable.

References 1. Richards, W.L., Madaras, E., Prosser, W.H., Studor, G.: NASA applications of structural health monitoring technology. In: 9th International Workshop on Structural Health Monitoring, 10–12 September 2013 2. Mckenzie, I., Karafolas, N.: Fiber optic sensing in space structures: the experience of the european space agency. In: 17th International Conference on Optical Fibre Sensors, vol. 5855, pp. 262–269. International Society for Optics and Photonics (2005) 3. Mancini, S., Giorgio, T., Paolo, G.: Structural health monitoring for future space vehicles. J. Intell. Mater. Syst. Struct. 17(7), 577–585 (2006) 4. Speckmann, H., Henrich, R.: Structural health monitoring (SHM)–overview on technologies under development. In: Proceedings of the World Conference on NDT, Montreal-Canada (2004) 5. Balageas, D., Claus-Peter, F., Alfredo, G. (eds.): Structural health monitoring. Vol. 90. John Wiley & Sons, Hoboken (2010) 6. Liu, Y., Seung, B.K., Aditi, C., Derek, D.: Application of system-identification techniques to health monitoring of on-orbit satellite boom structures. J. Spacecraft Rockets 48(4), 589– 598 (2011) 7. Ohanian III, O.J., Naman, G., Matthew, A.C.: Integrated fiber optic structural health sensors for inflatable space habitats. In: A Tribute Conference Honoring Daniel Inman, vol. 10172, p. 101720B. International Society for Optics and Photonics (2017) 8. Ursu, I., Mihai, T., Daniela, E.: Qualification of PWAS-Based SHM technology for space applications. In: Structural Health Monitoring from Sensing to Processing, p. 117 (2018) 9. Prosser, W.H., Allison, S.G., Woodard, S.E., Wincheski, R.A., Cooper, E.G., Price, D.C., Hedley, M., Prokopenko, M., Scott, D.A. and Tessler, A.: Structural health management for future aerospace vehicles (2004) 10. Tansel, I.N., Chen, P., Wang, X., Yenilmez, A., Ozcelik, B.: Structural health monitoring applications for space structures. In: Proceedings of 2nd International Conference on Recent Advances in Space Technologies, 2005. RAST 2005, pp. 288–292. IEEE (2005) 11. Hoschke, N., Price, D.C., Scott, D.A., Richards, W.L.: Structural health monitoring of space vehicle thermal protection systems. In: Key Engineering Materials, vol. 558, pp. 268–280. Trans Tech Publications Ltd. (2013)

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12. Foote, P.: New guidelines for implementation of structural health monitoring in aerospace applications. SAE Int. J. Aerosp. 6(2013-01-2219) 525-533 (2013) 13. Ferdinand, P.: The evolution of optical fiber sensors technologies during the 35 last years and their applications in structure health monitoring (2014) 14. Campanella, C., et al.: Fibre bragg grating based strain sensors: review of technology and applications. Sensors 18(9), 3115 (2018) 15. Kingsley, S.A.: Distributed fiber-optic sensors: an overview. In: Fiber Optic and Laser Sensors III, vol. 566, pp. 28–36. International Society for Optics and Photonics (1986) 16. Thévenaz, L.: Review and progress in distributed fiber sensing. In: Optical fiber sensors, p. ThC1. Optical Society of America (2006) 17. Bao, X., Liang, C.: Recent progress in distributed fiber optic sensors. sensors 12(7), 8601– 8639 (2012) 18. Allwood, G., Wild, G., Hinckley, S.: Fiber bragg grating sensors for mainstream industrial processes. Electronics 6(4), 92 (2017) 19. Guinchard, M., Araújo, F., Barbosa, C., Bianchi, L., Cabon, M., Ferreira, L., Grosclaude, P., Pereira, A.: Mechanical strain measurements based on fiber bragg grating down to cryogenic temperature–precision and trueness determination. In: 26th International Conference on Optical Fiber Sensors, OSA Technical Digest (Optical Society of America, 2018), paper WF85 (2018) 20. Miguel Giraldo, C., Zúñiga Sagredo, J., Sánchez Gómez, J., Corredera, P.: Demonstration and methodology of structural monitoring of stringer runs out composite areas by embedded optical fiber sensors and connectors integrated during production in a composite plant. Sensors 17(7), 1683 (2017) 21. Rao, M.B., et al.: Structural health monitoring (SHM) using strain gauges, PVDF film and fiber bragg grating (FBG) sensors: a comparative study. In: National Seminar on NonDestructive Evaluation, NDE 2006. Citeseer (2006) 22. Atherton, A., Fitton, D.R.: Temperature definition and measurement using platinum resistance thermometers. Trans. Inst. Meas. Control 11(1), 15–24 (1989) 23. Childs, P.R.N., Greenwood, J.R., Long, C.A.: Review of temperature measurement. Rev. Sci. Instrum. 71(8), 2959–2978 (2000) 24. Worden, K.: Rayleigh and lamb waves-basic principles. Strain 37, 167–172 (2001) 25. Giurgiutiu, V., Cuc, A.: Embedded non-destructive evaluation for structural health monitoring, damage detection, and failure prevention. Shock Vib. Dig. 37, 83–105 (2005) 26. Giurgiutiu, V., Bao, J.: Embedded-ultrasonics structural radar for in situ structural health monitoring of thin-wall structures. Struct. Health Monitor. 3, 121–140 (2004) 27. Zhao, X., Gao, H., Zhang, G., Ayhan, B., Yan, F., Kwan, C., Rose, J.L.: Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I. defect detection, localization and growth monitoring. Smart Mater. Struct. 16, 1208 (2007) 28. Kessler, S.S., Spearing, S.M., Soutis, C.: Damage detection in composite materials using Lamb wave methods. Smart Mater. Struct. 11, 269 (2002) 29. Ihn, J.B., Chang, F.K.: Pitch-catch active sensing methods in structural health monitoring of aircraft structures. Struct. Health Monitor. 7, 5–19 (2008) 30. Dong, T., Kim, N.H.: Cost-effectiveness of structural health monitoring in fuselage maintenance of the civil aviation industry. Aerospace 5(3), 87 (2018) 31. Forster, F.: Akustische untersuchung der bildung von martensitnadeln. Z. Metallk 29, 245 (1936) 32. Dong, Y., Ansari, F.: Non-destructive testing and evaluation (NDT/NDE) of civil structures rehabilitated using fiber reinforced polymer (FRP) composites. In: Service Life Estimation and Extension of Civil Engineering Structures, pp. 193–222. Woodhead Publishing (2011)

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33. Garcia-Souto, J.A., Lamela-Rivera, H.: High resolution (< 1nm) interferometric fiberoptic sensor of vibrations in high-power transformers. Opt. Express 14(21), 9679–9686 (2006) 34. Fu, T., et al.: Application of fiber bragg grating acoustic emission sensors in thin polymerbonded explosives. Sensors 18(11), 3778 (2018) 35. Beattie, A.: Acoustic emission, principles and instrumentation. Sandia National Labs. Albuquerque (1983)

Standardization and Guidelines on SHM and NDT: Needs and Ongoing Activities

Methods to Quantify the Utility of NDT in Bridge Reassessment Stefan Küttenbaum1(&), Sascha Feistkorn2, Thomas Braml3, Alexander Taffe4, and Stefan Maack1 1

2

Bundesanstalt für Materialforschung und -prüfung (BAM), Division 8.2, Berlin, Germany [email protected] SVTI—Swiss Association for Technical Inspections, Nuclear Inspectorate, Wallisellen, Switzerland 3 Institute of Structural Engineering, Bundeswehr-University Munich, Neubiberg, Germany 4 HTW Berlin, University of Applied Sciences, Berlin, Germany

Abstract. There is a continuing need for reassessments of existing bridges. The validity of reassessment results depends to a large extent on the information used for the calculations. In the meanwhile, the application of non-destructive testing (NDT) methods on concrete is suitable for gathering quantitative information about individual structures that are both relevant and accurate. Such measured information can be explicitly incorporated into probabilistic models used for the bridge reassessment. This way, the level of approximation of the considered model and therewith the validity of the reassessment results can be increased. The purpose of this contribution is to introduce and to apply the developed approach of incorporating non-destructively gathered measurement results (instead of deterministic information and assumptions) into a reassessment model of a typical prestressed concrete road bridge and to outline the advantages. An essential part is the quality evaluation of the non-destructively measured information, that deals primarily with two questions. Could the object or parameter to be obtained reliably detected and if, how accurate are the inspection results achieved? Therefore, the importance of the combination of a probability of detection (POD)-approach and measurement uncertainty calculations is emphasized. With regard to the introduced case-study it is shown, for which structure parameters an assumption deviating from the actual (and measurable) situation has a particularly strong (and possibly arithmetically unfavorable) influence on the structural reliability. Measurements on such parameters are particularly beneficial for a reliable and robust reassessment. In conclusion, the individual reassessment results without consideration and with consideration of evaluated non-destructive inspection results are compared. Keywords: Non-destructive testing (NDT)
Reassessment
Concrete bridges
Measurement uncertainty
Structural safety
Reliability
Probability of detection (POD)

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 403–413, 2021. https://doi.org/10.1007/978-3-030-64594-6_40

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1 Introduction The validity of computation results basically depends on the condition of the used information. If an information is to be used for the reassessment of a structure, its relevance, correctness and accuracy have to be evaluated [1]. Furthermore, the comparability and objectivity of the measured information are essential requirements to include them in reassessment models. The suitability of information generally depends on the intended purpose. It’s relevance regarding structural reassessments can be estimated based on preliminary probabilistic studies. These include sensitivity analyses and studies of initial distribution parameters of the basic variables. The correctness (trueness) and accuracy of measured information has to be determined by measurement uncertainty calculations, whereby the application of the internationally harmonized and accepted rules in metrology provided within the Guide to the Expression of Uncertainty in Measurement (GUM)-framework maximizes the comparability. The objectivity is guaranteed by the transparent and unambiguous documentation of both, the calculation and the measurement result. Thus, the model of the measurand (the measured quantity) can be disproved. The requirement for the measurability of a quantity using the methods of non-destructive testing is in turn, that the quantity can be reliably observed under the individual boundary conditions. Therefore, the POD-approach for an objective measurement data analysis can be applied to minimize for instance inspection personnel effects such as different levels of training, prior-knowledge or experience. The concept of measured data-based reassessments using probabilistic methods is summarized in Fig. 1. Based on an initial calculation model (cf. Sect. 2), the measurands and measurement procedures can be defined in a targeted way (see Sect. 3). The quality of the measured information has to be evaluated individually (cf. Sect. 4). The quality evaluated measurement result shall than be transferred to a measured databased stochastic model being used in the reassessment using NDT results (see Sect. 5). 1

Pre-investigation Crucial basis variables? Needed accuracy? 0,268

0,08

0,12

effective depth of a crosssection

30

uncertaintyof load effects

0,264

2

Measurement + uncertainty evaluation

3

Stochastic modelling based on measurement results

precise? true? accurate?

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Reliability analysis based on the updated model

scat tering characteristics

height of construction

-0,04

uncertainty of resistance

0,63

density function

traffic loads

E

R

-0,59

expectat ion Measurement specificaons

Measurement capability

Refined reassessment model

Measured data-based reassessment

Fig. 1. Concept of measured data-supported reassessments using probabilistic methods [2]

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2 Prestressed Concrete Bridge and Initial Computation Model The methodology and the advantages of the summarized approach will be demonstrated by means of a case-study. The investigated structure is a prestressed concrete bridge, that was located in southern Germany until it’s very recent dismantlement. The bridge represents a promising research project due to the associated possibility of dedicated verifications of the non-destructively gathered measurement results. The lengthways and transverse prestressed concrete bridge carried a two-lane federal road as well as pedestrian paths over the river “Regen”. The total length is about 133 m, the spans lengths are 39 m + 55 m + 39 m and the width of the single cell hollow box cross-section is about 12 m (cf. Fig. 2). The bridge was built in 1965 and is typical for a large amount of prestressed concrete road bridges in Germany. The bridge is assessed below regarding its shear force bearing capacity. In particular, the proof of the shear reinforcement VR,s  VE is performed at a distance d of about two meters from the edge of the support according to EN 1992–2 [3]. It should be noted that this proof provides rather conservative (in the sense of “too safe”) results in comparison to the proof of main tensile stresses according to DIN 4227. In other words: Although the bridge has not shown any shear cracks over a long period of time, a small computational reliability can be found. The limit state function is:  
Asw g VR;s ¼ HR;s

fy
ð0; 9
ðhS  d1 ÞÞ
ðcot h þ cot aÞ
sin a  HE sw
ðVG þ VQ þ VP Þ

ð1Þ

with H – model uncertainties, VG, VQ, VP – effects of actions, Asw/sw – shear reinforcement stirrup area per unit length (i.e. per spacing of the stirrups), fy – yield strength and z  0,9 ∙ (hs - d1) – inner lever arm. The mounting angle of the stirrups is

Fig. 2. Photo, lateral view and standard cross-section of the investigated bridge [2]

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S. Küttenbaum et al. Table 1. Initial computation model (extracted i. a. from as-built documents). Quantity HR;s Asw sw fy hs d1 HE VG VQ (BK60/30) VP

Distr. Type LN N N N N N LN N GUMBEL N

Mean 1; 1 8; 09
104 0; 175 400 2; 25 0; 18 1; 0 5; 393 1; 706 1; 474

CoV 10,0% 3,0% 5,7% 7,5% 0,4% 5,6% 10,0% 5,6% 15,0% 5,0%

Dimension  m2 m MN=m2 m m  MN MN MN

a  90°. The concrete strut angle was calculated deterministically (h  31°). The initial computation model (without consideration of measured data) is given in Table 1.

3 Pre-investigation and Definition of the Measurand Preliminary investigation means a reliability analysis based on the initial computation model without incorporating measured information. The First Order Reliability Method (FORM) is applied for this purpose. The strict solution for calculating the probability of failure is approximated by an optimization problem with constraints. The reliability index according to Hasofer/Lind [4] can be defined by: b ¼ ky k ¼ minfkykg fy : hðyÞ  0g:

ð2Þ

Essentially, the most probable point of failure y in standard space is determined. The problem can be solved using e. g. the Rackwitz-Fießler-algorithm [5]. The b-point y describes the most unfavorable combination of the realizations of the considered basic variables [6]. The method was proposed in [4] and further developed by i. a. [5]. Compared to the absolute value of b, other probabilistic parameters are of major importance for the efficient definition of the measurands and the specification of requirements from the structural reassessment on the measurement accuracy. These include the sensitivity coefficients ai xpressing whether the basic variable Xi is crucial or not and the elasticities ei describing how a small change in the value of a distribution parameter affects the calculation result, namely the structural reliability. Further findings can be generated from functional curves of the computed reliability index against the distribution parameters, which are the result of a more global sensitivity analysis and are permissible at least for normally distributed basic variables. The diagrams (Fig. 3) contain selected results of the pre-investigation. It can be derived from the pie chart that e. g. the spacing of the stirrups sw has a significant influence (ar;sw  0; 3). For this quantity, the elasticity of the standard deviation

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indicates, that more precise knowledge is not to be expected to have an excessive effect. However, the elasticity of the expectation shows, that a change in the mean of sw has a significant influence on the structural reliability. Accordingly, it is advisable to verify the actual spacing of the stirrups during the reassessment, especially if the information available prior to the measurements is not adequate in terms of quality or quantity.

Fig. 3. Chosen pre-investigation results consisting of sensitivity coefficients, elasticities of mean and standard dev. and reliability index depending on the mean of the stirrup spacing sw

Consequently, the spacing of the shear reinforcement sw is defined as measurand. The specification of the requirements on the measurement uncertainty is delimited here, since the uncertainty attributed to the measurand sw will be shown to be comparatively small. A Ground Penetrating Radar (GPR) measurement procedure is chosen for the measurement. This choice is justified by relatively low measurement efforts and the high detection probability considering the expected concrete cover of a few centimeters. In addition, or alternatively, the diameter of the reinforcement bars could be measured non-destructively using magnetic-inductive methods. The actual angle between the shear reinforcement and bridge axis could also be determined using GPR.

4 Quality Evaluation of NDT Results 4.1

Probability of Detection (POD)

To use non-destructive inspection results for a static recalculation in terms of bridge reassessment, the performance of the applied GPR-inspection system has to be

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evaluated beforehand. Radar has recently emerged as one of the technologies, which can be employed in various civil engineering applications. What makes GPR particularly attractive is the speed of data collection as well as the broad range of field applications such as the location of metallic tendon ducts and steel rebars, thickness estimation or the detection of moisture differences. Although the general field applicability of GPR has been demonstrated, its inspection task-specific capabilities have to be determined objectively. In this case study, the detection capability for reinforcement is of interest. One statistical approach to determine the inspection reliability under predefined conditions is the POD-approach, which calculates detection probabilities on a specified level based on a certain number of independent observations objectively [7, 8]. This systematic approach was transferred to civil engineering applications in a research study conducted at BAM [9]. As one result of the experimental investigations of GPR, the parameter a90/95 - the so called “reliable detection depth” - will be calculated to describe the reliability of a specific inspection system objectively. This parameter defines the concrete cover, at which a predefined metallic reflector can be detected in 95 out of 100 cases with a probability of 90%. For determining this reliability value of a GPR-system, the “â vs. a” model was applied.

Fig. 4. “â vs. a” model: reflector depths “a” and GPR system responses “â”, referring to [9]

As shown in Fig. 4, the “â vs. a” model represents the reflector depth a on the xaxis as the characteristic value against the system response â, which is plotted on the yaxis. These pairs (reflector depth a; system response â) are represented as black data points and in addition as the probability density functions [POD(a1); POD(a2); POD (a3)] of the system responses â. For calculating the detection probabilities POD(ai), a decision threshold âdec, displayed as a horizontal black dotted line in Fig. 4, has to be defined, since this value defines if a system response â is evaluated as noise or signal.

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The “â vs a model” has to satisfy four criteria for a valid POD calculation [7], which has been demonstrated in [9] based on statistical tests. These four criteria are the linearity between the parameters â and a, a uniform variance of the system responses â, uncorrelated observations â and multivariate normal distributed errors of â. Based on this model, a POD-curve as shown in Fig. 5 can be calculated for each GPR-system considering the specific boundary conditions. The characteristic value a90/95 obtained could serve as an input parameter for the evaluation of the measurement uncertainty and defines the maximum depth range, where a metallic reflector such as a tendon duct or a reinforcement bar can be detected reliably with a selected probability, which was defined to 90% in this example.

Fig. 5. POD-analysis for one GPR system (28 mm rebar diameter; concrete age of 203 days); a) POD curve with 95%-confidence bounds; b) B-scan with rebar depths from 21 cm to 33 cm [9]

4.2

Measurement Uncertainty Calculation

The calculation of measurement uncertainties serves generally to establish confidence in a measurement, to ensure the comparability of measurement results and to assess the quality of the information measured including the elimination of systematic measurement deviations. The internationally harmonized rules published within the Guide to the Expression of Uncertainty in Measurement (GUM [10])-framework are well suited for evaluating the accuracy of individual non-destructive measurements. Essentially, a model of the measurement is built by identifying and quantifying directly measurable input quantities or input quantities that can be determined by non-statistical methods (e. g. based on expert’s knowledge). The measured quantity is calculated by means of a model equation, i.e. by the synthesis of the input variables and by applying the Gaussian error propagation law. The measurement result usually consists of a single measured value representing the observations and the attributed measurement uncertainty, which is to be regarded as a measure of the quality of the measured information about a quantity. The scheme of the measurement uncertainty calculation according to

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the GUM is shown in Fig. 6. It was applied in [11] for the validation of NDT methods in civil engineering. The model used for the GPR measurements on the prestressed concrete bridge specifies the determination of the lateral position of sampling points on bar-shaped reflectors running approximately perpendicular to the measuring lines. Although the application of the principle of time-of-flight measurement is necessary to identify the true indications in the measured data, the analysis of the reflector positions is in the present case based on the choice of the volume element with the maximum amplitude (of the envelope) in lateral direction. The Synthetic Aperture Focusing Technique (SAFT [12]) was used to migrate the data. The determined sampling points were then combined within the individual measurement volumes in such a way, that the average spacing of the stirrups in the longitudinal bridge axis could be specified over a range of approx. 1,30 m on both sides around the investigated cross-section. An exemplary input variable is the limited resolution of the lateral axes. The authors plan to provide the used model shortly as an orientation for future individual measurements.

Fig. 6. Scheme for calculating measurement uncertainties according to GUM, extracted from [13], according to [11] and [14]

5 NDT-Supported Structural Reassessment As shown in Fig. 7, the vertically mounted shear reinforcement bars located inside the investigated volumes could be detected. The x-axis points in the direction of the bridge longitudinal axis and serves to describe the distance between the stirrups, the z-axis provides information about the reinforcement location in the vertical direction. The

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concrete cover is only a few centimeters. From these visualized migrated data, the measurement result was determined as shown in Fig. 8. The averaged spacing between 0 the bars is normally distributed with l ¼ ^sw ¼ 14; 827 cm (measured quantity value) 0
and r ¼ u ^sw ¼ 0; 011 cm (combined standard measurement uncertainty). In this case, the sampling points on four horizontal lines were combined (cf. Fig. 7).

Fig. 7. Imaging of the shear reinforcement detected by GPR measurements. The spacing is derived from the horizontally scattering sampling points on j = 1, …, 4 horizontal lines.

Fig. 8. Measurement result expressing the spacing of the shear reinforcement bars

This measurement result shall be transferred into a basic variable sw suitable for the structural reassessment. A general methodology for the conversion of nondestructively collected measurement results into basic variables for the use in the reassessment with probabilistic approximation methods is part of the ongoing research at BAM and will be published soon. In the present case, the following remarks should be made: Both, the initial model (which is consistent with existing modelling recommendations (e.g. [6])) and the measurand are normally distributed. Accordingly, the tail sensitivity problem (see e.g. [15]) has no effect in relation to the probabilistic pre-investigations (cf. Sect. 3). The statistical uncertainty is negligible (ustat < 0,01cm), since the number of analyzed observations is comparatively large (n = 448). However, the effort of the measurement does not exceed half a day. An arbitrary choice of one out of many based on the available information competing models is not to be made. On one hand, the number of observations is sufficient. On the other, the rules provided within the GUMframework are transparent and unambiguous. Further, no prior knowledge about sw has to be incorporated into the model of the basic variable, since the measurand can be

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comprehensively described by the measurements. However, an additional uncertainty would exist (and sometimes takes on large values), if the investigated volume (sample) is not representative for the area to be modelled. In case of doubt, additional measurements should be conducted. In this specific case, the measurement result as shown in Fig. 8 may be explicitly used to model the basic variable. The initial computation model according to Table 1 and Eq. 1 is used for the reliability analyses supported by measured data. The initial quantity sw is replaced by the measurement result shown in Fig. 8. Consequently, the mean value of sw changes by 14,8 cm–17,5 cm  −2,8 cm. In this individual case, the standard deviation decreases to a negligible value of a tenth of a millimeter. The application of the FORM provides a measured data-based value for the structural reliability index of b  1; 60 (initial: b  0; 65:). This result was as expected according to the curve of the reliability index shown in Fig. 3. In this way, the computation model used for the structural reassessment can be refined successively, even in several steps. This increases the level of approximation (LoA, cf. [16]), the robustness of the calculation and the validity of the computation results, until finally, in the ideal case, a sufficiently meaningful result for the engineer’s judgement about the reliability of a structure could be made. It should be noted, that the reliability does not necessarily have to increase through the incorporation of measured (actual) parameters describing an individual structure.

6 Results and Conclusion The utility of using NDT methods in bridge reassessments can be specified in different ways. From the pre-investigations it can be derived which inspections are particularly useful. By means of the POD approach it can be shown, how likely the quantity of interest can be reliably detected with the chosen NDT-method. The measurement uncertainty calculation in turn provides information about the accuracy of the measured information. On the one hand, the utility of using NDT can be estimated. On the other, it can be predicted whether the measurement will be possible and sufficiently accurate. In the case-study discussed above, the increase in numerical reliability compared to the initial computation is Db  1; 60  0; 65 ¼ þ 0; 95. Such values are only intended to show the valuable effects of using evaluated non-destructive inspection results (in this case by applying the GPR method) in relation to the reference model (cf. Sect. 3). It should be noted for the present case, that the inspection task introduced is simple and therefore economical, if there are doubts about the prior available information or if information about the quantity is missing. Compared to common NDT applications on concrete structures, the individually determined measurement uncertainty is very small, since the averaged relative distance between the stirrups is of interest, and not their absolute position. The conclusion, that we can locate built-in parts as precisely as this is inadmissible. The combination of non-destructive measurements, the application of different concepts (POD, GUM) for the quality assessment of measured data and the reliability analysis proposed here, allows measured information to be prepared in a transparent, revisable and thus objective and comparable manner for the use in reassessments. In some cases, the extension of calculated lifetimes of a structure, and in the best case, the

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saving of resources is conceivable. Conversely, not measuring can be ignorant and lead to a waste of resources. Moreover, the aim is to simplify the approach to a semiprobabilistic one. It has to be investigated, to what extent we may reduce partial safety factors depending on the scope of the measurements and the quality of the results without limiting the standardized reliability level. For this purpose, the GUM and the POD method should be interwoven: Which quantity can we measure how reliably and what accuracy should we expect? Both questions are related in the case of composite measurement quantities like the position of a tendon duct bunch. The more completely a measurand can be described, the more accurate the measurement result.

References 1. Nielsen, L., Tølbøll Glavind, S., Qin, J., et al.: Faith and fakes – dealing with critical information in decision analysis. Civil Eng. Environ. Syst. 36(1), 32–54 (2019) 2. Küttenbaum, S., et al.: Refining stochastic models for the reassessment of bridges using advanced NDT-methods. In: Proceedings 17th International Probabilistic Workshop, pp. 99– 105, Edinburgh (2019) 3. EN 1992–2:2005 + AC:2008: Eurocode 2: Design of concrete structures - Part 2 4. Hasofer, A.M., Lind, N.C.: Exact and invariant second-moment code format. J. Eng. Mech. Div. 100, 111–121 (1974) 5. Rackwitz, R., Fiessler, B.: Structural reliability under combined random load sequences. Comput. Struct. 9(5), 489–494 (1978) 6. JCSS: Probabilistic Model Code. Joint Committee on Structural Safety, Zurich, 2001/2002 7. MIL-HDBK-1823A: Department of Defence Handbook, Nondestructive Evaluation System Reliability Assessment, 7 April 2009 8. Berens, A.P.: NDE Reliability Analysis, Reprinted from METALS HANDBOOK® Volume 17, 9th Edition: Nondestructive Evaluation and Quality Control, University of Dayton Research Institute, ASM International (1989) 9. Feistkorn, S.: Gütebewertung qualitativer Prüfaufgaben in der zerstörungsfreien Prüfung im Bauwesen am Beispiel des Impulsradarverfahrens, Schriftenreihe des Deutschen Ausschusses für Stahlbeton, Beuth Verlag Berlin, Heft 603 (Dissertation TU-Berlin, 2011) 10. JCGM 100:2008. Guide to the expression of uncertainty in measurement 11. Taffe, A.: Zur Validierung quantitativer zerstörungsfreier Prüfverfahren im Stahlbetonbau am Beispiel der Laufzeitmessung, DAfStb-Heft 574. Beuth, Berlin (2008).(German) 12. Langenberg, K.-J., Marklein, R., Mayer, K.: Theoretische Grundlagen der zerstörungsfreien Materialprüfung mit Ultraschall. Oldenburg, München (2010). (German) 13. Braml, T., Taffe, A., Feistkorn, S., Wurzer, O.: Assessment of existing structures using probabilistic analysis methods in combination with nondestructive testing methods. Struct. Eng. Int. 23 (4), 376–85 (2013) 14. Sommer, K.-D., Siebert, B.: Systematic approach to the modelling of measurements for uncertainty evaluation. Metrologia 43(4), 200–210 (2006) 15. Benjamin, J.R., Cornell, C.A.: Probability, Statistics, and Decision for Civil Engineers. McGraw-Hill, New York (1970) 16. International Federation for Structural Concrete: fib Model Code for Concrete Structures 2010. Ernst & Sohn, Berlin (2013)

Structural Health Monitoring System for Furnace Refractory Wall Thickness Measurements at Cerro Matoso SA Diego A. Tibaduiza1(B) , Jersson X. Leon-Medina2 , Ricardo Gomez1 , Jose Ricardo3 , Bernardo Rueda3 , Oscar Zurita3 , and Juan Carlos Forero3 1

Departamento de Ingenier´ıa El´ectrica y Electr´ onica, Universidad Nacional de Colombia, Bogot´ a, Colombia [email protected] 2 Departamento de Ingenier´ıa Mecanica y Mecatr´ onica, Universidad Nacional de Colombia, Bogot´ a, Colombia 3 Cerro Matoso S.A, Montel´ıbano, Colombia

Abstract. In the smelting industry, the knowledge of the integrity of furnaces is a critical information because it allows well-informed decisionmaking during furnace operation, allows adjustments in furnace efficiency, and to prolong its remaining lifetime. To inspect furnace integrity, multiple methods have been explored. Particular characteristics of the furnace and the process make those solutions in most cases unique. This work explores the Ground Penetrating Radar-GPR method to evaluate the thickness of the refractory wall of an electric furnace during its operation in the production plant of Cerro Matoso SA, which is one of the world’s major producers of ferronickel. Results showed that the GPR method is limited by the metallic shield around the wall of the furnace because of the refraction of the signals, however a reliable measurement can be performed by locating the antenna in direct contact with the furnace’s refractory wall. In the latter configuration, it was possible to find different thicknesses at each measurement point and also to detect the phase boundary limit between the refractory wall and molten material. Keywords: Ground Penetrating Radar-GPR · Refractory thickness measurement · Furnace monitoring · Structural health monitoring

1

Introduction

Structural Health Monitoring (SHM) is an area of relevance for evaluating the health of the structures used in the industry [3,7]. Some of the advantages in its application include the use of a sensor network permanently attached to the structure for the inspection of damages, the possibility of on-line monitoring, the use of data pre-processing [2], and data analysis for damage identification [8]. In the case of the smelting industry, it is necessary to ensure the c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 414–423, 2021. https://doi.org/10.1007/978-3-030-64594-6_41

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integrity of the furnaces because it is there where the transformations of the materials occur. Cerro Matoso SA (CMSA) is a ferronickel producer company located in northern Colombia [5], currently is one of the world’s major producers of ferronickel. To hold his production and the quality of the process it is necessary to ensure that each element in the production line is working in optimal conditions during the operation for 24 h a day, 7 days per week to avoid runout of molten material, which is a catastrophic hazard for the safety of the workers, and also avoid economic losses. The bottleneck equipment in the production line is the furnace which allow obtaining the ferronickel from the material mined in an open-cut mine. Due to its importance for the process, to ensure its structural integrity is critical control for the operation. Continuous monitoring reduce the risk of incidents such as run-outs of the liquid metal and slag due to the reduction of failure probability of the components in the sidewall. In general, the sidewalls are composed of refractory bricks which contain the material by allowing the separation of the slag and metal inside the furnace by the application of electrical currents through three electrodes that are in contact with the material to process. Depending on the material to process, sidewalls can suffer wear or erosion because of the material flow and the refractory chemical corrosion. To avoid this problem, a structural health monitoring system is proposed in this work by the use of continuous monitoring and ground-penetrating radars (GPR) located at some positions of the walls of the furnace. The evaluation of the usefulness of GPR was carried out in a prototype and the furnace during its normal operation with excellent results showing the potential for its implementation as a SHM system. In general, this work is organized in six sections starting with this introduction, followed by a small theoretical background, the experimental setup, the experimental results, and the thickness monitoring system proposal. Finally, conclusions are addressed in the last section.

2 2.1

Theoretical Background Ground Penetrating Radar-GPR

GPR (Ground Penetrating Radar) is an electromagnetic inspection technique, oriented to the detection and location of elements, formations and anomalies of the subsoil, buildings and material objects [1]. This technique works in the frequency band between 100 MHz AND 1.5 GHz by emitting electromagnetic pulses between 0.6 ns and 10 ns, and sensing the reflections of these waves that propagate through the medium. This propagation is defined by Maxwell’s equations for electromagnetic waves and its operating principle is similar to that of a conventional radar with a wider bandwidth. The application and collection of the signals requires transmitting and receiving antennas which are directed to the medium to be inspected and the propagation considers characteristics such as the permittivity or relative dielectric constant, the relative magnetic permeability

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and the electrical conductivity. These parameters allow calculating the depth of the object to be studied and the speed of propagation of the electromagnetic wave in a medium. Specifically, the equipment works as follows: The central unit allows the configuration of the electromagnetic pulse generated by means of the transmitting/receiving antenna, which is specific for a certain characteristic frequency. The antenna can be characterized by its central frequency. The wave is propagated through the medium and interacts with objects with electromagnetic properties different from those of the medium, before a part of the wave is reflected and the other continues with its propagation. This interaction allows having an image of the elements present in the propagation of the wave. The reflected waves are captured through the receiving antenna and recorded in the central unit where the image of the received signals is displayed. This image is known as a radargram [1].

3

Experimental Setup

Currently, CMSA has two production lines, each one uses an electric furnace with 22 m of diameter and 7 m of height. Each furnace has a nominal power of 75 MW produce between 100 to 200 t of FeNi/day [6] and has a nominal capacity of 160 dmt/h of calcine to produce between 100 to 200 t of FeNi/day. Figure 1 shows a scheme of an electric furnace.The structural health monitoring system is oriented to evaluate the medium sidewall condition because at this level the smelting process occurs. Smelting implies the separation of the slag and metal at a liquidus temperature and the result of this separation is obtained by the tapholes as it is shown in Fig. 1. Upper and medium sidewalls consist of a steel shield, an expander material, and some refractory bricks. The medium sidewall also includes a cooling system that allows refrigerating the wall. The furnace consists of 72 sections around it, which are also called panels and used to determine the position of the instrumentation in the different sections of the furnace. To validate the use of GPR in the measurement of wall thickness, two scenarios were reviewed. First, a prototype of the skew that correspond to the part where inferior and medium sidewall are in contact, it was reproduced in the refractory laboratory of CMSA. Figure 2 shows the prototype evaluated. This experimental setup also allows to calibrate the equipment with some known distances. Second scenario considers the sidewall of one of the furnaces during its normal operation. Second scenario corresponds directly to the furnace as it is show in Fig. 3.

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Fig. 1. Electric furnace scheme. Adapted from [4]

Fig. 2. Prototype for GPR calibration, skew section

To inspect the structures, a MALA X3 system and a GSSI SIR 4000 were used. The first is compatible with 100, 250, 500 and 800 MHz antennas and includes a monitor that allows viewing the acquisitions and an antenna that interacts with the structure under inspection. This setting is the same for GSSI SIR 4000 but antennas with higher frequency can be arranged. Figure 4 shows a photos of the manual inspection process.

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Fig. 3. Furnace panoramic

Fig. 4. Tests carried out in the middle wall (panel 11) and lower hull (panel 12) of the furnace of line 2.

4 4.1

Experimental Results GPR at the Skew Level in the Test Bench

Two kind of experiments were performed in this experimental setup. First, the calibration and the evaluation of the wall thickness and second the evaluation of skew gap. These evaluations are an important measure for maintenance applications. For this purpose, different types of blocks and configurations were reviewed, changing the type of material present at the opposite end of the measurement made. These measurements were made on the blocks of the prototype as in Fig. 5, since the outer metallic layer behaves as a shield for the antenna and do not allow the propagation of the waves. As result, it was possible to calibrate the equipment to work with a 1600 MHz antenna that brings a depth range of approximately 1.2 m, which is more than the thickness reported by the furnace manufacturer.

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Fig. 5. Inspection to the skew prototype and antenna location

Once the calibration is applied, the evaluation of the gap was performed. Figure 6 shows one of the results obtained by the radargram of the prototype where the fourth refractory block was separated from the rest of the blocks, simulating a skew gap. By analyzing Fig. 6, it can be observed that after the third dielectric variation in the radargram, the gray range darkens, showing the presence of a gap. This is an indicator that allows to identify the presence of gap. However, from additional tests, it could be evidenced that it is not possible to quantify it. In this sense, it can be concluded that it is a viable method to determine the presence or not of a gap, but not exactly the value in cm of this separation. To determine this value, more experiments are required. This is one important result because demonstrates that it is possible to determine the presence of possible gaps that can produce run outs and accidents during maintenance tasks. Although the tests were not carried out at temperatures above ambient, the results obtained in the measurements made in the furnace allow to define that it is possible to replicate it at its operating temperatures. 4.2

GPR in the Areas with Visible Refractory Brick

To evaluate the usefulness of the GPR as a thickness monitoring system in a furnace, the furnace of the line 2 was used. Measurements were made on the exposed walls of a refractory brick. These tests were performed in the panels 11, 12 and 13, where the refractory is exposed as in Fig. 7. Figure 8 shows one of the results obtained, specifically in panel 12. The scale on the right side of the graph corresponds to the depth measured in cm. In this Figure, there are strong discontinuities evidenced by a dark tone in the gray scale. This varies according to the evaluated position. For example, the points of the third row (Fig. 7) evidence that the wall thickness has a distance between 57 and 60 cm. However, a small change at 35 cm can also be observed, which may correspond to the union of the two refractory bricks. In the positions 1 to 14, a variation of this first brick is observed, as well as in the darkest points of the graphs that

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Fig. 6. Radargram of the skews with gap

vary between 50 and 70 cm. It is not certain if the ledge may be influencing this variation. The ledge is the solid metal phase next to the walls of the refractory brick, although it is possible to see that there is a uniform material at those depths. These results are corroborated in panel 13, where measurements were close to those obtained in panel 12, but with greater uniformity in the results. Finally, it is deduced that the thickness measurement of the furnace refractories can be carried out as long as the GPR antenna makes direct contact with the refractory brick. The test at the position number 3 in the radargram of panel #12 (Fig. 8) shows two readings at different depths: one belonging to the first refractory lining and the other to the refractories that make contact with the slag.

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Fig. 7. Positions of the antenna in panels #11, #12 and #13 of the furnace in line 2.

5

Thickness Monitoring System

Due to the results obtained in the experimental setup, it was demonstrated that it is possible to locate the antennas at the medium sidewall to monitor the wall thickness. According to the results, the location of the antennas is proposed to be located at the medium sidewall in direct contact with the refractory bricks. In order to avoid cuts and holes in the shell, the installation of the sensors are recommended to be installed in the current windows of the shell as in the second experimental setup. The number of antennas depends of the resolution required to determine the thickness of the sidewall, however in the most basic mode it is possible to define a setup as in the Fig. 9. This configuration allows to have a thickness measurement of each of the four sections in the furnace. Due to the work temperature, these antennas require to be designed to work with temperatures higher than 200 C in order to allow a permanent installation for the continuous monitoring.

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Fig. 8. Radargrams obtained on the 21 positions of the refractory brick in panel 12 of the furnace in line 2.

Fig. 9. Sensor position for thickness and gap monitoring

6

Conclusions

Geo-radar method is limited by the presence of the furnace shield, but it is possible to use it for measuring the thickness of the refractory bricks when the antenna is in direct contact to refractory bricks or to the expander in the case of the skew. Results show different distances at each measurement point, even though these are close to each other. These distances are important to corroborate that with close measurement points there is uniformity. It was possible to identify gap zones in the prototype of the refractory in the laboratory. Although the data on the size of the gap could not be achieved, it is possible to monitor its presence and then closure. Although it was experimentally demonstrated that the georadar measurement method allows monitoring the presence of gap and wall thickness, it has some limitations for practical utilization because most commercial antennas are designed to work at temperatures close to room temperature. This would make it difficult to have the antenna connected at these

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points permanently. The solution to this issue is the possibility of using accessories that allow the antenna to be covered, nevertheless, the only thing they would do is to delay the heating. Another solution would be to design antennas for higher operating temperatures. The design of these antennas is possible and some related research groups are working with this aim to provide continuous monitoring. Acknowledgment. This work has been funded by the Colombian Ministry of Science through the grant number 786, “Convocatoria para el registro de proyectos que aspiran a obtener beneficios tributarios por inversi´ on en CTeI”.

References 1. Al-Qadi, I.L., Lahouar, S.: Measuring layer thicknesses with GPR - theory to practice. Constr. Build. Mater. 19(10), 763–772 (2005). Non Destructive Testing: Selected papers from Structural Faults and Repair 2003 2. Anaya, M.: Design and validation of structural health monitoring system based on bio-inspired algorithms. Ph.D. thesis, Universitat Polit`ecnica de Catalunya, July 2016 3. Burgos, D.A.T., Vejar, M.A., Pozo, F.: Pattern Recognition Applications in Engineering. IGI-Global (2020) 4. Caro, J.C.F.: Propuesta para mejorar la eficiencia energ´etica de un horno el´ectrico de arco para la producci´ on de ferron´ıquel con capacidad nominal de fundici´ on de 175 t/h y potencia el´ectrica de 75 000 kw. Master’s thesis, Universitat de Barcelona (2019) 5. Gleeson, S.A., Herrington, R.J., Durango, J., Velasquez, C.A., Koll, G.: The mineralogy and geochemistry of the Cerro Matoso S.A. Ni Laterite Deposit, Montelibano, Colombia. Econ. Geol. 99(6), 1197–1213 (2004) 6. Hatch: Cerro Matoso S.A. Montelibano, Colombia: Furnace basic engineering final report for cmsa expansion project. Technical report, Hatch Project Report PR21982.003 (1998) ´ Vitola, J., Anaya, M., Pozo, F.: A dam7. Tibaduiza, D., Torres-Arredondo, M.A., age classification approach for structural health monitoring using machine learning. Complexity 2018 (2018) 8. Burgos, D.A.T., Vargas, R.C.G., Pedraza, C., Agis, D., Pozo, F.: Damage identification in structural health monitoring: a brief review from its implementation to the use of data-driven applications. Sensors, 20(3) (2020)

Numerical and Experimental Assessment of FRP-Concrete Bond System Emma La Malfa Ribolla1(B) , Giuseppe Giambanco2 , and Antonino Spada2 1

Czech Technical University in Prague, Prague, Czech Republic [email protected] 2 Universit` a degli Studi di Palermo, Palermo, Italy {giuseppe.giambanco,antonino.spada}@unipa.it

Abstract. Fiber reinforced polymer (FRP) composite systems are widely used to repair structurally deficient constructions thanks to their good corrosion resistance, light weight and high strength. The quality of the FRP-substrate interface bond is a crucial parameter affecting the performance of retrofitted structures. In this study, ultrasonic testing have been used to assess the quality of the bonding. In the case of FRP laminates adhesively bonded to concrete, high scattering attenuation occurs due to the presence of concrete heterogeneities. The substrate material behaves almost like a perfect absorber generating a considerable number of short-spaced echo peaks that make the defect echo not distinguishable. In order to avoid scattering, waves longer than the discontinuity have to be used, but this expedient makes bonding defects undetectable. The presented technique is based on the energy distribution measurement of ultrasonic signals by means of a statistical parameter, named Equivalent Time Length (ETL). A preliminary numerical study involving a 1-D system with a material discontinuity was performed. 2D finite element (FE) analyses were also performed. The experimental study involved laboratory FRP reinforcements bonded to concrete substrates with imposed well-known defects, and seismic retrofitted concrete walls. The experimental and the numerical findings showed that the ETL is sensitive to the presence of bonding defects in the sense that lower values mean higher reflection of wave energy (low quality of bonding) and higher values mean lower reflection and higher penetration through the bonding (good quality of bonding). Keywords: Ultrasonic NDT Fiber reinforced polymer

1

· Debonding · Equivalent time length ·

Introduction

Fiber reinforced plastic (FRP) composite systems have been used since the mid1980s to repair and retrofit concrete structures. FRP composite laminate is usually externally bonded towards the existing structural elements with adhesive c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 424–434, 2021. https://doi.org/10.1007/978-3-030-64594-6_42

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material to increase their shear, flexural as well as torsional loading capacities. The advantages with respect to traditional strengthening techniques are good immunity to corrosion, high strength/weight ratio, good chemical stability and excellent mechanical properties [1]. The mechanical performance and the durability of this system is strongly affected by the interfacial bonding quality, considering the interfacial zone as the weak and thin layer between fiber and the bulk material where the mechanical properties can be different from the bulk material and the FRP [2,3]. This bonding can be often deteriorated, due to improper workmanship during installation process, hygrothermal aging, and freeze–thaw cycles. Bonding defects such as debonding, delamination, and air void can reduce the life expectancy of FRPretrofitted structure [4,5]. To maintain the structural integrity of FRP-bonded civil infrastructures, it has become more necessary to inspect them and figure out the hidden defect location, shape, and size through reliable, cost-effective, and efficient techniques. In the past several decades, many Non Destructive Techniques (NDTs) have been developed for inspecting the bonding integrity of FRP-bonded structural system. They were reviewed by Qiu [6] and include infrared thermography [7], microwave testing [8], acoustic emission [9–12], radar [13] and ultrasonic inspection [14]. The latter offer some advantages over other ND methods such as versatility, low cost, portability and ease to perform in situ measurement [6]. La Malfa Ribolla et al. [14] have firstly applied the Equivalent Time Length (ETL) to assess the quality of FRP bonding. They have carried out a numerical and experimental study showing the sensitiveness of the ETL, a time parameter that measures the distribution of the energy signal. Later, Campione et al. [15] have showed the potential of ETL in detecting air voids at the bonding between glass structures. In the present study, the concept of ETL is recalled and is applied to assess the quality of the FRP-concrete bonding by means of some novel applications. In Sect. 2 the technique is presented and discussed, Sect. 3 presents a 1-D numerical investigation, Sect. 4 illustrates the experimental results. Section 5 ends the article with some concluding remarks and recommendations.

2

Equivalent Time Length

The technique developed in this study compares the energy distribution of the signal with respect to its onset, and relies on the fact that the acoustic impedance mismatch between the FRP and the concrete is small when the two elements are perfectly bonded [16]. In this case, the sound speed measured in the FRP and the substrate is of the same order of magnitude, as a consequence the energy of vibration is almost entirely transmitted to the concrete where it is largely absorbed by scattering (Fig. 1(a)). On the contrary, when the bond between FRP and concrete is weak or absent due to air gap, the acoustic mismatch is large and a higher amount of energy is reflected back (Fig. 1(b)). To quantify the aforementioned phenomenon, the most common practices involve the use of

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the first echo amplitude [16], the peak-to-peak or the average amplitude of the signal in a given time window [17]. However, it is known that signal amplitudes are affected by the couplant between the probe and the sample and the pressure of the transducers on the sample. (a)

(b)

Concrete Defect Adhesive Fibers

Fig. 1. Scheme of a FRP laminate bonded to concrete subjected to the oblique incident of a bulk wave. (a) A satisfying bond causes the transmission of a high amount of energy which is diffracted inside the concrete. (b) The presence of the defect causes the transmission of a small amount of energy into the concrete substrate, together with the the prompt reflection of most of the excitation energy. Source: adapted from Bastianini et al. [16].

To exceed this limit, we propose the ETL to characterize the energy of the propagating waves defined as   N  A(tk )2 (tk − tAIC )2 k = [1, 2, ..., N ], (1) ET L =  k=AIC+1 N 2 k=1 A(tk ) where A(tk ) is the amplitude of the signal at the time tk , tAIC is the onset time of the received signal which is calculated using the Akaike Information Criterion (AIC) [18–20] which allows the exact onset determination when signal and noise are in the same frequency range. The AIC consists of dividing the time series of interest into locally stationary segments, each modelled as an autoregressive process. A signal including the onset and a first estimate of the onset time is needed. The intervals before and after the onset time are assumed to be two different stationary time series. The point at which the AIC is minimized determines the separation point of the two time series (noise and signal) and therefore the onset point of the signal. The AIC values are obtained through the following function

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-0.2

ETL AIC

0.5

-0.6

0

-1.0

-0.5

-1.4

-1.0 0

1

2 3 Time [μs]

4

AIC [104]

Amplitude

1.0

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Fig. 2. ETL of a signal: 500 kHz sine wave with different amplitudes and the associated ETL. The AIC function calculated for the sine wave of unitary maximum amplitude is overlapped.

AIC[k] = k log(var(A(k, 1))) + (N − k − 1) log(var(A(1 + k, N ))),

(2)

where var(A(k, 1)) is the variance function calculated from the current value of k, while A(1 + k, N ) means that all samples ranging from 1 + k to N are taken. By calculating the minimum of Eq. (2), the onset time tAIC is obtained AIC(kmin ) = min(AIC[k]),

(3)

tAIC = kmin Δt,

(4)

being Δt the sampling period. A similar parameter, also named ETL, is adopted in the framework of timedomain measurements of partial discharge pulse [21] and, together with the equivalent bandwidth, provides an intuitive and compact representation of the detected signals. Figure 2 illustrates the concept of ETL with the first cycle of a sine wave centered at 500 kHz and different amplitudes. The corresponding ETL is calculated analytically as    T 2 t2 dt  sin(ωt) 1 −3 + 8n2 π 2 = √ , (5) ET L =  0 T f2 2 6π sin(ωt)2 dt 0 where ω is the angular frequency, n the number of cycles, T the time associated to n wave period. The AIC referred to the sine wave with unitary maximum amplitude is also plotted as a dashed line. In the range 0–2 µs AIC assumes a negative infinity value due to the zero amplitude of the signal, while in the range 2–4 µs the minimum value is associated to the onset of the signal, i.e. 2 µs. The analytical expression (5) shows that the ETL is independent of the wave amplitude, proportional to the number of cycles and inversely proportional to the actuation frequency of the signal.

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Numerical 1-D Defect Detection System

As a preliminary study to test the ETL for detecting imperfections, the 1-D mechanical system schematized in Fig. 3 was considered. It consisted of three bars with different elastic and/or geometric properties. Bars 1 and 3 (corresponding to Ω1 and Ω3 ) were fixed at one end with properties listed in Table 1. The presence of an internal defect was simulated by varying Ω2 Young’s modulus in the range 100% to 1% of the Ω1 − Ω3 one. The lengths of the three bars were Ω1 : [0 ≤ x ≤ L1 ], Ω2 : [L1 ≤ x ≤ L2 ], Ω3 : [L2 ≤ x ≤ L3 ], respectively.

Z

T R

1

X

2

3

d 0

L1L2

L3

Fig. 3. ETL for defect detection: 1-D medium with the presence of a defect represented by Ω2 domain.

Table 1. 1-D defect detection system: bars’ sizes. Bar domain Length Ω1

14–16.5, 0.5 step

Ω2

0.5

Ω3

5–2.5, 0.5 step

Let u(x, t) be the longitudinal displacement at time t, the wave equation for longitudinal waves is ∂ 2 ui ∂ 2 ui = vi 2 2 , x ∈ [0, L], t ∈ [0, T ], i = 1, 2, 3 (6) 2 ∂t ∂x where L is the total length of the 1-D system and vi the wave velocity. For the sake of simplicity, we assumed that vi are constant over the domains Ωi . The following Dirichlet boundary conditions were considered u1 (0, t) = 0

(7)

u3 (L3 , t) = 0 u1 (L1 , t) = u2 (L1 , t)

(8) (9)

u2 (L2 , t) = u3 (L2 , t)

(10)

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100 80 0 60 0 1 2 40 E [%] 3 4 5 2 20 6 7 8 1 -3 9 Time [10 ]

4.8 4.4 4.0 3.6 3.2 2.8 2.4 2.0

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d=1 d=1.5 d=2 d=2.5 d=3 d=3.5

25.3% 19.1%

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25

50 E [%]

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Fig. 4. ETL for defect detection: (a) 1-D medium with the presence of a defect represented by Ω2 domain. (b) ETL trends. Percentages of EΩ2 indicate the difference with respect to EΩ1 or EΩ3 , the biggest for 1%.

and Neumann boundary conditions ∂u1 (L1 , t) = ∂x ∂u2 (L2 , t) = ∂x

∂u2 (L1 , t) ∂x ∂u3 (L2 , t). ∂x

(11) (12)

The initial conditions read u1 (x, 0) = I(x), u2 (x, 0) = 0, u3 (x, 0) = 0 ∂ui ∂t (x, 0) = 0

(13) (14)

where I(x) represents the source of excitation which was assumed as a Gaussian pulse of amplitude a, location of the peak at x = b, width c, along the bar I(x) = ae−(x−b)

2

/c2

.

(15)

The second-order hyperbolic system of Eqs. (6–12) was solved by using finitedifferences method in MATLAB. Different bars lengths of Ω1 and Ω3 were considered (Table 1) in order to study the effect of the distance d between a measurement point (indicated by ‘R’ in Fig. 3) and the location of the defect on the energy distribution. A gaussian pulse of amplitude a = 0.25 × 10−6 located at x = 12 (indicated by ‘T’ in Fig. 3) and width c = 0.2 was applied as initial condition to Ω1 . At each iteration the Courant-Friedrichs-Lewy (CFL) condition was satisfied. Figure 4(a) shows the axial displacement of bar 1 at point R considering different moduli of bar 2 when d was equal to 2. The first pulse is the incident wave generated at point T while the second pulse is the wave backscattered from the Ω1 –Ω2 interface. As expected, the amplitude of this pulse is strongly dependent on the elastic properties of Ω2 . In particular, the amplitude decreases as long as the Ω1 –Ω2 Young’s moduli mismatch decreases, trend in agreement

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with the theoretical limit case of zero amplitude, if Ω1 and Ω2 had the same elastic modulus. The third pulse is the wave transmitted through the Ω1 –Ω2 interface and reflected from the Ω2 –Ω3 interface. The time of flight between the first and second echo is dependent on the elastic properties of Ω2 . A small pulse subsequently visible is the consequence of the bounce in Ω2 boundaries. Finally, the pulse visible after 7 ×10−3 represents the two-ways transmission of the incident pulse through Ω2 and Ω3 which includes the reflection from the end of Ω3 . Both amplitude and time of flight are dependent on the Young’s modulus of Ω2 . Figure 4(b) presents the ETL as a function of Ω2 Young’s modulus for different values of d. Each trend qualitatively shows an asymptotic increase. Percentages indicate the total variation obtained. It was found that the increase of the distance R-Ω2 interface, maintaining the time window of the signal fixed, causes a shift of the ETL trend over low values and low variations. This result was somehow expected, in fact the ETL is calculated by considering the energy distribution with respect to the starting point of the signal which, in this case, is coincident with the arrival in R of the first echo amplitude. Hence, shifting Ω2 position corresponds to higher values of tAIC or, that is the same, considering smaller time windows and less pulses sensitive to Ω2 characteristics. The numerical result suggests that the material properties of Ω2 can be evaluated by observing the amplitude of the reflected echo, however, the time of flight of the reflected echo signals, except for the first one, is also associated to the characteristics of Ω2 . Therefore, if the first echo is no significantly distinguishable from the scattered signal components, the subsequent time of flights of the echoes can be considered to predict structural changes. In this sense, since the ETL measures both the amplitudes and the time of flights of a group of reflected waves, it may be a promising indicator of structural changes. (a)

(b)

Large defect Medium defect

70

20 μs 40 μs 80 μs 120 μs 163.84 μs

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Pristine state

ETL [ µs]

50 40 30 20 10

Teflon foil CFRP bonded area

0

Total debonding

Large defect

Medium defect

Small defect

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Fig. 5. ETL for defect detection: (a) Concrete samples with well-known bonding defects and (b) ETL as a function of the bonding quality calculated for different time windows.

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4 4.1

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Experimental Study Laboratory-Scale Measurements

Laboratory tests were conducted on four concrete specimens with outer dimensions of 180 × 250 × 150 mm3 , reinforced by carbon fiber reinforced polymer plates set in accordance with the manufacturer’s application guideline, using commercial epoxy resin. In order to simulate the lack of bonding of the FRP, three types of defects of well-known dimensions were located on three of the samples by means of the interposition of foils of Teflon between the concrete surface and the adhesive layer Fig. 5(a). The samples included the limit case of absence of defect, i.e. pristine state, and other defects, classified according to the size of the employed Teflon foil(s). The setups involved the use of an excitation signal equal to five cycles of sine function generated by a house-built ultrasonic pulser-receiver, two ultrasonic transducers arranged in a pitch-catch mode, and an ultrasonic preamplifier. Figure 5(b) displays the ETL vs the quality of bonding extracted for different time windows. From the polynomial trends, it is visible that 20 µs gives back a constant ETL trend. As expected, an adequate time- window is needed to reasonably estimate the energy propagation through the bonding. Specifically, 20 µs is twice the period of the excitation signal and can include only the wave packet reflected from the FRP/adhesive interface, without including pulses that can be affected by the defect’s characteristics. Time windows larger than 20 µs are instead associated to ETL trends that decrease with bigger defects. (a)

(b)

good impregnation of the fabrics low impregnation of the fabrics

Fig. 6. ETL for defect detection: (a) Reinforced wall at San Giovanni Di Dio hospital (ME - Italy) (b) Close-up view of some fabrics with different level of impregnation.

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4.2

In-Situ Measurements

In situ ultrasonic tests were carried out on the wall which constitutes the stair-lift core of the building C of San Giovanni Di Dio hospital (ME - Italy) (Fig. 6(a)). A total number of 416 points were selected as location to perform the measurement. For the bonding quality evaluation criterion, we measured the ETL resulting from a FRP laminate made in laboratory with the same materials used of the reinforced wall (Kimitech CB 1200, Kimitech EP-IN, Kimitech EP-TX). This measurement was considered as the reference one and it gave ET LR = 15.44 µs, where R indicates the reference one for no existence of bonding. At the reinforced wall, when the ETL of the received signal was bigger than the ET LR , the greatest part of the energy was absorbed by the substrate and the characteristics of the bonding were considered good (Fig. 6(b) green mark). The opposite condition was considered for poor bonding quality (Fig. 6(b) red mark). In Fig. 7 the ETL is plotted as a function of the measured position before (red curves) and after (green curves) the saturation of the FRP sheets with epoxy. Again, higher values are associated to a proper installation.

Fig. 7. ETL for defect detection at the reinforced wall of San Giovanni Di Dio hospital (ME - Italy): red curves represents FRP sheets simply installed on the surface, green curves the ones well saturated with the epoxy.

5

Conclusions

This article presented an ultrasonic-based NDE/SHM technique for the evaluation of bonding quality between FRP and concrete. The technique is based on the use of a pair of ultrasonic transducers for the generation and detection of

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surface waves and on the extraction of a novel damage-sensitive feature called Equivalent Time Length (ETL). The technique was tested numerically on a simple 1D application and validated experimentally by testing concrete samples reinforced with CFRP strips. We found that ETL increases with the quality of the bonding and this aspect suggests that it is a valid energy distribution indicator of the transmitted/detected signal, being not dependent on the amplitude of the peaks signal. In the future, this model could be expanded to see if it is possible to establish a relationship between the ETL and the area of the debonding. Moreover, the testing of much larger specimens is warranted prior to field applications. The precise knowledge of each mode behaviour would be also required as, when the bonding damage occurs, the changes affecting energy distribution include velocity and attenuation, multi-modality, dispersion and mode conversion.

References 1. Cottone, A., Giambanco, G.: Minimum bond length and size effects in FRPsubstrate bonded joints. Eng. Fract. Mech. 76(13), 1957–1976 (2009) 2. Spada, A., Giambanco, G., La Malfa Ribolla, E.: A FE-Meshless multiscale approach for masonry materials. Proc. Eng. 109, 364–371 (2015) 3. Giambanco, G., La Malfa Ribolla, E.: A phase-field model for strain localization analysis in softening elastoplastic materials. Int. J. Solids Struct. 172, 84–96 (2019) 4. Parrinello, F., Borino, G.: Non associative damage interface model for mixed mode delamination and frictional contact. Eur. J. Mech. A/Solids 76, 108–122 (2019) 5. Parrinello, F.: Analytical solution of the 4ENF test with interlaminar frictional effects and evaluation of Mode II delamination toughness. J. Eng. Mech. 144(4), 04018011 (2018) 6. Qiu, Q.: Imaging techniques for defect detection of fiber reinforced polymer-bonded civil infrastructures. Struct. Control Health Monit. (2020, in press) 7. Shih, J.K.C., Tann, D.B., Hu, C.W., Delpak, R., Andreou, E.: Int. J. Mater. Prod. Technol. 19(1–2), 174–187 (2003) 8. Akuthota, B., Hughes, D., Zoughi, R., Myers, J., Nanni, A.: Near-field microwave detection of disbond in carbon fiber reinforced polymer composites used for strengthening cement-based structures and disbond repair verification. J. Mater. Civ. Eng. 16(6), 540–546 (2004) 9. Carpinteri, A., Lacidogna, G., Paggi, M.: Acoustic emission monitoring and numerical modeling of FRP delamination in RC beams with non-rectangular crosssection. Mater. Struct. 40(6), 553–566 (2007) 10. Degala, S., Rizzo, P., Ramanathan, K., Harries, K.A.: Acoustic emission monitoring of CFRP reinforced concrete slabs. Constr. Build. Mater. 23(5), 2016–2026 (2009) 11. Spada, A., Giambanco, G., Rizzo, P.: Elastoplastic damaging model for adhesive anchor systems. I: theoretical formulation and numerical implementation. J. Eng. Mech. 137(12), 854–861 (2011) 12. Spada, A., Rizzo, P., Giambanco, G.: Elastoplastic damaging model for adhesive anchor systems. II: numerical and experimental validation. J. Eng. Mech. 137(12), 862–876 (2011) 13. Yu, T., Cheng, T.K., Zhou, A., Lau, D.: Remote defect detection of FRP-bonded concrete system using acoustic-laser and imaging radar techniques. Constr. Build. Mater. 109, 146–155 (2016)

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14. La Malfa Ribolla, E., Rezaee Hajidehi, M., Rizzo, P., Fileccia Scimemi, G., Spada, A., Giambanco, G.: Ultrasonic inspection for the detection of debonding in CFRPreinforced concrete. Struct. Infrastr. Eng. 14(6), 807–816 (2018) 15. Campione, G., Orlando, F., Fileccia Scimemi, G., Pauletta, M.: Bond characterization of monolithic and layered glass panels and ultrasonic tests to control glued surfaces. Eng. Struct. 198, 109545 (2019) 16. Bastianini, F., Di Tommaso, A., Pascale, G.: Ultrasonic non-destructive assessment of bonding defects in composite structural strengthenings. Compos. Struct. 53(4), 463–467 (2001) 17. Kundu, T., Ehsani, M., Maslov, K.I., Guo, D.: Ultrasonic non-destructive assessment of bonding defects in composite structural strengthenings. NDT E Int. 32(2), 61–69 (1999) 18. Akaike, H.: Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes. Ann. Inst. Stat. Math. 26(1), 363–387 (1974) 19. Maeda, N.: A method for reading and checking phase times in auto-processing system of seismic wave data. Zisin 38(3), 365–379 (1985) 20. Kurz, J.H., Grosse, C.U., Reinhardt, H.W.: Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete. Ultrasonics 43(7), 538–546 (2005) 21. Contin, A., Cavallini, A., Montanari, G.C., Pasini, G., Puletti, F.: Digital detection and fuzzy classification of partial discharge signals. IEEE Trans. Dielectr. Electr. Insul. 9(3), 335–348 (2002)

Wireless Sensing Systems for Structural Health Monitoring

Detecting Road Pavement Cracks Based on Acoustic Signature Analyses Rosario Fedele(&)

and Filippo G. Praticò

DIIES Department, Mediterranea University, Reggio Calabria, Italy [email protected]

Abstract. Transportation infrastructures can benefit from structural health monitoring in terms of pavement management systems and risk management. Pavement cracks, both visible and concealed, impact road agency budget but unfortunately there is lack of nondestructive methods to assess them. Consequently, the objectives were confined into setting up and improving a nondestructive, acoustic- and sensor-based method. An experimental investigation that was carried out on an asphalt concrete road pavement, aiming at deriving the Structural Health Status (SHS) of road pavements based on their acoustic response to a proper mechanical excitation (acoustic signature). The method was applied using as sensor device a microphone-based electronic system, which is able to gather only the ground-born sounds. Sensor data (i.e., the acoustic responses) were analyzed in three domains of analysis, i.e., the time, the frequency, and the time-frequency domain. Consequently, meaningful features (e.g., energy and entropy of the Continuous Wavelet Coefficients, spectral centroid) were extracted and used to derive the SHS of the road pavement under investigation, which represents a valuable information for different stakeholders (e.g., authorities, drivers, etc.). Results show that by using a small number of meaningful features and by applying a hierarchical clustering procedure, it is possible to recognize the variation over time of the acoustic signature of the infrastructure due to the presence and the propagation of internal and external cracks. Hence, the proposed method can be efficiently used to monitor the SHS of road pavements during their lifetime, and, consequently, to improve pavement management systems and risk management processes. Keywords: Cracks detection
Acoustic signature
Road pavement extraction
Hierarchical clustering
Wavelet transform


Feature

1 Introduction Transportation infrastructures can benefit from Structural Health Monitoring (SHM) in terms of Pavement Management System (PMS or Pavement Asset Management System, PAMS cf. [1]) and risk management [2]. In fact, the resiliency, safety, and sustainability of road infrastructures build on their structural health conditions [3] and on the effectiveness of the approach (e.g., PAMS) adopted to monitor the conditions above, estimate the remaining asset service life and select the optimum maintenance strategies. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 437–446, 2021. https://doi.org/10.1007/978-3-030-64594-6_43

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Destructive test (DT)-based or Nondestructive tests (NDT)-based methods and systems are commonly used to assess and monitoring the performance of road pavements. Usually, NDT aim at overcoming the drawbacks of DT acting on: 1) Sustainability: they are based on analysis of images (for surface failures; see e.g., [4, 5]), or of radargrams (for concealed failures) derived using the Ground Penetrating Radar (GPR)[6], or texture profiles derived using lasers [7], or using structural response (i.e., deflections using Falling or Light Weight Deflectometer, FWD and LWD respectively, and Rolling Wheel Deflectometer, RWD; [8], and on instrumented vehicles or other remote sensing technologies[9]; 2) Energy consumption: they are less consuming, which mainly depends on the use of networks of low-power devices (e.g., Wireless Sensor Networks; [10]), such as those based on Micro-Electro-Mechanical-Systems (MEMS, see e.g.,[11]), or of self-powered devices [12, 13]), or of the inclusion of Energy Harvester Technologies (EHTs, c.f. [14]); 3) Smart solutions: the assessment of the performance of the monitored assets is carried out bearing in mind the emerging concepts of Smart City, Smart Road, Intelligent Transportation System (ITS), and Internet of Things (IoT) [1, 15]), which are based on efficient data analysis (e.g., [16, 17]), and real time communications (i.e., vehicles-to-users, vehicles-to-road agency, vehicles-to-vehicles, vehicles-toinfrastructures; see e.g., [18]). Despite the strengths listed above, the NDT-SHM approach has some drawbacks, which can be analyzed classifying the NDT-based methods in high-speed and slowspeed monitoring systems. Many high-speed monitoring systems (e.g., laser-based methods, image-based methods) allow detecting the cracks once they are visible to the laser/cameras, that is to say, when cracks have reached the surface, through their path from the bottom of bituminous layers to the top (bottom-up cracking). This implies unacceptable delays and costs in terms of pavement management, as well as an increase of the P-F interval (time lag between the time when the cracks start, symptom detectable, point P, and the time when the failure is detected, point F). Consequently, road agencies undergo delays in prioritizing investments which entails agency and user costs growth. Slow-speed methods (e.g., FWD, LWD and GPR) or high-speed deflectometers (RWD) are expensive, they do not provide a continuous monitoring of the pavement (they are usually run periodically or failure-based), and they variability is still an issue. Overall, they do not gather data for a given specified surface over time, but, on the contrary, they refer to an extended surface (e.g., a motorway) in that given day. Based on the above, the main objectives of the study are confined into improving a monitoring method which has been recently set up [19–21]. It consists in “listening” to the pavement structure using a Wireless sensor network (WSN), and associating its acoustic signature (i.e., the acoustic response to a mechanical load) to its Structural Heath Status (herein called SHS) by means of a hierarchical clustering algorithm that uses as input meaningful features extracted from the signature mentioned above. Hence, this method can be used for crack detection and quantification (Fig. 1).

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The remaining parts of this paper are organized as follows. In Sect. 2, the monitoring method is presented together with the experimental investigation that was carried out to apply the method. Section 3 reports the results of the study, which is followed by conclusions and references.

2 Method Presentation and Application The main objective of this study is to report an application of an innovative, NDT-, WSN-based SHM method specially designed for road pavements. The proposed method is briefly described in Fig. 1. In particular, a WSN, consisting in self-powered wireless sensing nodes (Fig. 2) attached on the roadside, is used to gather the acoustic signature of the monitored road pavement, i.e., its acoustic response (acoustic signal) to a mechanical load (e.g., vehicle pass-by). Importantly, the road pavement is considered as a filter of seismic and/or acoustic waves originated elsewhere. This filters transforms the signals that cross the pavement based on its characteristics, under given assumptions in terms of traffic (cf. [22–26]). Hence, if these characteristics change, e.g., because of the occurrence of cracks or holes, the behavior of the filter response will change as well. In particular, it is expected that the presence of holes or surface distress affect the way in which the pavement transmits the signals leading to the dissipation of their energy, which may be attributed to multiple diffractions and reflections around the holes. Note that, in previous studies (cited above), different in-lab and on-site experiments have been carried out to understand which is the most effective method to gather the acoustic signatures of road pavements, and to identify any variation of their SHS based on the analysis of their acoustic signatures. These preliminary studies pointed out that the complexity and the amount of the data set recorded by each sensing unit require a massive computational effort that can be overcome using proper machine learning (ML) classification algorithms. Despite the high accuracy demonstrated by the ML tools [21], they can be considered as “black boxes” that use “hidden features” to carry out the classification (i.e., the SHS identification) and, hence, do not allow understanding which feature (or set of features) can be monitored to identify a change of structural state in a road pavement (e.g., from the un-damaged to the damaged, or from a certain level of damage to the next worse one). Hence, alternatively to the ML approach, a Hierarchical Clustering, HC-based algorithm, and a specific set of features can be used. The acoustic signals used in this study were collected using the sensing node showed in Fig. 2 and a car as mechanical source. In order to generate different SHSs, lines of holes were drilled on the asphalt concrete layers (traditional Hot Mix Asphalt), which can be compared to the well-known wheel paths (i.e., two longitudinal stripes, accordingly with the traffic direction, where the main effects of the vehicles wheels - especially of the heavy ones can be localized; cf. [27]). In more detail, three lines of holes (diameter = 1 cm; depth = 15 cm; distance between two holes = 5 cm; distance between two lines = 10 cm). For this reason, the HC algorithm aimed at classifying the recorded signals in four classes associated to the four SHSs, from the first one (i.e., SHS0 that means un-cracked road), to SHS1, SHS2, and SHS3 that stand for cracked with one, two, and three lines of holes drilled into the pavement, respectively.

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Wireless Sensor Network Road Acoustic Signatures (data) Analysis 1: Time Domain

Un-Cracked road = 1st SHS identification

Analysis 2: Frequency Domain Set of meaningful features Hierarchical Clustering

Analysis 3: TimeFrequency Domain

Cracked road = 2nd, 3rd, …, Nth SHS identification

Fig. 1. Framework of the method.

Fig. 2. a) IoT board used as core of the sensing nodes; b) sensing nodes in its case, with microphone (b.1) and isolating material (b.2); c) sensing node installed on-site fed by a photovoltaic panel (c.1), a battery and a recharge circuit (c.2).

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The data set used in this study consist of 8000 acoustic signals (2000 for each SHS). Signals were derived applying a specific processing technique (i.e., using the data augmentation technique on the signal combined with a proper averaging of the features extracted from the signals [21]), based on the data set of 40 signals, gathered while the car passed 2 m far from the sensing node (and the lines of holes were about 1 m far the sensing node), and using a sampling rate of 1920 Samples/s. The figure below is an example of signals recorded during the experimental investigation (Fig. 3).

Fig. 3. Example of acoustic signature related to the SHS0 (un-cracked road).

3 Results and Discussion The following figures show the trends of the nine features extracted (using a proper Matlab code) in the three domains of analyse related to the four Structural Health Statuses (SHSs) in which the road pavement was tested. In more detail, the following set of features were extracted 1) Difference between the absolute maximum (P) and the absolute minimum (N) of the signal amplitudes (arbitrary unit, a.u.). 2) Time Delay of N from P (millisecond, ms). 3) Standard deviation of the signals (arbitrary units, a.u.). 4) Maximum of the PSD of the signals into the range 20–500 Hz (dBW/Hz). 5) Slope of the linear regression model of the PSD of the signal into the frequency range 20– 500 Hz (dBW). 6) Spectral centroid of the spectrum (PSD vs. frequency) in the frequency range 20–500 Hz (Hz). 7) Maximum of the Entropy of the Continuous Wavelet Coefficients (CWCs) (a.u.). 8) Pseudo-frequency of the Wavelet Ridge (i.e., the value on the y-axis of the scalogram peak) (Hz); 9) Energy of the CWCs between 60 and 64 (i.e., red areas of the scalogram) (a.u.). Note that CWCs are the result of the application of the Continuous Wavelet Transform (CWT), i.e., of the convolution of the signal with a properly scaled and shifted function called Mother Wavelet. Scalograms are graphs in which x-axis shows the time, the y-axis shows the pseudo-frequency (from the convolution), and the z-axis shows the percentage of energy of each CWC using a color palette from blue to red (e.g., from 0 to 64, respectively). The energy of the CWCs is used to derive the entropy of the CWCs, which is related to the chaos of the received signals, or, in other words, the loss of information of the original message. In fact, pure noise signals have big entropies while systematic signals have almost zero entropy [28].

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Fig. 4. Features extracted from the acoustic signatures of the road pavement under test in the time domain of analysis: a) amplitude variation; b) delay; c) standard deviation.

Fig. 5. Features extracted in the frequency domain of analysis: a) maximum of the Power Spectral Density (PSD); b) slope of the PSD; c) PSD spectral centroid.

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Based on Fig. 4, 5 and 6, it is possible to state that the presence and the growing of the induced cracks into the road pavement 1) Reduce the amplitude (cf. Fig. 4.a) of the acoustic signatures of the road, which is also highlighted by the reduction of the standard deviation and the maximum of the PSD (cf. Fig. 4.c, and (cf. Fig. 5.a), and the increase of the slope of the linear regression model applied on the PSD and the spectral centroid (cf. Fig. 5.b and 5.c). 2) Stretch the signals (cf. Fig. 4.b), acting on the high frequency components of the signals as a low-pass filter that allows passing the longer wavelengths only (cf. Fig. 6.b) that are well correlated with the wavelet at the low frequency (cf. Fig. 6.c). 3) Reduce the maximum entropy of the CWC (cf. Fig. 6.a) confirming the loss of information of the transmitter signals due to the presence of “interruptions” into the medium of propagation.

Fig. 6. Features extracted in the time-frequency domain: a) Maximum Entropy (Ent_max) of the Continuous Wavelet Coefficients (CWCs); b) Pseudo-frequency (p-fWR) of the Wavelet Ridge; c) Maximum Energy (Eng_max) of the CWCs.

A HC algorithm was created in the Matlab environment to classify the features that represent the recorded signals (i.e., the observations). This algorithm uses the Euclidean distances between pairs of observations to assign them to number of clusters defined by the user. In this study, each cluster refers to a SHS of the monitored road, and the processing technique of the signals mentioned above was carried out considering the value of 80% as the model accuracy target (i.e., the signals were augmented from 40 to 8000 and the features were averaged from 8000 to 160; cf. Fig. 4, 5 and 6). Table 1

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reports the results of the clustering carried out using the HC algorithm described above. Results show that using the three features 4, 6, and 8 (frequency and time-frequency domains), it is possible to correctly classify the acoustic signals with a good accuracy (86% on average). Table 1. Results of the HC algorithm. #F F

SHS Max SHS0 SHS1 SHS2 SHS3 OPCAV (%) 1 n.a. PC – – – – – Max OPC (%) – – – – 2 4+8 PC 3 2 1 4 79 Max OPC (%) 75 95 52 92 3 4+6+8 PC 3 1 2 4 86 Max OPC (%) 100 72 75 95 4 5 solutions including all the features PC 3 1 2 4 86 accept the #2 Max OPC (%) 100 72 75 95 5 10 solutions including all the PC 3 1 2 4 86 features Except the #2 Max OPC (%) 100 72 75 95 6 10 solutions including all the PC 3 1 2 4 86 features accept the #2 Max OPC (%) 100 72 75 95 3 1 2 4 86 7 3+4+5+6+7+8+9 PC Max OPC (%) 100 72 75 95 3 1 2 4 86 8 1+3+4+5+6+7+8+9 PC Max OPC (%) 100 72 75 95 9 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9 PC 2 1 3 4 52 Max OPC (%) 52 47 45 62 Symbols. #F = number of features used as input during the clustering; F = combination of features that led to a correct classification; SHSi = i-th Structural Health Status of the road pavement under test, where i = 0, 1, 2, and 3, corresponding to the lines of drilled holes; PC = Predicted cluster; Max OPC (%) = highest percentage of observations associated to PC; Max OPCAV (%) = average of the values Max OPC; n.a. = not available, i.e., misclassification.

4 Conclusions An innovative, NDT-, WSN-based SHM method specially designed for road pavements was presented and applied in experimental investigation. Results show that by using a small number of meaningful features and by applying a hierarchical clustering procedure, it is possible to recognize the variation over time of the acoustic signature of the infrastructure due to the presence and the propagation of internal and external cracks. Hence, the proposed method can be efficiently used to monitor the SHS of road pavements during their lifetime, and, consequently, to improve pavement management systems and risk management processes.

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18. Yi, C.W., Chuang, Y.T., Nian, C.S.: Toward crowdsourcing-based road pavement monitoring by mobile sensing technologies. IEEE Trans. Intell. Transp. Syst. 16, 1905– 1917 (2015) 19. Fedele, R., Della Corte, F.G., Carotenuto, R., Praticò, F.G.: Sensing road pavement health status through acoustic signals analysis. In: PRIME 2017 - 13th Conference on PhD Research in Microelectronics and Electronics, Proceedings, pp. 165–168 (2017) 20. Fedele, R., Praticò, F.G.: Monitoring infrastructure asset through its acoustic signature. In: INTER-NOISE 2019 MADRID - 48th International Congress and Exhibition on Noise Control Engineering (2019) 21. Praticò, F.G., Fedele, R., Naumov, V., Sauer, T.: Detection and monitoring of bottom-up cracks in road pavement using a machine-learning approach. In: Algorithms, Kraków, Poland (2020). https://doi.org/10.3390/a13040081 22. Burger, C.P., Singh, A.: An ultrasonic technique for sizing surface cracks. In: DARPA/AFWAL Review of Progress in Quantita-tive NDE, October 1979–January 1981, vol. 65 (1981) 23. Rodríguez-Castellanos, A., Ávila-Carrera, R., Sánchez-Sesma, F.J.: Scattering of Rayleighwaves by surface-breaking cracks: an integral formulation. Geofis. Int. 46, 241–248 (2007) 24. Ávila-Carrera, R., Rodríguez-Castellanos, A., Sánchez-Sesma, F.J., Ortiz-Alemán, C.: Rayleigh-wave scattering by shallow cracks using the indirect boundary element method. J. Geophys. Eng. 6, 221–230 (2009) 25. Lee, F.W., Chai, H.K., Lim, K.S.: Assessment of reinforced concrete surface breaking crack using Rayleigh wave measurement. Sensors (Switzerland) 16 (2016). https://doi.org/10. 3390/s16030337 26. Praticò, F.G., Vaiana, R., Gallelli, V.: Transport and traffic management by micro simulation models: operational use and performance of roundabouts. In: Brebbia, C.A., Longhurst, J.W, S.W.P. (eds.) WIT Transactions on the Built Environment, pp. 383–394 (2012). https://doi. org/10.2495/UT120331 27. Huang, Y.H.: Pavement Analysis and Design, 2nd edn. (2004) 28. Reda Taha, M.M., Noureldin, A., Lucero, J.L., Baca, T.J.: Wavelet transform for structural health monitoring: a compendium of uses and features (2006)

Development of Autonomous UHF RFID Sensors Embedded in Concrete for the Monitoring of Infrastructures in Marine Environments K. Bouzaffour1,2, B. Lescop1, F. Gallée2, P. Talbot1, and S. Rioual1(&) 1

2

Univ Brest, Lab-STICC, CNRS, UMR 6285, 29200 Brest, France [email protected] Lab-STICC/MOM, Telecom Bretagne, Technopôle Brest-Iroise, CS 83818, 29238 Brest Cedex, France

Abstract. Chloride ingress in reinforced concrete infrastructures is of crucial importance when considering structural health monitoring applications in marine environments. It indeed leads to the depassivation and corrosion of steel and hence to the degradation of the whole infrastructure. The present study reports the development of an embedded wireless autonomous sensor dedicated to the monitoring of corrosivity of concrete initiated by such ingress. The sensor is based on the ultra high frequency (UHF) radiofrequency identification (RFID) technology. The communication between a commercial RFID reader and a specific optimized embedded antenna in concrete is experimentally demonstrated. In the last part of the study, a resistive corrosion sensor connected to the RFID chip is proposed for the evaluation of chloride ingress. Keywords: Concrete
Corrosion
Battery-less sensor
Wireless sensor
UHF RFID

1 Introduction Reinforced concrete is the most commonly used construction material in the world. Under normal conditions, due to the high pH-value of concrete, passivated stable film forms on the steel surface and prevents corrosion. However, in marine environment, with the presence of chloride, the passive layer breaks leading to the degradation of rebars and hence of the whole infrastructure. Within this context, there is a real interest in the monitoring of the degradation of steel, moisture and chloride content in reinforced concrete. For this purpose, nondestructive testing (NDT) techniques [1] including ultrasonic, electrical or electromagnetic methods were widely developed in the past. In particular, ground penetrating radar has become an emerging technique [2]. Wireless autonomous sensors embedded in the infrastructure under monitoring provides a second suitable solution. In this case, the absence of battery and cable facilitates the integration of sensors in infrastructures during the construction and allows a long service lifetime. Many strategies can be used for the development of such autonomous © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 447–454, 2021. https://doi.org/10.1007/978-3-030-64594-6_44

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sensors. Radiofrequency (RF) resonators sensitive to the dielectric properties of the surrounding material, here concrete, were reported as candidates for the monitoring of moisture content in concrete [3, 4]. Some investigations concerns also the development of RF resonators dedicated to the monitoring of the corrosion potential of steel [5] and to the loss of metals [6]. In this this case, the sensitive resonators are integrated in RFID (Radio Frequency Identification) chipless architectures. However, despite the success of the method, the communication between embedded sensors in concrete at few centimeters from the surface and a reader localized in air has never been proved. Another strategy concerns the application of the Ultra High Frequency (UHF) RFID technology operating at 868 MHz to the development of autonomous sensors. Due to its long range compared to LF (Low frequency) and HF (High frequency) RFID, it is very attractive [7]. Moreover, some progresses were recently made by developing innovative RFID chips which communicate with commercial sensors through SPI/I2C communication [8, 9]. This study aims therefore at developing a sensor embedded in concrete based on this technology and which can be interrogated from air by a commercial UHF RFID reader. In contrast to conventional applications, the development of embedded antennas is a challenging task since concrete displays variable electromagnetic (EM) properties which depend of the degree of moisture and chloride content. Manzari et al. [10] reported the main problems associated with the development of such embedded sensors. In particular, a loss of 100 dB were observed when immersing a dipole antenna in concrete at 15 cm from the surface, making the sensor unreadable from outside. Development of UHF RFID sensors was also proposed as a promising technique for humidity monitoring in concrete by Stangfeld et al. [11]. This was done by considering an RFID tag powered through a dipole antenna. However, the presented results concerned mainly low frequencies (HF RFID). Within this context, to investigate the impact of concrete on the UHF RFID communication, the first part of the present study details the RF losses associated with the presence of concrete. The second part deals with the optimization of an antenna embedded at a depth of 7 cm in concrete and operating in the UHF RFID band at 868 MHz. Results obtained by electromagnetic simulations are promising for embedded UHF RFID applications. Experimental characterization of the realized antenna demonstrates indeed that the transmission of RF waves between the embedded antenna and a conventional RFID antenna is feasible. This result is further proved by interrogating an RFID chip connected to the embedded antenna by a conventional commercial RFID reader. Many different type of sensors can be integrated in the proposed device via the SPI port of the RFID chip. As an example, a sensor connected to the chip and sensitive to the corrosivity initiated by chloride ingress is presented.

2 Development of an Embedded UHF-RFID Sensor in Concrete An RFID tag is composed of an antenna and a chip, which contains an identification number. It is energized by an RF wave coming from a reader and then returns back its identification code. Due to the autonomous property of tags, a reading distance, i.e. the distance between the tag and the reader, of about 5 m can be reached in air. This value

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is obtained by carefully designing the antenna connected to the chip with respect to the environment. When considering the immersion of the antenna in concrete, the same considerations have to be taken into account. 2.1

RF Losses Associated with Concrete

To evaluate the feasibility of the method, the first point concerns the determination of additional RF losses associated with the presence of concrete at the chosen frequency (868 MHz). The dielectric property of concrete is not constant and strongly depends on the degree of moisture. The values of the dielectric constant (er) and tangent loss (tand) are available from [12–16] for different ranges of frequencies and degree of hydration. The dielectric constant (tangent loss) ranges at 868 MHz from er = 6 (tand = 0.1) from dry concrete to 12 (tand = 0.25) for saturated concrete in seawater [16]. In the present study, these parameters were measured by several methods and are in a good agreement with these values. To determine the additional RF losses associated with the propagation length in concrete, the linear attenuation constant a for a plane wave propagation in a medium of dielectric permittivity (er, tand) is considered [17]. The expression of a is given by this relation:  qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 12 pffiffiffiffiffi 1 2 1 þ tan ðdÞ  1 a ¼ x le 2

ð1Þ

The propagation loss ap inside over a length d in concrete can be written: ap ¼ 10 logðe2ad Þ

ð2Þ

Additionally, RF losses occurs due to the presence of the air/concrete interface. In this case, the transmission loss at is given by this relation: at ¼ 10 logðjT j2 Re(

n0 )Þ n1

ð3Þ

Where T is the transmission coefficient and ηi the intrinsic impedances of concrete (i = 1) and air (i = 0). The ap and at losses for a propagating distance of d = 7 cm for a dried concrete are −1.3 and −0.8 dB, respectively, leading to a total loss of −2.2 dB. When considering the saturated concrete, a loss of about −6.3 dB is calculated. It is mainly associated with the propagation length and not with the interfacial loss. As the consequence, the impact of the high value of tand with respect to air on the EM wave propagation is relatively weak and does not hinder the proposed RFID application if antennas displaying similar gains as in air are produced. Note that at higher frequency, namely the ISM band at 2.45 GHz, an important value of losses of −15 dB is found by considering saturated concrete. This limits the proposed application to 868 MHz [7].

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Optimization and Realization of an Embedded Patch Antenna Operating at 868 MHz

Dipolar antennas are widely used in RFID applications due to their almost isotropic radiation patterns. A directive antenna in concrete is more adapted to communicate with the reader localized in air. Patch antennas are known to display such property and are therefore considered in the following. Figure 1 shows the geometry of the antenna. It consists of a metallic radiated element on a foam substrate with a relative dielectric permittivity of er = 1 and a thickness of 3 mm. A ground plane is added on the bottom of the dielectric substrate. A coaxial feed is used to power the antenna. The radiation of a patch occurs mainly through the fringing electric fields between the patch and the ground plane and there is no fields on the patch. As the consequence, a low thickness protective layer with a low loss tangent between the patch and concrete is sufficient to limit the destructive effect of concrete on the antenna property. This strategy was used by Shams and Ali [18] to produce a patch antenna at 5.7 GHz in concrete and more recently by Richard et al. at 1.5 and 2 GHz [19].

Fig. 1. Parameters used for the optimization of the patch antenna. Front and side views.

The sketch of the antenna embedded in concrete is depicted in Fig. 2. The antenna was optimized by HFSS software by considering a protective layer of foam of thickness d over the antenna and a localization at a depth h with respect to the surface. This was done for dry concrete (er = 6; tand = 0.1). For h = 7 cm, the optimization of the antenna leads to the gains of 0.8 dBi and 3.8 dBi for d = 1 cm and d = 3 cm, respectively. These values are promising and definitely higher than measured gains of unprotected antennas in concrete. However, the main requirement for the development of the antenna concerns its non-sensitivity to any variation of the dielectric properties of concrete from er = 6 to 12. Figure 3(a, b) display the reflection parameter S11 for the two antennas when dielectric constant of concrete is varying within this range. As seen in Fig. 3(a), the frequency of the antenna protected by d = 1 cm of foam is slightly sensitive to a change of er. It may however be

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Fig. 2. Sketch of the patch antenna embedded in concrete localized at a depth h with respect to the surface.

used since an S11 level lower than -10 dB can be achieved whatever the value of er. For this purpose, the central frequency should be rescaled by changing the length of the antenna. For a foam thickness d = 3 cm, in Fig. 3(b), the variation of the resonant frequency is strongly suppressed. This configuration is thus chosen here. Note that the influence of metals associated with rebars at higher depth than d + h was tested in simulations. No degradation of the results was observed due the presence of the ground plane of the patch antenna.

Fig. 3. Electromagnetic simulations of the S11 reflection parameter when the dielectric constant of concrete is varying. Thickness of the protective layer: a) d = 1 cm and b) d = 3 cm.

To demonstrate experimentally the achieved results, the patch antenna was realized with a protective foam layer of d = 3 cm and embedded in concrete with h = 7 cm. Copper foils were used as conductors. Specimens with dimensions of 30  30  7 cm3 were prepared with 300 kg of sand/m3 [20] in a mould. The antenna was placed in the mould when pouring the concrete over it. The specimens were then allowed to dry for few months in air. The antenna was then characterized by a vectorial network analyzer (VNA) (HP 8720B). The S11 reflexion parameter of the embedded antenna is depicted in Fig. 4(a) for dry concrete and after absorption of water. As shown, the resonant frequency does not change with water ingress and only a variation of the S11

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level is observed, in agreement with electromagnetic simulations. We note also on the figure that the antenna is not resonating at 868 MHz but at lower frequency. The S12 transmission parameter was also measured by connecting the produced antenna at one port of a VNA and a conventionnal RFID antenna of 5 dBi at the second port. The reading distance, i.e. the distance between the two antenna was changed. Figure 4(b) displays the results. The S12 transmission parameter varies from −25 to −45 dB around 868 MHz when moving the reader antenna from the surface to a reading distance of 2 m. Similar measurements were made by considering two RFID antenna with 5 dBi gain operating in air. The loss for a distance of 2 m was found to be −30 dB instead of −45 dB. This difference of 15 dB is higher than the loss values calculated above and is ascribed mainly to the resonant frequency which does not fit with the 868 MHz and also to the mismatch of polarization between the developed linear polarized antenna and the RFID one which is circularly polarized.

Fig. 4. a) Experimental S11 reflection parameter for dried concrete (blue) and after penetration of water (red). b) experimental S12 transmission parameter between the embedded antenna and an RFID antenna localized in air.

Finally, the communication between a Rocky 100 chip [8] connected to the embedded antenna and a commercial reader (Impinj R420) was subsequently tested. This implies to implement in the device a balun and to adapt the impedance of the antenna to that of the Rocky chip. The discussion between both was possible by localizing the RFID antenna in air at a distance of about 50 cm from the surface, demonstrating thereby the method. 2.3

Development of a Resistive Sensor Sensitive to Depassivation of Steel

Many different sensors may be associated with the Rocky 100 chip due to the presence of SPI ports. This includes electrochemical sensors, chloride sensors, pH sensors. Here, we propose to develop a resistive corrosion sensor sensitive to chloride content. Such sensors are widely developed due to their robustness, small size and relative low cost for atmospheric corrosion monitoring. They are constituted by a single line or multiple lines elaborated in a metallic sensitive material. From the change in geometry of this

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metallic element due to corrosion, it is possible to evaluate the corrosity of the environment [21]. Figure 5(a) displays the strip used in the present study. It is produced by magnetron sputtering in iron with a width of 2 mm and a thickness of 70 nm. Such strip was first immersed in a saturated Ca(OH)2 at pH = 13 to simulate concrete pore solution. As expected, due to the ultra thin layer of passivation of iron, no variation of the electrical resistance of the strip (12 kX) was reported. The strip was subsequently immersed in an 3% NaCl concentration solution leading to pitting corrosion. This is highlighted in Fig. 5b which shows the strip after 6 h of immersion time. In this case, the electrical resistance of the strip is equal to infinity. The transition from an electrical resistance of 12 kX to infinity is related to the amount of chloride available for corrosion of iron and is consequently an indicator of the corrosivity, or depasivation of steel. Very low thicknesses are used here to provide a very high sensitivity to chloride. However, this can be tuned by changing the thickness of the strip for real applications. This strong variation of electrical resistance is detected by using one of the digital lines available at the Rocky chip by simply implementing a resistance bridge circuit. The advantage of such method is to limit the number of electronic components to the chip only without any additional microcontroller. This ensures the robustness of the embedded sensor.

(a)

(b)

Fig. 5. Iron strip sensitive a) before immersion in artificial seawater and b) after immersion.

3 Conclusion The present study reports the application an embedded UHF RFID sensors to monitoring the corrosion of steel in concrete. As shown, reading distance of such autonomous sensor is few tens of centimeters if antennas are carefully optimized. The main parameters for this purpose is the determination of i) the thickness of the protective layer (d) and ii) the desired depth (h). The study proposes the development of a resistive corrosion sensor dedicated to the monitoring of chloride content which can be directly connected to the RFID chip without any other microcontroller. Further work are currently under progress to provide strips with different sensitivities to chloride. Acknowledgement. The authors acknowledge the Region Bretagne for the PhD support of K. Bouzaffour. This work is supported by the European Union through the European Regional Development Fund (ERDF), the Ministry of Higher Education and Research, the Région Bretagne, the Conseil général du Finistère and Brest Métropole Océane, through the CPER Project 2015-2020 MATECOM.

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References 1. McCann, D.M., Forde, M.C.: Review of NDT methods in the assessment of concrete and masonry structures. NDT&E Int. 34, 71–84 (2001) 2. Maierhofer, C.: Nondestructive evaluation of concrete infrastructure with ground penetrating radar. J. Mater. Civ. Eng. 15, 287–297 (2003) 3. Khalifeh, R., Gallée, F., Lescop, B., Talbot, P., Rioual, S.: Application of fully passive wireless sensors to the monitoring of reinforced concrete structure degradation. In: Proceedings of the 9th European Workshop on Structural Health Monitoring (EWSHM 2018) (2018) 4. Lázaro, A., Villarino, R., Costa, F., Genovesi, S., Gentile, A., Buoncristiani, L., Girbau, D.: Chipless dielectric constant sensor for structural health testing. IEEE Sens. 18(13), 5576– 5585 (2018) 5. Khalifeh, R., Yasri, M., Lescop, B., Gallée, F., Diler, E., Thierry, D., Rioual, S.: Development of wireless and passive corrosion sensors for material degradation monitoring in coastal zones and immersed environment. IEEE J. Ocean. 99, 776–782 (2016) 6. Yasri, M., Lescop, B., Diler, E., Gallée, F., Thierry, D., Rioual, S.: Monitoring uniform and localised corrosion by a radiofrequency sensing method. Sens. Actuators B: Chem. 257, 988–992 (2018) 7. Nikitin, P., Ensworth, J., Rao, K.V., Pesavento, A., Kim, J.: 2.4 GHz Passive Gen2 RFID System. IEEE International Conference on RFID (2019) 8. Rocky100 IC datasheet. http://www.farsens.com/en/products/ 9. EM4325 IC datasheet. http://www.emmicroelectronic.com 10. Manzari, S., Musa, T., Randazzo, M., Rinaldi, Z., Meda, A., Marrocco, G.: A passive temperature radio-sensor for concrete maturation monitoring. In: IEEE RFID Technology and Applications Conference, pp. 121–126 (2014) 11. Strangfeld, C., Johann, S., Muller, M., Bartholmai, M.: Moisture measurements with RFID based sensors in screed and concrete. In: 8th European Workshop on Structural Health Monitoring (EWSHM 2016) (2016) 12. Soutsos, M.N., Bungey, J.H., Millard, S.G., Shaw, M.R., Patterson, A.: Dielectric properties of concrete and their influence on radar testing. NDT and E Int. 34(6), 419–425 (2001) 13. Klyz, G., Balayssac, J.P., Ferrieres, X.: Evaluation of dielectric properties of concrete by a numerical FDTD model of a GPR coupled antenna-Parametric Study. NDT and E Int. 41(8), 621–631 (2008) 14. Van Beek, A., Hilhorst, M.A.: Dielectric measurements to characterize the microstructural changes of young concrete. Heron 44(1), 3–17 (1999) 15. Kim, S., Surek, J., Baker-jarvis, J.: Electromagnetic metrology on concrete and corrosion. J. Res. Nat. Inst. Stand. Technol. 116(3), 655–669 (2011) 16. Dérobert, X., Villain, G.: Effect of water and chloride contents and carbonation on the electromagnetic characterization of concretes on the GPR frequency band through designs of experiment. NDT and E Int. 92, 187–198 (2017) 17. Jiang, S., Georgakopoulos, S.V.: Optimum wireless powering of sensors embedded in concrete. IEEE Trans. Antennas Propag. 60, 1106–1113 (2012) 18. Shams, K.M.Z., Ali, M.: Wireless power transmission to a buried sensor in concrete. IEEE Sens. J. 7, 1573–1577 (2007) 19. Richard, T., Latrach, M., Ihamouten, A., Borderon, C., Gundel, H.W., Dérobert, X.: Design of an UHF antenna insensitive to the concrete dielectric characteristics. Electromagnetic Non-Destructive Evaluation (XXI) (2018) 20. Parex Lanko. https://www.parexlanko.com/fr/154-beton-universel 21. Li, S.Y., Kim, Y.G., Jung, S., Song, H.S., Lee, S.M.: Application of steel thin film electrical resistance sensor for in situ corrosion monitoring. Sens. Actuators B: Chem. 120, 368–377 (2007)

Integrated Approaches for SHM: Models, Data and Experiments

Improving the Capability of Detecting Damages in the Early State by Advanced Frequency Estimation Nicoleta Gillich , David Lupu , Codruta Hamat(&) Gilbert-Rainer Gillich , and Dorian Nedelcu

,

Babes Bolyai University of Cluj, 320085 Resita, Romania [email protected]

Abstract. Detecting damage in the early state is crucial in assessing structural integrity. Most current vibration-based damage detection methods use frequency shifts to assess the damage, observed as a change of the positions on which the peaks in the spectrum are located. However, accurate estimation of the natural frequencies can be challenging due to the raw frequency resolution obtained for short signals. We propose in this paper a signal post-processing algorithm that permits obtaining a spectrum with significantly enhanced resolution, without being necessary to increase the length of the signal. The super-resolution is obtained by overlapping numerous spectra calculated for the signal cropped iteratively. The spectral peaks are distributed in accordance with a pseudosinc function, which is asymmetrical, but the estimated frequencies are close to the real one. By interpolation, we improve the estimate. Moreover, by applying a correction term we find the true frequency. The algorithm is implemented in a Python application that can be linked to any virtual instrument developed in LabVIEW. The algorithm is tested for signals with known frequencies, in the absence and presence of noise and for real-world signals. It provides accurate results that permit observing the occurrence of damage in the very early state. Keywords: Frequency estimation method
Overlapped spectrum


Discrete fourier transform
Interpolation

1 Introduction The response of a structure to an excitation allows identifying the state of the structure. Structural health monitoring uses this advantage. Currently, there are many methods available that conclude on the integrity of the structure based on the modifications of the modal parameters, see for instance [1–3]. Among the modal parameters involved, the most commonly used is the natural frequency, as it is easily measured with the basic instrumentation [4]. Estimating the natural frequencies is typically made involving the Discrete Fourier Transform (DFT). It consumes more resources as the Fast Fourier Transform (FFT) but it ensures better results [5] because it permits setting the time length of the analyzed signal and in consequence the frequency resolution [6]. The clear advantage of the DFT against the FFT is the accuracy of the frequencies estimated, which is crucial in early state damage detection [7]. However, for short © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 457–466, 2021. https://doi.org/10.1007/978-3-030-64594-6_45

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signals, accurate estimation of the natural frequencies becomes challenging due to the raw frequency resolution obtained [8]. Nowadays, numerous methods to increase the accuracy of the estimate exist. The simplest and most efficient techniques involve interpolation to obtain a correction term [9]. With this technique, we can find the position of the estimated frequency in an inter-bin position in the DFT spectrum. To obtain the correction term, interpolation is made for two [10–12] or three [13–15] DFT samples, including the maximizer found in the frequency range of interest. Even if the estimation gain precision, we found the results still depend on the signal acquisition strategy (i.e. the time length) [8]. In this paper, we propose an alternative approach for an accurate estimation of the harmonic components of the vibration signal. First, we repeatedly cut two samples from the initial signal that was attained by measurements. Next, we calculate a DFT for each of the resulting signals. All the obtained spectra are overlaid, resulting in a dense so-called overlapped spectrum. The largest of the individual maximizer is identified in the frequency range of interest in the overlapped spectrum and further interpolation is performed involving its two neighbors. Note that these two neighbors are also maximizers in the individual spectra. Because the maximizers are distributed in the overlapped spectrum in accordance with a pseudo-sinc function, which is asymmetric, we introduce a correction term to improve the estimate. The algorithm is implemented Python, resulting in an application that provides extremely precise results that can be used to assess damage in a very early state.

2 The Algorithm and Its Theoretical Background The signals under consideration contain one or more harmonic components that are estimated with the proposed algorithm. The way the signals obtained from measurements are treated is identical, and we can assume that the results fall within a similar margin of error if the acquired signal has the same parameters as the generated signal. Let us consider that the signal x[n] has a time length tS and is generated with a frequency rate r. The number of samples necessary to create the signal is N, which can be calculated with the mathematical relation: N ¼

r þ1 tS

ð1Þ

The frequency resolution Df is inversely proportional to the signal length tS, so that their product is equal with 1. The frequency components are displayed in the spectrum at lines k, i.e. frequencies fk ¼ k
Df are indicated. Because the single-sided spectrum displays N/2 lines, the number of necessary spectral lines is k 2 ½0; N=2. If the signal has a length that does not involve an integer number of cycles for a given harmonic component, the maximizer will be displayed on the spectral line k indicating frequency fk closest to the true frequency fS and a value Xk smaller than the amplitude A of the component in question will be displayed. The maximizer illustrated in Fig. 1 is located on the spectral line k = 5 that indicate the frequency fk = 5 Hz. The true frequency is fS = 5.31 Hz.

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Fig. 1. Distribution of the spectral lines and the associated amplitudes

Along with the maximizer, due to leakage, the spectrum displays amplitudes on other spectral lines. The distribution of the amplitudes in the spectrum for a sinusoid that’s frequency fS does not fit a spectral line, i.e. fS 6¼ fk , follows approximately the sinc function rule. The way how the frequency lines and their associated amplitudes in a DFT spectrum are calculated is well-known and not detailed here. The idea based on which the algorithm is built is that by modifying the original signal length, different distributions of the spectral lines are obtained. By superposing the spectra, we obtain an overlapped spectrum with very fine resolution at which maximizer and the neighbors are displayed at frequencies very close to the true frequency. Figure 2 shows the peak of the overlapped spectrum, i.e. maximizer of the individual spectra obtained from the signals with different time lengths.

Fig. 2. Zoom on the peak of the overlapped spectrum highlighting the position of the maximizer achieved by iteratively cropping the signal

By performing an interpolation based on the three main peaks in the overlapped spectrum we obtain the amplitude AE and frequency fE that are even closer to the true one as that obtained from the overlapped spectrum. Note that in the algorithm we use amplitudes that are not adjusted with the number of cycles N, thus for a different number of cycles different amplitudes will be obtained.

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This permits easily associating the maximizer to the number of cycles contained in the signal. After choosing the curve for a given number of cycles, we calculate the Power Spectral Density (PSD) before interpolation in order to normalize the amplitudes to the energy contained in maximizer (obtained from signals with different time lengths). The error obtained when the same number of cycles is considered e ¼

fS  fE fS

ð2Þ

can be normalized with the true sinusoid’s frequency. It results in a coefficient j that is the same irrespective to the sampling strategy. Since: j ¼

e fS  fE ¼ fS fS2

ð3Þ

the true frequency is calculated from the algebraic equation: jfS2  fS þ fE ¼ 0

ð4Þ

If the harmonic components are quite far from each other, the effect of the leakage generated by one of them does not affect the other components. Therefore, we can treat each harmonic component of the signal individually. This approach is also motivated by the fact that each component contains an integer number of cycles for a different signal length. In the section dedicated to testing the algorithm, we also analyze the case where the signal has several harmonic components and show how close they can be to allow accurate frequency estimation. In conclusion, running the algorithm involves the following steps: 1. Import the acquired/generated original signal S_1 (it should contain at least 5 cycles for the fundamental frequency); 2. Calculate DFT_1 for this signal and extract the maximizer M_1; 3. Extract two samples from the end of the signal and perform DFT_2; 4. Extract the maximizer M_2; 5. Repeat steps 3 and 4 until the signal S_1 is shortened with 2.5 periods T calculated for the frequency of interest. It should result j = 1…J maximizer; 6. Overlay all extracted maximizers M_j - three curves result, each for a certain number of cycles; 7. Select a curve for which the maximum M_max has two neighbors; 8. Convert the curve from DFT to PSD; l 9. Perform interpolation to find the trendline and its maximum MAX; 10. Apply the correction coefficient to find the estimated frequency. The algorithm is implemented as an application written in Python programming language. The results obtained when the steps described above are performed are illustrated in Fig. 3.

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a.

b.

c. Fig. 3. The results displayed when performing the steps described in the algorithm: (a) the DFT calculated for the original signal with the maximizer; (b) the overlapped maximizers for the iteratively cropped signal; (c) the overlapped maximizers for the selected number of cycles with the identified peak.

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As one can observe in Fig. 3a, the DFT calculated for the original signal has two points with fairly close amplitudes, which means the real frequency and amplitude are not properly identified. Figure 3b shows all maximizers M_j found in the frequency range 4.5–6.5 Hz when the cropped signal contains at least four and maximum seven cycles. One can observe we obtained adequate maxima for both five and six cycles. Finally, selecting the number of cycles six, we obtain the curve represented in Fig. 3c. The interpolation is performed and the correction coefficient is applied, resulting the correctly estimated frequency and amplitude.

3 Tests Performed to Improve the Method’s Accuracy To demonstrate that the algorithm implemented in Python is feasible, we perform tests that involve one or more sinusoids generated with known frequencies and amplitudes. Mainly we test the accuracy and repeatability of the results and the limit at which two harmonics with close frequencies can be detected. 3.1

The Effect of the Signal Length on the Estimation Accuracy

As we have shown in Sect. 2, the DFT calculation does not ensure the consistency of the results, because the frequency values determined depend on the signal length used for the calculation. Usually, the entire length of the acquired or generated signal is used, because the general idea is that a longer signal permits estimating more accurately the frequencies. This length is defined by the operator and usually remains unchanged until the experiment is finalized. In this first example we show how the frequencies are estimated if the signal length varies in a limited range of around one period T. To this aim we generate a sinusoid with the frequency fS = 6.33 Hz and amplitude AS = 1 mm/s2. The original signal has N = 868 samples by a sampling rate r = 400, resulting in a time length tS = 2.1675 s. From the original signal we repeatedly cut 2 samples, until it contains only 784 samples. For each signal thus obtained, the DFT is calculated by the standard method and the spectral component is determined again using the PyFEST application. The results, presented in Fig. 4, show that the values of the frequencies obtained using a standard DFT are strongly affected by the signal length and are in general not estimated correctly. The variation of the values obtained by the standard DFT method is framed in respect with the frequency resolution Df, the range in which the estimated frequency may vary being indicated with purple lines in Fig. 4. On the contrary, using PyFEST we obtain the frequency fPy2 = 6.2999 Hz for the signal generated with even number of samples and the exact frequency fPy1 = 6.33 Hz if the signal is generated with an odd number of samples. Even if this effect is vanished for longer signals, we recommend using signals containing odd number of samples. We performed analysis involving signals with different frequencies, time lengths and generated with different frequency rates and have obtained always accurate results [16–18]. Therefore, we conclude the frequency estimation made with PyFEST is accurate and can be used for demanding applications of physics or engineering.

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Fig. 4. The frequencies estimated using the standard DFT and with PyFEST.

3.2

The Capacity to Detect Small Frequency Changes

If the signal is short, a coarse frequency resolution Df is achieved and a big gap between two consecutive spectral lines results. This makes it impossible to observe small frequency changes, because, if the frequency changes slightly, the DFT still indicate the signal on the same spectral line but with different amplitude. The frequency change is observed only if the amplitude is shifted to a neighboring spectral line. So, for an interval ±Df/2 around a spectral line the same frequency will be read. The question is whether, with PyFEST, we can find very fine frequency changes even for short signals. To determine this, we generate one by one three sinusoids with close frequencies, see Table 1. These have the amplitude AS = 1 mm/s2 and are generated with the frequency rate r = 1000 Hz. Each of the three sinusoids is considered containing 970, 1970 and 2970 samples, respectively. For the nine resulted signals we calculated the standard DFT and performed an analysis with PyFEST. The frequencies obtained by the two methods are also shown in Table 1. Table 1. Comparison of the results obtained with the standard DFT and involving PyFEST Generated signal N (-) 970 r (Hz) 1000 na (-) 12 fS (Hz) 12.66 12.68 AS (mm/s2) 1 1 Estimation with standard DFT 12.3839 12.3839 fDFT (Hz) ADFT (mm/s2) 0.8769 0.8613 Estimation with PyFEST 12.6599 12.6799 fPy (Hz) APy (mm/s2) 1.0009 1.0015

1970

12.70 1

24 12.66 1

2970

12.68 1

12.70 1

37 12.66 1

12.68 1

12.70 1

12.3839 12.6968 12.6968 12.6968 12.7989 12.7989 12.7989 0.8447 0.9952 0.9970 0.9990 0.7536 0.8178 0.8742 12.7 1.002

12.66 1.0004

12.68 1.0009

12.7 1.0005

12.66 1.0002

12.68 1.0003

12.7 1.0001

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One can observe from Table 1 that the results obtained with the standard DFT do not allow detecting the frequency change, the same values being estimated for a given number of cycles. Conversely, PyFEST allows detection of frequency change with high accuracy, which has an important role in early-state detection of physical or engineering systems. 3.3

Frequency Estimation for an Acquired Signal

In this example we show how the natural frequencies of a beam in the intact state and with a complex-shaped crack are found from measured signals. The specimen is a carbon steel cantilever beam fixed in a machine vise at the left end, as shown in the schematic of the experimental stand, see Fig. 5. The beam has the active length L = 1 m and the rectangular cross-section has the width B = 50 mm and thickness H = 5 mm. In Fig. 5, the equipment used for excitation and acquisition is indicated and detailed information about the experimental setup is presented in [19].

Fig. 5. Schematic of the experimental stand.

The target was to find the natural frequencies for the out-of-plain vibration modes of the intact and damaged beam. In the case of damage, this has the depth 0.5 mm and is produced by electro-erosion with a wire of 2 mm diameter. The location of the crack is 225 mm from the fixed end. The two modes are analyzed independently, this meaning a tailored excitation was applied involving the sound speaker when the acquisition system was activated, followed by post-processing of the acquired signal.

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We exemplify the case of modes four and five, for which the excitation was applied involving a sound speaker with a frequency close to that of the targeted vibration mode. The excitation time was, tE = 1…5 s. This kind of excitation was made to ensure a bigger amplitude for the targeted mode in comparison to all other modes. The subsequent acquisition was made after a time tP = 0…3 s measured from the moment the excitation has stopped. The acquisition time was set to achieve at least 20 cycles in the signal. This strategy permitted the selection of response signals with different amplitudes, which was necessary to test the robustness and repeatability of the results. Obviously, PyFEST was used for signal post-processing. The estimations results are presented in Table 2. Table 2. Estimation results for the measured signals for modes two to five Intact beam: Mode 4 Freq. Ampl. 136.9767 0.2532 136.9842 0.166 136.9849 0.3523 136.9856 0.4347

Damaged Mode 4 Freq. 136.9017 136.9103 136.9214 136.9109

beam: Ampl. 0.5255 0.6986 0.621 0.7106

Intact beam: Mode 5 Freq. Ampl. 226.8998 2.3408 226.9186 1.1823 226.9197 0.9275 226.9098 1.3734

Damaged Mode 5 Freq. 226.7112 226.6924 226.7024 226.7123

beam: Ampl. 2.3408 1.1823 0.9275 1.3734

Analyzing Table 2, we can observe that the achieved frequencies are not the same as we obtain when involving DFT, but the differences are less than 0.01%. On the other hand, small frequency changes due to incipient crack is observable and the shift is certainly quantifiable, permitting to assess damage. This demonstrates the excitation method and the post-processing algorithm are feasible and permit observing the frequencies with accuracy, and, if the frequencies are altered, the change is observable.

4 Conclusion We propose estimating the natural frequencies of a beam by an interpolation method performed on DFT samples belonging to spectra obtained for different time lengths. The method is proved reliable and errors less than 1% are obtained. The precision increases with the time signal’s length becoming negligible when considering numerous cycles in the original signal. We improved the frequency estimates by taking for interpolation PSD samples and applying a correction coefficient which we have determined for all combinations of frequencies, sampling rates and time lengths. Using the PSD has the advantage that, for the different cropped signals, the effect of the time length is suppressed by normalization. The results are significantly improved compared with the case when we use the DFT samples, especially for a small number of cycles.

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References 1. Doebling, S.W., Farrar, C.R., Prime, M.B.: A summary review of vibration-based damage identification methods. Shock Vibr. Digest 30, 91–105 (1998) 2. Khatir, S., Dekemele, K., Loccufier, M., Khatir, T., Wahab, M.A.: Crack identification method in beam-like structures using changes in experimentally measured frequencies and particle swarm optimization. C. R. Méc. 346(2), 110–120 (2018) 3. Gillich, G.R., Minda, P.F., Praisach, Z.I., Minda, A.A.: Natural frequencies of damaged beams - a new approach. Rom. J. Acoust. Vibr. 9(2), 101–108 (2012) 4. He, K., Zhu, W.D.: Structural damage detection using changes in natural frequencies: theory and applications. J. Phys: Conf. Ser. 305, 012054 (2011) 5. Chioncel, C.P., Gillich, N., Tirian, G.O., Ntakpe, J.L.: Limits of the discrete fourier transform in exact identifying of the vibrations frequency. Rom. J. Acoust. Vibr. 12(1), 16– 19 (2015) 6. Gillich, G.R., Mituletu, I.C.: Signal post-processing for accurate evaluation of the natural frequencies. In: Yan, R., Chen, X., Mukhopadhyay, S.C. (eds.) Structural Health Monitoring. SSMI, vol. 26, pp. 13–37. Springer, Cham (2017). https://doi.org/10.1007/ 978-3-319-56126-4_2 7. Gillich, G.R., Maia, N., Mituletu, I.C., Praisach, Z.I., Tufoi, M., Negru, I.: Early structural damage assessment by using an improved frequency evaluation algorithm. Lat. Am. J. Solids Struct. 13(8), 2311–2329 (2015) 8. Gillich, G.R., Maia, N.N.N., Mituletu, I.C.: Problem of detecting damage through natural frequency changes. Comput. Exp. Methods Struct. 10, 105–139 (2018) 9. Djukanović, S., Popović, T., Mitrović, A.: Precise sinusoid frequency estimation based on parabolic interpolation. In: Proceedings of the 24th Telecommunications Forum TELFOR, pp. 1–4. IEEE, Belgrade (2016) 10. Grandke, T.: Interpolation algorithms for discrete fourier transforms of weighted signals. IEEE Trans. Instrum. Measur. 32, 350–355 (1983) 11. Quinn, B.G.: Estimating frequency by interpolation using fourier coefficients. IEEE Trans. Signal Process. 42, 1264–1268 (1994) 12. Jain, V.K., Collins, W.L., Davis, D.C.: High-accuracy analog measurements via interpolated FFT. IEEE Trans. Instrum. Measur. 28, 113–122 (1979) 13. Ding, K., Zheng, C., Yang, Z.: Frequency estimation accuracy analysis and improvement of energy barycenter correction method for discrete spectrum. J.Mech. Eng. 46(5), 43–48 (2010) 14. Voglewede, P.: Parabola approximation for peak determination. Glob. DSP Mag. 3(5), 13– 17 (2004) 15. Jacobsen, E., Kootsookos, P.: Fast, accurate frequency estimators. IEEE Signal Process. Mag. 24(3), 123–125 (2007) 16. Minda, A.A., Gillich, N., Mituletu, I.C., Ntakpe, J.L., Manescu, T., Negru, I.: Accurate frequency evaluation of vibration signals by multi-windowing analysis. Appl. Mech. Mater. 801, 328–332 (2015) 17. Gillich, G.R., Nedelcu, D., Malin, C.T., Biro, I., Wahab M.A.: Efficient algorithm for frequency estimation used in structural damage detection. In: Proceedings of the 13th International Conference on Damage Assessment of Structures, pp. 283–300. Springer, Porto (2020) 18. Gillich, G.R., Nedelcu, D., Barbinta, C.I., Gillich, N.: A versatile algorithm for estimating natural frequencies with high accuracy. Vibroeng. Procedia 27, 37–42 (2020) 19. Tufisi, C: Detection of branched cracks in beam-like structures, Ph.D. thesis, Universitatea “Eftimie Murgu” Resita, Romania (2020)

False Alarm-Improved Detection Capabilities of Multi-sensor-Based Monitoring of Vibrating Systems Daniel Adofo Ameyaw(&) and Dirk Söffker Chair of Dynamics and Control, University of Duisburg-Essen, Duisburg, Germany {daniel.adofo-ameyaw,soeffker}@uni-due.de

Abstract. Monitoring the State-of-Health of vibrating mechanical systems is useful but complex. Besides challenges associated with dynamical behaviors of the systems monitored, supervision tasks are complex with respect data acquisition, feature extraction, and/or statistical modeling for feature classification. Data acquisition strategy addresses sensor types, quantities, and locations. Feature extraction task details the selection and processing of features sensitive to a change/fault present and if required the development of statistical models for change/fault classification. In this contribution, the above-mentioned challenges associated with supervision are explained and detailed using an elastic mechanical structure applying the Probability of Detection method. Previously solved problems relating to simultaneously accessing all mentioned challenges are briefly repeated for understanding. This serves as a prelude to the newly developed data driven noise analysis and improved detection procedure. An experimental example using different real sensor types in combination with mechanical modifications of an elastic beam is presented. The adapted Probability of Detection method helps to determine a suitable feature, sensor type, and position for least change/fault detection. In this article a new data driven noise analysis approach is introduced to ensure optimal sensor-specific flaw size detection. Optimality in this context is related to the selection of the appropriate feature and threshold values for desired false alarms. The noise analysis permits the selection of a decision threshold (threshold beyond which change/fault is considered present in a signal) with the corresponding detectable flaw size and related false alarm rate. Selecting different sensors implies changing the signal distribution character and the decision threshold. This change results in different values and hence can be exploited to decide the optimal sensor. The implemented noise analysis allows a trade-off between flaw size detection and probability of false characterization of faults with 90% detection at 95% reliability level. The novel approach provides a graphical representation that illustrates the diagnostic capabilities of a sensor as its decision threshold is varied. Keywords: Vibration diagnostics


Multi-sensor
Probability of detection

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 467–480, 2021. https://doi.org/10.1007/978-3-030-64594-6_46

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1 Introduction Elastic mechanical systems refer to structures that adapt to workload changes provided the elastic limit is not exceeded. Notable structures are skyscrapers, bridges, aircrafts, wind turbines, among others. These structures are complex, safety-critical, and capital intensive. Ensuring structural and functional safety guarantee optimal performance and Structural Health Monitoring (SHM) aims to achieve this optimality [1, 2]. Structural health monitoring is the process of implementing a damage detection and characterization strategy for monitoring engineering structures [1]. Several SHM procedures exist with vibration-based health monitoring a well-established technique. Vibrationbased SHM use structural behavior for fault diagnosis and operates on the principle that structural defects result in changes in dynamical properties. Fault identification is mainly based on displacement, velocity, or acceleration measurements at a single point [3]; however, useful assessments may include observable effects and multipoint measurements [4, 5]. State of health monitoring of vibrating structures are complex. The complexity is related to data acquisition, feature extraction, and/or statistical modeling for feature classification [6]. Data acquisition is mainly based on hardware and software. An important component of the hardware system are sensors. Sensors are devices to acquire data for onward processing by other components in the acquisition system. The selection of suitable types of sensors, quantities, and locations need to be effectively addressed. This ensures optimal sensor placement and prevent redundancy. Feature extraction details the selection and processing of features. This is important due to different sensitivity levels of features to faults. A suitable solution may be to examine different features and conclude on the most sensitive to a fault present in a structure. The development of statistical models for fault classification usually completes the health monitoring process though other processes may be implemented. Monitoring dynamic systems requires all outlined phases to be examined concurrently [5]. Decision making for SHM systems requires the collection of data from the system to be monitored and proper data interpretation. This requires data to be processed to easily understood form for appropriate decisions to be made. In this contribution, the Probability of Detection (POD) reliability measure is adapted and implemented in vibration-based health monitoring to process and analyze data to easily understood form. The POD approach allows a general assessment of the reliability of Nondestructive testing (NDT) [7, 8]. The aim of the POD approach is to specify a damage size, which can be detected applying a specific NDT procedure, considering statistical variability of the sensor and measurement properties [8, 9]. The advent of SHM led analysts to consider how to integrate POD in SHM systems. In contrast to NDT, SHM systems are permanently mounted and should provide reproducible results. Aging of the structure leads to adverse effects. In Aldrin et al. [10] Monte Carlo simulation of flaw size as a function of time is proposed. The results demonstrate the sensitivity of flaws to degradation of SHM system, thereby making the implementation of POD in SHM time/duration sensitive. The POD approach is employed in this work to evaluate the reliability of individual sensors within a sensor network monitoring a dynamic system. Previous publications [4, 5] highlighting methods to simultaneously analyze complexity are briefly repeated for sequential flow of thought. This serves as

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introduction to the newly developed data-driven noise analysis and improved detection procedure. The new data-driven noise analysis procedure is presented providing a graphical tool to illustrate diagnostic capabilities of a sensor as its decision threshold is varied. The article is organized as follows: in Sect. 2 the POD measure is briefly introduced, followed by description of the experimental setup and fault simulation procedure in Sect. 3. The obtained results are detailed in Sect. 4. The new data-driven noise analysis procedure permitting a tradeoff between the false alarm rate and detectable flaw size is presented in Sect. 5. The summary and conclusion finalize the contribution.

2 Description of the POD Measure A brief introduction required to understand POD is presented in this section. Probability of Detection is an established certification method [8]. The POD is a probabilistic method to quantify the reliability of an NDT procedure considering statistical variability of sensor and measurements properties [7, 8, 11]. Data used in producing POD curves are categorized by the main POD controlling variables. These variables are either discrete or continuous and can be categorized as: I. Hit/miss approach: produce binary or qualitative statement about the existence of a flaw. II. Target response approach: data which provide quantitative information about the related target size to be detected. The target response approach is adapted and implemented in this work because the data to be analyzed relates an excitation to a flaw’s signal response quantitatively. The target response approach is used when there exists a relationship between a dependent function and an independent variable [7]. In the derivation of the POD curve, a predictive modeling technique is required. One such method is regression analysis of the data gathered [7, 11, 12]. The data distribution could be linear or not. A strategy to linearize the data distribution is by plotting four models: X vs Y, log X vs Y, log Y vs X, and log X vs log Y. The model with best linearity and variance is used in the construction of the POD curve [13]. The regression equation for a line of best fit to a given data set is given by y ¼ b þ mx;

ð1Þ

where m is the slope and b the intercept. The Wald confidence bounds are constructed to define a confidence interval that contains 95% of the observed data. Here the 95% Wald confidence bounds on y is constructed by yða ¼ 0:95Þ ¼ y þ 1:645sy ;

ð2Þ

where 1.645 is z value of 0.95 for a one-tailed standard normal distribution and sy the standard deviation of the regression line. The Delta method, a statistical technique for deriving the variance of a function of asymptotically normal random variables with

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known variance, is used to transition from regression line to POD curve. It is useful to construct the confidence bounds of the POD curve. This is done by computing the covariance matrix for the mean and standard deviation POD parameters µ and r respectively [7]. To estimate the entries, covariance matrix for the parameters and distribution around the regression line need to be determined. This is done using the Fisher’s information matrix I. The information matrix is derived by computing the maximum likelihood function f of the standardized deviation z of the regression line values. The Fisher’s matrix entries are calculated by the partial differential of the logarithm of the function f using the parameters of Hðm; b; sÞ of the regression line [7]. From ðyi  ðb þ mxi ÞÞ s

zi ¼ fi ¼ Pnl¼ 1

1 12 e 2p

ð3Þ


y  ðb þ mx Þ2 i

i

s

ð4Þ

the information matrix I can be computed as Iij ¼ E



@ @Hi @Hj

 logð f Þ :

ð5Þ

The inverse of the information matrix yields £ as 2

£ ¼ I 1

r2b 4 ¼ rm rb rs rb

3 rb rs rm rs 5 : r2s

rb rm r2m rs rm

ð6Þ

Next the mean µ and standard deviation r of the POD (a) curve has to be computed. The parameters are related by l ¼ ythm b, where yth is the decision threshold and r ¼ ms . The cumulative distribution function U is calculated as h i Uðl; rÞ ¼ 1 þ erf xpffiffi2rl :

ð7Þ

The POD as a function of flaw size a is derived as PODðaÞ ¼ U

al r

:

ð8Þ

Using Eq. 8 the POD-curve can be constructed for varying flaw sizes and hence integrate vibration analysis results.

3 Experimental Results Illustration of the complexity related to data acquisition, feature extraction, and statistical modeling for feature classification for vibrating system is presented in this section. The POD is adapted and implemented for diagnosis. Using a benchmark test

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rig, the principal difficulty associated with implementation of POD in dynamic systems is demonstrated. The experimental system to be considered for illustration is an elastic beam. Displacement, strain, and acceleration measurements are recorded. As features, eigenfrequency analysis are carried out on the first two modes of the mechanical system. In addition, band power (0–500 Hz) feature is selected and analyzed. The results and the analysis are discussed in detail. 3.1

Experimental Setup

The experiment is carried out on an elastic mechanical beam using the test rig shown in Fig. 1. An elastic steel beam of dimensions 545 mm  30 mm  5 mm is clamped on the left side. The length of the beam is divided into five equal parts defining sensors position (Fig. 2). Two displacement measurements are taken at the two positions (P2, P4) using noncontact laser sensors. Piezoelectric accelerometers are attached at three positions (P1, P2, and P3) on the beam. Two strain gauges are bonded onto the beam at positions P1 and P3 as indicated by the schematic in Fig. 2. The beam is excited for vibrational experiments using modal hammer.

Fig. 1. Test rig (Chair SRS, UDuE) consisting of a one side clamped elastic beam with bonded strain gauges, laser sensors, and accelerometers.

Fig. 2. Schematic of sensor position relative to beam length

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Injected Faults as Changes to Be Investigated

In this paper, additive masses are applied to modify the existing initial system to simulate a fault. Two cases of point mass placement are examined. Case I involves placing the mass at midpoint of positions 2 and 3. Case II involves the placement of mass at the midpoint of positions 3 and 4. For every incrementally placed mass (Fig. 3), the lower frequencies (up to 2 kHz) of the beam are excited, and the corresponding data are recorded. A complete description of the experimental setup, data acquisition process, and the resulting data can be found in [5, 14].

Fig. 3. Additive mass to stimulate fault

4 Results and Discussions The results and analysis are presented in this section. Classical NDT approaches utilize raw probe data for POD analysis. However, SHM systems are permanently mounted therefore features are selected from the dataset to reduce dimensionality and decrease the degree of randomness. The analysis is carried out for band power (0–500 Hz) feature and the first and second mode for each situation of mass placement (cases I and II). The technique to map the data to the POD curve is shown (Fig. 4). In Fig. 4, a graphical representation of the target response approach explained earlier is given. It involves plotting the observed data, setting inspection threshold (least detectable flaw size), saturation threshold (maximum detectable flaw size) and decision threshold (threshold below which observation is considered noise) are constructed. Regression analysis is performed to determine line of best fit to the data. Confidence and prediction bounds are constructed on both sides of regression line. Probability density functions at each flaw size are established. The area above the decision threshold is used to construct the POD curve.

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Fig. 4. Technique: from data to POD curve [5]

The analysis for all eight sensors is performed and the 90% probability of detecting a flaw at a 95% confidence level (90/95 criterion) is indicated in Table 1. The least values represent best results in that the sensor can detect least flaw changes with 90/95 reliability level.

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Mass between P2 and P3 (Case I) Mode 1 Mode 2 Band power POD [g] POD [g] POD [g]

Mass between P3 and P4 (Case II) Mode 1 Mode 2 Band power POD [g] POD [g] POD [g]

ACC 1 at P1 ACC 2 at P2 ACC 3 at P3 SG 1 at P1

74.04 74.04 74.04 85.19

48.15a 55.78

45.28b 34.63

72.59b 72.38

20.20a 29.34

52.21 52.15 52.15 52.15

SG 2 at P3 Las 1 at P1 Las 2 at P4

126.70b 67.30a 74.04

62.23 61.03 -

34.37 27.64 23.44

54.08b 52.10a 52.15

9.293a

84.56b 22.78

13.36 11.07

17.29a 28.69

9.394

27.93 25.54 25.85

9.915

43.49b -

The results indicate that the detection and quantification capabilities of sensors depends on sensor type, sensor location, feature selected, and the fault position relative to the sensors. The introduced POD measure helps to assess change detection and quantification capabilities of individual sensors within a sensor network monitoring a vibrating system. It is therefore useful to examine the sensor types, locations, selected feature, and the fault location concurrently to conclude on an effective combination for best fault detection. The introduced approach provides the means to access all these combinations concurrently.

5 Diagnostic Capabilities of Sensors Qualifying sensor diagnostic capabilities for detection and quantification also requires noise analysis. Noise is always present in measured data. Assuming sensor observability, the observed signals aggregates features and characteristics of the monitored system including noise. Noise in this context refers to observed signal with no useful flaw characterization information. In this contribution a new method is developed where the healthy state data are used to compute the noise. The healthy and faulty states data are plotted together and the effect of a selected decision threshold yth on the Probability of False Positive (PFP) for a specific sensor is inferred from the healthy state data. The PFP is the percentage of healthy data that the system wrongly classifies as damage. Classical POD methods usually evaluate independently the noise or infer noise from data not associated with target size [7, 11]. However, a new data-driven approach is introduced. For illustrative purpose, accelerometer measurements at position P3 filtered as eigenfrequency (mode 1 case II) are used (as shown in Fig. 5). Flaw detection probability and false positive probability are dependent on each other through definition of system decision threshold. The decision threshold signifies system response value above which the system is considered faulty.

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Fig. 5. Proportion of healthy data above selected threshold

However, from Fig. 5, it becomes evident that there are still healthy state data (circled points) above the defined threshold to be analyzed. This implies that the region above the threshold cannot be considered to correspond entirely to faulty states. The healthy state data, with no flaw characterization information, can thus be used to determine false positives associated with a selected threshold.

Fig. 6. Accelerometer at P3 eigenfrequency noise data

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For noise analysis, first the nature and distribution of the noisy data needs to be established. One technique to ascertain the distribution of noise data is through hypothesis testing. A v2 (chi-square) hypothesis test is undertaken to identify the nature of noise distribution. Various distributions are tested, with the Gaussian distribution emerging most plausible (as shown in Fig. 6). The v2 produced a p-value of 0.95, thereby rejecting the null-hypothesis that the distribution is non-Gaussian. A regression analysis is carried out on the noisy data and the mean l and standard deviation r are calculated (Fig. 6). For a Gaussian noise distribution, the PFP is calculated as PFP ¼

R1 yth

1 pffiffiffiffi e 2pr

ðylÞ2 dy 2r2

ð9Þ

The distribution regarding PFP and the corresponding POD values for accelerometer measurements at position P3 is illustrated in Fig. 7.

Fig. 7. POD and PFP values for eigenfrequency feature

The results indicate that for every selected decision threshold, a unique PFP value is evaluated but detected flaw size is dependent on the probability level (50%, 90%, etc.). This implies known evaluation approaches like the receiver operating characteristic is deficient in applications which require detectability to be related to flaw size. In this work the 90% and 90/95 POD sizes are used (similar to typical standards in industry [7]). Changing the sensor results in changing the distribution character. Other sensors

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will result in different distribution characters and different values; however, the character of the introduced strategy remains. The procedure is repeated for all sensors for the same feature (here: mode 1 eigenfrequency case II). A summary of the 90% and 90/95 POD and PFP values for a selected decision threshold are illustrated in Table 2 and 3 respectively.

Table 2 Tradeoff between decision threshold, PFP, and 90% POD Sensor ACC 1 ACC 2 ACC 3 SG 1 SG 2 Las 1 Las 2

13.2 1.000 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13.4 0.996 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Decision threshold

13.6 0.966 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13.8 0.841 5.85 7.64 7.64 7.64 6.71 3.71 7.64

14.0 0.568 23.36 24.53 24.53 24.53 24.38 21.90 24.53

14.2 0.255 40.62 41.18 41.18 41.18 41.80 39.83 41.18

14.4 0.0685 57.64 57.59 57.59 57.59 58.98 57.52 57.59

14.6 0.0103 74.43 73.78 73.78 73.78 75.92 74.96 73.78

14.8 0.0008 90.99 89.75 89.75 89.75 92.63 92.16 89.75

PFP

Table 3 Tradeoff between decision tradeoff, PFP, and 90/95 POD Sensor ACC 1 ACC 2 ACC 3 SG 1 SG 2 Las 1 Las 2

5.1

13.2 1.000 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13.4 0.996 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13.6 0.966 0.418 1.79 1.79 1.79 2.10 0.00 1.79

13.8 0.841 15.83 16.82 16.82 16.82 17.40 14.54 16.82

14.0 0.568 31.46 32.06 32.06 32.06 33.1 30.61 32.06

14.2 0.255 47.68 47.80 47.80 47.80 49.40 47.32 47.80

14.4 0.0685 64.79 64.32 64.32 64.32 66.80 65.08 64.32

14.6 0.0103 82.74 81.55 81.55 81.55 85.10 83.80 81.55

14.8 0.0008 101.13 99.15 99.15 99.15 103.9 103.05 99.15

Improved POD Analysis

A new method to improve the detection capabilities of sensors is introduced in this section. The main idea is to discuss the sensitivity of features to the fault to be detected. The POD results (Table 1) comparing different sensor/feature combinations indicate that the most sensitive feature to point mass changes for case I is band power. By utilizing the introduced noise analysis procedure, direct comparison can be made between the eigenfrequency and band power detection capabilities for the same sensor (here: accelerometer at position P3 for case I).

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Fig. 8. PFP value for band power feature

The corresponding PFP for band power feature for accelerometer 3 for a similar threshold is shown in Fig. 8, producing a better PFP value of 0.119 compared to 0.2825 for mode 1 frequency. This PFP value corresponds to a single decision threshold and a specific POD flaw size. The PFPs and POD values corresponding to the entire flaw sizes can be analyzed by repeating the procedure for all decision thresholds (response values). By considering all thresholds, the tradeoff between PFP, 90/95 POD (specific POD), and flaw size can then be constructed for both features (eigenfrequency mode 1 and band power) as illustrated in Fig. 9.

Fig. 9. Tradeoff between PFP, POD and flaw size (a): Eigenfrequency (b): Band power

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The analysis indicates for a selected PFP value of 0.1 (1) at 90% probability and 95% reliability level (2), the eigenfrequency feature detects a flaw of 60.2 g (3) while the band power feature detects a flaw size of 24.9 g (4). This procedure provides an effective and simple method to improve the detection capabilities of sensors without affecting (here increasing) the false alarm rate.

6 Summary and Conclusion In this contribution challenges associated with monitoring dynamical systems are presented. The difficulties are related to data acquisition, feature extraction, and statistical modeling for features classification. A data acquisition system consists of software (signal data) and hardware (sensors). In previous publications [4, 5] it was shown that the sensor type and position play a significant role in fault diagnosis. Feature selection is also critical due to different sensitivity levels of features to faults. In vibration analysis, it is useful to consider this concurrently. The POD measure is adapted and implemented in this work to provide a statistical modeling technique to select the best sensor (type and position)-feature combination for optimal fault diagnosis especially considering their noise characteristics. A new data driven noise analysis procedure is also presented to analyze the PFP and corresponding POD value for any selected decision threshold. An effective method to improve sensor detection without affecting the false alarm by utilizing sensitive features is introduced. The noise analysis procedure provides a graphical representation that illustrates the diagnostic capabilities of a sensor as its decision threshold is varied.

References 1. Ko, J.M., Ni, Y.Q.: Technology developments in structural health monitoring of large-scale bridges. Eng. Struct. 27(12), 1715–1725 (2005) 2. Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Phil. Trans. Roy. Soc. London A: Math. Phys. Eng. Sci. 365(1851), 303–315 (2007) 3. Fritzen, C.P.: Vibration-based structural health monitoring–concepts and applications. In: Key Engineering Materials, vol. 293, pp. 3–20. Trans Tech Publications (2005) 4. Ameyaw, D.A., Rothe, S., Söffker, D.: Adaptation and implementation of Probability of Detection (POD)-based fault diagnosis in elastic structures through vibration-based SHM approach. In: 9th European Workshop on Structural Health Monitoring (EWSHM), Manchester, 10–13 July 2018 (2018) 5. Ameyaw, D.A., Rothe, S., Söffker, D.: A novel feature-based probability of detection assessment and fusion approach for reliability evaluation of vibration-based diagnosis systems. Struct. Health Monit. 19(3), 649–660 (2020) 6. Farrar, C.R., Doebling, S.W., Nix, D.A.: Vibration–based structural damage identification. Phil. Trans. Roy. Soc. London. Ser. A: Math. Phys. Eng. Sci. 359(1778), 131–149 (2001) 7. MIL‐HDBK‐1823A: Department of Defense Handbook: Nondestructive Evaluation System Reliability Assessment. Washington, DC: Department of Defense (2009) 8. Georgiou, G.A.: PoD curves, their derivation, applications, and limitations. Insight-NonDestruct. Test. Cond. Monit. 49(7), 409–414 (2007)

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9. Ginzel, E.: Introduction to the Statistics of NDT. NDE. net–e-J. Nondestruct. Test. 11(5) (2006) 10. Aldrin, J.C., Medina, E.A., Lindgren, E.A., Buynak, C.F., Knopp, J.S.: Case studies for model‐assisted probabilistic reliability assessment for structural health monitoring systems. In: AIP Conference Proceedings, vol. 1335, no. 1, pp. 1589–1596, June 2011 11. Annis, C.: Statistical best-p ractices for building Probability of Detection (POD) models. Rmh1823, Version, 2(4.4), Florida (2017). http://StatisticalEngineering.com. Accessed 7 Jan 2018 12. Gandossi, L., Annis, C.: Probability of detection curves: statistical best-practices. ENIQ report 41. Office for Official Publications of the European Communities, Luxembourg (2010) 13. Neter, J., Kutner, M.H., Nachtsheim, C.J., Wasserman, W.: Book: Applied linear statistical models (1996) 14. Ameyaw, D.A., Rothe, S., Söffker, D.: Probability of detection (POD)-oriented view to fault diagnosis for reliability assessment of FDI approaches. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 51852, p. V008T10A041. American Society of Mechanical Engineers (2018)

A Computer Vision-Based Approach for Non-contact Modal Analysis and Finite Element Model Updating Marco Civera1(&), Luca Zanotti Fragonara2, and Cecilia Surace3

2

3

1 Department of Mechanical and Aerospace Engineering-DIMEAS, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy [email protected] Centre for Autonomous and Cyber-Physical Systems, Cranfield University, Cranfield, Bedford MK43 0AL, UK [email protected] Department of Structural, Geotechnical and Building Engineering-DISEG, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy [email protected]

Abstract. Computer vision-based techniques for modal analysis and system identification are rapidly becoming of great interest for both academic research and engineering practice in structural engineering. For instance, this is particularly relevant in fields such as bridge or tall building monitoring, where the large size of the structure would require an expensive sensor network, and for the characterisation of very slender, highly-flexible structural components, where physically-attached sensors cannot be deployed without altering the mass and stiffness of the system under investigation. This study concerns the latter case. Here, an algorithm for the full-field, non-contact extraction and processing of useful information from vibrational data is applied. Firstly, video acquisition is used to capture rapidly very spatially- and temporally-dense information regarding the vibrational behaviour of a high-aspect-ratio (HAR) prototype wing, with high image quality and high frame rate. Video processing is then applied to extract displacement time histories from the collected data; in turn, these are used to perform Modal Analysis (MA) and Finite Element Model Updating (FEMU). Results are benchmarked against the ones obtained from a single-point laser Doppler vibrometer (LDV). The study is performed on the beam-like spar of the wing prototype with and without the sensors attached to appreciate the disruptive effects of sensor loading. Promising results were achieved. Keywords: Parameter estimation
Model updating
System identification Video processing
Computer vision
Experimental modal analysis




1 Introduction To perform realistic numerical simulations, a reliable predictive Finite Element Model (FEM) is required. To achieve such a FEM, the unknown material parameters of the corresponding real-life system need to be estimated from experimental acquisitions. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 481–493, 2021. https://doi.org/10.1007/978-3-030-64594-6_47

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This is generally achieved by attaching mounted sensors, such as accelerometers, to the structure of interest. However, this classic experimental setup has at least two main disadvantages. Firstly, it only allows a sparse, point-wise disposition of the output channels, which limits the amount of available information. Secondly, both the additional weight of and the additional stiffness induced by the physically attached transducers and by their connection to the investigated system affect the dynamical response of the structure-sensors ensemble. The first issue negatively affects the robustness and reliability of the model since it is derived from relatively few recordings. Indeed, while the global dynamic behaviour can be estimated even from few points, the lack of local information severely hampers some more specific investigations, such as damage localisation, where high or very high spatial density is required [1, 2]. Regarding the latter point, the effects of the additional masses and stiffness are negligible on massive buildings such as bridges or bell towers, yet become predominant for very lightweight, very slender structural elements. This is especially relevant for the aeronautical industry, where in recent years more and more efforts have been dedicated to producing lighter and more flexible wings [3]. Thus, the recorded behaviour of the system-sensors ensemble can diverge substantially (both locally and globally) from the one corresponding to the system alone with no transducers attached. The linear dynamics of the XB-1 high-aspect-ratio (HAR) wing [4] are the subject of this study. Importantly, the prototype highly flexible skin is supposed to transfer all the aerodynamic loads to the spar, making the structural behaviour of the latter the one of greatest interest; thus, all experimental tests were performed on the spar alone. The specific aim is to perform the FE model updating of its material parameters (Young’s Modulus E; Poisson Ratio m; density q; and damping ratio f) in a non-contact way, by extracting the vibrational response of the structure from video acquisitions. These displacement time histories (THs) are compared to the one acquired by a singlepoint Laser Doppler Vibrometer (LDV), showing good consistency. The rest of this discussion is organised as follow. In Sect. 2, the basics of FEM updating are briefly recalled. Section 3 discusses the algorithm applied for the extraction of displacement time histories from the recorded video. In Sect. 4 the case study of this dynamic investigation is introduced. Section 5 describes the results and Conclusions follow in Sect. 6.

2 FE Model Updating The concept itself of FE-based Model Updating (FEMU) has been put forward since several decades [5]. A large variety of algorithms have been proposed for this aim at least since the 1990s. A quite exhaustive review can be found in the relatively recent work of Reference [6], while a comparative study is available in Reference [7]. FEMU approaches may be mainly classified as direct and indirect methods; the members of this latter group are also known as sensitivity-based techniques [8]. In direct methods, the individual elements in the system matrices of masses and stiffnesses are adjusted through comparison between the initial model prediction and the experimental data, generally without recurring to iterative algorithms. In the case of indirect techniques, as the name suggests, the adjustments are applied not directly to the system

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matrices but rather to some specific physical property of the model finite elements. In turn, this causes a variation of the resulting matrices and – hopefully – brings the predicted output closer to the measured data. The interest audience may refer to the classic book of Friswell & Mottershead [9] for further general information about the topic. A shorter yet effective introduction to indirect techniques can be found in Reference [10]. In the case of this study, an input-output procedure has been applied as an iterative and indirect technique operating in the frequency domain. This approach belongs to the broad family of the response function methods (RFM) [11]. The process is quite straightforward: by taking the recorded inputs and outputs, one or more experimental Frequency Response Functions (FRFs) which define the linear system under exam are compared with the results from the numerical simulations at the same points. This is done here by computing the Normalised Mean Square Error (NMSE) between the numerical and the experimental data in a short frequency range around the first natural frequency. This can be carried out at any output channel of interest, thus allowing a Single-Input Multi-Output (SIMO) characterisation of the investigated system. The iterative algorithm is sketched in the flowchart of Fig. 1.

Fig. 1. Flowchart of the iterative FEMU algorithm.

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The minimization of the error function was performed on MatLab ® using the patternsearch() function, which implements a variant of the generalised pattern search (GPS) algorithm [12]. Convergence was set to occur accordingly to three requirements: 1. cumulative NMSE of all the M output channels considered below an arbitrary limit set to e ¼ 0:01
M; 2. change in NMSE less than 0.001 respect to the previous iteration; 3. change in pattern search mesh size less than 10−6 respect to the previous iteration. The FE model of the wing spar (Fig. 2), recently used for some related works [13] is made up by 400 8-noded quadratic shell elements, for a total of 1369 nodes, with 6 degrees of freedom per node. The input was applied as a harmonic acceleration to the clamped base, while the output THs were computed at all nodes corresponding to the LDV point of application and close-by investigated cross-sections.

Fig. 2. The geometry of the FE model to be calibrated. The input harmonic excitation was applied to the elements coloured in light grey. The closest node to the LDV dot is highlighted in cyan.

3 Video Acquisition Algorithm The Virtual Video Vibrometer (VVV) technique, firstly proposed by the Authors in [14], has been utilised here to extract the displacement THs from the video recordings at the cross-sections of interest. The basic concept is that the moving wing edge profile produces a sharp change in the pixel brightness respect to the background, which in turn can be easily detected at any frame. The results are pixel-wise time series of displacements referred to the targeted wing cross-sections, similar to what can be achieved with a Laser Doppler Vibrometer aimed at the same points. As for the LDV, the implicit assumption of this approach is that the trajectory of the transverse motion can be approximated by a straight line. While this assumption may not hold true for larger transverse deflections [15], this is not an issue when the output amplitude of

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motion is relatively low, as commonly done for the task of system identification (SI) of structure behaving linearly at low energy levels. The VVV procedure is very straightforward and can be summarised as follow (all the steps are depicted in Fig. 3). Firstly, the original video sequence is converted frameby-frame to a greyscale image and a frame slice of interest is selected, with arbitrary narrow width, here set at 6 pixels (indicated by the green lines in the top left image of Fig. 3). This selection is then isolated (Fig. 3, step i) and its brightness is defined at any pixel as 8-bit unsigned integers, thus spanning in a range from a minimum of 0 to a maximum of 256 (step ii). A mean brightness profile is then defined over the six-pixel columns (step iii). The 2-mm-long thickness of the wing spar is noticeable, while the laser dot is revealed by the peak in brightness. Through any nonlinear detrending algorithm, it is then possible to remove the illumination gradient on the background panel (step iv). Here, a Savitzky-Golay sliding polynomial filter [16] of order 3 and window width 27 was applied. This step is also useful to remove any unrelated object included in the frame as long as it is not moving during the recording. At this point, the moving (local or global) maximum can be targeted utilising a peak picking method. The final result is the framewise profile of a Brightness Index (BI). This index is defined as the signed deviation of the brightness respect to the background trend. By following any BI peak of interest frame after frame, the THs of vertical displacement are thus obtained for any given cross-section. While this can be more easily implemented for the most prominent peak, the approach is not limited to it, allowing to select the spar intrados or extrados as well (as long as they are distinguishable from the background at any frame). These THs are finally converted from pixels to millimetres.

Fig. 3. A pictorial description of the VVV algorithm (steps i–iv).

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Respect to the classic LDV acquisitions, this video-based method has both benefits and drawbacks. The main limitation is in terms of spatial resolution, as the accuracy is limited by the pixel dimension and thus depends on the distance of the camera from the target structure (here, the focal length was about 240 mm). This can be improved by several techniques for subpixel resolution and/or by interpolating, for which numerous algorithms exist in the literature. Motion magnification techniques have been proposed in recent years [17], which can be used directly or combined with techniques such as the one described here to obtain displacement THs. Yet, the actual resolution will be inferior respect to the LDV one in most of the cases. On the other hand, the video processing procedure can be applied to any slice of the frame, thus capturing multiple THs from a single experiment. This can be otherwise achieved only employing multipoint LDV, which is much more expensive and difficult to use than single-point LDV or high-speed HD cameras. Moreover, the VVV technique directly measures the displacement of a point, without the need of numerical integration (even if a conversion step from pixel to SI units is still required and can introduce error in the inferred quantities). Another technical issue derives from the internal memory capacity, which is limited and inversely proportional to the pixel density and the frame rate set. In the case of this study, with 1280  1024 pixels per frame (width x height), the storage capacity was limited to 4897 frames.

4 Experimental Setup The whole experimental setup is shown in Fig. 4. The studies were performed in the facilities of Cranfield University. The instrumentation is the same as appeared in Reference [18, 19], and [20]; in detail, an Olympus® I-speed 3™ video camera and a Polytec® OFV-505 Sensor Head™ LDV were utilised. More details can be found in Table 2 of Reference [18]. The geometric details of the investigated wing spar are reported in Table 1. The characterisation has been performed via harmonic analysis, by dwelling the spar at its first natural frequency, and with an input acceleration of 0.01 g, low enough to ensure the linearity of the response. The input was applied to the clamped base with a Data Physics® Signal Force™ shaker and directly recorded from its DP760 close-loop™ control software. The camera was set to acquire 2000 frame per second (fps), the same sampling frequency as the laser vibrometer, for better comparability. Thus, the resulting available recording duration is 2.4485 s. The corresponding frequency resolution is therefore limited to 0.4084 Hz, which is relatively coarse yet proved sufficient for the aim of updating the spar FE model. The camera was aimed at the spar trailing edge and the focus was adjusted consequently; the very short focal length can be seen in Fig. 4.c. Points on this edge are considered representative of the behaviour of the whole spar at that cross-section (i.e., torsion effects are neglected). This is still viable with negligible effects at very low input amplitudes if the flapwise deflections are the only motion of interest.

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Fig. 4. The experimental setup. (a) top view of the wing spar clamped to the modal shaker. (b) the whole apparatus: the high-speed camera [A] with its acquisition user interface [B], the shaker utilised to apply the input [C], the LDV [D] and the light source [E]. (c) close distance acquisition. Table 1. Geometrical properties of the wing spar.

Parameter

Value

Free length (clamp to tip) l tip Thickness t Max width at clamped section bmax Mid-length width at the section of changing tampering ( l 258 mm) bl Min width at the tip section bmin

258

Measurement unit

706.00

mm

2.00

mm

180.00

mm

56.10

mm

17.04

mm

5 Results Eleven equally spaced cross-sections, represented in Fig. 5 and enlisted in Table 2, were considered. The resulting 11 time series, as well as the derived FRFs of displacement per unit of applied acceleration, are reported in Fig. 6 and Fig. 7, respectively. Convergence according to the requirements expressed in Sect. 2 was reached after circa 100 iterations, even if the NMSE Cost Function was already relatively low and almost plateauing after the first 70 iterations. The results are enlisted in Table 3 for the four parameters considered. The values guessed as a first attempt are also reported.

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For completeness, an early estimation of the parameters, as reported in [18], is included as well. The relatively large divergence in Young’s Modulus can be explained by both the relatively imprecise first estimation and by the non-negligible differences in the experimental setup, especially in the exact position of the clamped cross-section and clamp load. The resulting FRFs are reported in Fig. 8. As a validation of the obtained results, it can be seen that the numerically simulated behaviour matches well the experimental results obtained from the LDV acquisition (bottom right corner of Fig. 8).

Fig. 5. (a) Zoom on the laser dot emanated by the LDV, clearly visible and highlighted in the video recordings. (b) the eleven points investigated along the trailing edge.

Table 2. Location (in pixels) of the selected 6-pixels-wide cross-sections.

Distance from the left border [pixels]

Marker

Distance from the left border [pixels]

Marker

Distance from the left border [pixels]

106-111

506-511

906-911

206-211

606-611

1006-1011

306-311

706-711

1106-1111

406-411

806-811

LDV: X = 710

Marker

The results of the modal analysis run on the calibrated model are then compared to the experimental findings reported of Pontillo et al. [4] in Table 4. It must be remarked that the slight difference is again due to the different experimental setup. In that study, a

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single vibrometer was applied, thus torsional modes went undetected. The neighbouring 4th flexural flapwise and 1st torsional mode generated some unclear response in their range of frequencies. An unclear result at circa 202 Hz may again be due to imperfectly detected torsion or flection in the chord direction.

Fig. 6. Resulting time histories (pixels amplitude along time expressed in terms of frames). The magnified portion is highlighted in red. The colour scheme reflects the positions highlighted in Fig. 5 and Table 2.

Fig. 7. The frequency response function between these displacement outputs and the acceleration input. The colour scheme reflects the positions highlighted in Fig. 5 and Table 2.

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The experimental investigation of Pontillo et al. was upper bounded to 300 Hz, so the 7th flexural flapwise mode most probably fell out of the range. It is noteworthy how the FE Model, while calibrated only on the first mode, can provide a relatively good estimation of all the higher modes for which the comparison with the experimental data is feasible. This proves the reliability of the FE Model, even if it is a very basic and simple approximation of the target plate-like structure. It is important to remark that, as it can be seen from Fig. 8, the video-extracted vibrational information is strongly redundant. This is very useful for Model Updating. On one hand, the overdetermination of the problem means that more parameters can be calibrated. On the other hand, if few parameters have to be estimated, as in this case, extrapolating information from an arbitrarily large amount of closely spaced output channels also makes the resulting system identification more robust than in the case of a single-point LDV.

Table 3. Estimated and Updated mechanical parameters. Parameter

Young’s modulus [MPa ] Density [kg / m 3 ]

Early estimates [18]

73.1000·10

3

First attempt assumptions

69.1000·10

3

Final updated values

59.0162·10

3

2893.0649

2850.000

2850.000

Damping ratio [%]

-

0.1000

0.8634

Poisson’s ratio [-]

0.3300

0.3300

0.2616

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Fig. 8. Results of the fitting procedure at convergence. Numerical FRFs reported as dashed red lines superposed to experimental data from video. Experimental data from the LDV shown for comparison in the bottom right corner. Table 4. First ten vibrational modes.

ID #

1 2 3 4 5 6 7 8 9 10

Mode

1st flexural flapwise 2nd flexural flapwise 3rd flexural flapwise 4th flexural flapwise 1st torsional th 5 flexural flapwise 1st flexural chordwise 2nd torsional th 6 flexural flapwise 7th flexural flapwise

Experimental values ([4]) [Hz]

5.12 22.02 55.30 110.10 174.10 202.20 259.10 Out of range

Video acquisition [Hz]

5.40 Out of range Out of range Out of range Out of range Out of range Out of range Out of range Out of range Out of range

Calibrated FE Model [Hz]

5.49 23.16 55.80 103.99 125.11 172.09 189.15 219.90 255.62 333.89

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6 Conclusions The work presented here detailed a simple yet effective video-based FEMU procedure. The object of this experimental investigation was the spar of a very flexible and HAR wing prototype. This target system was proven in previous studies to be highly affected by the local and global changes in mass due to sensor loading. Therefore, the main difficulty for its dynamical characterisation lies in the invasiveness of the acquisition procedure. Non-contact approaches are very useful from this point of view. Acknowledgements. The authors would like to thank dr Mudassir Lone for kindly providing the high-speed camera and the wing prototype, and dr Ivan Petrunin and dr Alessandro Pontillo for their help with the experimental setup.

References 1. Civera, M., Surace, C., Worden, K.: Detection of Cracks in Beams Using Treed Gaussian Processes, pp. 85–97. Springer, Cham (2017) 2. Martucci, D., Civera, M., Surace, C., Worden, K.: Novelty detection in a cantilever beam using extreme function theory. J. Phys: Conf. Ser. 1106, 012027 (2018) 3. Dussart, G., Portapas, V., Pontillo, A., Lone, M.: Flight dynamic modelling and simulation of large flexible aircraft. In: Flight Physics - Models, Techniques and Technologies; InTech (2018) 4. Pontillo, A., Hayes, D., Dussart, G.X., Lopez Matos, G.E., Carrizales, M.A., Yusuf, S.Y., Lone, M.M.: Flexible high aspect ratio wing: low cost experimental model and computational framework. In: Proceedings of the 2018 AIAA Atmospheric Flight Mechanics Conference; American Institute of Aeronautics and Astronautics, Reston, Virginia (2018) 5. Mottershead, J.E., Friswell, M.I.: Model updating in structural dynamics: a survey. J. Sound Vib. 167, 347–375 (1993) 6. Sehgal, S., Kumar, H.: Structural dynamic model updating techniques: a state of the art review. Arch. Comput. Meth. Eng. 23(3), 515–533 (2015) 7. Modak, S.V., Kundra, T.K., Nakra, B.C.: Comparative study of model updating methods using simulated experimental data. Comput. Struct. 80, 437–447 (2002) 8. Boscato, G., Russo, S., Ceravolo, R., Fragonara, L.Z.: Global sensitivity-based model updating for heritage structures. Comput. Civ. Infrastruct. Eng. 30, 620–635 (2015) 9. Friswell, M., Mottershead, J.E.: Finite Element Model Updating in Structural Dynamics (2013) 10. Mottershead, J.E., Link, M., Friswell, M.I.: The sensitivity method in finite element model updating: a tutorial. Mech. Syst. Signal Process. 25, 2275–2296 (2011) 11. Ewins, D.J.: Adjustment or updating of models. Sadhana - Acad. Proc. Eng. Sci. 25, 235– 245 (2000) 12. Hooke, R., Jeeves, T.A.: “Direct search” solution of numerical and statistical problems. J. ACM 8, 212–229 (1961) 13. Civera, M., Ferraris, M., Ceravolo, R., Surace, C., Betti, R.: The Teager-Kaiser energy cepstral coefficients as an effective structural health monitoring tool. Appl. Sci. 9, 5064 (2019)

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14. Civera, M.; Zanotti Fragonara, L.; Surace, C. Video processing techniques for the contactless investigation of large oscillations. In: Proceedings of the proceedings of the AIVELA XXVI Meeting. https://doi.org/10.1088/1742-6596/1249/1/012004 15. Civera, M., Zanotti Fragonara, L., Surace, C.: Nonlinear dynamics of cracked, cantilevered beam-like structures undergoing large deflections. In: Proceedings of the Metrology for Aerospace (2019) 16. Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964) 17. Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.T.: Phase-based video motion processing. ACM Trans. Graph. 32, 1 (2013) 18. Civera, M., Zanotti Fragonara, L., Surace, C.: Using video processing for the full-field identification of backbone curves in case of large vibrations. Sensors 19, 2345 (2019) 19. Civera, M., Zanotti Fragonara, L., Surace, C.: An experimental study of the feasibility of phase-based video magnification for damage detection and localisation in operational deflection shapes. Strain 56(1) e12336 (2020) 20. Civera, M., Calamai, G., Zanotti Fragonara, L.: Experimental Modal analysis of structural systems by using the fast relaxed vector fitting method. Accepted for publication, Structural Control and Health Monitoring (2020)

On Metrics Assessing the Information Content of Datasets for Population-Based Structural Health Monitoring Chandula T. Wickramarachchi1(B) , Wayne Leahy2 , Keith Worden1 , and Elizabeth J. Cross1 1

Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S10 2TN, UK [email protected] 2 Element Six Global Innovation Centre, Harwell Campus, Fermi Ave, Didcot OX11 0QR, UK

Abstract. Within databases designed for population-based structural health monitoring, diagnostic information can be transferred between structures allowing inferences to be made across them. Information metrics can be developed for this case, where similarities and differences between data collected for monitoring of structures can be evaluated easily without relying on an in-depth, physics-based understanding of the data. By doing so, feature extraction for monitoring will be faster and more informed than through current methods. This paper focusses on adopting the maximum mean discrepancy, to find the distance between probability distributions of tool wear data from tools in a population, in order to find similarly-behaving tools for diagnostic and prognostic purposes. Keywords: PBSHM · Information metrics discrepancy · Tool wear · Machining

1

· Maximum mean

Introduction

Population-based structural health monitoring (PBSHM) expands the concept of SHM from looking at single structures to multiples in a population. This approach enables inferences to be made between structures by sharing information across them [1]. Machine learning techniques can then be used to make diagnostic and prognostic decisions about the structures using data from others in a population. One of the first tasks in a PBSHM setting is to identify structures behaving in a similar manner, or, indeed, to quantify how dissimilarly they are behaving. Many of the tools commonly used for SHM rely on the assumption that the data under comparison share the same distribution. A model or algorithm is only able to predict or detect accurately when the relationships it learns remain valid across all datasets it is applied to. If this is not the case, the model could give false positives; dissimilarities could be misinterpreted as damage. c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 494–504, 2021. https://doi.org/10.1007/978-3-030-64594-6_48

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Transfer learning [2] has been identified for use in PBSHM for when datasets don’t share the same underlying distributions. Nevertheless, in a large population of structures, an understanding of the similarities between members of the population will be useful before any transfer of knowledge is attempted. This paper focusses on using the maximum mean discrepancy (MMD) between datasets as a measure of similarity. The MMD essentially provides a measure of the distance between two distributions of data. Gretton et al. formally defined the MMD as a metric in [3], where it was introduced as a kernel method for addressing the two-sample problem (the statistical test of assessing whether two distributions are equal [null hypothesis] or not [alternative hypothesis]). The authors use the metric for evaluating whether two random variables are independent, for attribute matching in databases, and discuss its suitability for determining distributions of structured data, such as graphs. Gardner et al. [4] reviewed the MMD as a metric for assessing the similarity between simulator outputs and observational data for validation purposes. As the MMD is nonparametric and has the ability to present the difference in probabilities, it was considered as a useful tool for verification and validation. In cancer research, the MMD has been used as a measure to evaluate the distance between distributions of data from patients before and during treatment, where a large MMD suggested that the treatment was effective [5]. In the field of computer science, the MMD was employed to identify the difference between ‘honest’ and ‘malicious’ users of a cognitive radio-based network [6]. In the formation of generative adversarial networks, the MMD has been used as a tractable choice for the adversary to speed up the optimisation process [7]. Domain adaptation, a form of transfer learning, also utilises the MMD to reduce the distance between source and target data. Chen et al. [8] presents a framework that uses graph embeddings for domain adaptation where the MMD is used to reduce the scatter within classes. Yan et al. [9] uses a weighted MMD method to address the drawbacks of unsupervised domain adaptation caused by class weight bias. Gardner et al. [2] uses the MMD to perform domain adaptation in four PBSHM case studies; heterogeneous populations with topologically similar and dissimilar structures are considered. The domain adaptation techniques consistently outperformed the k-nearest neighbour classification without transfer in all four case studies. It is clear that the MMD is useful for assessing the similarity of datasets. However, its use as a metric to identify similarity of structures for PBSHM has not been researched in the past. The MMD is calculated empirically, alleviating the need to make model and parameter decisions that follow more complex approaches for this problem, such as machine learning classification. As a result, the MMD can be a simple and preliminary metric to find similarities in structures in order to gather the relevant datasets for analysis. In particular, this paper explores the following scenarios: – The task of identifying similar structures in a population, without reference to identifying labels (either for structure type or damage state). This strategy takes into account the fact that comprehensively-labelled datasets are

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rarely available, and allows one to assess similarity between members of the population without the use of prior knowledge. This approach may lead to unanticipated discoveries of similarly and dissimilarly behaving members of the population. – The task of identifying similar structures in a population, with reference to identifying labels. Here, for example, a subset of the data can be used to study the similarity of damage mechanisms in a population. In this paper, a brief introduction to the MMD is given in Sect. 2. A PBSHM case study is then presented in Sect. 3. The study focusses on determining the similarity of tools across a population during finish turning using the MMD as a distance metric.

2

The Maximum Mean Discrepancy (MMD)

The MMD uses a covariance/kernel function to provide a measure of the distance between two features (which can be univariate or multivariate; here one considers an entire dataset and so has the multivariate case). The use of the kernel function here can be considered as a projection in a reproducing kernel Hilbert space (RKHS). For a more comprehensive understanding, the reader is referred to [3,10]. The MMD can be used to check if two distributions, p and q are different. The probability measures p and q are defined on a metric space. There exists a class of functions that map the metric space to the coordinate space over the real numbers. The random variables x and y are sampled from distributions p and q respectively. The MMD is considered a distance metric as it follows the four rules set by Dudley [11]. Firstly a distance metric must be non-negative, it must follow the triangular inequality and it must be symmetric. Finally, the MMD is a metric as it takes the value of zero if – and only if – the two distributions of interest are the same, i.e. if p = q, then MMD = 0. In order for the MMD to qualify as a metric, the class of continuous, smooth functions F, is a unit ball in a universal RKHS; there exists a restriction that the norm of the function should be less than or equal to 1 (the term smooth indicates that the function is infinitely differentiable everywhere). The unbiased estimation of MMD is given by m

MMD2u [F, X , Y ] = + −

m

 1 k(xi , xj ) m(m − 1) i=1 1 n(n − 1) 2 mn

j=i n n 

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where X := {x1 , ..., xm }, Y := {y1 , ..., yn } and m and n are the number of data points in X and Y respectively. The kernel k(· , · ) used here is a Gaussian kernel

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Eq. (2) as it is universal, and is therefore required to be continuous in the RKHS [3]. The width of the kernel is a hyperparameter, denoted here as σ,    x − y 2 k(x, y) = exp − (2) 2σ 2 In Eq. (1), the top line can be considered the average similarity of every pair of samples drawn from distribution p. The middle line corresponds to the same for samples from q. The third line is the average similarity of points drawn from p and q. In the case where p and q are dissimilar, i.e. points from p are more similar to each other than they are to points from q, then the top two lines would produce a large value compared to the bottom, giving a large value for MMD2 . On the other hand, where p and q are similar, the top two lines would produce a value similar to the bottom line, giving a value close to zero for MMD2 .

3

Case Study: PBSHM of Machining Tools – Assessing the Similarity of Structures Using the MMD as a Metric

The monitoring of tool wear is a field of research that has spanned decades because of the significant effect that damaged tools have on machined components; machining with a broken tool introduces defects into the finished surface that then lead to damage over time, causing early retirement of safety-critical parts [12]. As with civil structures, such as bridges and turbines, monitoring of tools requires the installation of sensors to the structure and extraction of features from the signals to either predict failure or detect damage. The MMD has been used previously in assessing the similarity of data from machining tools. Jia et al. [13] was able to separate three wear conditions of boring tools using the MMD; it was possible to distinguish between overlaying clusters within a principal component analysis of time-frequency data. In this paper, the MMD is used prior to modelling in order to identify structures that can be used for analysis in the first place. In particular, two scenarios where the MMD will be useful in a PBSHM setting are explored in this case study, as discussed in Sect. 1: 1. The task of identifying similar structures in a population, without reference to identifying labels (either for structure type or damage state). – One of the most crucial challenges in data-driven health monitoring strategies is the availability of descriptive labels of damage states. It is not always possible to acquire damage labels, owing mainly to accessibility and cost of downtime. Collecting machining datasets is expensive and time consuming, as the cutting process must be interrupted to obtain damage labels; it is not possible to measure wear when the tool is in use. However, by treating the tools as a population, it may be possible to identify irregular behaviours and tools that deviate from the norm. Although a PBSHM approach that does not take labels into account may

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not establish precisely whether the outliers are damage, any anomalous behaviour could be used as an indicator for intervention. – Another benefit of adopting a PBSHM strategy is the transfer of information from one structure to another to enable prognostic and diagnostic decision making. However, the structures could belong to homogeneous or heterogeneous populations; in a homogeneous population, the structures are nominally identical in composition whereas in a heterogeneous population, the structural make-up can vary [14,15]. In order to transfer information across these varying populations, there is a need to form networks and communities of similarly-behaving structures. This issue is especially important in tool monitoring as the material composition of tools has a significant influence on the type of operation they are designed to perform [16]. 2. The task of identifying similar structures in a population, with reference to identifying labels. Here, for example, a subset of the data can be used to study the similarity of damage mechanisms in a population. – The case study also assesses whether different damage cases can be identified by studying a population of structures during failure. The possession of information regarding damage mechanisms of structures can help to reverse-engineer the entire process of sensor selection and positioning, signal processing and feature selection in order to focus on and utilise features that are sensitive to damage. In a PBSHM setting, damage sensitive features can be used to understand whether all structures undergo the same damage mechanisms from a feature perspective or whether variations exist. 3.1

The Tool Wear Dataset

Numerous studies have focussed on identifying indirect measurements from machining that correlate well to tool wear. Out of the many options, acoustic emission (AE) has been widely accepted due to its high sensitivity to both continuous and transient behaviours of the tool wear process [17]. Therefore, the aim of this case study is to use features from AE signals produced during machining trials as inputs to the MMD, in order to identify similarities and differences of tool behaviours that will aid PBSHM strategies. The dataset consists of acoustic emission data from 14 Polycrystalline cubic Boron Nitride (PcBN) tools, collected during outer diameter turning of casehardened steel. Each tool in the dataset belongs to one of two ‘grades’ (7 tools from Grade A and 7 tools from Grade B); the grade is defined by the percentage of cBN particles in the material composition. As a result, the dataset constitutes a heterogeneous population comprising of smaller, homogeneous populations (tools A1 to A7 and tools B1 to B7). Each of the 14 tools were machined until catastrophic failure, with AE signals recorded throughout at a sampling frequency of 1 MHz, making this one of the most extensive AE datasets of machining to date. For each tool, the machining process was interrupted 600 m

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intervals of sliding distance (the distance travelled by the tool) in order to collect damage labels through microscopic inspection. 3.2

Signal Processing and Feature Selection

The feature selection process is an important consideration when using the MMD, as the kernels are a dot product of features. As a result, the type of feature used must be indicative of the changes one wish to observe when using the MMD. For this paper, features from the frequency domain are used due to their strong sensitivity to damage [18].

Fig. 1. An example spectrogram of AE data collected 1 MHz during machining of case hardened steel using a PcBN tool (at specific cutting conditions).

An example spectrogram for tool B6 can be found in Fig. 1 (the spectrogram consists of 129 frequency bins, representing a frequency range of 0–500 kHz). By studying the signal in this view, it is possible to track the power of frequency content as a measure of sliding distance, a unit that is analogous to time. Two sets of features from the spectrograms are chosen to demonstrate the PBSHM scenarios explored in this paper. Wickramarachchi [18] found that the frequency bands with the highest intensity within these spectrograms vary according to the tool grade. As a result, the multivariate feature set considered here consists of the power in each of 129 frequency bins across the 0–500 kHz range, in order to average out the dependency of features on material composition. The first feature set contains the power for the whole test duration, the second feature set only considers a subset of this and is restricted to the 600 m sliding distance: Feature set 1 – The entire spectrogram of each tool is used as the input feature, in order to investigate the behavioural variation of tools across grades. Feature set 2 – Features consist of the 600 m of sliding distance to capture the damage in each tool.

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Results of Using the MMD

The unbiased MMD is calculated using Eq. (1) for each tool against all others in the population. The results are presented in the form of matrix graphs in Fig. 2. The shade in each plot represents the distance between distributions p and q, where 0 (white) is when p = q and darker shades indicate a larger distance. Figure 2a presents the resulting MMD when using the first feature set. Interestingly, only Grade B tools display uniformly-low MMD values when compared to others within the grade. This result was unexpected given the homogeneity of the tool grades; the MMD was expected to be lower in the top left and bottom right quadrants of the plot compared to the others, as homogenous tools were expected to behave similarly. The high MMD values with Grade A are due to tools A2, A5 and A7. It is clear that tools A2 and A7 are dissimilar to all other tools in the dataset. However, tool A5 is more similar to tools from Grade B on average than it is to its own homogeneous population. It is likely that these inconsistencies may stem from the tool wear. Figure 3 shows the maximum volume loss from the tools due to wear at 600 m of sliding distance; tools A2 and A7 have high volume losses near the end of life, whereas tool A5 has higher loss during the middle of life compared to others in the population. Consequently, the use of Grade A features to make inferences across grades is not advised without considering a transfer learning approach. However, Grade B tool features are possible candidates for use in PBSHM without further manipulation.

Fig. 2. MMD results to explore the similarity of unlabelled datasets in (a) and labelled dataset during damage in (b).

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Fig. 3. Maximum volume loss from the tools in the population.

(a) Sliding distance of 600 m before failure

(b) Tool at failure

Fig. 4. 3D scans of a PcBN tool (a) 600 m sliding distance prior to failure and (b) after tool failure.

Figure 2b presents the MMD between tools as they undergo damage. It is assumed that large MMD values are witnessed as a result of the erratic nature of catastrophic failure; catastrophic failure of tools used in this study occurs due to the sudden loss of the sharp edge as a result of excessive cutting forces. Figures 4a and 4b present an example of this mechanism from tool B2 600 m prior to, and after failure, respectively. Interestingly, there are a number of tool combinations that display low MMD results compared to the rest. For example, tools A1, A4, B2 and B6 all show low MMD results compared with all other tools in the population. Figure 5 presents the input features of the MMD including 600 m preceding tool failure for clarity (spectrograms of tools B4 and B5 are also shown to compare between the spectrograms of tools judged to be similar and dissimilar). In each spectrogram for the four similar tools, there is a downward shift in the frequency bins with the most energy. These frequency bin are associated with sawtooth chip formation [18], a material removal mechanism of the machining process. This shift in the energy suggests that the loss in cutting

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Fig. 5. Spectrograms of tools A1, A4, B2 and B6 during the 1.2 km sliding distance. The spectrograms of tools B4 and B5 have been provided for comparison.

edge reduces the frequency at which chips are formed for tools A1, A4, B2 and B6. The low MMD values indicate that the failure mechanism that led to this shift in energy is similar in each of these tools. Often data from damage cases are difficult to obtain when monitoring health of structures; maintenance steps are usually undertaken to avoid catastrophic failure, or in this particular case, tools are discarded to avoid introducing defects to the component that machining with a broken tool may cause. As a result, using the MMD to find the similarity of features in a population-based setting could be important in order to compare datasets without damage labels with ones such as this to understand whether it may undergo a similar damage mechanism.

5

Conclusions

In this paper, the MMD has been used as a metric to find the similarity between tool wear (AE) datasets for two PBSHM scenarios. When damage labels were unavailable, the MMD was able to distinguish similar tools in the population. These structures can then be used for information transfer in PBSHM. It was also possible to identify tools that behave similarly during damage evolution using the MMD, thus enabling further investigation into the types of damage that are present in the population. Acknowledgements. This work was funded by Engineering and Physical Sciences Research Council grant numbers EP/J016942/1, EP/K003836/2, EP/S001565/1 and EP/R004900/1: A New Partnership in Offshore Wind. The author would also like to

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thank Element Six Ltd. for providing the tools, workpieces, machining and operator time used for this work.

References 1. Worden, K., Cross, E.J., Dervilis, N., Papatheou, E., Antoniadou, I.: Structural health monitoring: from structures to systems-of-systems. IFAC-PapersOnLine 28, 1–17 (2015) 2. Gardner, P.A., Liu, X., Worden, K.: On the application of domain adaptation in structural health monitoring. Mech. Syst. Signal Process. 138, 106550 (2020) 3. Gretton, A., Borgwardt, K.M., Rasch, M.J., Smola, A., Sch¨ ollkopf, B.: A Kernel Two-Sample Test, technical report (2012) 4. Gardner, P.A., Lord, C., Barthorpe, R.J.: A unifying framework for probabilistic validation metrics. J. Verification Validation Uncertainty Quantification 4(3), 1–11 (2019) 5. Gangeh, M.J., Tadayyon, H., Sannachi, L., Sadeghi-Naini, A., Tran, W.T., Czarnota, G.J.: Computer aided theragnosis using quantitative ultrasound spectroscopy and maximum mean discrepancy in locally advanced breast cancer. IEEE Trans. Med. Imaging 35, 778–790 (2016) 6. Yuan, S., Li, L., Chigan, C.: Maximum mean discrepancy based secure fusion strategy for robust cooperative spectrum sensing. In: IEEE International Conference on Communications, July 2018 7. Dziugaite, G.K., Roy, D.M., Ghahramani, Z.: Training generative neural networks via maximum mean discrepancy optimization. In: Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, UAI 2015, pp. 258–267 (2015) 8. Chen, Y., Song, S., Li, S., Wu, C.: A graph embedding framework for maximum mean discrepancy-based domain adaptation algorithms. IEEE Trans. Image Process. 29, 199–213 (2020) 9. Yan, H., Ding, Y., Li, P., Wang, Q., Xu, Y., Zuo, W.: Mind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 945–954 (2017) 10. Gretton, A.: Introduction to RKHS, and some simple kernel algorithms (2016) 11. Dudley, R.M.: Real Analysis and Probability. Cambridge University Press, Cambridge (2002) ˇ 12. Solaja, V.: Wear of carbide tools and surface finish generated in finish turning of steel. Wear 2, 40–58 (1958) 13. Jia, X., Zhao, M., Di, Y., Yang, Q., Lee, J.: Assessment of data suitability for machine prognosis using maximum mean discrepancy. IEEE Trans. Ind. Electron. 65, 5872–5881 (2018) 14. Gosliga, J., Gardner, P.A., Bull, L.A., Dervilis, N., Worden, K.: Foundations of Population-Based SHM, Part II: heterogeneous populations - graphs, networks, and communities. In: Submitted to Mechanical Systems and Signal Processing (2019) 15. Gosliga, J., Gardner, P.A., Bull, L.A., Dervilis, N., Worden, K.: Towards population-based structural health monitoring, Part II: heterogeneous populations and structures as graphs. In: Proceedings of IMAC XXXVIII - the 38th International Modal Analysis Conference, Houston TX (2020) 16. Element Six Ltd.: PCBN Metalworking, technical report, Element Six Ltd., Shannon (2008)

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17. Li, X.: A brief review: acoustic emission method for tool wear monitoring during turning. Int. J. Mach. Tools Manuf. 42, 157–165 (2002) 18. Wickramarachchi, C.T.: Automated Testing of Advanced Cutting Tool Materials. PhD thesis, University of Sheffield (2019)

Experimental and Numerical Aspects of Lamb Waves Excitation and Sensing by Rectangular Piezoelectric Transducers Alisa N. Shpak1(B) , Mikhail V. Golub1 , Inka Mueller2 , and Claus-Peter Fritzen3 1 2

Kuban State University, Krasnodar, Russian Federation [email protected] Bochum University of Applied Sciences, Bochum, Germany 3 University of Siegen, Siegen, Germany

Abstract. Experimental and theoretical investigations of Lamb waves excitation and sensing by rectangular piezoelectric transducers perfectly and imperfectly mounted on the surface of a plate are presented in this study. Out-of-plane velocities measurements via laser Doppler vibrometry and voltage signal measurements by the piezoelectric transducers are compared in the context of SHM. The semi-analytical hybrid approach to simulate Lamb waves excitation and sensing by rectangular PWAS in a plate is presented. Reasons for the deviations between the simulated and the measured signals are analyzed. The effects of partial debonding of the piezoelectric transducers are considered and influence of the debonding on sensing abilities is analysed using obtained mathematical model. Keywords: Piezoelectric sensor Debonding · Sensing

1

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Introduction

A typical guided wave-based structural health monitoring (SHM) system consists of transducers for actuation and sensing, e.g. piezoelectric wafer active sensors (PWAS) [4,5]. Numerical analysis based on rigorous mathematical models is used to accumulate knowledge how piezo-induced guided waves interact with damages and to develop reliable damage detection algorithms. Employing these physically essential models, performance analysis of the SHM system could be implemented. For this purpose, a lot of calculations are to be made with various defects’ and PWASs’ characteristics. Therefore, a fast and effective mathematical model is necessary. Meanwhile, a full coupled three-dimensional model, either semi-analytical, or pure numerical, demands great computational resources. As a result, 2D mathematical models are frequently used for limited range of problems. Though, concrete conditions, when a 2D model may be employed, should be estimated and rigorously defined. c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 505–514, 2021. https://doi.org/10.1007/978-3-030-64594-6_49

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In this paper the excitation and sensing of guided waves with rectangular PWAS is focussed. The possibility and effects of partial debonding of these PWASs are taken into account. First, the 2D mathematical model, used for simulating the actuation, traveling and sensing of guided waves is presented. Afterwards experimental data is presented. In Sect. 3 the effect of debonding of an actuating square PWAS, as a 3D case, is shown for different bonding scenarios and different excitation frequencies. The results motivate to have a closer look on partial debonding of the actuator and the sensor. This is realized in Sect. 4 using the described mathematical model. It is validated with experimental data of a simple setup to be then used for the analysis of partially debonded rectangular actuators and sensors. All results emphasize the importance of detailed analysis in the whole frequency range of interest, as debonding has a significant effect, which highly varies with excitation frequency.

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The application of the semi-analytical hybrid approach (SAHA) is explained in detail in [2] for the case of an actuator attached at the surface of an elastic waveguide. The SAHA is based on the combination of the frequency domain spectral element method [6] for the discretization of a transducer and the boundary integral equation method [1] for the simulation of the induced wave-fields in the layer. Here, the SAHA is expanded to the case of an actuator and a sensor attached at the surface of a waveguide. The statement of the problem is very similar to the one given in [2], therefore, only the boundary conditions, concerning the transducer acting like a sensor, are given here. The solution is constructed in the frequency domain and the Laplace transform is used to calculate the time dependant solution. Let us consider an elastic layer of height H mm, occupying domain Ω 1 = {|x1 | < ∞, −H ≤ x3 ≤ 0} with Lame constants λ and μ and density ρ1 . Two piezoelectric transducers are attached at the upper surface of the layer x3 = 0, so that Ω (a) = {0 ≤ x1 ≤ wa , 0 ≤ x3 ≤ ha } and Ω (s) = {|x1 − χ| ≤ w2s , 0 ≤ x3 ≤ hs }, see Fig. 1. The common area between the transducers and

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507 (p)

the waveguide is composed of the contact area Sc and debonded area Sd :  (p)  (p) Ω 1 Ω (p) = {Sc Sd }, hereinafter index p stands for an actuator (p = a) or a sensor (p = s). Though both transducers can operate as an actuator and as a sensor, for convenience we here fix the first transducer operating like an actuator, while the domain with all related variables is denoted by superscript (a), and the second transducer operating like a sensor and its characteristics are denoted by the superscript (s). Material properties for two transducers are determined by the mass density and elastic, piezoelectric and dielectric constants: ρ(p) , C (p) , e(p) , (p) . The problem under consideration is to be treated whithin plane strain assumption. Therefore, tensors of elastic, piezoelectric and dielectric constants are recalculated in order to consider two-dimensional problems. The governing equations for a piezoelectric structure Ω (p) in terms of displacement u(p) and potential function ϕ(p) are given as in [2,3]. All boundaries (p) of the transducers are stress-free except for the contact area Sc . The same is assumed for the surfaces of the waveguide Ω 1 . The side boundaries of the (p) transducers are considered free of charge, while lower boundaries S− of both transducers are grounded. The electric potential with the voltage V = V0 and (a) modulation g(t) is applied on the upper surface of an actuator S+ , while the potential function ϕ(x) is unknown at the upper surface of a sensor. ϕ(a) (x, t) = V0 g(t), ϕ(s) (x, t) = φ,

(a)

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(1)

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s of a sensor: Electric charge is equal to zero on the upper surface S+  (s) (s) D3 (x1 , hs , t)dx1 = 0, x ∈ S+ . (s)

(3)

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In the contact area Sc , the continuity of displacement and traction vectors (p) is assumed, while in the debonded area Sd both displacement and traction vectors are equal to zero. This way, the extended SAHA model can be used for the analysis of 2D problems including the actuation and the sensing process.

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Square PWASs

To analyse effects of Lamb waves actuation by perfectly bonded and partially debonded rectangular PWASs, several experiments have been conducted. The measurements have been performed with a one-dimensional Laser Doppler vibrometer from OptoMET using a compact optical head with an infrared measurement laser 1550 nm wavelength. Additionally, sensor voltage signals have been registered with a Handyscope HS-5 from TiePie Engineering. The measurements have been performed for aluminium plate and PWASs made of PIC 155 from PI Ceramic GmbH. To describe the experimental results, Cartesian

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coordinates {x1 , x2 , x3 } are employed so that Ox3 is orthogonal to the plate’s faces. In this section, the details of the conducted experiments are explained and the results of the analysis based on the experimental data are presented. 5 mm

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Four square PWASs 10 × 10 × 0.5 mm are attached to the surface of an aluminium plate with dimensions 400 × 600 mm and height H = 1 mm. The distance between neighbouring PWASs is 100 mm to ensure that no reflections from the neighbour PWAS are registered. Three of four PWASs are partially debonded, the scheme of the bonding conditions is given in Fig. 2. An electric voltage signal g(t) with Nc = 5 cycles    2πf0 t Nc 1 (4) , 0 120 kHz. It is very important to take into account this effect while designing guided-waves based SHM methods, because the same square PWAS may excite a very different wave front, depending on its excitation frequency. This effect is closely connected with the sensing features explained in Sect. 4.

Experimental and Numerical Aspects of Lamb Waves Excitation and Sensing 20 kHz P1

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Another effect is related to the debonding of the PWASs. One can observe, that amplitudes of the measured signals are highly dependent on the debonding geometry. For example, Fig. 3(a,e) show experimental data for PWAS 1, which is half debonded; and amplitudes of the registered velocities are 2–3 times larger when measured from the bonded side of the PWAS, comparing to the data, measured from the debonded side. The influence of the debonded area on the excited wave front is clearly visible in Fig. 4, where the Hilbert transform applied to the results of the grid measurements v(t) are presented for a specific time tc . To aspects are clearly visible. Firstly, the excited wave front replicates the bonding area between the PWAS and the plate. Secondly, for a regular excitation frequency f = 60 kHz, the deboning of a PWAS results in a reduction of the total amplitudes of the excited Lamb waves. While the last effect is valid for the regular excitation frequencies, this is not the case for PWAS specific resonance frequencies. If a PWAS is excited with its resonance frequency, which also depends on the contact characteristics, the total amplitudes may significantly increase comparing to the perfectly bonded PWAS, see Fig. 5, where the Hilbert transform of the results of the grid measurements are presented for the resonance frequency of each PWAS. Additionally, comparing Figs. 4(d) and 5(d) circle-like and cross-like excitation is visible for lower (f = 60 kHz) and higher (f = 230 kHz) frequencies.

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Rectangular PWASs

The results motivate to have a closer look on partial debonding for the actuating and the sensing PWAS. This analysis is to be conducted using the 2D model, which needs to be validated in the first place. Therefore another experiment has been conducted with rectangular PWASs of dimensions 30 × 10 × 1 mm glued at the surface of a 300 × 300 × 2 mm aluminium plate. The distance between the PWASs is 200 mm (χ = 215 mm) and the total geometry of the prepared specimen is symmetric. Each of the PWASs is excited turn by turn with the same electric voltage signal (4) and the resulting velocities are measured at the surface x3 = 0 of the plate along the line x2 = 0 with the LDV. The resulting signals are compared with the simulation results. For the simulation, the obtained two-dimensional SAHA model described in Sect. 2 and the 2D and 3D Comsol Multiphysics models have been used. In Comsol, quartic Lagrange discretization with 620 elements for 2D problem and cubic Lagrange discretization with 45160 elements for 3D problem have been used to calculate excited and registered Lamb waves.

Experimental and Numerical Aspects of Lamb Waves Excitation and Sensing Experiment 1

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The resulting comparison is shown in Fig. 6, where two experimental signals are presented for each of the PWASs operating like an actuator (green and blue dotted lines). The small discrepancy between the experimental signals is visible, despite the symmetric specimen. This effect practically shows the deviation of the real-life specimen from the designed scheme and illustrates the necessity of advanced post-processing of the registered signals inside the SHM system. However, the simulation signals coincide very good with the experimental data even for relatively high frequency f = 240 kHz (Fig. 6b), despite the 2D assumption made in the mathematical model. Such coincidence is achieved due to the use of a rectangular PWAS with a high length to width ratio. Here it equals 3, which results in rather directional wave front. As a result, if a measurement line is situated on the line connecting centres of the PWASs (x2 = 0) the 2D assumption can be successfully applied. Experiment

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Apart from LDV measurement, the sensor voltage signals were registered with the Handyscope HS-5 from TiePie Engineering. The same signal has been calculated with the obtained 2D hybrid mathematical model, the comparison

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of the experimental and simulation data is presented in Fig. 7. A very bad coincidence between the experiment and 2D simulation signals is clearly visible with the excitation frequencies f = 65 kHz (Fig. 7a). While LDV measures velocities in a certain point on the surface of the plate, the sensor registers the whole wave front over the contact area. Due to the fact, that sensor voltage signal is an integrated characteristic of the guided waves, and the excited wave front has a circular shape with the lower frequency like it was shown in the previous section, the 2D model describes the sensor voltage signal inaccurately. The results obtained using the 3D Comsol model confirms this hypothesis – the coincidence with the 3D simulation signal is much better. At the same time, with the higher excitation frequency f = 170 kHz (Fig. 7b), the comparison between the experiment and 2D simulation signals is good enough. It is therefore useful to analyze the effect of debonding on actuating and sensing PWAS using the developed model.

Bonded Actuator One-side debonded Actuator Bonded Actuator Two-sides debonded Actuator Bonded Actuator

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To investigate the influence of the PWAS debonding on the sensing ability of a PWAS, the output electric potential φ in dependence on frequency has been calculated with SAHA. Figure 8 illustrates plots φ(f ) when at least one of the PWASs is perfectly bonded. One-sided (Scp = 6 mm) and two-sided (Scp = 5 mm) debondings are considered. Figure 9 shows the same plots, but for mutual debonding of the PWASs. For most frequencies, the debonding of a PWAS leads to a decrease of the voltage φ, however, sharp peaks are visible at some frequencies, corresponding to resonance, like it was shown for the impedance curve by the authors in [7]. If both PWASs are symmetrically debonded (two red lines in Fig. 8), the resulting potential value is estimated to be the same. In general, such analysis might be helpful for a self-monitoring of PWASs, installed in an SHM system.

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5

Conclusion

In this study, investigations of Lamb waves excitation and sensing by a rectangular PWAS operating under various bonding conditions with the host structure has been performed. Experiments with perfectly bonded and partially debonded rectangular PWASs have been conducted. Out-of-plane velocities of the excited wave-field have been measured on the surface of the plate with LDV, and sensor voltage signal have been registered with the sensing PWAS. The partial debonding of a PWAS influences the excited wave-fields, while the radiation pattern replicates the geometry of the remaining contact area. Amplitudes of the excited Lamb waves are reduced if an actuating PWAS is debonded, except for the resonance frequencies. It has been shown, that LDV signals can be simulated with the semi-analytical 2D model quite efficiently, while sensor signals being an integral characteristic are predicted accurately only at higher frequencies f > 120 kHz. The research is supported by the grant of the President of the Russian Federation for state support of young Russian scientists (project MK-470.2020.1).

References 1. Glushkov, E.V., Glushkova, N.V.: On the efficient implementation of the integral equation method in elastodynamics. J. Comput. Acoust. 9(3), 889–898 (2001) 2. Golub, M.V., Shpak, A.N.: Semi-analytical hybrid approach for the simulation of layered waveguide with a partially debonded piezoelectric structure. Appl. Math. Model. 65, 234–255 (2019) 3. Komatitsch, D., Tromp, J.: Introduction to the spectral element method for threedimensional seismic wave propagation. Geophys. J. Int. 139(3), 806–822 (1999) 4. Memmolo, V., Monaco, E., Boffa, N., Maio, L., Ricci, F.: Guided wave propagation and scattering for structural health monitoring of stiffened composites. Compos. Struct. 184, 568–580 (2018)

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5. Rose, J.L.: Ultrasonic Guided Waves in Solid Media. Cambridge University Press, Cambridge (2014) 6. Shi, L., Zhou, Y., Wang, J.M., Zhuang, M., Liu, N., Liu, Q.H.: Spectral element method for elastic and acoustic waves in frequency domain. J. Comput. Phys. 327, 19–38 (2016) 7. Shpak, A.N., Golub, M.V., Mueller, I., Eremin, A.A., Kathol, J., Fritzen, C.P.: Influence of a delamination on lamb wave excitation by a nearby piezoelectric transducer. J. Intell. Mater. Syst. Struct. (2020)

Recent Results in Active and Passive SHM Victor Giurgiutiu(&) Laboratory for Active Materials and Smart Structures (LAMSS), University of South Carolina, Columbia, USA [email protected]

Abstract. This paper reviews recent active and passive results obtained at the Laboratory for Active Materials and Smart Structures (LAMSS) of the University of South Carolina, USA. The active SHM research has focused on detecting various types of composite damage using guided-wave interrogation and sensing. The composite damage considered covered seeded delaminations associated with barely visible impact damage (BVID). The seeded delaminations were created through the insertion of thin Teflon-film patches during composite fabrication. Multiple delaminations at the same x-y location were also studied. [+45/90/−45/0]nS quasi-isotropic layup plates were considered. The Teflon insert specimens were modeled and tested using guided waves transmitted and received with piezoelectric wafer active sensors (PWAS). Scanning laser Doppler vibrometer (SLDV) measurements were also performed. The passive SHM research was focused on recording acoustic emission (AE) wave signals. The major thrust of the AE work focused on detecting AE signal created during fatigue loading of aerospace-grade sheet-metal coupons. Both low-cycle fatigue (LCF) and high-cycle fatigue (HCF) tests were performed. The AE signals were analyzed with the scope of finding specific signatures associated with fatigue crack growth. It was found that not all crackoriginating AE signals were associated with fatigue crack growth. In fact, some AE signals were recorded in fatigue-cracked specimens even after the crack growth was stopped by reducing the load to a level below the crack-growth threshold. These AE signals were attributed to clapping and/or rubbing of crack faying surfaces. Similar AE signals were also observed on fatigue-cracked specimens subjected to low-frequency lateral-vibration resonances. Keywords: SHM


AE
PWAS
Composites
LCF
HCF

1 PWAS Transducers Developments The piezoelectric wafer active sensors (PWAS) play a central role in most active and passive SHM due to their low cost and ready availability. Two recent developments in PWAS transducers are reported here: (a) PWAS tuning in damped structures; and (b) PWAS transducers for harsh environments. 1.1

PWAS Tuning in Damped Structures

The tuning between PWAS transducer and the guided waves present in the structure [1] has been successfully used by many researchers in active and passive SHM studies. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 515–524, 2021. https://doi.org/10.1007/978-3-030-64594-6_50

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However, the effect of structural damping was not included in the original work [1]. Since then, we found that structural damping may play a very important role in the PWAS tuning process [2]. As a result, frequency shifts and the suppression of highfrequency tuning peaks was observed and analyzed. Figure 1 shows a comparison between prediction and experiment performed on an aluminum plate with a protective polymer film left in place. The effect of the polymer film damping is quite significant: apparently, the guided wave excitation is limited to around 700–800 kHz, beyond which no guided wave can be excited. When the film was peeled off, the excitation could be extended to much higher frequencies (2–3 MHz). Similar damping effects that limit the guided-wave excitation to 700–800 kHz were also observed in composites [2]. 1.2

PWAS for Harsh Environments

The use of PWAS in harsh environments is of great interest. Two harsh-environment effects were studies: temperature up to 250 °C and radiation up to 225 kGy. Two PWAS material were considered: (a) conventional PZT piezoceramic; (b) singlecrystal Gallium orthophosphate GaPO4 [3, 4]. It was found that GaPO4 is better suited for harsh environments than conventional PZT.

2 Active Guided-Wave SHM of Composites Active guided-wave SHM of composites has attracted considerable attention in recent years especially for the detection of barely visible impact damage (BVID) due to lowvelocity events. Considerable effort was put into developing a fast and efficient predictive methodology that would be able to predict the signals received at the sensors when guided waves were scattered from a BVID site. It was found that a hybrid globallocal (HGL) approach would permit efficient analytical solution for guided-wave propagation in the pristine composite whereas wave damage interaction coefficients (WDIC) would be used to add the scatter waves originating from the damage site. 2.1

Damped Guided-Wave Prediction in Composite Plates

The composite dispersion curves and modeshapes were predicted using a stable and efficient semi-analytical finite element (SAFE) approach which only needs to discretize the plate thickness at a single generic location [5]. Damping was introduced into the calculations using a frequency-dependent complex stiffness matrix:   f ~ Cpq ¼ Cpq 1  i gpq ; f0

p; q ¼ 1; . . .; 6

ðKevinVoigt modelÞ

ð1Þ

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Extensive experiments were performed in order to tune the Kevin-Voigt ηpq parameters through curve fitting the experimental guided-wave data over a wide frequency rage. The following results were obtained

ð2Þ

Examination of Eq. (2) reveals that damping is especially strong in the transverse and shear moduli, but much smaller in the longitudinal modulus. This implies that modes that have strong shear component, e.g., A0, will be much more attenuated than other modes, e.g., S0 which is dominantly longitudinal. The fitted model was used to efficiently predict guided wave propagation which was experimentally validated with scanning laser Doppler vibrometry measurements (Fig. 2). 2.2

Detection of Multilayer Delamination

BVID in composites is accompanied by extensive multilayer delamination in a fir-tree pattern with the largest delamination closer to the composite surface opposed to impacted surface. A study was conducted to quantify the capability of guided-wave SHM to distinguish between different number of delaminations in a 3-mm quasiisotropic [+45/90/−45/0]nS CFRP composite plate [6]. A full-scale multiphysics FEM simulation of guided wave propagation and interaction with plate delaminations was performed using ANSYS 17 software package. Nonreflective boundaries were used to avoid boundary reflections. Guided waves were excited with a surface-mounted PWAS (Fig. 3). Five delamination scenarios were considered, from a single large delamination close to the bottom through five delaminations arranged in a fir-tree pattern with the smallest on top (Fig. 3). The FEM simulation indicated that (a) the magnitude of guided-wave scatter increases with the number of delaminations; and (b) strong trapped-wave patterns can be observed. The bottom surface showed strong trapped waves even for only a single delamination, which can be explained by the fact that the single delamination is close to the bottom. The top surface showed trapped-wave patterns when the number of delaminations was large, but not when only one or two delaminations were present. This observation is

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important because, in practice, access is not possible to the “bottom” surface of the composite panel but only to the “top” surface, i.e., to the outside surface of the composite structure subjected to low-velocity impact. The simulation results were compared with SLDV measurements. A 3-mm quasiisotropic CFRP composite plate was constructed with a 4-quadrant arrangement. One quadrant was left pristine; the second quadrant had a 25-mm delamination (Teflon film insert) created near the bottom layer. The third quadrant had two delaminations, one 25-mm near the bottom layer and one 20-mm a few layers above. The last quadrant was left pristine for future application of a BVID-generating impact. The ultrasonic scan imaging of the delaminations is shown in Fig. 4. The plate was instrumented with PWAS transducer and subjected to SLDV measurements (same setup as in Fig. 2). Strong trapped-wave patterns and wave scattering were observed on bottom surface. 2.3

EMIS Detection of Delaminations

The presence of trapped waves in the delamination region signifies local vibration that is specific to the delaminated plate but do not appear in the pristine plate. This aspect is especially apparent at high frequencies. A comparative study using SLDV and electromechanical impedance spectroscopy (EMIS) was performed study on unidirectional and crossply CFRP plates [7]. It was found that presence of 25, 50, 75 mm diameter delaminations created vibration spectrum peaks in the 100–200 kHz range, with the larger delaminations giving lower frequencies. EMIS measurements confirmed the SLDV results [7]. Very interesting trapped wave patterns were observed in both FEM modeling and experiments (Fig. 5). 2.4

Wave Damage Interaction Coefficients (WDIC)

Essential for the application of the HGL technique to composites is the quantification of complex-number WDICs. This can be achieved with a small FEM model similar to that shown in Fig. 3 but modified as follows: • the analysis is “harmonic response” over a wide frequency range • the excitation is a straight crested wavefront, not a PWAS • the response is recorded at all thickness locations on a circular contour around the damage site • the analysis is performed for the pristine case and then for all the damage situations considered in the analysis. Subtraction of pristine response from the “damage” response yields the WDICs for that particular damage. Furthermore, experimental SLDV verification can be achieved by comparing the predicted surface displacements with SLDV measurements. A preliminary study [6] revealed that the scatter-field directivity patterns modify considerably as damage progresses from one, to two, to three, …, to five delaminations.

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Analytical Prediction of Wave Damage Interaction Coefficients Using the Complex Mode Expansion with Vector Projection (CMEP) Method

In metallic structures, the WDICs can be also predicted analytically using the complex mode expansion with vector projection (CMEP) method [8]. This method has been validated experimentally [9] on a plate specimen with a step discontinuity encountering a straight-crested interrogative Lamb wave (Fig. 6). Further work in the CMEP method consisted in predicting the interaction of Lamb waves in a stiffened aluminum panel with a horizontal crack at the root of the stiffener [10]. The specimens used in the study are shown in Fig. 7. A comparison of CMEP analytical prediction vs. experiment are shown in Fig. 8. The CMEP method relies on using the complete population of guided-wave modes, propagating, evanescent, and decaying propagating, i.e., associated with real, imaginary, and complex wavenumbers. For metallic structures, the complete population of these modes is readily available as a complex-number solution of the Rayleigh-Lamb dispersion equation. The CMEP method could be extended to composites if the complete wavenumber complex-number population is calculated and understood as discussed in ref. [11].

3 Passive SHM via Acoustic Emission Acoustic emission (AE) is an attractive method for passively monitoring cracks in metallic structures. The AE concept assumes that AE signals emitted when a crack advances can be recorded and counted as “hits” by AE instrumentation. The hit rate is indicative of crack growth rate and could be integrated to estimate how close to failure the structure is. However, there are several barriers to the widespread use of the AE concept. One barrier is that AE sensors are quite expensive and that their placement in large numbers on a structure may be cost prohibitive. To address this barrier, we studied the possibility of using PWAS transducers instead of conventional AE sensors and found that this gives very good results [12]. The second barrier is the lack of fast and efficient modeling of how the AE signal are generated at the crack tip and progress to the sensor location. To this goal, we developed an analytical approach based on the extension of Helmholtz potentials [13] which was able to efficiently predict the AE signals that would be recorded at various locations away from the crack [14]. A third barrier identified in AE research stems from that fact that AE signals seem to be also emitted when the crack does not grow. This phenomenon happens even when all the sources of external noise had been eliminated. Our research has focused on detecting AE signal created during fatigue loading of aerospace-grade sheet-metal coupons. Both low-cycle fatigue (LCF) and high-cycle fatigue (HCF) tests were performed. The AE signals were analyzed with the scope of finding specific signatures associated with fatigue crack growth. It was found that not all AE signals were associated with fatigue crack growth. In fact, some AE signals were recorded in fatiguecracked specimens even after the crack growth was stopped by reducing the load to a level below the crack-growth threshold. These AE signals were attributed to clapping

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Fig. 1. Comparison of experimental and theoretical 2-D Lamb-wave tuning curves: (a) A0 mode; (b) S0 mode (η0 = 8%, f0 = 1.5 MHz, 9-mm-diameter circular PAWS, 1.016-mm 2024T3 aluminum plate with PVC adhesive film, circular crested Lamb waves) [2]

Fig. 2. Experimental verification of guide-wave propagation in CFRP composite plate using scanning laser Doppler vibrometer (SLDV) instrumentation [5]

and/or rubbing of crack faying surfaces. Similar AE signals were also observed on fatigue-cracked specimens subjected to low-frequency mechanical vibration of the cracked structure [15]. Such vibration happens at frequencies three four order of

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Fig. 3. Multiphysics FEM simulation of guided wave propagation and interaction with delaminations in a quasi-isotropic CFRP composite plate [6]

Fig. 4. Ultrasonic scan of multilayer delaminations in a quasi-isotropic CFRP plate: (a) pristine; (b) one delamination; (c) two delaminations (the upper smaller delamination is seen as a shadow on the lower larger delamination) [6]

Fig. 5. Trapped wave patterns predicted by FEM analysis [7]

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Fig. 6. Analytical prediction of WDIC values validated by SLDV experiment: plate specimen with thickness step; (b) SLVD of incoming Lamb wave front; (c) SLDV of wave-damage interaction; (d) comparison of CMEP predictions and SLDV measurements [9]

Fig. 7. Specimens used for the experimental validation of CMEP prediction of the interaction of Lamb waves in a stiffened aluminum panel with a horizontal crack at the root of the stiffener [10]

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Fig. 8. Validation of CMEP prediction of the interaction of Lamb waves in a stiffened aluminum panel with a horizontal crack at the root of the stiffener: analytical predictions vs. experiment [10]

Fig. 9. AE emission from fatigue cracks subjected to structural vibration: (a) shaker setup; (b) typical AE signals generated by crack but without crack growth [15].

magnitude lower then the frequencies found in the AE spectrum, hence the AE signals are NOT the vibration signals, but are produced by the crack due to mechanical vibration of the structure. Vibration shaker experiments at 35 and 180 Hz produced AE signals similar to those recorded during fatigue crack growth in a fatigue machine under cyclic axial loading (Fig. 9).

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References 1. Giurgiutiu, V.: Tuned lamb-wave excitation and detection with piezoelectric wafer active sensors for structural health monitoring. J. Intell. Mater. Syst. Struct. 16(4), 291–306 (2005) 2. Mei, H., Giurgiutiu, V.: Effect of structural damping on the tuning between piezoelectric wafer active sensors and Lamb waves. J. Intell. Mater. Syst. Struct. 29(10), 2177–2191 (2018) 3. Haider, M.F., Giurgiutiu, V., Lin, B., Yu, L., Lam, P.S., Verst, C.: Effects of gamma radiation on resonant and antiresonant characteristics of piezoelectric wafer active sensors. DASME J. Nondestr. Eval. Diagn. Progn. Eng. Syst. 2(1), 011001 (2019) 4. Mei, H., Migot, A., Faisal Haider, Md., Joseph, R., Giurgiutiu, V.: Recent advances in piezoelectric wafer active sensors for structural health monitoring. MDPI Sens. 19, 383 (2019). https://doi.org/10.3390/s19020383 5. Mei, H., Giurgiutiu, V.: Guided wave excitation and propagation in damped composite plates. Struct. Health Monit. – Int. J. 18(3), 690–714 (2018) 6. Mei, H., Giurgiutiu, V.: Characterization of multilayer delaminations in composites using wavenumber analysis: numerical and experimental studies. Struct. Health Monit. – Int. J. 1– 26 (2020). https://doi.org/10.1177/1475921720939616 7. Mei, H., Haider, Md.F., Joseph, R., Migot, A., Bhuiyan, Y.Md., Giurgiutiu, V.: Vibrationbased in-situ detection and quantification of delamination in composite plates. MDPI Sens. 19, 1734 (2019). https://doi.org/10.3390/s19071734 8. Poddar, B., Giurgiutiu, V.: Complex modes expansion with vector projection using power flow to simulate Lamb waves scattering from horizontal cracks and disbonds. J. Acoust. Soc. Am. 140(3), 2123–2133 (2016) 9. Haider, M.F., Poddar, B., Giurgiutiu, V.: Experimental validation of an analytical method to predict lamb wave scattering from a discontinuity. Smart Mater. Struct. 28, 015012 (2019) 10. Haider, Md.F., Bhuiyan, Y., Poddar, B., Lin, B., Giurgiutiu, V.: Analytical and experimental investigation of the interaction of lamb waves in a stiffened aluminum plate with a horizontal crack at the root of the stiffener. J. Sound Vibr. 231, 212–225 (2018). https://doi.org/10. 1016/j.jsv.2018.06.018 11. Haider, Md.F., Giurgiutiu, V.: Propagating, evanescent, and complex wavenumber guided waves in high-performance composites. MDPI Mater. 11, 269 (2019). https://doi.org/10. 3390/ma12020269 12. Bhuiyan, Y., Lin, B., Giurgiutiu, V.: Characterization of piezoelectric wafer active sensors for acoustic emission sensing. Ultrasonics 92, 35–49 (2019). https://doi.org/10.1016/j.ultras. 2018.08.020 13. Faisal Haider, Md., Giurgiutiu, V.: A Helmholtz potential approach to the analysis of guided wave generation during acoustic emission events. ASME J. Nondestr. Eval. Diagnost. Progn. Eng. Syst. 1(2), 021002–021002-11 (2018). https://doi.org/10.1115/1.4038116 14. Haider, M.F., Giurgiutiu, V.: Theoretical and numerical analysis of acoustic emission guided waves released during crack propagation. J. Intell. Mater. Syst. Struct. 1045389X18798948 (2018) 15. Joseph, R., Bhuiyan, M.Y., Giurgiutiu, V.: Acoustic emission from vibration of cracked sheet-metal samples. Eng. Fract. Mech. 217, 106544 (2019). https://doi.org/10.1016/j. engfracmech.2019.106544

Comparison of CWRU Dataset-Based Diagnosis Approaches: Review of Best Approaches and Results Xiao Wei(&) and Dirk Söffker Chair of Dynamics and Control, University of Duisburg Essen, Duisburg, Germany [email protected]

Abstract. Bearings are the most common mechanical components in machines. Once a bearing fails (or components in it), other adjacent components or the machine itself are effected up to failure. Therefore, bearing health condition is of great interest in practice. Several benchmark datasets are developed to evaluate development in bearings health state (diagnosis) and remaining useful lifetime (prognosis). Among these datasets, Case Western Reserve University (CWRU) dataset is one of the most cited ones used to validate the performance of different diagnostic approaches. Over recent years, a significant amount of research approaches are developed using CWRU data. Most approaches are focused on specific performance parameters like detection rate or accuracy etc. The main problems in connection with CWRU dataset use are: no overview about latest results is available. Furthermore several results published are not complete, for example published accuracies rate without false alarm rates. In this contribution an overview about the development change over the last years, the approaches applied, and specifically the results obtained will be given. Additionally, the new approaches emerging in recent years like deep learning (DL) also in combination with fusion methods and related performance will be given in comparison with conventional machine learning (ML) methods. Special care will be given to the completeness of published results also in combination with shown robustness. As outcome of this contribution the newest and best results are noted, furthermore a recommendation how to complete research work using benchmark dataset will be given. Although most approaches using CWRU dataset as benchmark get high accuracy, for further bearing fault diagnosis research, more and more suitable measures as well as other datasets are needed for increased performance evaluation. Keywords: Bearing dataset
CWRU learning
Performance
Review


Diagnosis
Deep learning
Machine

1 Introduction Within the last decades, rotating machinery equipment plays an irreplaceable role in modern industry [1]. As one of the most common components of rotary machinery, bearing is a mechanical component used to reduce friction among other moving parts. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 525–532, 2021. https://doi.org/10.1007/978-3-030-64594-6_51

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Once a bearing fails (or components in it), other adjacent components and machines are effected up to failure. Several surveys regarding the likelihood of induction machine failure conducted by the IEEE Industry Application Society (IEEE-IAS) and the Japan Electrical Manufactures’ Association (JEMA) reveal that bearing fault is the most common fault type and is responding for 30 to 40% of all machine failures [2]. Therefore, condition monitoring and fault diagnosis of bearings is of increasing interest [3]. Several benchmark datasets are developed to evaluate development in bearings health state (diagnosis) and remaining useful lifetime (prognosis). Among these datasets, Case Western Reserve University (CWRU) dataset is one of the most cited ones used to validate the performance of different approaches on bearing diagnosis. In general, there are three kinds of bearing fault diagnosis methods: signal-based methods, model-based methods, and data-driven methods. Due to the development of smart manufacturing and the widely application of intelligent sensors, the data-driven fault diagnosis methods have attracted many studies in recent years [4]. Machine learning (ML), deep learning (DL), and transfer learning (TL) are powerful data-driven methods. Many approaches applying to bearing fault diagnosis have been proposed in the last years, however, there is no common standard to judge the performance of these approaches. Usually, several options are known to evaluate the outcome of algorithms and the classifiers: accuracy, precision, recall (sensitivity), specificity, F-score and receiver operating characteristic (ROC). Every metric has its pros and cons: accuracy assess the overall effectiveness of algorithms, precision assesses the predictive power of algorithms, sensitivity and specificity access the effectiveness of the algorithm on a single class; F-score benefits algorithms with higher sensitivity and challenges algorithms with higher specificity; ROC shows a relation between sensitivity and specificity of algorithms [5]. At present, accuracy has been widely used as the metric to evaluate the fault diagnosis approaches. However, fault diagnosis is by definition an imbalanced classification problem where the positive class (machine faults) is greatly outnumbered by the negative class. The accuracy metric is therefore not an appropriate measure for assessing model performance—a classifier with a focus on merely getting the negative instances correct will have a high accuracy by definition, but it will not be useful for identifying the few positive instances (i.e. machines faults) when it really matters [6]. Therefore, more metrics should be included in the results of algorithms. The structure of this paper is organized as follows. A brief introduction of CWRU bearing dataset is given in Sect. 2. In Sect. 3, the approaches applying for CWRU dataset are summarized. Results and resulting challenges of these approaches are presented in Sect. 4. Suggestion for fault diagnosis and conclusions are given in Sect. 5.

2 Case Western Reserve University Bearing Dataset Collected by Prof. Kenneth Loparo’s research group at Case Western Reserve University, CWRU dataset provides access to ball bearing test for normal and faulty bearings.

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As shown in Fig. 1, the test stand consists of a 2 hp motor (left), a torque transducer/encoder (center), a dynamometer (right), and control electronics. Motor bearings were seeded with faults using electro-discharge machining (EMD). Faulted bearing were reinstalled into the test motor and vibration data was recorded for different motor loads (labeled as 0, 1, 2, 3) horsepower (motor speeds of 1797 to 1720 rpm). Faults ranging from 7 mils in diameter to 40 mils (1 mil = 0.0001 inch) in diameter were introduced separately at the inner raceway, ball, and outer raceway. As the placement of outer raceway faults is relative to the load zone of the bearing and has a direct influence on the vibration signal, therefore, the position of outer raceway faults was located at 3 o’clock (directly in the load zone), at 6 o’clock (orthogonal to the load zone), and at 12 o’clock [7].

Fig. 1. Test rig used in the Case Western Reserve University Lab [7].

Vibration data was collected using accelerometers, which were placed at the 12 o’clock position both the drive end and fan end of the motor housing. For drive end bearing faults, data was collected at 12,000 samples/second and 48,000 samples/ second. All fan end bearing data was collected at 12,000 samples/second [7].

3 Approaches Used for CWRU Bearing Dataset Data-driven fault diagnosis can be divided into three types: machine learning-based, deep learning-based, and transfer learning-based. In this section, approaches applying for CWRU bearing dataset would be summarized. 3.1

Machine Learning Approaches

Machine learning algorithms which remove manual observation or interpretation on model-based approaches is one of the main methods to handle the data in fault diagnosis [8]. Many researchers have applied machine learning algorithms to fault diagnosis, including: support vector machines (SVM), artificial neural networks (ANNs), expert system, fuzzy logic (FL), principle component analysis (PCA), and K-Nearest Neighbors (k-NN) [2]. Comparing with other machine learning approaches, SVM-based approaches [9–19] are used most in the last 5 years when detailed to CWRU bearing dataset.

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Deep Learning Approaches

According to [20] deep learning approaches have the potential to overcome the inherent shortcomings of the traditional intelligent diagnosis methods. The most success of deep learning methods is the ability to automatically learn the representative features from raw data [21]. Deep belief network (DBN), convolutional neural network (CNN), autoencoder (AE), recurrent neural network (RNN) and generative adversarial network (GAN) are popular deep learning methods for fault diagnosis. Many approaches based on DBN [22–26] take the CWRU bearing dataset as input. Fault diagnosis methods applying to CWRU dataset based on CNN are presented in [8, 27–30]. Auto-encoder based papers [31–38] also illustrate the effectiveness and efficiency of employing autoencoder to serve for fault diagnosis. Besides, other deep learning algorithms are also applied on CWRU bearing dataset, such as: low-delay lightweight recurrent neural network (LLRNN) [39] and multi-manifold spectral clustering based on particle swarm optimization (MMSC-PSO) [4]. 3.3

Transfer Learning Approaches

Transfer learning (TL) performs learning on training datasets (called source problem) and then to perform the same task on the test dataset (called target problem) from a related distribution. Compared with shallow structures, TL offers greater flexibility in extracting high-level features transferred from the source to the target problem [32]. Contributions [6, 32, 40] prove that TL is a powerful algorithm.

4 Results and Resulting Challenges A systematic comparison of the different algorithms and related results employing the CWRU bearing dataset is presented in Table 1. From Table 1, the following conclusions could be drawn: 1. Approaches differ with respect to feature extraction algorithms and classifier. To calculate dataset effectively, researchers design various structures and combine multifarious algorithms. Namely, most of these approaches are novel and unique so the CWRU dataset only serves as application example. 2. Most of the contributions select individual data combinations from CWRU dataset: different work conditions, fault sizes, training/test samples ratios. In [6, 12, 13, 29, 32], as the training/test ration is variant, the detection accuracy shifts. Besides, some contribution select the data from CWRU dataset to build up their own dataset, in [4, 6, 15, 19, 27, 32, 40], different dataset are build. The difference of selecting data affects the test accuracy. 3. Most of approaches could reach high detection accuracy. Especially in [19, 24] detection accuracy could reach 100%. However, most approaches with high accuracy are applied on a specific sub-dataset with fixed operating conditions. 4. Most of these approaches use detection accuracy as metrics. Few approaches use other standards to judge the performance of these approaches. Only [6, 10, 13, 14,

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33] apply precision, specificity, recall, F-score, PPV, and ROC curve as approach’s metrics. 5. Although there are many methods for bearing fault detection, there are few methods used for predicting fault size, only in [9, 16, 17] the fault severity is calculated.

5 Recommendation and Conclusions At present no standard metrics is applied to judge the overall performance of algorithms, however, from Table 1, the following conclusions could be drawn: 1. Different operating conditions and different fault sizes data are chosen in [6, 8, 9, 14, 16, 17, 22, 25–28, 30, 32, 38, 40], therefore, the robustness of these algorithms is higher than those of others. Approaches [6, 9, 17, 26, 27, 32, 40], are trained on one load, but tested on other loads. Additionally, in [9] the approach is also trained on another fault size. In this sense, [9] choses broader data as input. 2. The perfect solution to deal with the illustrated complexity is to use N-fold cross validation. N-fold cross validation is applied in [9–11, 13, 14, 17, 29, 33, 37–40]. Especially, in [14], other metrics besides accuracy are calculated. Due to the results achieved this approach appears as the actual best approach applied to CWRU dataset. 3. To verify the performance of algorithms, some contributions apply their algorithm to other dataset, [6, 8, 10, 12, 22, 25–27, 30, 37, 39]. Therefore, the results of these algorithms applying to other dataset should be considered in evaluating the performance of these algorithms. From the analysis of the existing results (Table 1) some formal conclusions can be drawn: 1. Results are dataset- and metric-specific, so they can not be compared really. 2. Results are often only shown as accuracy, so they are not representative for unbalanced datasets. 3. Results are often only applied to CWRU dataset, so their applicability to other benchmark data sets can not be concluded. Some further statements can be concluded: The performance of a fault detection and diagnosis algorithm usually depends on the trade-off between robustness and sensitivity, so suitable metrics instead of only one should be used. As lessons learned from this comparison, it can be stated: 1. Results getting from CWRU dataset always state very high values. 2. New approaches should demonstrate their robustness to variations in operating data sets, fault size, training/test data sets etc. 3. Results should only be accepted as N-fold cross validated results to avoid effects by too precise tuning of algorithms. 4. Applicability to different benchmark data sets should be demonstrated to learn about the inter applicability problem of the individual approaches.

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X. Wei and D. Söffker Table 1. Comparison of approaches on CWRU bearing dataset Data selection

Load

Fault size

Feature extraction Tr/te ratio

N-fold

Classifier

algorithms

Results Accuracy

Other results

Fault

Applied

Load

size

on other rence

tested

Cross

Refe-

Other

detection dataset

validation 0

7

1:2

Y

DSLS-SVM

SVM

te:99.9

pr:99.96;re:99.95;F-

--

--

Y

[10]

s:99.9 4:1

--

DBN+IWV

0

0,7,14,21

8:1

Y

LLRNN

softmax

te:96.2-99.5

0

0

0,7,14,21,28

0,7,14,21

2:1

Y

EDAEs

softmax

DBN+IWV te:96.95 te:97.18

--

1

0,7,14,21

3:1

--

Semi-DBN

softmax

te:100

--

--

--

--

[24]

1

0,7,14,21

variant

Y

CNN

SVR

94.6-100

--

--

--

--

[30]

1

0,7,14,21

2:1

Y

EEMD/MPE+

softmax

99.6

--

--

--

Y

[38]

-pr:73.-100;re:67-

--

--

--

--

Y

[40]

--

--

--

--

[34]

[23]

100;F-s: 76.6-100

SSDAE 1

0,7,14,21,28

variant

Y

PCA

SVM-OAA Mean:96.98

--

--

--

[13]

2

21

variant

--

LCD/GDA

CRSVM

95-100

sp:99.2; re:100;ROC --

--

--

Y

[12]

3

7

9:1

Y

BPFG

SVM

99.05

--

--

--

--

[11]

3

0,7,14,21

10:19

--

CMFE

ESVM

100

--

--

--

--

[19]

0,1

0,7,14,21,28

1:1

--

DTCWPT

DBN

te:98.75

--

Y

--

Y

[26]

1,2,3

0,7,14,21

variant

--

FFT+DACNN

softmax

pr:90.81-100; re:79.88-

Y

--

Y

[6]

model mean:99.6

100 0,1,2,3

0,7,14,21

1:1

Y

CWT/SVD

KMCSVM

above 95.6

--

Y

Y

--

[17]

0,1,2,3

0,7,14,21

1:1

--

HES

DBN

te:98--99.55

--

--

--

Y

[22]

1:3

--

WPT+DBN

--

--

Y

[25]

0,1,2,3

7

softmax

te:99.58

--

0,1,2,3

0,7,14,21

5:1

--

CNN

FC layer

mean: 99.79

--

--

--

Y

[27]

0,1,2,3

0,7,14,21

3:1

--

AWMSCNN

FC layer

97.97-99.98

--

Y

--

Y

[28]

0,1,2,3

0,7,14,21

4:1

--

FFT+IDSCNN

softmax

98.4

--

--

--

--

[29]

0,1,2,3

0,7,14,21

14:3

--

CNN

softmax

te: 99.41

--

--

--

Y

[31]

0,1,2,3

0,7,14,21

variant

--

SAE

softmax

mean:99.82

--

Y

--

--

[33]

0,1,2,3

0,7,14,21

1:9

Y

CLAE

softmax

te:99.73±0.15

--

--

--

--

[39]

0,1,2,3

0,7,14,21

132:5

Y

few-shot learning

FC layer

71.16-99.84

--

Y

--

--

[42]

0,1,2,3

7,14,21,28

2:3

Y

EEMD

ICDSVM

te:96.48-100

--

Y

Y

--

[9]

0,1,2,3

0,7,14,21,28

5:3

Y

WPT+Fisher’s

SVM with

98.9-100

--

--

--

[14]

rankgin+KPCA

Gaussian

BPSO-RFC

SVM

--

Y

--

[16]

0,1,2,3

0,7,14,21,28

1:1

--

sp:98.5-100;re:98.6100;PPV:98.5-100

90.62-100

--

Note: --: not mention; tr: training; te:test; sp: specificity; pr: precision; re:recall; F-s:F-score; PPV: positive prediction value; Y:yes; EEMD: ensemble empirical mode decomposition; ICDSVM: support vector machine optimized by inter-cluster distance; DSLS-SVM: deep stacking least square support vector machine; GDA: generalized discriminant analysis; CRSVM: chemical reaction support vector machine; BPSO: binary particle swarm optimization; KMCSVM: kernel matrix construction for support vector machine; CMFE: composite multiscale fuzzy entropy; ESVM: ensemble support vector machine; DTCWPT: dual-tree complex wavelet packet transform; AWMSCNN: adaptive weighted multiscale convolutional neural network; IDSCNN: deep convolutional neural networks and improved Dempster-Shafer; SSDAE: stacked sparse denoising auto-encoder; DACNN: domain adaptive convolutional neural network; LCD: local characteristic-scale decomposition; DAE: deep autoencoder; FFT: fast Fourier transformation; BPFG: bandpass filter group; OAA: one against all; WPT: wavelet packet transform; RFC: regularized Fisher's criterion; CWT: continuous wavelet transform; SVD: singular value decomposition; IWV: improved weight voting; HES: Hilbert envelope spectrum; FC layer: fully connected layer; SAE: stacked auto-encoder; SVR: support vector regression; CLAE: class level auto-encoder; EDAE: ensemble deep auto-encoder; KPCA: kernel principal component analysis.

Regarding future research directions using CWRU bearing dataset, the following trends can be detected from the actual analysis: 1. Due to its excellent classification abilities, SVM and improved SVM still would be applied on CWRU dataset and other similar problems in practice in the next years. 2. More different structure of DL approaches can be expected. 3. Although TL approaches and few-shot learning are new now, new related developments can be expected in the near future.

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Analyzing the Robustness of Hybrid, OutputOnly, Kalman Filtering–Based System Identification Method Esmaeil Ghorbani and Young-Jin Cha(&) University of Manitoba, Winnipeg, MB R3T 5V6, Canada [email protected]

Abstract. This paper investigates, in detail, the robustness of a previously introduced approach to output-only structural system identification using the random decrement method and unscented Kalman filter (RD-UKF) [1]. Unscented Kalman filters have been widely used for structural system identification and damage detection purposes. These filter’s divergence in estimating the desired states of a structural system with unknown excitations is a wellknown weakness, considerably limiting their application. To overcome this difficulty, the current study initially employs the random decrement method to extract a system’s free decaying response from its measured responses. Subsequently, it applies an unscented Kalman filter to the extracted free response in order to estimate the system’s dynamic properties. Our previous study demonstrated this method’s proficiency. The present study conducts further sensitivity analysis to show the RD-UKF method’s robustness vis-à-vis different uncertainties in the process of identification. First, we estimate the stiffness and damping matrices of a three-degrees-of-freedom (DoF) system with three different kinds of excitations. Next, we examine the RD-UKF method’s robustness in 100 experiments (Monte Carlo simulation). Besides, it will be shown that the method is robust in addressing uncertainties related to mass distribution and missing data (sensor malfunction or a loss of communication connectivity) during the modelling and measurement process. The results of the study show that the RD-UKF method is sufficiently robust for all the uncertainties of the system identification process. Keywords: Structural damage detection
Unscented Kalman filter
Random decrement
Large degrees of freedom
Robustness analysis
Output-only system identification

1 Introduction The structural damage identification process comprises three main steps: damage detection, damage localization, and damage quantification. Damage identification methods can be categorized into three groups: data-driven methods, model-based methods, and hybrid methods. Model-based methods use the advantages of a physicsbased model, compared to data-driven approaches, to evaluate the extent of the damage. Recently, some studies have attempted to combine both model-based and data© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 533–542, 2021. https://doi.org/10.1007/978-3-030-64594-6_52

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driven approaches as a hybrid method in order to harness both method’s advantages for damage identification purposes [2–4]. Measuring excitation is not viable in the course of normal operation of many civil infrastructures for their damage identification purposes [2]. During the last decade, many studies have attempted to identify a solution for extracting excitation information for the identification process. As the first solution, shakers and actuators simulates excitations on the structure level, and the corresponding system response is used for damage identification. However, shakers’ enormous mass changes a structure’s modal parameters [5]. Estimating input with the help of a mathematical model and the measured response of the system using different methods, such as Kalman filtering, could constitute another approach. Joint input-state methods [6] and dual Kalman filtering [7] are two of the main methods that have been widely used for output-only system identification, utilizing this approach. The predicted state’s accuracy strongly depends on the first Kalman filter’s estimated input excitation [7]. Reconstruction of the input using mathematical methods [8]—such as autoregressive moving average (ARMA) methods, subspace methods [9], and Markov chain Monte Carlo (MCMC) methods [10]—is the third solution to unknown excitation problems. Selecting an appropriate sampling distribution plays a vital role in these kinds of approaches. Also, these approaches are not robust enough for combined excitations [10]. This study investigates the robustness of a previously introduced output-only damage quantification approach that takes advantage of both data-driven and modelbased methods [1]. We believe that estimating input causes an estimation error during the damage identification process. We extract a free decaying response from the measured response of a structure using a data-driven method called random decrement (RD) [11], and we input the free-response into an unscented Kalman filter (UKF) for damage identification. This approach reduces the complexity and uncertainty of the physical model, and the combined method (RD-UKF) [1] could be used for damage identification of large-scale systems under the various kinds of loading involved in the identification process. Therefore, in this paper, we investigate the RD-UKF method’s robustness in resolving various uncertainties associated with the structural damage quantification process. This paper is organized as follows. Section 2 briefly introduces the basics of the RD and UKF methods. This section also presents RD-UKF’s algorithm. Section 3 outlines extensive studies on 1) RD-UKF’s robustness for damage identification, using a 3-DoF numerical model subjected to three different loading conditions, 2) the method’s robustness for about 100 Monte Carlo simulation experiments, 3) the method’s robustness for uncertainty in a model’s mass matrix, and 4) the effect of a missing sensor signal in the identification process, which could be due to sensor malfunction or a loss of communication connectivity. The results emphasize that the RD-UKF method is robust enough for all four cases of uncertainty.

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2 RD-UKF Conceptually, the vibration response x(t) of a linear system subjected to any excitation consists of a linear combination of free and forced vibration responses, as expressed in Eq. (1). xðtÞ ¼ xðtÞjxð0Þ þ xðtÞjx_ ð0Þ þ xðtÞjf ðtÞ :

ð1Þ

The first two parts of Eq. (1), xðtÞjxð0Þ þ xðtÞjx_ ð0Þ , refer to the system’s free vibration response carrying information about the system’s modal parameters and the third part of Eq. (1), xðtÞjf ðtÞ , returns to the forced vibration response. In this study, we eliminate the third part of the system’s total vibration response using the RD method. The RD method was proposed by Cole [11] to extract the free vibration response from the vibration response of a dynamic system subjected to random or ambient excitation. The method averages out the time segments of the measured\responses with a standard initial or triggering condition. To implement the RD method, a trigger point pffiffiffi 2r (r is the standard deviation of the measured response) should be defined, and a segment of the signal is saved when the signal’s amplitude yields the trigger point. The average of these segments represents the system’s free vibration response (mathematically provable) [1]. The extracted free vibration response is input into the UKF to estimate the desired state. UKF is a derivative-free Kalman filtering method defined for the state estimation of nonlinear systems [1]. This model-based method has been widely used for structural system identification and damage detection purposes [12, 13]. A discrete state-space model of the system has been derived to identify the system’s dynamic properties, using UKF, as: xk þ 1;k ¼ Gðxk ; uk Þ;

ð2Þ

  Yk ¼ H xk þ 1;k ; vk :

ð3Þ

Dt is a time-step, xk is a state variable vector, uk is process white noise, Q is the covariance matrix, Yk is the measurement vector, vk is a discrete measurement noise vector, and R is the corresponding covariance matrix. In contrast to a linear Kalman filter, UKF propagates a set of predefined points, called sigma points, through its transition and measurement functions, and it calculates the covariance matrix in each iteration. Assuming prior information of a Gaussian distribution with mean m and i covariance error matrix PXX 0 , sigma points (v ) and their corresponding weights are defined as follows:

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w0m ¼ n þk k ; qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi  ðn þ kÞPXX w0c ¼ n þk k þ ð1  a2 þ bÞ; vi ¼ m þ 0 i ; i ¼ 1; 2; . . .; n qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  i i 1 ðn þ kÞPXX vi ¼ m  0 in ; i ¼ n þ 1; . . .; 2n wm ¼ wc ¼ 2ðn þ kÞ ; i ¼ 1; . . .; 2n;

v0 ¼ m,

ð4Þ where n is the number of states, k is a scaling parameter, and i is a column vector number. Using these points and the state-space model, the algorithm for the proposed RD-UKF is followed by:

3 RD-UKF Robustness Studies Extensive numerical studies have examined the robustness of the proposed output-only damage identification method. We should mention that the algorithm’s tuning parameters are the same in all cases. Thus, the proposed technique is assured to be robust for tracking a system’s clean stiffness values.

Analyzing the Robustness

3.1

537

Case 1: 3-DoF System Under Different Loading Conditions

A 3-DoF damped mass-spring system was simulated in MATLAB software for 50 s with a sampling rate of 1,000 Hz. The mass, stiffness, and damping values were 500 kg, 50,000 N/m, and 300 N.s/m for each DoF, respectively. The input excitations are white noise with a power of 10−4, the periodic force with a frequency of 2p, and the historic EI Centro earthquake as an ambient vibration. Figure 1 shows the estimated stiffness and damping matrices for three different loading cases.

Fig. 1. Estimated stiffness (left column) and damping values (right column)

Figure 1 shows that the RD-UKF method is capable of tracking a clean response for the three kinds of loadings. Only small fluctuations for the periodic forces were observed, which might be due to the elimination of periodic force from the measured response. For identification purposes, the RD method was used to extract the free pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi vibration response data with a selected trigger point equal to 0:3 varð€ x1 Þ of the 1st DoF and 10 s selected as the segment length. Prior information on UKF for structural identification was assumed as m ¼ 500 kg, k ¼ 30000 N/m, c ¼ 200 N.s/m, and x ¼ x_ ¼ 0 for each DoF. Detailed information about the model’s transition and measurement functions, as well as the procedure to remove the periodic signal from the measured acceleration response, is available in our previous work [1].

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Case 2: RD-UKF Robustness Under Monte Carlo Simulations

Because we were working with stochastic models and stochastic signals, Monte Carlo simulations were necessary to show the RD-UKF method’s robustness under different initial conditions. Therefore, we generated 100 signal realizations using a simulation model with different seed numbers (that is, random signal generation), estimated the dynamics, and presented the estimates for all realizations (multiple curves) in Fig. 2. For a more realistic evaluation, we also added white noise with a signal-to-noise ratio (SNR) of 30 to each measurement.

Fig. 2. Stair-step graphs and box-and-whisker plots of stiffness values for each floor with 100 tests (signal-to-noise ratio = 30).

Figure 2 shows that the error rates for k1–k3 are about 0.14%, 0.34%, and 0.18%, respectively. The error rates for C1–C3 are about 2.1%, 2.6%, and 2.3%, respectively, in Fig. 3. In Fig. 2 and Fig. 3, the orange lines are related to the estimated stiffness and damping value of the previous case study. it should be note that different seed numbers are assigned to each test, and white noise (SNR = 30) is added to the measured signal. The figures show that the results are repeatable.

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Fig. 3. Stair-step graphs and box-and-whisker plots of damping values for each floor with 100 tests (signal-to-noise ratio = 30).

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Case 3: Mass Matrix Sensitivity Analysis

We examined the RD-UKF method’s robustness vis-à-vis changes in stiffness and damping from the previous study [1], while we had assumed that enough information about the mass matrix is available. However, to show the method’s proficiency regarding mass matrix uncertainty, we randomly changed the mass values in the transition and measurement functions by 5%. The updated measurement equation and new results of the UKF are provided in Eq. 5. The estimated results are shown in Fig. 4.

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Fig. 4. Estimated stiffness (left column) and damping values (right column) of the model under mass distribution uncertainty. 8 9 2 3 ðx7 þ x8 Þx1 þ x8 x2  ðx10 þ x11 Þx4 þ x11 x5 =ðm1 þ 0:05m1 Þ > < €x1 > = 7 6 y ¼ €x2 ¼ 4 ½x8 x1  ðx8 þ x9 Þx2 þ x9 x3 þ x11 x4  ðx11 þ x12 Þx5 þ x12 x6 =ðm2  0:05m2 Þ 5 þ vðtÞ > : > ; ½x9 x2 þ x12 x5  ðx12 x6 þ x9 x3 Þ=ðm3 þ 0:05m3 Þ €x3 ¼ hðx; tÞ þ vðtÞ:

ð5Þ Figure 4 also shows that RD-UKF is not too sensitive to changes in mass. One damping value cannot track the correct response because of a significant difference in absolute values between K and C. This damping value deviation is inevitable, and it is a well-known limitation of Kalman filter-based system identification in many journal articles [14]. 3.4

Case 4: Missing Sensor Sensitivity Analysis

This section aims to show the RD-UKF method’s robustness in situations where the response of some DOFs is not available (not measured due to an absence of sensors). For the 3-DOF system using earthquake excitation, we assumed that the second-floor acceleration response was not available, and the estimated stiffness and damping values are shown here. Because we expected no differences in transition function, we have just rewritten the new measurement function in Eq. 6.

Analyzing the Robustness

 y¼

€x1 €x3





ðx7 þ x8 Þx1 þ x8 x2  ðx10 þ x11 Þx4 þ x11 x5 =ðm1 Þ ¼ ½x9 x2 þ x12 x5  ðx12 x6 þ x9 x3 Þ=ðm3 Þ

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 þ vðtÞ

ð6Þ

¼ hðx; tÞ þ vðtÞ: Figure 5 shows that the missing measurement has no significant effect on the stiffness estimation and that it only leads to some overestimation of damping values (c2), which we saw in our previous study as well [4]. However, we should mention that the RD-UKF method is more sensitive to missing information than to mass matrix uncertainty. This difference in sensitivity could be due to the equation’s stochastic nature, which yields uncertainties in the model while missing information reduces the rank of observability for the system as well as estimation accuracy.

Fig. 5. Estimated stiffness (left column) and damping values (right column) of the model in missing-sensor situations.

4 Conclusion This paper conducted a sensitivity analysis of a hybrid, output-only system identification method, based on an unscented Kalman filter and the random decrement method (RD-UKF) [1]. The proposed hybrid method takes advantage of the RD to overcome the drawbacks of traditional model-based methods (which require both input and output

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for damage quantification). This study demonstrates that the RD-UKF method is robust, with various loadings exciting the structure and different initial conditions for different realizations. The study also examined the method’s robustness vis-à-vis missing data and mass matrix uncertainty in the identification process. These extensive numerical investigations emphasize that the RD-UKF method is sufficiently capable of damage quantification in different real structural systems.

References 1. Ghorbani, E., Buyukozturk, O., Cha, Y.-J.: Hybrid output-only structural system identification using random decrement and Kalman filter. Mech. Syst. Signal Process. 144, 106977 (2020) 2. Limongelli, M.P., et al.: Towards extraction of vibration-based damage indicators. In: EWSHM-8th European Workshop on Structural Health Monitoring (2016) 3. Ghorbani, E., Cha, Y.-J.: Identification of large-scale systems with noisy data using an iterated cubature unscented Kalman filter. In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018. International Society for Optics and Photonics (2018) 4. Ghorbani, E., Cha, Y.-J.: An iterated cubature unscented Kalman filter for large-DoF systems identification with noisy data. J. Sound Vib. 420, 21–34 (2018) 5. Fu, Z.-F., He, J.: Modal Analysis. Elsevier, Amsterdam (2001) 6. Maes, K., et al.: Joint input-state estimation in structural dynamics. Mech. Syst. Signal Process. 70–71(Supplement C), 445–466 (2016) 7. Eftekhar Azam, S., Chatzi, E., Papadimitriou, C.: A dual Kalman filter approach for state estimation via output-only acceleration measurements. Mech. Syst. Signal Process. 60–61 (Supplement C), 866–886 (2015) 8. Sanchez, J., Benaroya, H.: Review of force reconstruction techniques. J. Sound Vib. 333 (14), 2999–3018 (2014) 9. Masjedian, M., Keshmiri, M.: A review on operational modal analysis researches: classification of methods and applications. In: Proceedings of the 3rd IOMAC, pp. 707– 718 (2009) 10. Erazo, K., Nagarajaiah, S.: An offline approach for output-only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering. J. Sound Vib. 397, 222–240 (2017) 11. Cole Jr, H.A.: Failure detection of a space shuttle wing flutter model by random decrement (1971) 12. Cha, Y.-J., Chen, J., Büyüköztürk, O.: Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters. Eng. Struct. 132, 300–313 (2017) 13. Cha, Y.-J., Chen, J.G., Büyüköztürk, O.: Motion magnification based damage detection using high speed video. In: Structural Health Monitoring 2015 (2015) 14. Wu, M., Smyth, A.W.: Application of the unscented Kalman filter for real-time nonlinear structural system identification. Struct. Control Health Monit. 14(7), 971–990 (2007)

Production-Induced Variance of Guided Wave-Based SHM Systems – A Case Study Inka Mueller1(B) , Alisa Shpak2 , Claus-Peter Fritzen3 , and Mikhail Golub2 1

Bochum University of Applied Sciences, Bochum, Germany [email protected] 2 Kuban State University, Krasnodar, Russia 3 Siegen University, Siegen, Germany

Abstract. To show the effectiveness and sensitivity of SHM systems, proof of concept-experiments have been state of the art for quite some time. For the detailed analysis of SHM systems, frequently numerical modeling is used. Its quality is shown with a comparison to experimental data. Deviations are often called to be caused by the influence of environmental and operational conditions as well as simplifications in the setup of the model. Another aspect, often neglected in guided wave-based SHM, are inaccuracies during production leading to deviations from the nominal structure and SHM system setup. An intelligent structure using active guided waves for SHM consists of the structural component, piezoelectric transducers, data acquisition and data analysis units. Production processes may lead to small deviations in the structure itself, deviations of the bonding process for the transducers as well as geometric inaccuracies, i.e., production-induced variance. For guided wave-based SHM, a case study is presented, which shows the effect of production-induced variance for a simple experimental setup consisting of an aluminum plate with rectangular piezoelectric transducers. The influence highly depends on the type of data acquisition and feature extraction. Transducer signals as well as laser Doppler vibrometer signals and different extracted features are evaluated. Using a very simple artificial change of the structure by adding an acrylic additional mass, it is shown that the effect of this change is approximately the same scale as production-induced variance. It is a significant influence, which needs to be taken into account when aiming for model-based sensitivity analysis. Keywords: Guided waves · Experimental variance vibrometer · Piezoelectric transducers

· Laser Doppler

c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 543–552, 2021. https://doi.org/10.1007/978-3-030-64594-6_53

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Introduction

Reliability and repeatability are basic requirements for long term usage of SHM. Uncertainty quantification is therefore absolutely necessary to improve the quality of SHM system results and facilitate the transition to industrial applications. This holds true for a variety of SHM methods, including vibration-based as well as wave-based methods. First approaches to quantify and model uncertainty, based on features derived in the frequency domain, exist, see [7]. For guided wave-based methods, uncertainty quantification has not been in the focus of current research, but the analysis of probability of detection to enable quality assessment for these kind of systems is an ongoing important topic, see [2,5,12]. Many approaches in SHM rely on numerical modeling of intelligent structures. For guided wave-based active SHM methods, a variety of modeling approaches exist, see [1,4,6,13]. If these models are compared to experimental data, their quality is often checked by comparing with experimental data of a laser Doppler vibrometer (LDV) of the structure, see [3]. Sometimes, if the model includes the sensing process, they are compared using measured data of a piezoelectric transducer network, see [11]. Differences between model and experiment can be caused by various reasons. Model assumptions are one of these reasons. The influence of changing environmental and operational conditions, as well as methods to compensate these, have been in the focus of the SHM community and are still a challenging topic. Seldom, the influence of production-induced variance is taken into account. The focus of this paper is therefore to document the influence of productioninduced variance, which the authors have seen in a variety of projects in the past, using a very simple experimental setup with guided waves. At first, the concept of the study is explained, before introducing the experimental setup. The main part is presented in the results and discussion section, which is followed by final conclusions.

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Methods

The idea of this case study is to show the effect of production-induced variance on guided wave-based SHM systems. Especially three points are focused on: 1) effect of production-induced variance on the generated wave field, 2) effect of production-induced variance on the sensor signal, 3) effects of the application of 2D models for simulating guided waves. The above mentioned effects are compared to the changes of the wave field caused by a simple artificial variation. For the analysis of the production-induced variance on the generated wave field, an LDV is used to measure the generated wave field without using contact techniques. For the analysis of production-induced variance on the sensor signal, the signal of piezoelectric transducers (PWAS - piezoelectric wafer active sensors) is used. They are used as a sensor and in turn as an actuator. To analyze the effects due to 2D assumptions, LDV-measurements are used. To enable these analyses, a number of structures, which are nominally the same are produced

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and used to generate measurement data. The data is analyzed to show its variety. This is compared to the effect caused by a simple artificial change, applied to all structures in the same manner. One might argue that the resulting differences are caused by a low level of reproducibility during the manufacturing process and indeed this is partly true, but it is intentionally introduced. To enable the usage of SHM systems in an industrial context, we should not expect perfectly manufactured structures with exact dimensions. Moreover, the PWAS application is often done manually. Therefore, the case study takes into account important influencing factors for SHM in an industrial context, i.e., machine- and human-induced variance in the production process.

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Experimental Setup

The experimental setup of this case study consists of a simple rectangular aluminum plate of size 300 mm × 300 mm × 2 mm. It is equipped with two rectangular PWAS from PI Ceramics, PIC255, 30 mm × 10 mm × 1 mm, which are placed symmetrically to enable their use as actuator and sensor enlarging the number of experiments, see Fig. 1a). Three plates P1, P2 and P3 of this kind have been manufactured. With two sensors each, this results in a number of instances n = 6.

Fig. 1. Setup of the plate including two rectangular PWAS, a) dimensions, b) photo of P1 with PMMA block.

The plates are analyzed with an LDV measuring the generated wave field with its out-of-plane velocities in two directions on the plates, called grid G1

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and grid G2. G1 shows a possible effect in direction of the traveling wave along x2 . Five measurement points are defined equally distributed on the axis between the two transducers. G2 along x1 shows a possible effect perpendicular to the axis between the two transducers. The focus of G2 is to analyze the effect of 2D simplifications. It is therefore located close to the sensor position in the far field of the actuator, see Fig. 2. All LDV-measurements are realized with two frequencies, f1 = 80 kHz and f2 = 240 kHz.

Fig. 2. Visualization of grid G1 and grid G2.

As equipment, a digital infrared LDV from Optomet, NB-DF Nova Basis with D-VD-1N encoder and dual fiber head with a short range objective is used, which is mounted on a table, movable in two axes. The actuator is excited by a five cycle windowed cosine signal of 70 V amplitude. The signal is generated by a TiePie Handyscope HS5, which is also used as an oscilloscope for the LDV-signal, and amplified. This equipment has already been combined, used and described in [8,10]. Moreover, all plates are tested with a pitch-catch measurement using the same TiePie HS5 with a five cycle windowed cosine. All pitch-catch measurements are realized with 80 kHz and 240 kHz. All measurements are performed for the reference state and the modified state. The reference state is the state described above, the modified state is realized by adding a very simple artificial change. A PMMA block of 300 mm × 10 mm × 10 mm is bonded with double-sided adhesive tape exactly in the middle between the two transducers, see Fig. 1b). Hereby, energy dissipation is introduced into the system.

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Results and Discussion

For the analysis of the effects of production-induced variance on the generated wave field, as well as of effects due to 2D assumptions for modeling of guided waves, the LDV-measurements are used. As a first parameter of interest, the maximum amplitude of the direct signal without reflections is used. It is calculated by using the Hilbert transform and extracting its maximum amplitude in the time period of the direct signal, see Fig. 3. For both frequencies, the A0-mode was selected, which is measured dominantly with the out-of-plane velocity.

Fig. 3. Time data and extracted maximum out-of-plane velocity amplitude of A0-mode at excitation frequency of 80 kHz.

When exciting with 80 kHz, the results of grid G2, along the width of the PWAS used as sensor, show a high variation among the six different instances. This variation among the six instances is higher than the variation over the PWAS width for each individual instance. Along the width, a decrease of amplitude from the center to the edges can be seen, which is expected, see Fig. 4a). Moreover, the effect of the wrap-around electrode is visible for x1 < 0. The boxplots in Fig. 4b) show the variation. For all six instances, the PMMA block results in a decreased amplitude of the maximum velocity. While this can be clearly recognized when comparing a sample in both states, this is not statistically significant when comparing both groups. The effect, realized by attaching the PMMA block, is very consistent among all points and instances, with an amplitude reduction of about 5 mm/s. For the higher excitation frequency of 240 kHz, the results of grid G2 show a high variation among the six different instances as well as among the measurement positions distributed along the width of the PWAS. Compared to this variation, the influence of the PMMA block is very small, resulting in a nondetectability of this modification at this frequency range, even for plate P3, which exhibits the smallest variation compared to the other two plates. For this high frequency, the variation is not showing the same pattern for all transducers. This makes the usage or validation of 2D models in this frequency regime complicated, see Fig. 5.

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Fig. 4. a) Extracted maximum out-of-plane velocity amplitude of A0-mode over the width of the PWAS according to G2 for all six instances, b) Comparison of data for the baseline and the measurement with PMMA block, for an excitation frequency of 80 kHz.

The result shows that the transducers of plate P2 and P3 are attached slightly different than the transducers on the first plate. The resulting velocities are significantly reduced for P1 for 80 kHz and increased for 240 kHz. The smallest variation is found for plate P3. If an industrial process could ensure an improved, more steady application quality, i.e., by a quality check using the electro-mechanical impedance spectrum, see [9], a clear distinction between both states can be also realized by comparing the groups in the lower frequency range. The variation is significantly higher for the higher frequency range making an ID-specific baseline necessary. Variation over the transducer’s width is small compared to the variation among transducers for 80 kHz. It is therefore reasonable for simple problems to include 2D models, e.g., for parameter studies, at a lower frequency range but not for higher frequency ranges. Moreover, the simplification used for 2D models is not valid for any indirect path, which is important for damage influence analyses. Therefore, detailed 3D models are absolutely necessary, e.g., for quality assessment analyses. The influence of the PMMA block is also different for the various distances between block and measurement point. This is visible using data of grid G1. The results of this analysis are shown in Fig. 6. The first two measurement points are located between the actuator and the PMMA block. The direct signal, as it is analyzed in this case study, is not changed and shows a high repeatability of the measurements. Reflections, which are changed by the block accordingly,

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Fig. 5. a) Extracted maximum out-of-plane velocity amplitude of A0-mode over the width of the PWAS according to G2 for all six instances, b) Comparison of data for the baseline and the measurement with PMMA block, for an excitation frequency of 240 kHz.

are not analyzed here. The third measurement point is located directly at the block location and cannot be measured while the block is attached. The last two measurement points are located between the block and the sensor position. Therefore, the block changes their direct signals. The maximum velocity amplitude is shown in Fig. 6 for all instances and x2 -coordinates. The results show significant variation between the six instances for both states, without and with PMMA block. The pattern of the differences among the six instances is not the same for all measurement points. Especially in the far field, actuator A2 on plate P1 as well as A1 on P2 exhibit significantly lower amplitudes. While the effect of the block has approximately the same size for the last point at x2 = 200 mm, the effect is smaller for these two instances at the fourth point, x2 = 152.5 mm. The production-induced variance does not only influence the generated amplitude of the direct signal, but also the arrival time. Small differences in the transducer position, transducer orientation and structure size also have a significant effect. This effect is shown for the central point of grid G1, see Fig. 7. At this location, it is easily possible to distinguish the effects in the direct signal, first reflection and multiple reflections.

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Fig. 6. Changed wave field along the path between actuator and sensor due to a PMMA block attached to the plate, shown for all six instances using an excitation frequency of 80 kHz.

Fig. 7. Variances in the time signal including direct signal and reflections for the central point of grid G1 shown for all six instances using an excitation frequency of 80 kHz.

For an excitation frequency of 80 kHz, the time shifts of the direct signal are small, as it can be seen in the first wave package, which is very much in phase for all instances. The first reflections in all instances, resulting from a reflection on the left boundary of the plate, are already showing significant shifts in the second recognized wave package. From 0.2 ms on, multiple reflections lead to large differences within the signal. Especially for all methods of data evaluation, which use these parts of the signal, these production-induced variances have a significant influence, which cannot be easily included into a mathematical model. The LDV-measurements show a high production-induced variance in the data. The usage of sensor signals, as it is used for most guided wave-based SHM systems, exhibits slightly weaker influence, as the transducer area has the effect of averaging the signal. Nevertheless, the same effects apply to the transducer signal: amplitudes are not equal and time shifts are present, especially for multiple reflections. The effect is more dominant for 80 kHz in this case study, see Fig. 8. Due to the reciprocal usage of A1 and A2, only three instances are available.

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Fig. 8. Variances in the sensor signal for a) 80 kHz and b) 240 kHz.

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Conclusion

The results of this case study clearly show that there is a high influence of production-induced variance on the resulting wave field and sensor signals. This influence depends not only on the exact size of the structure, the exact orientation and location as well as bonding quality of the transducer, but it is also frequency-dependent and varies with measurement location. It is therefore necessary to take into account the resulting differences, if model-based analysis of data is planned. The effect of this production-induced variance on the SHM data evaluation result moreover depends on the type of data analysis, the SHM system is based on. Robust feature selection might help reducing the influence of production-induced variance. To take into account these ID-specific variations in numerical models in future approaches for model-based quality assessment, model-updating can be applied, as soon as data of a specific structure is available. Alternatives like meta or surrogate models are currently discussed among the authors. Acknowledgements. The authors would like to thank Divya Singh for his help in conducting the experiments and the German-Russian Interdisciplinary Science Center, supporting this research (A-2019b-9d).

References 1. Bartoli, I., Marzani, A., di Scalea, F.L., Viola, E.: Modeling wave propagation in damped waveguides of arbitrary cross-section. J. Sound Vib. 295, 685–707 (2006). https://doi.org/10.1016/j.jsv.2006.01.021 2. Buethe, I., Dominguez, N., Jung, H., Fritzen, C.P., S´egur, D., Reverdy, F.: Pathbased MAPOD using numerical simulations. In: Woelcken, P., Papadopoulos, M. (eds.) Smart Intelligent Aircraft Structures (SARISTU) - Proceedings of the Final Project Conference, pp. 631–642. Springer (2015) 3. Glushkov, E., Glushkova, N., Lammering, R., Eremin, A., Neumann, M.N.: Lamb wave excitation and propagation in elastic plates with surface obstacles: proper choice of central frequencies. Smart Mater. Struct. 20(1), 015020 (2011). https:// doi.org/10.1088/0964-1726/20/1/015020 4. Golub, M.V., Shpak, A.N.: Semi-analytical hybrid approach for the simulation of layered waveguide with a partially debonded piezoelectric structure. Appl. Math. Model. 65, 234–255 (2019). https://doi.org/10.1016/j.apm.2018.08.019

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5. Janapati, V., Kopsaftopoulos, F., Li, F., Lee, S.J., Chang, F.K.: Damage detection sensitivity characterization of acousto-ultrasound-based structural health monitoring techniques. Struct. Health Monit.: Int. J. 15(2), 143–161 (2016). https://doi. org/10.1177/1475921715627490 6. Lugovtsova, Y., Bulling, J., Prager, J., Boller, C.: Efficient modelling of guided ultrasonic waves using the scaled boundary FEM towards SHM of composite pressure vessels. In: EWSHM (2018) 7. Mao, Z., Todd, M.: Uncertainty modeling and quantification for structural health monitoring features derived from frequency response estimation. Key Eng. Mater. 569–570, 1148–1155 (2013). https://doi.org/10.4028/www.scientific.net/KEM. 569-570.1148 8. Moll, J., Golub, M.V., Glushkov, E., Glushkova, N., Fritzen, C.P.: Nonaxisymmetric lamb wave excitation by piezoelectric wafer active sensors. Sens. Actuators A: Phys. 174, 173–180 (2012). https://doi.org/10.1016/j.sna.2011.11. 008 9. Mueller, I., Fritzen, C.P.: Inspection of piezoceramic transducers used for structural health monitoring. Materials 10, 71 (2017). https://doi.org/10.3390/ma10010071 10. Mueller, I., Shpak, A., Golub, M.V., Fritzen, C.P.: Effects of debonding of PWAS on the wave propagation and the electro-mechanical impedance spectrum. In: EWSHM (2016) 11. Schulte, R.T., Fritzen, C.P.: Simulation of wave propagation in damped composite structures with piezoelectric coupling. J. Theor. Appl. Mech. 49(3), 879–903 (2011) 12. Tsch¨ oke, K., Gaul, T., Schubert, L., Mueller, I.: Rechnergest¨ utzte PODBestimmung f¨ ur SHM-Verfahren basierend auf gef¨ uhrten Wellen im Automobilbereich. In: DACH Jahrestagung (2019) 13. Tsch¨ oke, K., Gravenkamp, H.: On the numerical convergence and performance of different spatial discretization techniques for transient elastodynamic wave propagation problems. Wave Motion 82, 62–85 (2018). https://doi.org/10.1016/ j.wavemoti.2018.07.002

Damage Identification by Inverse Finite Element Method on Composite Structures Subject to Impact Damage Luca Colombo(&)

, Daniele Oboe, Claudio Sbarufatti, and Marco Giglio

Mechanical Engineering Department, Politecnico di Milano, via La Masa 1, 20156 Milan, Italy {luca1.colombo,daniele.oboe,claudio.sbarufatti, marco.giglio}@polimi.it

Abstract. One main limitation to the implementation of an SHM system on real structures is the difficulty to accurately define the load boundary conditions and the material properties, possibly leading to damage misclassification, especially with heterogeneous materials like composites. In this framework, the inverse Finite Element Method (iFEM) enables to reconstruct the complete displacement, and thus, the strain field starting from discrete strain measures without any a priori knowledge of the loading condition and the material properties. Structural assessment is then performed by computing an anomaly index identifying discrepancies between the strain reconstructed and measured in some testing positions and exploiting the latter for computing the Mahalanobis distance to further highlight discrepancies. Though the anomaly identification framework is general for any arbitrary component geometry and damage type, the procedure is experimentally verified with a CFRP reinforced panel subjected to a compressive load with propagating delamination generated from bullet damage. Keywords: iFEM
Shape sensing
Inverse problem
Damage identification
Composite
Delamination

1 Introduction The recent widespread application of composite materials for aeronautical and aerospace applications has introduced new mechanisms of degradation, enforcing structures inoperability for dedicated controls. Structural Health Monitoring (SHM) systems aim to mitigate the unavailability of the structure based on a network of permanently installed sensors to assess in real-time the structural integrity. Different SHM techniques exist in the literature, mainly classified into data-based and model-based methods. The former makes use of machine learning or pattern recognition to diagnose the structure condition based on the input sensor data [1]. The latter relies on a model that simulates the behavior of the system both in the healthy and damaged condition, then inferring over the structural heath state, often based on inverse processing of sensor signals [2]. However, model-based techniques often require © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 553–563, 2021. https://doi.org/10.1007/978-3-030-64594-6_54

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detailed modeling of the environmental variability and physical properties of the structure, resulting in a challenging and cost demanding task for most of the real applications. Some methods exploit data normalization to limit the effect of disturbing influences, precisely temperature, on diagnosis in a model-based framework [3]. Other methods consider the machine learning approach to decouple the strain field from the applied load, thus identifying anomalies in the strain field as regards the healthy condition [4]. However, its main limitation and drawback is related to the training process required by the neural network, which entails full coverage of the system operating conditions. In this framework, a model-based technique developed by Tessler et al. [5], the inverse Finite Element Method (iFEM), allows one to reconstruct the deformed shape of a structure based on discrete strain measurements. Only a geometrical model of the structure with its boundary conditions and a specifically designed strain sensor grid are required as input for calculating nodal displacements. From the latter, strain and stresses can be computed on the entire structure with minimal computational burden, thus favoring a real-time application. This technique has already been developed for shape sensing of beams [6] and shell-like structures [7, 8], recently extending the algorithm to curved shell structures [9], and solid elements [10]. However, limited applications for damage identification are reported in the literature. Recently, the iFEM has been successfully applied to identify damages on metallic structures [11–16]. In particular, the load-adaptive baseline proposed in [13] is claimed to be independent of the applied load and relies on a comparison between the reconstructed strain field and the measured one. Although this technique looks promising, to the best of the authors’ knowledge, no application to composite material structures has been reported yet in the literature. In this work, the authors extend previous studies on damage detection techniques with iFEM [13] to composite structures, thus exploiting the independence of the method from the knowledge of material properties. The monitored structure is a cocured stiffened panel subjected to a ballistic impact and a compression-compression fatigue test for damage progression. The panel is equipped with a distributed fiber optic sensor network acquired with the Luna ODISI-B system, providing the strain pattern as input to the iFEM algorithm for full strain field reconstruction, feature extraction, and damage identification. Particular care was taken to model the boundary conditions. An error on the definition of boundary conditions would lead to a wrong estimation of structural displacements and, as a consequence, to damage misclassification. This work leverages on boundary condition effect linear superimposition to approach the real behavior of the structure better, due to its simplicity and consolidated application in very different fields. Finally, the damage identification results are verified with ultrasonic non-destructive techniques. The paper is structured as follows. The general framework of the iFEM is briefly described in Sect. 2. The damage identification framework based on the iFEM is described in Sect. 3 and is subsequently applied to the experimental test described in Sect. 4. The results of damage identification are reported in Sect. 5. Finally, a conclusive section is provided.

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2 The Inverse Finite Element Method A summary of the iFEM approach to displacement and strain field reconstruction is provided in this section. The interested reader can refer to [7, 13] for a detailed treatment of the algorithm formulation. The iFEM algorithm relies on a weighted least-squares optimization to compute the global displacement field of a structure based on a comparison between measured (
e ) strains and a numerical formulation (
ðuÞ) of the same, with u referring to the implicit optimization target, i.e., the displacement. Supposing the structure is discretized in nel shell-like inverse elements, a weighted least-squares functional can be defined, accounting for membrane (e), bending (k), and transverse shear (g) deformations of the element mid-plane, hereon referred to as reference plane. In particular, the functional of the ith inverse element is defined as: 
 2 
 2 
 2
 Ui ui ¼ wm e ui  eei  þ wb k ui  kei  þ ws g ui  gei 

ð1Þ

Where ui refers to the vector of nodal degrees of freedom in local coordinates and wm ; wb ; ws are positively valued parameters associated with the membrane, bending, and shear deformations, governing the coherence between numerical and measured strains. The implementation of the iFEM procedure requires two features: (i) a numerical formulation of the e; k; g strain components and (ii) a vector of input strain measurements (ein ) to define ee ; ke ; ge . The former is defined following a procedure similar to the direct FEM and, thus, is not detailed here for brevity sake. The latter is computed starting from surface strain measures. In particular, considering the ith inverse element is instrumented with n strain sensors, each 3 strain tensor components
one measuring  and located at n discrete positions xj ¼ xj ; yj ; h ðj ¼ 1; . . .; nÞ on both the top ( þ h) and bottom (h) surfaces (with h referring to the distance of the element surface from the reference plane), the e and k strain components can be computed as: 8 þ 9 < exx þ e xx = eei;j ¼ 12 eyyþ þ e : c þ þ cyy  ; xy 8 xyþ 9i;j ðj ¼ 1; . . .; nÞ  e  e < xx xx = 1 e þ  e kei;j ¼ 2h : cyyþ  cyy  ; xy xy i;j

ð2Þ

In contrast, the strain component g cannot be directly computed from the measured surface strain components. However, since the g contribution can be safely neglected in most of the engineering applications [7], its formulation is disregarded hereon. Once the numerical (
ðuÞ) and measured (
e ) strain components of the reference plane are defined, one can derive a global system of equations as in Eq. (3) applying a standard finite element procedure to sum the contribution of each nel element in a single functional. The latter can be minimized with respect to the global displacement vector

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U and, after the application problem-dependent boundary conditions, the problem is reduced to: K FF UF ¼ FF

ð3Þ

where K FF is a positive definite non-singular matrix, implying a solution of the system exists, and the subscript ∙F indicates that Eq. (3) only includes the contribution of the unconstrained degrees of freedoms. After the global displacement field, U, is computed solving Eq. (3), the reconstructed strain field (eiFEM ) can be defined through Eq. (4): 8 9 > = < exx > eyy  eðui Þ þ zkðui Þ > ; : > cxy ð4Þ ( ) cxz  gðui Þ cyz Where z refers to the through-the-thickness coordinate. A model M iFEM is, thus, available for real-time numerical prediction of the strain field, eiFEM , as a function of a vector of strain measurements ein , without requiring any a priori knowledge of the loads or the material properties, since only strain-displacement relationships are involved in the calculations.

3 Application to Anomaly Identification The load-independent anomaly index defined in [13] is briefly described in this section for the purpose of this work. In particular, its reduced formulation for the case of monoaxial strain measures [12] is considered hereafter. The iFEM model M iFEM for strain field computation (eiFEM ) as a function of ein is used to define a synthetic index representative of the health state of the structure, allowing one to define a load adaptive baseline, without any requirement for algorithm training. The damage identification procedure assumes a defect alters the strain field of a structure as regards its pristine state. If the iFEM mesh geometry presents no hint of damage and the input strain ein are collected in a damaged scenario, a discrepancy between the measured and reconstructed strain fields appears, since M iFEM always reconstructs an eiFEM compatible with the geometrical discretization and the boundary conditions considered for the structure. If a pattern of test strain measures, et , collected from nt test sensors at xt positions is available, classification of the structural health state is achievable by comparing the test strain measures et with the iFEM reconstruction, eiFEM , in the same xt test positions. Despite the fact that the anomaly index defined in [13] considers an equivalent strain proportional to the second invariant of the deviatoric strain tensor, if one has to limit the number of measures, likely measuring a single strain component (e.g., through fiber optic sensors) in structures with preferential load transfer capabilities, the anomaly index assumes a simplified form. In the case of mono-axial strain component measurement, the

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anomaly index computed for each test positions xt  xt is defined as the percentage difference between measured exx;t and reconstructed exx;iFEM : iðxt Þ ¼

exx;t ðxt Þ  exx;iFEM ðxt Þ
100 exx;t ðxt Þ

ð5Þ

Collecting all the nt indices defined in Eq. (5) in a vector iðxt Þ 2 Rnt , health state classification and damage localization can be performed. For a healthy structure, iðxt Þ is expected to results in a null vector, iðxt Þ ¼ 0, meaning a perfect correspondence between exx;t and exx;iFEM holds for each test sensor position xt . Conversely, for a damaged structure, the index deviates from zero in the test positions close to the defect, due to the non-compatibility between the eiFEM , always compatible with the healthy structure, and the measured strain, et , function of the health state. Finally, it is worth remarking that if one exploits iðxt Þ for damage identification, a constant baseline pattern of anomaly indices iðxt Þ can be maintained regardless of the load or combination of loads acting on the structure. 3.1

Mahalanobis Distance Exploitation

Even though the anomaly index defined in Sect. 3 is directly applicable to the case of interest, one could consider possible small variabilities of the index due to model biases or noise disturbances. Thus, a squared Mahalanobis index is regarded in this work to consider possible statistical variability of the anomaly indexes of Eq. (5) from the null baseline condition and to further highlight discrepancies from the pristine condition. The index is defined as: d AI ¼ ðiðxt Þ  lðxt ÞÞT
C1
ðiðxt Þ  lðxt ÞÞ

ð6Þ

Where: iðxt Þ is a pattern of anomaly indexes in the positions xt , l is the vector of the related mean value, and C is the covariance matrix of the baseline of anomaly indexes i. Notice that the index d AI can be computed considering the entire component or sub-regions of variable size within the same. In particular, considering nsr subregions, one can collect the nsr indexes in a vector dAI 2 Rnsr for damage localization.

4 The Case Study Experimental verification of the iFEM-based method for anomaly identification is performed on a reinforced CFRP delaminated panel subjected to a compressive cyclic load. A description of the specimen, the test, the sensors grid, and the iFEM model are provided in this section. 4.1

The Specimen

The iFEM-based damage identification procedure is experimentally verified on a stiffened composite panel representative of the stabilizer of an Unmanned Aerial

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Vehicle (UAV). The panel, shown in Fig. 1, is a made of a co-cured Carbon Fiber Reinforced Polymer (CFRP) material and is composed of two main components: (i) a flat skin panel and (ii) two stringer to increase the overall stiffness of the component. As shown in Fig. 1a, the overall panel dimension is 270  400 mm, with a thickness of 3:348 mm for the skin and 2:604 mm for the two stringers. In particular, the skin is a symmetric laminate composed of 18 plies (stacking sequence: ½45=  45=0= 45=  45=  45=45=90=0s ), while each stringer is made of two layers of 7 plies (stacking sequence: ½90=  45=45=90=0=  45=45) glued together. Furthermore, delamination is initialized by a ballistic impact with a caliber 7:62  51 mm rifle bullet (Fig. 1b), simulating real damage to a military UAV during operational life.

Fig. 1. The CFRP Specimen: (a) The layout and main dimensions; (b) The specimen and the bullet damage details at the bottom (blue) and top (black) sides of the panel.

4.2

The Test Rig

The specimen described in Sect. 4.1 is mounted on a MTS 244.31 actuator (Fig. 2a) to perform a fatigue propagation test. The experiment data were collected at discrete intervals to reduce the post-processing time and to allow the NDT inspection of the panel for result validation. In particular, the test is performed in three steps. First, (i) a cyclic compressive load with a peak magnitude of 90 kN, a load ratio R ¼ 0:1 and a frequency of 4 Hz is applied for a certain number of cycles to propagate the damage. Then, (ii) the same load is applied for 30 cycles with a frequency of 0:25 Hz to acquire the data, and finally, (iii) the collected data are processed with an in-house developed iFEM software for anomaly identification. The test equipment includes temperature sensors, to compensate the thermal effect on the data, and lasers, for shape sensing validation. The latter are exploited to validate the displacement field reconstruction prior to the test execution when the influence of the damage on the measurements is minimal. Furthermore, the lasers’ acquisitions are exploited for the proper definition of the boundary condition in the model, as described in Sect. 4.3. The laser data are acquired in the fourteen target positions measuring the out of the plane displacement and in the three target locations measuring the vertical displacement defined in Fig. 2a.

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4.3

559

The iFEM Model and the Sensors Grids

The same panel reported in Fig. 1 is modeled by 2150 iQS4 shell elements, as shown in Fig. 2b. The numerical model is a slight simplification of the real structure in which the ribs of the two stringers are not considered as a result of a sensitivity analysis not reported here for brevity's sake. The element dimensions are chosen as a tradeoff between computational lightness and precision in the displacement reconstruction. Since at the beginning of the service life, the component can be reasonably considered undamaged, the model presents no hint of damage, thus possibly highlighting the discrepancies between reconstructed, eiFEM , and measured strains in the testing positions, et , as the damage progresses. The boundary conditions in the two regions reported in Fig. 2b are non-trivial due to the compliance of the two gripping systems with the two tabs and the type of test rig adopted, which applies both an axial and bending forces to the specimen. Specifically, the boundary conditions are determined by a weighted combination of three different simple models to model the compliance of the constraints and, thus, considering a non-ideal situation. The models considered are (i) clamp on the bottom and slider at the top; (ii) clamp only on the bottom of the panel and (iii) pin on the bottom and roller at the top. The weighting coefficients are determined by a least-squares optimization of the displacement reconstruction in the undamaged condition considering a subset of the lasers target positions and validating the results considering the complete set. As a result, the boundary conditions in Fig. 2b are obtained from 21:3% contribution of model 1, 71:5% of model 2, and 7:2% of model 3. Notice that the model allowing out of the plane movement of the upper tab (model 1) contributes the most to the final mixed boundary condition definition due to the combined vertical-horizontal movement of the actuator when it is loading. Finally, it is worth remarking that the algorithm doesn’t require any a priori knowledge of the load and the material properties; thus, no information about the two quantities is provided as input.

Fig. 2. Test rig and iFEM model: (a) Specimen with the target positions for the laser displacement acquisition (left) and test rig with laser and temperature sensors (right); (b) The iFEM mesh and boundary conditions.

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The last step of the anomaly identification procedure requires the definition of the input (ein ) and test (et ) strain measures positions. In this work, the applicability of the methodology is verified with a series of strain measures collected by the Luna fiber optic ODISI B system. The latter allows for an almost continuous measurement path along the entire fiber length. Specifically, a 5 m length fiber optic is exploited to provide the algorithm both the input strain measures in an almost symmetric configuration between the top and bottom sides of the panel, compatibly with the constrains from the test rig and the shot (Fig. 3a), and the test strain measures, reported in Fig. 3b. The latter comprises both the test strain position, et , exploited to compute the loadindependent anomaly index of Eq. (5) and the different zones of the panel exploited to compute the Mahalanobis distance, dAI . The latter are discretized in such a way to obtain the same number of iðxt Þ falling inside each area as well as to demonstrate the ability of the method to localize a damage in consideration of a possible in-field application.

Fig. 3. The sensor networks: (a) Input sensors grid in the top (left) and bottom (right) sides of the panel (b) Test sensors grid (blue lines) and the five regions for the Mahalanobis distance computation.

5 Results The damage identification results for the stiffened composite panel described in Sect. 4.1 and subjected to a fatigue delamination propagation under compressive loading are provided in this section. Subplots of Fig. 4 show the Mahalanobis distance results, computed for each sub-control area, as a function of the test cycles and damage progressions. For the sake of clarity, the same color scale is used, facilitating the understanding of the index development as a function of the damage growth. As defined in Sect. 4.1, a rifle bullet damage was adopted to facilitate the delamination initiation and, thus, the anomaly indexes of Fig. 4 are depurated from the initial damage influence to appreciate only the AI sensitivity to the delamination growth. In particular, despite literature widely recognizes the difficulty of detecting delamination defects, by looking at the results of Fig. 4, one can quickly notice how the peak index

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(a) N = 0 cycles

(b) N = 40000 cycles

(c) N = 83000 cycles

(d) N = 223000 cycles

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Fig. 4. Anomaly Index evolution as a function of the number of cycles and damage evolution.

varies as a function of the delamination growth, moving from a null value at the beginning of the propagation test to a maximum value (about 16.5) at the end of the experiment when the damage size is maximum, thus highlighting the correlation between the index and the defect. Furthermore, the plotted indexes are a function of the actual health state of the structure only, since the raw anomaly indexes used to compute the Mahalanobis distance are load-independent, i.e., they maintain a null value in an undamaged condition despite a load variation. The index distribution in Fig. 4 further validates the possibility of exploiting the method also for damage localization. Indeed, since the beginning (only after 40000 cycles), the peak value is correctly located in region number 1, where the delamination due to the bullet damage is positioned, and its value increases as far as the damage propagates through the test. Furthermore, one can appreciate the ability of the index to correctly highlight the presence of the delamination damage also far from the artificially initiated damage, further strengthening its potentiality for a future in-field application.

6 Conclusions A load-independent inverse Finite Element (iFEM) feature, previously defined for crack damage identification, is exploited in this work to identify a delamination defect produced by a bullet impact on a composite stiffened panel. The iFEM ability to

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reconstruct the displacement and, thus, the strain field, without any a priori knowledge of the load boundary condition or the material property, is exploited to define a defect sensitive anomaly index. A Mahalanobis distance-based anomaly index is defined relying on a previously described anomaly index defined as a percentage difference between a numerical and measured equivalent strain. Besides, since the feature used to derive the new index enables one to establish a load-adaptive baseline, the established index is loadindependent too in the undamaged condition. The experimental results validate the proposed method in a complex scenario where the specimen is composed of heterogeneous material, the load is multiaxial, and the boundary conditions are non-trivial. A proper damage sensitivity is observed both in terms of damage correlation and damage localization, being the method able to sense also damages developed from locations far from the artificially initialized defect. Finally, though not reported here, being a matter of present and future work by the authors, the method remains valid also with a less dense sensors grid and with a testing area optimized to target a predefined damage size. Future activity by the author will be devoted to the statistical characterization of the method and the testing with structures of increasing complexity in view of a realistic future application.

References 1. Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. Wiley, Hoboken (2012) 2. Leser, W.P., Leser, P.E., Warner, J.E., Bomarito, G.F., Hochhalter, J.D., Newman, A.J.: A computationally-efficient probabilistic approach to model-based damage diagnosis. Int. J. Progn. Heal. Manag. 8 (2017) 3. Salvetti, M., Sbarufatti, C., Cross, E., Corbetta, M., Worden, K., Giglio, M.: On the performance of a cointegration-based approach for novelty detection in realistic fatigue crack growth scenarios. Mech. Syst. Sig. Process. 123, 84–101 (2019) 4. Grassia, L., Iannone, M., Califano, A., D’Amore, A.: Strain based method for monitoring the health state of composite structures. Compos. Part B Eng. 176 (2019) 5. Tessler, A., Spangler, J.L.: A Variational Principle for Reconstruction of Elastic Deformations in Shear Deformable Plates and Shells. NASA (2003) 6. Gherlone, M., Cerracchio, P., Mattone, M., Di Sciuva, M., Tessler, A.: Beam shape sensing using inverse finite element method: theory and experimental validation. In: Structural Health Monitoring 2011: Condition-Based Maintenance and Intelligent Structures Proceedings of the 8th International Workshop on Structural Health Monitoring, pp. 578– 585 (2011) 7. Kefal, A., Oterkus, E., Tessler, A., Spangler, J.L.: A quadrilateral inverse-shell element with drilling degrees of freedom for shape sensing and structural health monitoring. Eng. Sci. Technol. Int. J. 19(3), 1299–1313 (2016) 8. Cerracchio, P., Gherlone, M., Tessler, A.: Real-time displacement monitoring of a composite stiffened panel subjected to mechanical and thermal loads. Meccanica 50(10), 2487–2496 (2015) 9. Kefal, A.: An efficient curved inverse-shell element for shape sensing and structural health monitoring of cylindrical marine structures. Ocean Eng. 188 (2019)

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10. de Mooij, C., Martinez, M., Benedictus, R.: iFEM Benchmark Problems for Solid Elements. Smart Mater. Struct. Struct. 28(6) (2019) 11. Colombo, L., Sbarufatti, C., Giglio, M.: Load adaptive baseline by inverse finite element method for structural damage identification. In: 9th European Workshop on Structural Health Monitoring, EWSHM 2018 (2018) 12. Colombo, L., Sbarufatti, C., Giglio, M.: Anomaly identification in mechanical structures exploiting the inverse finite element method (ECCM -ECFD 2018 conference). In: Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018, pp. 2234–2244 (2020) 13. Colombo, L., Sbarufatti, C., Giglio, M.: Definition of a load adaptive baseline by inverse finite element method for structural damage identification. Mech. Syst. Signal Process. 120, 584–607 (2019) 14. Roy, R., Gherlone, M., Surace, C.: Damage Localisation in Thin Plates Using the Inverse Finite Element Method, vol. 2 (2020) 15. Li, M., Kefal, A., Cerik, B.C., Oterkus, E.: Dent damage identification in stiffened cylindrical structures using inverse Finite Element Method. Ocean Eng. 198 (2020)

Comparison of Hilbert Transform and Complex Demodulation for Defect Identification in Cutting Discs using Vibration-Based Feature Extraction Sebastian Priebe1(B) , Lukas Brackmann1 , Ahmad Alabd-Allah1 , ottger1 , G¨ unther Meschke1 , and Inka Mueller1,2 Sahir Butt1 , Arne R¨ 1

2

Ruhr University Bochum, Bochum, Germany [email protected] Bochum University of Applied Sciences, Bochum, Germany

Abstract. This paper presents a novel concept for vibration-based feature extraction to identify damages in cutting discs of Tunnel Boring Machines (TBM). Defect frequencies resulting from repeated interaction of rock and disc defects are analysed. The data set is represented by the normal force acting on the edge of a cutting disc and the rock. Two different methods, the Hilbert transform and the complex demodulation, are used to generate the envelope of the time series, which was used to analyse whether the signal shows a feature representing an existing defect in the frequency domain. For the first proof of concept two numerical models were used - a multi-body system and a peridynamics 3D model simulating time series of normal forces. With both models, the linear motion of the disc on a rock sample with constant velocity was simulated. An experimental setup, mechanically similar to the simulations, was used in two experiments for further comparison. All implemented defects could be detected using vibration data of forces and one of the proposed data analysis techniques. Keywords: Cutting disc · Tunnel boring machine · Mechanised tunnelling · Defect frequencies · Structural health monitoring

1

Introduction

Despite the size and costs of the undertake to manage a tunnel boring machine (TBM) and the high costs related to unplanned maintenance activities due to failure of cutting discs, there are little efforts toward a condition monitoring (CM) system for cutting tools. Maintenance intervals for cutting discs of TBMs are currently based on experience rather than the assessment of the tooling using monitoring data. Without proper monitoring, tool failure can occur before the next maintenance interval, leading to increased downtimes, while non-mandatory maintenance decreases efficiency. The maintenance process of cutting tools is c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 564–572, 2021. https://doi.org/10.1007/978-3-030-64594-6_55

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currently planned based on a collection of laboratory test results and subjective evaluations [4]. Although the development of different CM systems started a decade ago [7,9,12], there is still no widely used CM system for cutting discs in place. The selection of performance parameters to be extracted is a major step when setting up CM systems. Information, extracted from vibrational data is suggested to be used for a variety of faults in different types of machines, including impeller faults [6]. Basic techniques for diagnostics using vibration data are described in [5] with a special focus on bearings in the application annexes. Using the variety of performance parameters and feature extraction methods for vibration-based CM systems, a manifold of damage types can be detected. The selection of distinct performance parameters is highly application-specific. It is therefore important to analyse which method of feature extraction is applicable in this specific context. In the context of this work, two different methods, namely Hilbert transform and complex demodulation, are investigated. Both methods generate an envelope around the vibrational signal and thus allow feature extraction, which can be used to identify damages in cutting discs of TBM. The Hilbert transform and the complex demodulation are applied to the datasets of two different numerical models. A multi-body system and a peridynamics 3D model are used to generate time-series data of the normal force acting on the disc. Both models simulate a linear motion of the cutting disc on a rock sample with constant velocity. Furthermore, both methods for envelope generation are applied to a set of experimental data similar to the numerical models. Finally, the generated envelopes are compared in the frequency domain based on predetermined damage frequencies and the different methods for envelope generation are evaluated.

2

Numerical Methods and Experiments

This work uses two numerical models and an experimental setup from which vibration data is gathered. Each set of data consists of a time-series of the normal force acting on a cutting disc with and without a defect. The defect of the cutting disc interacts with the rock sample once every rotation of the disc and therefore occurs with the same frequency as the disc revolves. This is called the damage frequency. First, the two models and the experimental setup are described before introducing the two methods used for envelope generation and subsequent feature extraction. 2.1

Numerical Models

A multi-body model, created in MSC ADAMS, and a peridynamics model are used to simulate the movement of a cutting disc over a rock surface. The multibody model is created to resemble the cutting discs used by [8], with a disc diameter of 19 inches and a thickness of 2 mm. It consists of three parts which are joined to each other. A horizontal cylinder is connected to the ground with a

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translational joint for horizontal motion, which allows the cutting disc to move as well. This cylinder is connected to a fork with a translational joint for vertical movement of the disc. The disc itself is connected with a rotational joint to the fork which lets the disc rotate freely [1]. The setup is shown in Fig. 1a.

Fig. 1. Numerical and experimental Models. a) Multi-body model used in simulations with MSC Adams with horizontal cylinder (A), fork (B), cutting disc (C) and rock sample (D). Radius x0 and distance between rock sample and disc midpoint xd used for impact force calculation. b) Experimental setup of the superstructure of the lathe with shaft (A), counter-body (B), disc (C), carriages (D, E) and force balancing rolls (F). The red arrows indicate the measurement points of the load cells.

Three boundary conditions are applied to the model: the linear motion of the disc at 2.37 m/s, an applied force FN of 250 kN acting in the normal direction to the rock surface and the contact between disc and sample surface, which is described by two forces, namely friction and impact force. The friction is defined as (1) FF = μi FN , with μi being either the static or kinetic coefficient depending on the relative motion of the disc to the ground (Table 1). The impact force, FI = k(x0 − xd )e − cx˙d ,

if xd < x0 else 0,

(2)

acts as a counterforce to the normal force with x0 as the radius of the disc and xd as the distance from rock surface to the midpoint of the disc (Fig. 1a). k is the stiffness, c the damping coefficient and e a dimensionless weighting factor (Table 2). Both, sample surface and disc, are defined as Kelvin-Voigt materials. To simulate a damaged disc the model was modified by cutting out a semicircle of 2 mm diameter at the cutting edge of the disc. Using the diameter of the disc and the linear motion this defect is expected to interact with the rock surface at a rate of 1.56 Hz. In the following, this frequency is called defect frequency. With this model, it is not possible to simulate the cutting process. The

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Table 1. Properties used to define the fric- Table 2. Properties used to define the tion between disc and ground. Values are impact force between disc and ground. taken from [1]. Values are taken from [1]. Property

Value

Property

Value

Static friction coefficient µs

0.62

Stiffness k

65 kN/mm

Kinetic friction coefficient µk

0.51

Damping coefficient c 0 kg/s

Shift velocity for static friction

0.1 m/s

Weighting factor e

2.2

Shift velocity for kinetic friction 1 m/s

data obtained from this simulation is the vibration of the normal force due to the excitation of the disc rolling over the sample surface [2]. As an alternative, data from a much more sophisticated physical representation of the cutting process is used. In the peridynamics model, the disc has a diameter of 5.6 cm and a linear velocity of 1 m/s. Thus, the expected defect frequency is 5.68 Hz. The data gathered from this model will provide a base for comparison to the first model and the performed experiments. Because it is a peridynamic continuum formulation, it allows the direct interaction of a material point with a set of material points within a volume defined by a cut-off radius, known as the peridynamic horizon. This model provides a suitable environment to model physical phenomena involving discontinuities, such as the simulation of the rock cutting process. Details about the formulation and implementation can be found in [3]. 2.2

Experimental Setup

The experimental setup is designed to recreate the mounting of the cutting disc as close as possible to real TBM. A modified lathe acts as the foundation of the experimental setup. It turns a metal shaft on which different counter-bodies can be mounted. In this case, the counter-body is a metal disc of 17 cm in diameter. The cutting disc, which has a diameter of 11.8 cm and is made of cold work tool steel (1.2379), is attached to a carriage which can be pressed against the counterbody with a piston. The exerted force on the shaft is transferred to two small rolls on the other side of the shaft so that the shaft does not bulge. The forces acting on the cutting disc are recorded by two load cells KM40 by ME, which are located at the positions indicated by the red arrows. The data is transferred to a universal measuring amplifier, MX840B by HBM (Fig. 1b). Since one of the experiments should represent a damaged state of the disc, but the disc is not supposed to get permanently damaged during this stage of work, a different kind of damage compared to the simulations is used. A small drop of epoxy resin (≈3 mm height) is applied to the edge of the disc. This yields similar peaks in the signal of the force sensors once every rotation of the disc compared to the simulations. However, this does represent a defect that is very

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unlikely to occur naturally. The disc in the experiments rotates at a frequency of 0.54 Hz, which is equal to the expected defect frequency. 2.3

Analysis

Two different methods for envelope generation are used, a complex demodulation algorithm (CD) and the Hilbert transform (HT). The CD, xdemod = x[n] exp(−i2πf0 t[n]),

with f0 =

5fs , 16

(3)

is applied to the discrete signal x[n] and the time signal t[n], with fs being the sampling frequency. Then, an FIR filter of order 50 with a cut off frequency of fs /16 is convoluted with the demodulated signal. This yields the envelope ECD of the original data. Finally, the real part of the data is transformed into the frequency domain [10]. In another attempt to analyse the data, the analytic signal, using the HT, is computed (e.g. [11]), xa = F −1 (F (x)2U ) = x + iH(x) .

(4)

Here, F is the Fourier transform, U the Heaviside step function and H the Hilbert transform. This data represents the envelope EHT , which is then analysed in the frequency domain as well.

3

Results

The dataset presented in this work includes two simulations for each model and two experiments, one set of each datatype without defect (DF - defect false) of the disc and one with the earlier mentioned defects (DT - defect true) (Table 3). Both analysis methods, CD and HT, are applied to the undamaged and damaged cases of each dataset. Graphs of each analysis method are plotted and compared to each other for both cases. The goal is to detect a peak in the frequency spectrum of the damaged case which is not present in the undamaged case. Such a result would suggest an existing feature that can later be used to identify a damaged disc in an automated manner. Table 3. Overview of datasets and respective properties. ID

Properties

multi DF

Simulation (multi-body), no defect

multi DT

Simulation (multi-body), with defect

peri DF

Simulation (peridynamics), no defect

peri DT

Simulation (peridynamics), with defect

exp DF

Experiment, no defect

exp DT

Experiment, with defect

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Multi-body Model Data

This set of data shows significant differences between damaged and undamaged discs with both analysis methods. Even more so, both analysis methods provide coinciding peaks, despite their different formulations and implementations. The peaks in both cases occur close to the expected frequency of 1.56 Hz and its multiples, which are overtones of the damage frequency. The analysed data using the HT shows an exponential drop from 0 Hz up 12 Hz, which makes the identification of overtones 8 Hz impossible without further analysis. On the other hand, the CD method does not show the decay of amplitude with increasing frequency. Another difference between both methods is the amplitude ratio of damaged and undamaged cases, which is significantly larger for the analysed data using the HT (Figs. 2 and 3).

Fig. 2. Frequency spectrum of the envelope EHT of the multi-body simulation using the HT.

3.2

Fig. 3. Frequency spectrum of the envelope ECD of the multi-body simulation using the CD.

Peridynamics Model Data

The frequency resolution of the peridynamics simulation is low, because it is resource-intensive, which limits the time frame of the simulation (0.6 s in this case). For the damaged disc, the analysed data using the HT shows peaks near the expected 5.56 Hz and multiples (overtones) of that. It also shows that damaged and undamaged instances differ. In case of the analysed data using the CD with a damaged disc, it does not seem to coincide with the damage frequency or differ significantly to the undamaged case for that matter. With this set of data, the amplitude ratio of the HT is again larger than that of the analysed data using the CD (Figs. 4 and 5).

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Fig. 4. The frequency spectrum of the envelope EHT of the peridynamics simulation using the HT.

3.3

Fig. 5. The frequency spectrum of the envelope ECD of the peridynamics simulation using the CD.

Experimental Data

The experimental dataset shows the most distinct peaks of all datasets if the HT is used as the analysis method. Some of these peaks can be related to different parts/phenomena of the setup and are present in either case, damaged and undamaged disc. At 0.4 Hz the shaft of the lathe itself produces a sharp spike, at 0.54 Hz is the peak of the cutting disc and the peak at 0.78 Hz is generated by the two force balancing rolls (Fig. 1b). When comparing undamaged to damaged data, the peak for the damaged case at 0.54 Hz is about twice the size. The amplitude ratio, in this instance, is close to 1, which is much smaller compared to the other sets of data. In the case of the analysed data using the CD, no distinct features could be detected and the differences between damaged and reference case are also unspecific (Figs. 6 and 7).

Fig. 6. Frequency spectrum of the envelope EHT of the experiments using the HT.

4

Fig. 7. Frequency spectrum of the envelope ECD of the experiment using the CD.

Discussion

Each of the presented sets of data - numerical and experimental - is afflicted with its shortcomings. While the experimental setup provides non-artificial data

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from a cutting disc and sensors, it is not used on a rock sample but instead on a rotating steel counter-body, which does not show any of the mechanisms involved in breaking and cutting rock. This certainly leads to different results than one would achieve when using a rock sample. Thus, the rather small feature found when using the HT analysis might need a method for analysis that is even better suited to make it unambiguous. However, this first assessment with the HT is still promising compared to the CD in this case, which does not show any features, not even the rotation of the lathe’s shaft, which visibly dominated the signal in the time domain. The peridynamics simulations most dominant shortcoming is the computational effort associated with it. Realising a viable resolution in the frequency domain with a reasonable fine spatial resolution might not be achievable at this point. This could lead to masking of important features, with peaks which are close to the peaks of irrelevant ones. Still, the HT was able to extract the damage frequency reasonably well, while the CD, again, does not show any viable features. Lastly, the simplicity of the multi-body simulation does not represent the physical mechanisms adequately, but can still be useful for swift data generation to analyse very specific cases or interactions. Contrary to the other two sets of data, the CD method provides better results than the HT with this set of data. A possible explanation for this might be its sensitivity to noise which is not present in the multi-body simulation. The peridynamics simulation provides a pseudo-noise caused by the simulated cutting process. These findings suggest that the HT might be better suited for data from real-world applications. However, the amount of data gathered is too little to confirm this and future studies in this aspect are therefore necessary.

5

Conclusion

In this study, forces experienced by a TBM cutting disc, obtained from three different methods, are analysed. Data sets are analysed using two different methods for envelope generation, namely complex demodulation (CD) and Hilbert transformation (HT). The damage frequency was known prior to all simulations and experiments, which allowed for a comparison between CD and HT. The CD provides clear features for the multi-body simulation but HT shows better results for the computationally expensive simulation and the experiment. This suggests that the HT is better suited for real-world applications. It must be noted that the data presented so far covered only a single set of parameters which are quite far from parameters observed in real-world applications. Additionally, the amount of data analysed is also not sufficient and further studies are needed. Future studies will have to prove that this concept is viable at real conditions, including variable rotation velocities for cutting discs and cutter head, rock conditions and applied pressure. A future dataset should investigate a range of parameters, for example rotation speed, normal force and rock conditions, as well as different kinds of defects, sizes of defects and methods for analysis.

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Acknowledgements. The authors gratefully acknowledge the support of the German Science Foundation (DFG) for providing financial support in the framework of projects C7, C5 and C4 of the Collaborative Research Center SFB 837.

References 1. Alabd-Allah, A.: Numerische Untersuchungen zum Vibrationsverhalten von Diskenabbauwerkzeugen im maschinellen Tunnelbau. Master’s thesis, RuhrUniversity Bochum, Bochum, Germany (2019) 2. Alabd-Allah, A., Butt, S., Galler, R., Meschke, G., Mueller, I.: Vibration-based feature extraction to identify damage in cutting discs - a concept description. In: Proceedings of ICGI-ETS Conference. Luxor (December 2019) 3. Butt, S., Meschke, G.: A 3D peridynamic model of rock cutting with TBM disc cutters. In: 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry, pp. 752–755 (November 2017) 4. Conrads, A., Scheffer, M., K¨ onig, M., Thewes, M.: Robustness evaluation of cutting tool maintenance planning for soft ground tunneling projects. Undergr. Space 3(1), 72–85 (2018). https://doi.org/10.1016/j.undsp.2018.01.005 5. Condition Monitoring and Diagnostics of Machines - Vibration condition monitoring - part 1: General procedures (July 2002). https://doi.org/10.31030/2838751. DIN ISO 17359 6. Condition Monitoring and Diagnostics of Machines - General Guidelines (June 2003) 7. Entacher, M., Galler, R.: Development of a disc cutter force and face monitoring system for mechanized tunnelling/Ortsbrustmonitoring und Leistungsprognose bei TBM-Vortrieben. Geomech. Tunn. 6(6), 725–731 (2013). https://doi.org/10.1002/ geot.201310013 8. Labra, C., Rojek, J., Onate, E.: Discrete/finite element modelling of rock cutting with a TBM disc cutter. Rock Mech. Rock Eng. 50(3), 621–638 (2017). https:// doi.org/10.1007/s00603-016-1133-7 9. Roby, J., Sandell, T., Kocab, J., Lindbergh, L.: The current state of disc cutter design and development directions. In: Proceedings of 2008 North American Tunnelling Conference (NAT2008), Society for Mining, Metallurgy & Exploration, pp. 36–45 (2008) 10. The MathWorks, Inc.: Envelope spectrum for machinery diagnosis (1994–2020). https://de.mathworks.com/help/signal/ref/envspectrum.html#mw f67ce4d6153f-47a1-bd39-c18fba4cc8ce. Accessed 07 July 2020 11. The SciPy Community: Scipy v1.4.1 reference guide (2019). https://docs.scipy.org/ doc/scipy/reference/generated/scipy.signal.hilbert.html. Accessed 23 Mar 2020 12. Willis, D., Shanahan, A., Box, Z.: Remote disc cutter monitoring in tunnelling. Tunnel 08, 68–75 (2011)

In-Service Inspections of Bondlines in Composite Structures by Distributed Optical Fiber Sensors Carlos Miguel Giraldo(&), Juan Zuñiga Sagredo, and Luis Miguel Garcia Vazquez Airbus Operations, S.L, Paseo John Lennon S/N, 28906 Getafe, Madrid, Spain [email protected]

Abstract. Stringers are commonly used as longitudinal stiffeners and load carrying elements in most composite aeronautical parts such as the wing skin panels, fuselage or empennage. Each stringer has necessarily two ends -stringer run outs- and are actually critical areas requiring in many cases not just a detailed structural analysis during the design phase but also a follow-up during in-service operation. The paper describes the demonstration and reliability of the online inspection of stringer disbond detection by permanent distributed optical fiber sensors integrated into the adhesive line. A set of test specimens with sensing fibers have been manufactured and tested under a controlled process that have enabled the creation and progressive growth of disbonds. The interrogation of the optical fiber sensors during the tests as well as the correlation with conventional ultrasonic inspection have demonstrated the potential of the technology for in-service inspections. The integration of this optical fiber technology in future aircrafts would enable operators to identify the initiation of damage debonding in these areas, without the expense or time required to take the structure out of service. Ideally, the technology will also determine the damage type, location and size as well as the structure’s health prognosis. Keywords: Distributed optical fiber sensors Inspection stringer run


Inspection of adhesives lines


1 Introduction Today the in-service inspection of composite aeronautical structures is mainly carried out by visual inspections or classical non-destructive inspection techniques. These inservice inspection are performed according to scheduled and unscheduled inspection programs always requiring the landing of the a/c and the corresponding time consuming inspections by qualified NDI inspectors. Among the different composites configurations, the inspection of adhesive lines especially the stringer run-outs (SRO) areas are considered critical from the design point of view since stringer disbond could reduce enormously the load capability of the structure. These inspection areas are repeatedly called in Aircraft Maintenance Manual instructions after the occurrence of certain unscheduled events such as hard landing, bird strike, flying in excessive turbulence or after tire burst. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 573–583, 2021. https://doi.org/10.1007/978-3-030-64594-6_56

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The purpose of this article is to present an alternative inspection approach based on the use of structural health monitoring (SHM) technologies, in particular distributed optical fiber sensors ((OFS) integrated into the stringer/skin bondlines. The main potentials of OFS in this application would be the use of an indirect but simple method to detect and monitor disbonds in these structural composite configurations.

2 Distributed Optical Fiber Sensing Technology The Rayleigh backscatter in optical fibers is caused by the elastic interaction of the photons with the random fluctuations in the core index of refraction profile along the fiber length. This scatter that varies from segment to segment in the fiber is highly repeatable and can be considered as a unique static property for a given fiber. Therefore changes in the local period of the Rayleigh scatter caused by an external stimulus produce changes in the locally reflected spectrum [1, 2]. The measurement system used in this work was a LUNA ODiSI-B system, that is based on the swept-wavelength coherent interferometry (SWI) (Fig. 1) to interrogate the fiber and measure the Rayleigh backscatter (amplitude and phase) as a function of position in the optical fiber. On one hand, the SWI collects the backscatter optical power in the spectral frequency domain. On the other hand, the detectors collect the light backscattered from the fiber under test as the laser spectral frequency is tuned through a range of frequencies. Backscatter power data is processed with a Fourier Transform to generate the backscatter optical power as a function of time delay that in turn can be converted into distance function. When physical parameter, such as temperature or strain, changes in the sensing fiber, a measurable change on the backscattered light along the optical fiber sensor is created. By cross correlation comparing the backscattered light of the sensing fiber under test in the two states, it is possible to determine the physical state of the fiber at the time of measurement. The two backscattered profiles are correlated -segment to segment- to determine the spectral shift of the backscatter along the length of the sensing fiber. The shift is analogous to the spectral shift produced in a Bragg grating: Dk=k ¼ KT
DT þ Ke
De

ð1Þ

where the default values for most germane-silicate core fibers are KT = 6.45  10–6 °C−1 and Ke = 0.780.

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Fig. 1. Scheme of swept-wavelength coherent interferometry

3 Test Specimens This section described the test samples manufactured to evaluate the disbond detection capabilities with distributed optical fibers. They are designed to systematically reproduce progressive disbond between two composite laminates being representatives of typical stringer run outs on a composite wing [3, 4], where the stringer ends and transfers its load to the skin underneath. The load transfer at the stringer termination creates a complex stress condition in which the composite laminate is subjected to significant inter-laminar shear and tension/compression stresses, which generate an equivalent peeling moment at the stringer run out. Figure 2 simplify the main stresses acting on a stringer run out subjected to tension/compression loads (Fig. 3).

Fig. 2. Shear stress and peeling moment on a stringer run out under tension and compression loads

In order to represent these effects, three types of test specimens were designed and manufactured: pure compression/tension specimens and Four Point Bending (4PB) specimens.

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Fig. 3. Scheme of tension /compression and four point bending tests specimens

3.1

Tension and Compression Samples

A total of eight samples were manufactured, half tension and half compression. These samples consisted of two co-bonded thicknesses integrating a distributed optical fibre in the bond line. Furthermore, artificial defects, to initiate the propagation of the disbond during load steps, were laid on the adhesive film for half of the samples of each type. The Fig. 4 illustrates the test specimens and path of the fiber inside the bond line.

Fig. 4. Details of tension /compression specimens

3.2

Four Point Bending Samples

Four samples of this kind were manufactured. They consist of two co-bonded laminate containing two grooves of 1.6 mm depth to generate the effect of the peak stress what

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happens in run out configuration. The optical fibers were embedded inside the adhesive line according to the path defined in the Fig. 5. Similarly to the previous samples, artificial defects were laid to promote the initiation and propagation of the disbond during load steps.

Fig. 5. Scheme of four point bending test specimens with optical fiber sensing route

The next Table 1 summarizes all the specimens for this test program. Table 1. Summary of the test samples. Designation T2 T3 T5 T6 C2 C3 C4 C6 4PBT1 4PBT2 4PBT3 4PBT4

Group of samples Tensile Tensile Tensile Tensile Compression Compression Compression Compression Four point bending Four point bending Four point bending Four point bending

Artificial defect/dimension (mm) Load range (kN) No 0 to 160 No 0 to 140 Yes/10  50 mm 0 to 130 Yes/10  50 mm 0 to 120 No 0 to 110 Yes/10  50 mm 0 to 105 No 0 to 105 Yes/10  50 mm 0 to 105 Yes/ 22  100 mm 0 to 10 Yes/ 22  100 mm 0 to 10 Yes/ 22  100 mm 0 to 9,5 Yes/ 22  100 mm 0 to 10

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4 Results 4.1

Tensile-Compression Results

They consist of the graphs of the strain profile, measured by the distributed OFS integrated into the bond-line, versus the fiber distance for each static load step. The Fig. 6 and 7 shows the sensing optical fiber segments inside the adhesive before any damage when tension/compression load are applied respectively. As the fiber is located in longitudinal direction respect to the sample and changing the direction inside, the strain profile shows a symmetrical curve where three areas can be considered. The segment 1 corresponds to the fiber length at the left of the sample, the segment 2 corresponding to the right side and the middle section that coincides with the fiber length where this changes the direction. The strain measured in this last segment is generally very low due to unaligned direction of the fiber to the main stress and also since this area can be partially clamped for the test machine gripes. The two maximum peaks -left and right- correspond to the last portion of the fiber that is embedded in the adhesive joint.

Fig. 6. Strain profile by OFS for tension specimens

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Fig. 7. Strain profile by OFS for compression specimens

4.2

Four Point Bending Results

As the previous samples, four point bending results comprise of the graphs of the strain profile, measured by the distributed sensing optical fiber, versus the fiber distance. The profile can be divided into three segments, as depicted in Fig. 8. Each segment presents two strain peaks corresponding to the locations of the fiber crossing the transversal groove. These peaks are representative of the stress concentration in stringer run out configuration.

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Fig. 8. Strain profile by OFS for four points bending test specimens

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Additionally, tension/compression and four points bending samples were inspected by manual ultrasonic pulse echo after each load step. The Fig. 9 shows as example the UT results marked on 4PBT1.

Fig. 9. Scheme of the results of the manual pulse echo inspection after each load step

5 Discussion Beginning with the tensile-compression tests results, since the fiber crosses twice through the run out, the strain profile of the embedded fiber shows clearly two peaks of maximum strain, positive or negative, depending on the sign of the load applied to the joint, and corresponding to the last portions of the fibers just before egress from the skin-stringer interface. When the load increases the strain profile curve move up. This ascent of the curve keep in all times a relation to the load increase providing no disbond has taken place in the joint. Nevertheless when the load step is enough high to produce the required energy to create fracture surface between skin and stringer, the disbond starts and consequently the strain profile changes significantly. The segments of the fiber monitoring the areas of the joint where they remains intact increase the level of the strain as consequence of the increment of load. In the fiber segments corresponding to the new fracture surface the load decreases drastically and so the strain level measured by the distributed fiber. Consequently, when stringer disbond occurs in these samples, the position of the peak of the strain shifts inwards the joint and the movement of the peak coincide with the growth length of the disbond area. This principle detection can also be appreciated in the four point bending specimens results where the strain profile

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of the fiber shows clearly eight peaks of strain corresponding to the locations of the fiber crossing the grooves. To sum up, the fiber strain peaks are characterized by the following parameters: a) The amplitude that is a proportional to the load level in the specimen. b) The shape that depends on the presence or not of artificial defects in the groove. Where no artificial defects, the strain peak is clean and unique, s in those locations where Teflon is embedded the peaks are split and broader. c) The position that is actually the parameter in the signal that is necessary to follow to monitor the detection and progression of the disbond in the samples through the bond-lines. When we have a disbond, the peak distance shift in the direction of the damage growth. This OFS detection principle concept was compared with manual ultrasonic pulse echo inspection after each load step for all samples. The results demonstrate that conventional inspection and OFS provide similar results as for detection and monitoring capability of the progression of the damage. For simplicity reasons only comparison results of the specimen 4PBT4 are compiled. The Fig. 10 compares the growth measured by the two technologies in each load step for the specimen 4PBT1. The growths are compared in six locations for each sample, being the growth location 1 the first groove that the fiber patch crosses, the location 2 the second groove that the fiber patch crosses and so on. The x-axis corresponds with the six locations and for each location the measurement by distributed OFS and NDI. The y-axis represents millimeters growth distance through each technique.

Fig. 10. Correlation between distributed fiber and NDI for 4PBT1 after the test

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6 Conclusions The work done demonstrates the potential of embedded distributed optical fiber sensors for in-service inspection of composites stringer/skin bondlines. This indirect but simple detection principle would aim to support the classical inspection program by oncondition maintenance, especially for unscheduled events, with the consequence benefits in terms of maintenance cost and aircraft down-time. The integration of the technology in the structures would enable operators to detect and identify the initiation of damage without the expenses or time associated to bring he structure out of service.

References 1. Stephen, T.K., Dawn, K.G., Mark, E.F., Alex, K.S., Roger, G.D., Matthew, S.W., Brian, J.S.: High-resolution extended distance distributed fiber-optic sensing using Rayleigh backscatter. In: Proceedings Volume 6530, Sensor Systems and Networks: Phenomena, Technology, and Applications for NDE and Health Monitoring 2007, 65301R (2007) 2. Stephen, K., Dawn, K.G., Brian, J.S., Matthew, S.W., Mark, E.F.: Making Distributed Strain and Temperature Measurements with the Optical Backscatter Reflectometer. Luna Technologies, Blacksburg, VA 24060 3. Miguel Giraldo, C., Zúñiga Sagredo, J., Sánchez Gómez, J., Corredera, P.: Demonstration and methodology of structural monitoring of stringer runs out composite areas by embedded optical fiber sensors and connectors integrated during production in a composite plant. Sensors 17(7), 1683 (2017) 4. Blazquez, J.R., Parıs, F., Estefani, A., Arevalo, E., Cruz, F.: Analysis of a stringer run-out concept including damage initiation and evolution at the interfaces. In: ECCM15–15th European conference on composite materials, Venice, Italy, 24–28 June 2012

Assessment of a Dual Kalman Filter-Based Approach for Input/Output Estimation in an Aluminum Plate Afshin Sattarifar(B)

and Tamara Nestorovi´c

Mechanics of Adaptive Systems, Ruhr University Bochum, Bochum, Germany [email protected]

Abstract. Vulnerability of structures to damage during their service time brings up the necessity of design and implementation of an intelligent procedure to assure the health of the structure. In the sight of this requisite, current work deals with extending the capability of a dual Kalman filter (DKF) state estimation scheme to assist vibration-based health monitoring methods. This is met by estimating the response of the structure for locations at which a sensor cannot be placed. The capability of the DKF method in the estimation of states of a linear system with an unknown input has been presented in various recent works. In this paper, a DKF approach incorporated with a reduced order structural model (in this case an aluminum plate) is utilized to obtain an estimation of applied force and the response of the structure in terms of acceleration, velocity, and displacement. These estimations are based on measured accelerations at a limited number of points on the aluminum plate as well as the state-space model of the dynamic system. Numerical simulations and experimental works are performed to obtain the mentioned datasets. To assess the robustness of the method concerning various conditions, the effect of the frequency, as well as type of the function of the input force on the validity of the method, is presented. Moreover, it is shown to what extent the number of selected modes in model reduction procedure can influence the accuracy of the DKF technique.

Keywords: Kalman filter measurement

1

· State estimation · Model reduction · Noisy

Introduction

Reconstruction and estimation of the input of a structure, based on its outputs is an ill-posed problem [13]. To solve this class of the problems, deterministic, regularized technique, and probabilistic approaches can be implemented. Kalman filters (KF) as a probabilistic algorithm can estimate unknown states of a dynamic system from measurements with associated uncertainty. The estimation of states of a dynamic system can be seen as measurements from fictitious sensors. There are several motivations, that designate the estimation of c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 584–593, 2021. https://doi.org/10.1007/978-3-030-64594-6_57

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the input/output of a dynamic system as a considerable issue for further investigations. First of all, having fewer sensors in a system reduce the economical costs and complexity of wiring. Thereafter, inaccessibility of some locations on the structure hinders the sensor installation at those point. Since time histories are the building block of the majority of the vibration based and guided wave based Structural Health Monitoring (SHM) techniques [11], estimation of dynamic response at those degrees of freedoms (DOF) of the structure where direct measurement is not possible, extend these algorithms potential. In recent years various studies have been performed with the emphasis on estimation of response or a joint input-state estimation. Among them, numerous scientists have developed stochastic based state estimation algorithms with the help of KF [4]. One of the earliest contributions in this filed was conducted to estimate the exerted heat fluxes in a heat conduction problem. [14] and [1] proposed an approach with incorporation of KF and a real-time least square algorithm to estimate the unknown state of the problem. Ma et al. [7] proposed an algorithm with the implementation of KF with a recursive estimator for determination of impulsive loads. The researches conducted in [6] and [5] investigate the estimation of three types of excitation forces by means of the method proposed in [7]. Papadimitriou et al. [9] proposed a methodology to estimate damage accumulation by utilizing the potential of KF-based state estimation. Zhang et al. [15] studied the input estimation by utilizing Monte Carlo Markov chain method to reconstruct the force jointly with determination of the model uncertainty. Gilligns et al. [3] extended the capability of state estimation by taking into account an unknown input force. To this end, they proposed a joint input-state estimation with a structure similar to KF. Eftekhar Azam et al. [2] used solely the noisy acceleration measurement to estimate the state as well as the input of a linear state-space model. Several applications of the joint input-state estimation scheme are proposed in [8] and [10]. To the best of authors knowledge, the effect of the number of retained modes in the reduced order model as well as type, and the frequency content of the input function, on the performance of DKF has not been investigated so far. Therefore, in the current paper a DKF scheme has been implemented to investigate the mentioned issues in estimation of response of a vibrating plate with unknown input. The acceleration data are obtained from numerical modeling and experiment, which is utilized as the input of the DKF algorithm. The realization of the DKF algorithm by employing a limited number of measurements is carried out by using a reduced order model. In the following sections, by using a numerical model and performing experiments the mentioned objectives will be investigated.

2

Mathematical Formulation

The equation of motion of a linear dynamic system can be formulated as: M q¨ + D q˙ + Kq = Bo u

(1)

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where M , D, and K represent mass, damping, and stiffness matrices of the vibrating system. Furthermore, q ∈ R is the displacement vector and the dot over it, shows the derivation with respect to time. u(t) is the input force of the system, and B0 relates the input force to the degree of freedom on which it is applied. In order to implement the KF algorithm, the state-space model of the system is required. Equation (2) presents a discrete state-space formulation of a dynamic system. xk+1 = Ac xk + Bc uk (2) yk = Cc xk + Dc uk where, A = eAc T , B = [A − I]A−1 c Bc , C = Cc and, D = Dc . To facilitate the flexibility in choosing the number of modes that are going to be considered for the model, it is required to express the state-space model in the modal form. Accordingly, Ac , Bc , Cc , and Dc in Eq. (2) can be expressed as:     0 I 0 , Bc = Ac = , −1 T −Ω 2 −2ZΩ Mm φ Bo (3) −1 −1 Cov − Coa M Coq ], Cc = [Coq − Coa M K T Dc = Coa φφ Coq here, Ω = diag{ωi }, Z = diag{ζi }, where ωi and ζi represent the ith natural frequency and damping ratio of the ith mode, respectively. Furthermore, φ ∈ Rn×p , is the modal matrix where n is the number of DOFs and p is the number of retained modes of the system. C0q , C0v , and C0a are responsible for selection of displacement, velocity, and acceleration as the outputs of the system on desired DOFs, respectively. After obtaining the discrete and reduced form of the statespace equation, the KF algorithm for state estimation can be implemented. For this purpose, Eq. (2) should be rewritten under a stochastic environment by considering the presence of the process noise (ωk ), and the measurement noise (νk ). The latter two vectors can be described as a random Gaussian process with the variances of Q and R, respectively. The conventional implementation of KF consists of two steps, the prediction, and the update step. Table 1 describes the equations in these two steps. Additionally, the extensive derivation of these parameters can be found in [12]. Yet, the aforementioned formulations are unable to estimate the response of the system with an unknown input. Therefore, DKF scheme is envisaged by regarding another fictitious equation for the input update at each time step (Eq. (4)). (4) uk+1 = uk + gk where gk is a white Gaussian process with the associated covariance matrix Qu . Accordingly, the state-space equation can be modified. Once more, for brevity of the text, the realization of DKF has not been expressed here, and can be found in [2]. By obtaining the DKF formulation the estimation of the response given an unknown input is possible. These estimations are based on the reduced order model of the system. As an input for DKF, acceleration measurement together with the reduced order state-space model of the structure are required. In Sects. 3

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Table 1. Conventional Kalman filter state estimation algorithm Prediction step State estimation

Update step x − = A xk−1 + Buk Innovation covariance k

Error covariance prediction Pk− = APk−1 AT + Q Kalman gain Output estimation

y k = C x − + Duk k

Sk = CPk− C T + R −1 K = Pk− C T Sk =

− P CT k − CP C T +R k

Updated state estimation x + =x − + K(yk − y k ) k k Updated error covariance Pk+ = (I − KC)Pk−

and 4 the outlines of implementation of numerical and experimental approaches to provide the described input of DKF are discussed.

3

Numerical Modeling and Results

In this section the details of the modeled structure and the steps to carry out the simulation are explained. The investigated structure in the current study is a rectangular aluminum plate with the properties listed in Table 2. The numerical simulation has been performed to extract the modal response of the structure, i.e. the eigenvectors and eigenfrequencies of the structure. Further, it can be utilized as a test platform to excite the structure and measure the responses. For the numerical model Finite Element (FE) method, performed in Abaqus has been used. To build the FE model, shell elements with four nodes (S4R) are used. To extract the eigenfrequencies in Abaqus, the linear perturbation step incorporated with Lanczos extraction algorithm has been utilized. The results of the first fourteen eigenfrequencies obtained from the numerical modeling is shown in Table 3. In the next step, by having the modal data of the structure, the Modal Dynamics (MD) step in Abaqus is performed to simulate the excitation of the structure, and obtain the response of the system from an excitation source. MD enables modeling of a transient linear dynamic system based on the modal superposition. The results of the MD analysis are then employed to reconstruct the response of the system at a point where no sensor is employed, i.e. a hidden state of the system. Table 2. Details of the aluminum plate Young’s Modulus

E = 70 × 109 MPa

Density

ρ = 2670 kg/m3

Length

600 mm

Width

600 mm

Thickness

2 mm

Number of Elements 10 × 10 Poisson’s ratio

0.33

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f2

f3

f4

f5

f6

f7

12.75

26.25

31.98

62.88

65.15

79.89

108.05

f8

f9

f10

f11

f12

f13

f14

121.88 161.61 186.59 187.48 190.38 214.63 245.64

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Numerical Results

For the excitation of the plate, a sine wave (u(t) = sin(2π × 25t)), an impulse, and a tone burst function are utilized. The input force is applied on the middle of the plate and the acceleration response is also measured at that point. Response estimation is performed for a node lying on the middle of a quarter of the plate. Accordingly, the result of the response reconstruction based on these three types of input is depicted in Fig. 3. As can be seen, the DKF algorithm has the best performance when the tone burst signal is employed as the source signal. Nonetheless, the accuracy of the estimation based on the sine wave and impulse is acceptable as well. Figure 1 and Fig. 2 represent the estimated force by DKF. A reasonable accuracy cannot be seen in the estimation of the input force, especially for the case with sine wave (Fig. 1) excitation. The problem of input force estimation needs a separate study, and in the current work will not be investigated. Here, the main focus remains on the estimation of response, with an assumption of unknown input. Next, the relation between the performance of the DKF method and the number of selected modes is investigated. Figure 4 depicts how the selection of number of retained modes can influence the accuracy of the reconstruction. Here the logic of selection of modes is based on their contribution in the frequency response function (FRF) of the structure (Fig. 8), and the corresponding eigenfrequency of each mode can be obtained from Table 3. Choosing the appropriate modes in the case of keeping two modes for the reduced system should be performed carefully. However, it is shown in the Fig. 5 that the sensitivity toward mode selection by retaining four modes drops marginally. On the other hand, an adverse effect on the accuracy of the reconstruction especially at the beginning of the signal is observable by comparing Fig. 4(a) and Fig. 5. This phenomenon illustrates also the importance of the correlation between the frequency spectrum of the excitation force and the selected modes to form the reduced system. For instance, in the case of u(t) = sin(2π × 25t), the adjacent modes to these frequency play the most important role in the accuracy of reconstructed response. The observed correlation between selected modes and frequency spectrum of the input necessitates further analysis and investigation, so that the sensitivity and a systematic recommendation for this purpose can be developed. In the next step, the capability of the DKF approach in response estimation in the case of excitation at several frequencies is studied. Figure 6 shows the reconstructed acceleration of the plate based on a sine input force with frequencies of 25, 50, 100, and 500 Hz. This graph shows the consistent accuracy of the DKF to reconstruct the response of the plate. However, the potential of DKF at higher frequencies requires further investigations.

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4 4.1

Experimental Verification Setup of the Experiment

Here, in the experimental analysis likewise the numerical investigation the structure should be regarded from two points of view: experimental modal analysis and the excitation of the structure with an external force and reconstructing the response. The modal analysis of the described plate is carried out by following a hammer test as well as a shaker test approach. In the former case, the location of the accelerometer is fixed on the plate while the position of the force is varied by roving the hammer over the plate. Contrarily, in the shaker test the location of driving force (electromagnetic shaker) is fixed and the position of the accelerometers is changed over the plate (Fig. 7).

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Results of Experimental Measurement

Figure 8 shows the FRF of the clamped aluminum plate by performing a hammer test on the structure. The peaks of this graph correspond to the natural frequencies of the plate. Moreover, in Fig. 9 the constructed response of the plate by implementation of the DKF algorithm and utilizing the acceleration measurement from them experiment is depicted. Here, an impulse force is employed as the source of the excitation. Figure 9 shows the capability of DKF algorithm to reconstruct the response of a plate under a noisy environment and an unknown source of the excitation.

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In the current work, the capability of DKF method to reconstruct the response of a structure with an unknown input has been investigated. The data required for DKF are provided by acceleration measurement as well as the physical model

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of the dynamic system. The objective of this work was to demonstrate the possibility of implementation of DKF on a plate structure, together with showing the performance of the method toward several input force conditions. It is revealed that DKF algorithm is robust with respect to utilization of a sine wave, an impulse, and a tone burst signal as the excitation force of the system. Additionally, the importance of selecting the proper modes of the system to generate the reduced order model of the structure was shown. Here, the driving factor has been found out to be in correlation with the frequency spectrum of the input force together with the contribution of different modes to FRF of the structure. The result of this work is important in extending the capability of the health monitoring algorithms whose input are based on dynamic responses of the system.

References 1. Chen, T.C., Tuan, P.C.: Input estimation method including finite-element scheme for solving inverse heat conduction problems. Numer. Heat Transf. Part B: Fundam. 47(3), 277–290 (2005) 2. Eftekhar Azam, S., Chatzi, E., Papadimitriou, C.: A dual Kalman filter approach for state estimation via output-only acceleration measurements. Mech. Syst. Sig. Process. 60, 866–886 (2015) 3. Gillijns, S., De Moor, B.: Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica 43(5), 934– 937 (2007) 4. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960) 5. Ma, C.K., Chang, J.M., Lin, D.C.: Input forces estimation of beam structures by an inverse method. J. Sound Vib. 259(2), 387–407 (2003) 6. Ma, C.K., Lin, D.C.: Input forces estimation of a cantilever beam. Inverse Prob. Eng. 8(6), 511–528 (2000) 7. Ma, C.K., Tuan, P.C., Lin, D.C., Liu, C.S.: A study of an inverse method for the estimation of impulsive loads. Int. J. Syst. Sci. 29(6), 663–672 (1998) 8. Maes, K., Nimmen, K.V., Lourens, E., Rezayat, A., Guillaume, P., Roeck, G.D., Lombaert, G.: Verification of joint input-state estimation for force identification by means of in situ measurements on a footbridge. Mech. Syst. Sig. Process. 75, 245–260 (2016) 9. Papadimitriou, C., Fritzen, C.P., Kraemer, P., Ntotsios, E.: Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering. Struct. Control Health Monitor. 18(5), 554–573 (2011) 10. Petersen, W., Øiseth, O., Nord, T.S., Lourens, E.: Estimation of the full-field dynamic response of a floating bridge using Kalman-type filtering algorithms. Mech. Syst. Sig. Process. 107, 12–28 (2018) 11. Sattarifar, A., Nestorovi´c, T.: Frequency-bounded delay and sum: a modified damage detection method in thin-walled plates. PAMM 19(1), e201900368 (2019) 12. Shrivastava, A., Mohanty, A.R.: Kalman filter-based force estimation in a clamped plate using reduced order model and noisy measurements. Inverse Prob. Sci. Eng. 27(8), 1170–1193 (2019)

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13. Trapp, M., Bogoclu, C., Nestorovi´c, T., Roos, D.: Intelligent optimization and machine learning algorithms for structural anomaly detection using seismic signals. Mech. Syst. Sig. Process. 133, 106250 (2019) 14. Tuan, P.C., Ji, C.C., Fong, L.W., Huang, W.T.: An input estimation approach to on-line two-dimensional inverse heat conduction problems. Numer. Heat Transf. Part B: Fundam. 29(3), 345–363 (1996) 15. Zhang, E., Antoni, J., Feissel, P.: Bayesian force reconstruction with an uncertain model. J. Sound Vib. 331(4), 798–814 (2012)

Monitoring Road Acoustic and Mechanical Performance Filippo G. Praticò , Rosario Fedele(&) and Gianfranco Pellicano

,

DIIES Department, Mediterranea University, Reggio Calabria, Italy [email protected]

Abstract. In the last decades, noise pollution has become a criticality, especially in residential areas. In more detail, the traffic noise produced by the interaction between tire and road surface (rolling noise) represents one of the main sources of urban noise. Tire characteristics (type/construction, size, belt stiffness, tire damping, non-uniformity, rubber hardness, wear and ageing, retreaded, studded, tread pattern and porosity, and tire cavity content) and road properties (e.g., acoustic absorption, surface texture, porosity, and mechanical impedance) greatly affect rolling noise. In particular, the mechanical impedance of pavement is defined as the ratio of a force applied on a structure to the induced velocity, where these latter are frequency-dependent vectors. Despite efforts and studies, mechanical impedance real effect on rolling noise is still a critical issue. Consequently, this study aims at shedding the light upon the relationship between acoustic response and mechanical impedance of road pavements. By using an impact hammer and a 3D accelerometer, several tests were performed on different types of samples and materials according to the EN 29052-part 1. Results were derived in terms of mechanistic (modulus, damping ratio, dynamic stiffness) and acoustic parameters. Based on results, both changes of the structural health status of pavements and their mechanical impedance affect the acoustic response. Keywords: Mechanical impedance
Rolling noise
Acoustic response
Road pavements
EN 29052-part 1
Impact hammer

1 Introduction Noise can cause sleep disturbs, cardiovascular and psychophysiological diseases, changes in social behavior (World Health Organization (WHO) [1]), and lives loss [2]. Traffic noise and vibrations impact urban livability [3–5], the life and cycle costs of pavements, and the built environment [6]. In more detail, tyre pavement noise includes aerodynamic effects and vibratory phenomena [7]. These latter depend on tyre (e.g., tread design) and pavement-related properties (e.g., macrotexture, friction between tyre and road surface, and mechanic properties [8]). Road mixture composition (i.e., presence of crumb rubber) and volumetrics affect mechanistic properties such as moduli (cf. AASHTO T 342 for dynamic

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 594–602, 2021. https://doi.org/10.1007/978-3-030-64594-6_58

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modulus and ASTM D4695, D4602, D4694, and D5858, for pavement deflection measurements and in situ equivalent elastic moduli) and mechanical impedance. The influence of pavement mechanical properties on noise contribution has been supposed or postulated many times in the past (cf. [3,9–17]). In particular, mechanical impedance represents a frequency response function (FRF) of a system to a sinusoidal force applied to it (namely, the force divided by the velocity [18]). Several studies and projects were carried out to assess mechanical impedance impact on the issues mentioned above. Li et al. [19,20] studied the mechanical impedance of different pavement types in the laboratory and on-site. In the project PERSUADE [21,22], mixtures containing a high percentage of rubber (i.e., greater than 50% on the weight of the mixture, which are known as Poroelastic Road Surfacing, PERS) were tested. To this end, note that Losa et al. [23] and Teti et al. [24] focused on rubberized mixes and their noise performance, and there is a growing interest in theoretical and experimental aspects related to the dynamic response and the frequency-based response of pavement structures. Unfortunately, despite this, the relationship between rolling noise and the frequency response of a pavement is still mainly unknown. The complexity of such an assessment is increased by the fact that mixture composition can affect, at the same time, both surface- and volumetric-related properties [25] and dynamic response, and this makes it difficult to split their contribution. Consequently, the main objective of this study is to investigate the relationships between acoustic response and mechanical impedance of road pavements. To this end, the tasks in Fig. 1 were carried out. The remaining parts of the paper is organized as follows. Section 2 reports the main findings of the literature review, Sect. 3 illustrates the methodology, Sect. 4 reports the measurements carried out and the results of the study and is followed by conclusions and references.

Fig. 1. Tasks of the study.

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2 Analysis of Literature and Standards 2.1

Frequency Response Function

Task 1 focused on the analysis of literature and standards about Frequency Response Functions (FRF, e.g., dynamic stiffness and mechanical impedance). FRFs give insights into a structure’s resonant frequencies (peaks or dips), damping (width of the peaks), and mode shapes [26]. Many types of input excitations and response outputs can be used to calculate an experimental FRF, with inputs in force (N) and outputs in acceleration (m/s2), velocity (m/s) or displacement (m). According to the literature, the following FRF-related standards can be listed: USAS S2.6 [27], EN 29052-1 [28], ISO 7626-5 [29], ASTM C125 [30], and EN 14146 [31,32]. Importantly, several mechanistic properties (e.g., moduli, ratio of stress to strain) are intrinsic properties of materials, while others (e.g., stiffness, ratio of force or stress to displacement) depends on geometry, and this implies the need for specifying sample geometry (cf. EN 29052-1 [28]) as well as the remaining boundary conditions (such as additional load plates, plastic foils, paste of plaster of Paris, exciting and measuring devices). Some examples of FRF’s are shown in Table 1: Table 1. Examples of frequency response function. FRF Mechanical systems

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The vibrating systems are affected by the damping coefficient c. This is usually expressed in Ns/m and governs the response of systems at natural frequencies. Higher damping coefficients correspond to lower deflections. Other damping-related parameters are the damping ratio (namely, the damping coefficient divided by the critical damping coefficient, dimensionless), the loss factor, the percent of critical damping, and the phase angle between cycling stress and strain: g¼

1 %Cr Dx3dB ¼ 2f ¼ ¼ tan/ ¼ Q 50 x0

ð1Þ

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where η is the loss factor (dimensionless); Q is damping factor or quality factor (dimensionless); f is the damping ratio, (dimensionless). This latter is given by the actual damping (damping coefficient) divided by the critical damping (2⋅(km)0.5, where m is the mass m and k is the spring stiffness). Finally, %Cr is the percent of critical damping (%Cr = 100⋅f), while / is phase angle between cyclic stress and strain. Note that the reciprocal of the damping factor (or quality factor) equals twice the damping ratio and both this factor and the damping factor are dimensionless. Furthermore, being c and f proportional, this implies that high damping corresponds to low Qs. According to Uglova and Tiraturyan [33], pavement layers damping ratios range from 0.03 to 0.23, while Hasheminejad et al. [34] found values ranging from 0.01 and 0.34. For damping estimates, note that in a FRF, the damping is proportional to the width (f2 − f1) of the resonant peak “around” the peak’s center frequency, f0 (“3 dB method”, also called “half power method” [35,36]): Q¼

f0 f2  f1

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3 Set Up of the Methodology Laboratory tests were carried out on different types of materials. In some measurements the sample tested was cylindrical, while in some other cases rectangular slabs were used. The equipment used for tests was connected to a system consisting of a laptop computer (Fig. 2a) and a Brüel & Kjaer front-end acquisition board (Fig. 2b), which was used to convert (using the Fast Fourier Transform, FFT) the hammer’s time series to frequency responses. With an external sound card ‘Roland quad-capture UA-55’ (Fig. 2c), sound pressure generated from each hammer hit was obtained. The remaining part of the system consists of an impact hammer ‘Brüel & Kjaer Type 8206’ to register the applied force (Fig. 2d), a microphone (omnidirectional prepolarized microphone ‘Audix TM1’, Frequency Response = 20 Hz–25 kHz ±2 dB, Sensitivity = 6 mV/Pa @ 1 kHz, Dynamic Range = 112 dB, Fig. 2e), and an accelerometer (piezoelectric accelerometer ‘Brüel & Kjaer Type 4507’ with a frequency range of 0.3–6000 Hz, Fig. 2f). As mentioned above, both rectangular slabs and cylindrical samples were used, as illustrated in Table 2. The Mechanical Impedance (based on force and velocity) and the Dynamic Stiffness (based on force and displacement) were derived using the accelerometer-hammer-based system, while the acoustic response (AR) was recorded through the microphone-based system.

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Fig. 2. Hammer test set up Table 2. Samples tested. Sample C02 C03 C04 P01-B P02 P04

Material 100% CR with resin HMA open graded HMA dense graded HMA dense graded 100% CR with resin Steel

Geometry Cylinder Cylinder Cylinder Rectangular slab Rectangular slab Rectangular slab

4 Design of Experiments and Results Task 3 included the design of experiments to carry out with the hammer tests. Subsequently (Task 4), each impact hammer test consisted of ten hits (sampling rate of 3.2 kHz) where the upper surface of samples (see Fig. 2g) was hit. The accelerometer was located 20 mm far from hammer spot (according to EN 29052-part 1). In order to study the boundary conditions, the accelerometer was placed in some cases on a steel slab (dimensions 40 mm  20 mm) glued with hot bitumen on the sample and in other cases directly on the surface of samples tested. According to the Resonant Method [28,37] some measurements on cylindrical samples were carried out placing a mass on the top of the studied surface while the specimen was covered with a waterproof plastic foil (approximately 0.02 mm thick) on which a thin paste of plaster of Paris and water was applied to cover any unevenness. Importantly, these cases were not considered in terms of acoustic response of the material under test. Results in terms of average Mechanical Impedance (Fig. 3), Dynamic Stiffness (Fig. 4) and Acoustic Responses (Fig. 5), in the frequency range of 0–40 Hz, 40–400 Hz, 400–1600 Hz and 1600–3200 Hz, respectively, are illustrated below.

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5 Discussion and Conclusions The study presented in this paper focused on the relationship between mechanistic and acoustic response (AR) of road pavements. Based on first results, the following conclusions can be drawn: 1) The European Standard EN 29052-1 provides quite sound estimates of resonant frequencies for cylindrical samples, especially in presence of rubber. 2) In laboratory tests, boundary conditions, like the geometry of the sample and underlayer, strongly affect the FRF estimates. 3) The presence of a steel load plate on the top of the specimen does not affect significantly Mechanical Impedance (MI) and Dynamic Stiffness (K) (variations lower than 5% in terms of MI and frequency are detected). 4) Even if the thickness is the same, the remaining dimensions (slab or cylindrical sample) affect MI and K. Wider surfaces (starting from a small sample) imply lower deflections and generally higher MIs. 5) MI and K appear to be sound indicators to evaluate the AR for frequencies in the range 40–3200 Hz. 6) Further investigations are required to have a higher accuracy of the results.

References 1. WHO Europe: Burden of disease from environmental noise: quantification of healthy life years lost in Europe. Copenhagen, Denmark (2011) 2. Praticò, F.G.: Roads and loudness: a more comprehensive approach. Road Mater. Pavement Des. 359–377 (2011). https://doi.org/10.1080/14680629.2001.9689908 3. Li, T.: Influencing parameters on tire-pavement interaction noise: review, experiments and design considerations. Designs 2, 38 (2018). https://doi.org/10.3390/designs2040038 4. Praticò, F.G.: On the dependence of acoustic performance on pavement characteristics. Transp. Res. Part D Transp. Environ. 29, 79–87 (2014). https://doi.org/10.1016/j.trd.2014. 04.004 5. Praticò, F.G., Fedele, R., Pellicano, G.: The prediction of road cracks through acoustic signature: extended finite element modeling and experiments. ASTM J. Test. Eval. 49 (2019). https://doi.org/10.1520/JTE20190209 6. Praticò, F.G., Ammendola, R., Moro, A.: Factors affecting the environmental impact of pavement wear. Transp. Res. Part D Transp. Environ. 15, 127–133 (2010). https://doi.org/ 10.1016/j.trd.2009.12.002 7. Sandberg, U., Beata, Ś.Ż., Ejsmont, J.A.: Tyre/road noise reduction of poroelastic road surface tested in a laboratory, pp. 1–8 (2013) 8. Van Keulen, W., Duškov, M.: Inventory study of basic knowledge on tyre/road noise. Delft, Netherlands (2005) 9. Berge, T., Storeheier, S.Å.: Low noise pavements in a Nordic climate. Results from a four year project in Norway. In: 38th International Congress and Exposition on Noise Control Engineering, INTER-NOISE 2009, pp. 359–367 (2009)

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10. Sandberg, U., Goubert, L.: PERSUADE - a European project for exceptional noise reduction by means of poroelastic road surfaces. In: 40th International Congress and Exposition on Noise Control Engineering, INTER-NOISE 2011, pp. 673–683 (2011) 11. 11. Storeheier, S.A.: Preliminary investigation on a poroelastic material used as a low noise road surface. In: SINTEF Foundation for Scientific and Industrial Research, pp. 41p (1987) 12. Nilsson, N.Å., Sylwan, O.: New vibro-acoustical measurement tools for characterization of poroelastic road surfaces with respect to tire/road noise. In: Proceedings of the Tenth International Congress on Sound and Vibration, pp. 4343–4350 (2003) 13. Świeczko-Zurek, B.: Biological hazards in low noise, poroelastic road surfaces. In: 20th International Congress on Sound and Vibration, ICSV 2013, pp. 2813–2818 (2013) 14. Bilawchuk, S.: Tire noise assessment of asphalt rubber crumb pavement. Can. Acoust. Acoust. Can. 32, 110–111 (2004) 15. Ponniah, J., Tabib, S., Lane, B., Raymond, C.: Evaluation of the effectiveness of different mix types to reduce noise level at the tire/pavement interface. In: 2010 Annual Conference and Exhibition of the Transportation Association of Canada: Adjusting to New Realities, TAC/ATC 2010 (2010) 16. Beckenbauer, T.: Akustische Eigenschaften von Fahrbahnoberflaechen. Strasse+Autobahn 54, 553–561 (2001) 17. Stenschke, R.: Activities of the German Federal Environmental Agency to reduce tire/road noise. In: Proceedings of International Tire/Road Noise Conference 1990, Gothenburg, Sweden (1990) 18. Harris, C.M., Piersol, A.G.: Harris’ Shock and Vibration Handbook. McGraw-Hill, New York (2002) 19. Li, M., Molenaar, A.A.A., van de Ven, M.F.C., van Keulen, W.: Mechanical impedance measurement on thin layer surface with impedance hammer device. J. Test. Eval. 40, 20120089 (2012). https://doi.org/10.1520/jte20120089 20. Li, M., Van Keulen, W., Ceylan, H., Cao, D., Van De Ven, M., Molenaar, A.: Pavement stiffness measurements in relation to mechanical impedance. Constr. Build. Mater. 102, 455– 461 (2016). https://doi.org/10.1016/j.conbuildmat.2015.10.191 21. 21. Bendtsen, H., Andersen, B., Kalman, B., Cesbron, J.: The first poroelastic test section in PERSUADE. In: 42nd International Congress and Exposition on Noise Control Engineering, INTER-NOISE 2013: Noise Control for Quality of Life, vol. 1, pp. 1–5 (2013) 22. Skov, R.S.H., Bendtsen, H., Raaberg, J., Cesbron, J.: Laboratory measurements on slabs from full scale PERS test sections. In: EuroNoise 2015 (2015) 23. Losa, M., Leandri, P., Licitra, G.: Mixture design optimization of low-noise pavements. Transp. Res. Rec. 25–33 (2013). https://doi.org/10.3141/2372-04 24. Teti, L., de León, G., Del Pizzo, A., Moro, A., Bianco, F., Fredianelli, L., Licitra, G.: Modelling the acoustic performance of newly laid low-noise pavements. Constr. Build. Mater. 247, 118509 (2020). https://doi.org/10.1016/j.conbuildmat.2020.118509 25. Praticó, F.G., Moro, A., Ammendola, R.: Factors affecting variance and bias of non-nuclear density gauges for porous European mixes and dense-graded friction courses. Balt. J. Road Bridg. Eng. 4, 99–107 (2009). https://doi.org/10.3846/1822-427X.2009.4.99-107 26. What is a Frequency Response Function (FRF)? 27. USAS S2.6: Specifying the Mechanical Impedance of Structures (1963) 28. EN 29052-1: Acoustics - Method for the determination of dynamic stiffness - Part 1: Materials used under floating floors in dwellings (1992) 29. ISO 7626-5: Vibration and shock - Experimental determination of mechanical mobility – Part 5: Measurements using impact excitation with an exciter which is not attached to the structure (1994)

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30. ASTM Standard C215: Standard Test Method for Fundamental Transverse, Longitudinal, and Torsional Resonant Frequencies of Concrete Specimens (2008). https://doi.org/10.1520/ C0215-08 31. NP EN 14146-2006: Determination of dynamic modulus of elasticity (by measuring the fundamental resonance frequency) (2005) 32. Bede, N., Kožar, I.: Determination of dynamic modulus of elasticity of concrete by impact hammer. HDKBR INFO Mag. 6, 8–11 (2016) 33. Uglova, E., Tiraturyan, A.: Calculation of the damping factors of the flexible pavement structure courses according to the in-place testing data. Procedia Eng. 187, 742–748 (2017). https://doi.org/10.1016/j.proeng.2017.04.431 34. Hasheminejad, N., Vuye, C., Van Den Bergh, W., Dirckx, J., Leysen, J., Sels, S., Vanlanduit, S.: Identification of pavement material properties using vibration measurements. In: Proceedings of ISMA 2016 - International Conference on Noise and Vibration Engineering and USD2016 - International Conference on Uncertainty in Structural Dynamics, pp. 2217–2231 (2016) 35. PJS: How to calculate damping from a FRF? https://community.plm.automation.siemens. com/ 36. Bonfiglio, P., Fausti, P.: Dynamic stiffness of materials used for reduction in impact noise: comparison between different measurement techniques. In: Proceedings of Acustica 2004 Paper ID: 066, pp. 1–8 (2004) 37. Vázquez, V.F., Paje, S.E.: Dynamic stiffness assessment of construction materials by the resonant and non-resonant methods. J. Nondestruct. Eval. 35, 1–1 (2016). https://doi.org/10. 1007/s10921-016-0350-z

Diagnostics and Prognostics of Composite Structures Towards a Condition-Based Maintenance Framework

Acoustic Emission Based Monitoring of Fatigue Damage in CFRP-CFRP Adhesive Bonded Joints Michele Carboni(&)

and Andrea Bernasconi

Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy [email protected]

Abstract. Adhesive bonded joints are more and more applied in modern structures. However, manufacturing defects and particularly harsh operative conditions might cause local de-bonding and catastrophic failures. Structural Health Monitoring and Non-destructive Testing procedures are, then, needed for evaluating the in-service structural integrity of adhesive bonded joints. In this research, an adhesive bonded single lap joint, whose both adherends are manufactured using a carbon fiber reinforced polymer composite, is subjected to constant amplitude fatigue loading. During such a test, the integrity and damage condition of the joint is continuously monitored by acoustic emission, while the test itself is periodically interrupted in order to apply micro-computed tomography to the specimen, with the aim to investigate the real features of the developing fatigue damage. Results show that monitoring by acoustic emission, after suitable elaboration and filtering my means of pattern recognition algorithms, allows identifying and characterizing effectively the development of fatigue damage in adhesive bonded joints. Keywords: CFRP-CFRP adhesive bonded joints emission
Micro computed tomography


Fatigue
Acoustic

1 Introduction Adhesive bonding is a solid-state mechanical joining technique where a suitable fluid adhesive, typically a polymeric resin, is inserted between the surfaces of two adherends and, after solidification/polymerization, joins them permanently [1]. In the last years, the adoption of structural adhesive bonded joints has increased, with respect to other more traditional mechanical joining techniques, due to their lighter weight and overall lower manufacturing cost. Other advantages consist in a high stiffness, the possibility to join dissimilar materials, a better stress distribution, a good performance against fatigue loading and a good performance against corrosion (but not in every case). On the other hand, the main drawbacks of adhesive bonded joints consist in the permanent layout, the onset of residual stresses due to manufacturing, the degradation over time due to environmental effects and an inherent difficulty to apply nondestructive testing (NDT) inspections. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 605–615, 2021. https://doi.org/10.1007/978-3-030-64594-6_59

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The application of adhesive bonding is particularly effective and widespread in the case of composite adherends, especially considering the increasing use of this kind of material in numerous industrial and civil fields. On the other hand, in real applications, adhesive bonding introduces more critical issues for structural integrity because, with respect to traditional joints, multiple and complex failure modes are observed. From this point of view, in the present research, single lap joints between adherends made of “carbon fiber reinforced polymer” (CFRP) are considered and Fig. 1a [1] shows the typical failure modes of this kind of joint. It is worth adding that such failure modes are classified in relevant standards [2] and it is possible to expect that, under some conditions, damage can initiate in the adhesive or in the adherends and, then, propagate into the different components of the joint with a complex behavior.

Fig. 1. (a) Possible failure modes of a single lap adhesive bonded joint between CFRP adherends [1]. (b) Common defects of the adhesive layer [1].

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A primary role, in the competition between the different failure modes, is taken by the typical manufacturing defects originating within the adhesive or at the interface between the adhesive and the adherends. Figure 1b [1] shows the main cases: the presence of such inhomogeneities and discontinuities in adhesive bonded joints requires, then, the application of maintenance, inspection and monitoring procedures able to guarantee the needed in-service reliability [3–5]. Many research studies are available, in the literature, on non-destructive testing (NDT) and structural health monitoring (SHM) of adhesive bonded joints. Summarizing, the most used NDT methods are [4]: visual testing, infrared thermography, ultrasonic testing, radiography and computed tomography (CT). On the other hand, the paradigm is recently shifting towards SHM, because it does not require service interruptions and allows a reduction of maintenance costs of components and systems up to 30% [5]. Today, the most widespread SHM methods applied to adhesive bonded joints are [5] acoustic emission (AE), strain measurement by strain gages or optical fibers and ultrasonic guided waves. In order to deepen the feasibility of a SHM approach to adhesive bonded joints, the present research analyzes the case of a single lap CFRP-CFRP adhesive bonded joint subjected to fatigue loading, while its damage condition is inspected by visual testing (VT) and x-ray lCT [6], at suitable interruptions of the test, and continuously monitored by AE [7].

2 Experimental Set-up A single lap CFRP-CFRP adhesive bonded joint (Fig. 2a) was fatigue tested. The single adherend has a total thickness equal to 5.3 mm and is composed of 0.66 mm thick laminas arranged as [+45/02/+45]s. A Scotch Weld 9323 B/A epoxy resin bonds the adherends composing the specimen and the length of the bonded part is equal to 25.4 mm (the total length of the specimen is 285 mm). A uniform adhesive thickness is obtained using 0.2 mm diameter glass spheres as spacers. The specimen was fatigue tested, under load-controlled conditions, by a uni-axial MTS 810 servo-hydraulic testing machine having maximum capacity equal to 100 kN. The applied cyclic load was characterized by an amplitude of 2 kN and a load ratio equal to 0.05. Particularly, the load level was set on the base of previous fatigue tests carried out on similar joints [8, 9] and chosen because able to guarantee an average fatigue strength of about 500.000 cycles. In order to focalize the analysis on the first stages of damage development, VT and lCT inspections were carried out at 0 fatigue cycles, at a first interruption of the test after 49100 fatigue cycles and at the forced end of the test (without failure of the specimen) after 109000 fatigue cycles. VT inspections were carried out on the flanks of the specimen (Fig. 2b) in order to evaluate the location and size of the surface cracks developing from the edges of the overlapped region. A high magnification Dino-Lite digital microscope, together with a Bosch GLI DeciLed LED light, was used. In order to get more insight on the developing damage, the specimen was scanned by x-ray lCT (Fig. 2c) using a North Star Imaging X25 micro-tomography scanner, as well. The main adopted parameters consisted in 76 kV voltage, 40 lA amperage, 3 lm focal spot size, magnification equal to 4x and 1200 projections on a 360° rotation of the

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specimen. A contrast medium was applied in order to enhance the contrast of damage and get a better information.

(a)

(b)

(c)

Fig. 2. (a) Specimen adopted for the fatigue test. (b) Set-up for the test, for visual inspections and for structural health monitoring by acoustic emission. (c) Set-up for lCT scans.

SHM by AE was, instead, continuously applied during the test. A Vallen AMSY-6 control unit, equipped with two Vallen VS150-M piezoelectric sensors having frequency range equal to 100–400 kHz with peak frequency equal to 150 kHz (Fig. 2b) and two 34 dB Vallen AEP4 pre-amplifiers, was used. Relevant AE events and their spatial localization, by means of the 1D time of flight triangulation made possible by the two sensors, were acquired. The distance between sensors was equal to 95 mm centered on the overlapped region (Fig. 2b). Sensors were coupled to the specimen by silicon grease and the coupling, along with the performance of the localization, was verified, before the beginning of the test and then periodically, using the standardized “pencil lead break” test by Hsu and Nielsen [10]. The same test allowed determining the sound wave velocity in the specimen, which, on average, resulted to be equal to 3300 m/s. Finally, the control unit was set up to record the displacement of the moving crossbeam of the testing machine, the load signal from the load cell and the number of applied fatigue cycles, as well.

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3 Analysis of AE Events Channel 1 recorded 467346 AE events, while channel 2 recorded 615039 events. It is reasonable to expect that just a portion of such acquisitions be related to damage, while the other one can be likely ascribed to background noise. For this reason, in order to have the possibility to evaluate accurately the performance of the proposed AE SHM approach, the recorded AE raw data were classified by a post-processing procedure after the end of the fatigue test. From this point of view, special methods for the classification of sets of numerous objects [11–13] are those defined by machine learning and artificial intelligence techniques. For the case at hand, a pattern recognition tool, already successfully applied to another kind of adhesive bonded joint in [14], has been considered. Such a method is based on an unsupervised artificial neural network [15] allowing individuating similar and common features of different objects and makes possible to classify them in homogeneous sets [16]. In detail, the classification procedure starts with the choice of the features to be used to look for similarities between the considered AE events. This was done, in a semiquantitative way, analyzing many typical features of the recorded AE events by a Multivariate Visual Analysis based on the Parallel Coordinates diagram [6]. The analysis highlighted, as relevant features for the present case, the amplitude, the energy, the rise time, the duration, the counts and the frequency centroid (Fig. 3, [17]). Other considered features did not prove to be significant for the classification of this particular case.

Fig. 3. Scheme of an AE hit (case of burst event) and its relevant features for the present case [17].

After having defined the classifying features, an automatized algorithm, named “kSOM” and proposed by Crivelli for CFRP composite materials [18], was applied. It is worth remarking that, as each sensor may have a slightly different response, data from channel 1 and 2 were analyzed separately. Nevertheless, k-SOM implements a sequence of steps to analyze the data:

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1. a Self-Organizing Map (SOM) [19] in order to get a preliminary indication on how many classes (clusters) the data can be subdivided into. It is an unsupervised algorithm because it is demonstrated [16] that this kind of algorithms is the most appropriate for classifying AE data, whose morphology is typically not known a priori; 2. the automatic optimization of the number of classes based on different standardized performance indexes; 3. the conclusive classification by a k-Means algorithm [20].

Fig. 4. Trend of classified AE events against the number of fatigue cycles (example of channel 1). (a) Fatigue cycles against amplitude in dB. (b) Fatigue cycles against cumulated energy.

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The optimized number of classes, provided by the automatized algorithm, resulted to be two. This could seem in contrast with the abovementioned multiple failure modes of a single lap adhesive bonded joint between CFRP adherends, but, actually, it suggests that, for the case at hand, the waveform of the events related to different failure modes is likely rather similar, even if different from that of noise. A deeper analysis should be carried out in order to classify the different failure modes, but this is beyond the aim of the present research, where the focus is just on detecting damage able to prevent in-service functionality, regardless its type. The complete set of the classified recorded AE data, for the case of channel 1, is shown in Fig. 4a in terms of amplitude in dB against the number of fatigue cycles, and in Fig. 4b, in terms of cumulated energy against the number of fatigue cycles. Channel 2 showed analogous results. As can be seen, Class 1 is more uniformly distributed in the low-amplitudes region (mean amplitude value equal to 60.3 dB for channel 1 and to 60.2 dB for channel 2) and spans the whole axis of the number of cycles (the typical expected behavior of noise), while Class 2 is more concentrated and variable in the high-amplitudes region (mean amplitude value equal to 71.6 dB for channel 1 and to 74.3 dB for channel 2) and its activity increases with the accumulation of fatigue cycles (the typical expected behavior of fatigue damage). Moreover (Fig. 4b), the cumulated energy of Class 1 shows an almost linear and time-independent trend (typical, again, of noise), while that of Class 2 shows sudden variations of slopes and is time-dependent (typical, again, of damage development). Considering, then, channel 1, 66.6% of AE acquisitions were classified into Class 1, while the remaining 33.4% into Class 2. Likewise, for channel 2, 69.8% of AE acquisitions were classified into Class 1, while the remaining 30.2% into Class 2. A further simple check consisted in the visual analysis of the morphology of the waveforms included into the defined classes. Figure 5 shows some emblematic examples: Class 1 includes the typical waveforms related to background noise, while Class 2 those related to damage phenomena (particularly, fracture).

Fig. 5. Typical waveforms included into the two classes. (a) Class 1. (b) Class 2.

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A final validation of this conclusion and of the proposed SHM approach is the comparison to the results obtained by the NDT techniques applied during the fatigue test. Particularly, in order to check if the proposed SHM approach is able to detect the first stages of damage development, the fatigue test was interrupted, for the first time, at 49100 cycles, i.e. just after the first AE events classified in Class 2 were observed (Fig. 4a). Figure 6 shows the results of visual testing applied at the edges of the overlapped region of the adherends after 49100 fatigue cycles. As can be seen, the upper edge presents the initiation of micro-cracks which were not detected by the same inspection carried out at 0 fatigue cycles, while the lower edge was still intact. This is confirmed by the lCT analysis of the overlapped region of the adherends after 49100 fatigue cycles (Fig. 7) and suggests that, actually, the first recorded AE events classified in Class 2 could be due to the initiation of fatigue cracks in the specimen.

Fig. 6. Visual testing of the edges of the overlapped region of the adherends after 49100 fatigue cycles.

(a)

(b)

Fig. 7. lCT analysis of the overlapped region of the adherends after 49100 fatigue cycles. (a) Region of interest. (b) Slice of the fatigue damaged region (false colors).

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4 Localization of AE Events A very fast release of elastic energy in the material, e.g. in the case of an advancing crack, generates an AE event in the form of an elastic wave that propagates in all directions. Depending on the position of the source, the released wave reaches the applied sensors with given, and generally different, delays (times of flight) [7]. Assuming the considered lap joint as a one-dimensional linear system, two sensors are enough and, consequently, localization was applied with two aims: 1. as a further check of the effectiveness of the classification algorithm; 2. to check the ability of the proposed SHM approach to localize the developing fatigue damage, i.e. the AE source, in the considered lap joint configuration. The analysis was carried out considering just the AE events classified in Class 2, i.e. the one here assumed to be related to the developing fatigue damage. Figure 8 shows, then, the energy distribution of the localized events as a function of the position along the specimen. As can be seen, the cumulative energy, i.e. the most energetic AE events, tended to gather in correspondence of the overlap ends, where damage initiated and propagated, as observed by VT and lCT. In particular, most of the energy was released at the overlap end close to channel 2, i.e. the one presenting, at the end of the fatigue test, the highest AE activity. It can be concluded that localization furtherly validates the proposed classification algorithm and proves to be effective in the perspective of in-field SHM application to the considered kind of lap-joints.

Fig. 8. Localization of AE events.

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5 Concluding Remarks The performance of a structural health monitoring approach, based on acoustic emission, was evaluated considering the applicative case of a single lap CFRP-CFRP adhesive bonded joint subjected to fatigue loading. Damage development was, also, investigated by visual testing and x-ray micro-computed tomography, which fully validated the proposed monitoring method. Results showed that monitoring by acoustic emission, after the application of suitable pattern recognition algorithms, allows detecting, in adhesive bonded joints of CFRP adherends, the early initiation of fatigue cracks in an effective way and that the application of localization of AE sources can be an effective help to interpret the developing damage. Acknowledgements. The authors would like to thank Mr. A Grossi for the active help given to the research. PoliNDT (Interdepartmental Lab for NDT and SHM set at Politecnico di Milano) is also acknowledged for providing the AE equipment.

References 1. Banea, M.D., Da Silva, L.F.M.: Adhesively bonded joints in composite materials: an overview. Proc. Inst. Mech. Eng. Part L J. Mater.: Design Appl. 223(1), 1–18 (2009) 2. ASTM D 5573-99: Standard practice for classifying failure modes in fibre-reinforced-plastic (FRP) joints. Annual Book of ASTM Standards (2002) 3. Adams, R.D., Cawley, P.: A review of defect types and non-destructive testing techniques for composites and bonded joints. NDT Int. 21(4), 208–222 (1988) 4. Adams, R.D., Drinkwater, B.W.: Non–destructive testing of adhesively bonded joints. Int. J. Mater. Prod. Technol. 14(5–6), 385–398 (1999) 5. Chang, F.K.: Introduction to health monitoring, context, problems, solutions. Presentation at the First European Pre-workshop on Structural Health Monitoring, Paris, France (2009) 6. Carmignato, S., Dewulf, W., Leach, R.: Industrial X-Ray Computed Tomography. Springer, Heidelberg (2018) 7. Grosse, C.U., Ohtsu, M.: Acoustic Emission Testing. Springer, Heidelberg (2008) 8. Bernasconi, A., Carboni, M., Comolli, L., Galeazzi, R., Gianneo, A., Kharshiduzzaman, M.: Fatigue crack growth monitoring in composite bonded lap joints by a distributed fibre optic sensing system and comparison with ultrasonic testing. J. Adhesion 92(7–9), 739–757 (2016) 9. Gianneo, A., Carboni, M., Bernasconi A.: Crack profile reconstruction of CFRP-CFRP bonded joints from optical backscatter reflectometry and comparison with X-ray computed tomography. In: 14th International Conference of the Slovenian Society for Non-Destructive Testing “Application of Contemporary Non-Destructive Testing in Engineering”, Portorož, Slovenia (2017) 10. ASTM E 976-15: Standard Guide for Determining the Reproducibility of Acoustic Emission Sensor Response. ASTM International, West Conshohocken, Pennsylvania (2015) 11. Batchelor, B.G.: Practical Approach to Pattern Classification. Plenum Press, New York (1974) 12. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)

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13. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Pearson Education Inc., Upper Saddle River (2009) 14. Carboni, M., Bernasconi, A.: Application of acoustic emission for monitoring fatigue damage in CFRP-CFRP adhesive bonded joints. In: Proceedings of 15th International Symposium on Nondestructive Characterization of Materials, Portorož, Slovenia (2019) 15. Miller, R.K., Hill, E.v.K., Moore P.O.: Acoustic emission testing. In: Nondestructive Testing Handbook, vol. 6, 3rd edn. American Society for Nondestructive Testing, Columbus (2005) 16. Batchelor, B.G.: Pattern Recognition: Ideas in Practice. Springer, Heidelberg (2012) 17. Carboni, M., Crivelli, D.: An acoustic emission based structural health monitoring approach to damage development in solid railway axles. Int. J. Fatigue 139, 105753 (2020) 18. Crivelli, D.: Structural health monitoring with acoustic emission and neural networks. Ph.D. thesis, Politecnico di Milano, Italy (2014) 19. Miljković, D.: Brief review of self-organizing maps. In: 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opotija (2017) 20. Krishna, K., Murty, M.N.: Genetic K-means algorithm. IEEE Trans. Syst. Man Cybern Part B (Cybern.) 29(3), 433–439 (1999)

Damage Diagnostics of a Composite Single-Stiffener Panel Under Fatigue Loading Utilizing SHM Data Fusion Agnes A. R. Broer1(B) , Georgios Galanopoulos2 , Dimitrios Zarouchas1 , Theodoros Loutas2 , and Rinze Benedictus1 1

Structural Integrity and Composites Group, Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629HS Delft, The Netherlands [email protected] 2 Laboratory of Applied Mechanics and Vibrations, Department of Mechanical Engineering and Aeronautics, University of Patras, 26500 Rio, Greece

Abstract. A case study is presented in which the first steps are made towards the development of a structural health monitoring (SHM) data fusion framework. For this purpose, a composite single-stiffener panel is subjected to compression-compression fatigue loading (R = 10). The carbon-epoxy panel contains an artificial disbond of 30 mm, which was created using a Teflon insert during manufacturing and placed between the skin and the stiffener foot. Under the applied fatigue load, the disbond is expected to grow and its propagation is monitored using two SHM techniques, namely acoustic emission (AE) and Rayleigh-scattering based distributed fiber optic strain sensing. Four AE sensors are placed on the skin, thereby allowing for disbond growth detection and localization. On each stiffener foot, fiber optic sensors are surface-bonded to monitor the growth of the disbond under the applied fatigue loading. The distributed strain measurements are used to localize and monitor the disbond growth. The strength of each technique is utilized by fusing the data from the AE sensors and the fiber optic sensors. In this manner, a data-driven approach is presented in which a data fusion of the different techniques allows for monitoring the damage in the stiffened panel on multiple SHM levels, including disbond growth detection and localization. Keywords: Data fusion · Acoustic emission · Distributed strain sensing · Composite single-stiffener panel · Fatigue loading

1

Introduction

Stiffened composite skin panels are commonly used in the aircraft industry and when used in-service, the occurrence of damage is inevitable. These damages can be caused by operational loads and environmental conditions or caused by an unexpected event such as a foreign object impact. Currently, commercial c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 616–625, 2021. https://doi.org/10.1007/978-3-030-64594-6_60

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aircraft do not include permanently installed sensors to monitor damage initiation and propagation, resulting in the need for manual inspections. By placing a sensor network, the health of the structure can be monitored at all moments. In order to fulfill the diagnostic requirements, information is required on multiple structural health monitoring (SHM) levels, i.e. 1) detection, 2) localization, 3) type, and 4) size. Many SHM techniques are available for this purpose, such as acoustic emission (AE), lamb waves (LW), and static and dynamic strain sensing. Two techniques will be used in this work: AE and distributed fiber optic sensing (DFOS). As will be seen next, each technique has its strengths and their combination can potentially allow for a damage diagnostic on multiple SHM levels. AE can be used to monitor the damage initiation and propagation in a composite, as well as for damage localization and damage type identification. Damage localization can be achieved when multiple sensors are used simultaneously in a cooperative fusion setting, followed by the application of an algorithm such as the time-of-arrival technique. Yet the error of localization might be large, especially for an anisotropic medium such as a composite panel, since the wave velocity is dependent on the propagation direction. Other methods have been proposed to resolve this disadvantage, such as the Delta T-Mapping approach [1,3,4,11] or by employing a close sensor placement that allows an assumption of constant wave velocity [2,9]. Though reducing the localization error, the Delta T-Mapping approach is user-intensive, as well as structure- and lay-up-dependent due to its manual mapping, while the second approach requires a larger number of sensors. Damage type can be identified by clustering AE feature data, yet it is not commonly seen in stiffened panels, which are the focus of this work. Generally, classification techniques are applied on a coupon level and the results are affected by overlapping feature values (overview given in [6,12]). Though an example of damage type identification on a panel level is shown by Kolanu et al. [8] who categorized AE feature data obtained from a single-stiffened carbon fiber-reinforced polymer (CFRP) panel in (post-)buckling. DFOS can be used to monitor the strain at the location of the fiber placement. Rayleigh-scattering based distributed fiber optic strain sensing provides a distributed strain measurement along the length of the fiber with intervals as low as 0.65 mm [10], as opposed to Fiber Bragg Gratings (FBGs) that provide point strain sensing. Strain measurements are affected by different sources, e.g. the applied load and the presence of damage, noise, or vibrations. If the influence of the damage on the strain measurement can be identified, it can be used for damage diagnostics. For example, the distributed strain measurements can be employed for the detection and the localization of impact damage in stiffened composite panels [14]. Furthermore, the disbond growth rate under fatigue loading can potentially be sized using distributed strain measurements [13]. Each monitoring technique has its strengths and only provides information about certain SHM levels. In this work, it is hypothesized that a fusion of sensor data from AE and DFOS will release synergistic effects that can result in an improved damage diagnostic on multiple SHM levels. In this view, AE can

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be used for damage detection and localization, while strain measurements from DFOS can be employed to localize and monitor disbond growth at the stiffener. The first steps in testing the hypothesis are made in the presented case study. A composite single-stiffener panel with an artificial disbond of 30 mm is tested in compression-compression fatigue until failure and monitored using AE sensors and DFOS. It is shown that their data can be fused to describe the damage process throughout the test until the final failure. In the next section, the experimental campaign is described, including details on each monitoring technique. Section 3 presents a data fusion of the different SHM techniques and describes the damage propagation per cycle period by simultaneously looking at damage detection, localization, disbond growth, as well as growth direction.

2

Experimental Campaign

A test campaign took place at the TU Delft Aerospace Structures and Materials Laboratory in November and December 2019 as part of the H2020 ReMAP project.1 During this test campaign, a composite skin-stiffener panel was tested in compression-compression (C-C) fatigue loading. The panel, based on a design by Embraer and shown in Fig. 1, consists of a skin panel and a single T-stiffener. Both the skin and the stiffener are made from IM7/8552 carbon fiber-reinforced epoxy unidirectional prepreg and its lay-ups are [45/−45/0/45/90/−45/0]S and [45/−45/0/45/−45]S , respectively. During manufacturing, which was performed by Optimal Structural Solutions, an artificial disbond of 30 mm was created by placing a Teflon film between the right stiffener foot and the skin. Additionally, two resin blocks were added to ensure a distributed load introduction. The panel’s dimensions, as well as the location of the disbond, are indicated in Fig. 1c. Six monitoring techniques were used to monitor the damage growth in the panel, namely (1) AE, (2) DFOS, (3) FBGs, (4) Lamb Wave Detection System (LWDS), (5) Digital Image Correlation (DIC), and (6) Camera. For the scope of this paper, only data from the AE system and DFOS is used to monitor the health of the panel, hence the remainder of this work will focus on these techniques. As previously stated, the panel was loaded in C-C fatigue loading with an R-ratio of 10 and a frequency 2 Hz. Initially, the fatigue load was set at a minimum load of −5.0 kN and a maximum load of −50.0 kN. After 100,000 cycles, the load was increased to a minimum load of −6.0 kN and a maximum load of −60.0 kN, while the R-ratio and frequency were kept constant. These load levels were maintained until the final failure of the panel, i.e. when the panel lost its load-bearing capability.2 The fatigue load was interrupted at certain intervals to allow for measurements by the SHM systems. The load cycle pattern is as follows and repeats itself every 5000 cycles. Every 5000 cycles, the applied load 1 2

ReMAP: Real-time Condition-based Maintenance for Adaptive Aircraft Maintenance Planning. https://h2020-remap.eu/. The test was interrupted at 30,000, 50,000, 70,000, 100,000, and 245,000 for limited time periods.

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Fig. 1. Single stiffener panel as seen from (a) skin and (b) stiffener side. Its dimensions in [mm] and the sensor locations are shown in (c), where the AE sensors are indicated as gray circles, the two optical fibers as red lines inside the SMARTape (blue), and the Teflon film as an orange square.

is reduced to 0 kN3 to allow for the LW measurements. Every 500 cycles (except for the 5000th cycle), a quasi-static (QS) load is applied from the minimum to the maximum load (either from −5.0 kN to −50 kN or from −6.0 kN to −60 kN) with a displacement rate of 0.5 mm/min. During the QS loading, measurements are taken by several techniques, namely by the DIC system, camera system, DFOS, and FBGs. The DFOS system records a strain measurement both at the minimum and maximum of the QS load and the AE system records continuously throughout the test. The latter two SHM systems are discussed in more detail next. Acoustic Emission. The AE sensors are VS900-M broadband sensors from Vallen Systeme GmbH with an operating frequency range of 100–900 kHz. An AMSY-6 Vallen acquisition system is used to record the AE hits. Four AE sensors are clamped on the panel in different locations to form a parallelogram and allow for damage localization. The [x, y] location of sensor 1, 2, 3, and 4 is [20.0, 190.0], [20.0, 20.0], [145.0, 50.0], and [145.0, 220.0] mm, respectively, and is also displayed in Fig. 1c. To prevent the capturing of noise signals, an amplitude threshold of 50 dB was set for recording of the hits. For localization, the internal Vallen processor for planar location was employed, which is based on Geiger’s method [5]. After testing, it was seen that the AE system localized a large number of signals, therefore an amplitude filter of 80 dB was applied to the localized events. Additionally, a filter was implemented to remove events with a location uncertainty 3

The load level was changed to −0.2 kN after 50,000 cycles.

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higher than 50 mm. Lastly, only localized events within the AE sensor region are considered. Distributed Fiber Optic Sensing. An optical fiber sensor has been placed inside a SMARTape [7], which was adhesively bonded to the surface of the stiffener foot using a co-polyamide-based adhesive. Two SMARTapes were used: one on each stiffener foot. Due to the placement of one piezoelectric transducer (PZT) on each stiffener foot, strain measurements are only available for part of the stiffener foot’s length as indicated in Fig. 1c. The strains were measured using a LUNA ODiSI-B Optical Distributed Sensor Interrogator with an acquisition rate of 23.8 Hz and a gauge pitch of 0.65 mm. Since the interrogator consists of only 1 interrogation channel, the two SMARTapes were spliced together. Additionally, a coreless fiber was added at the end of the fiber to minimize reflections. Note that the distributed strain measurements required no post-processing procedures such as filtering or smoothing.

3

Damage Monitoring Results

Based on the findings, the test has been split into different cycle periods to allow for discussion in this section. For each considered cycle period, the localized AE events are presented in Fig. 2. Moreover, the cumulative energy of all recorded AE hits is shown in Fig. 3, where bins of 5000 cycles have been adopted. Here, in contrast to the localization, no filtering was applied. The strain measurements at the surface of the left and right stiffener foot are presented in Fig. 4a and b, respectively, for different cycle numbers. These correspond to the start and end of each cycle period. Note that due to the LW measurements every 5000 cycles, the strains were not recorded at these intervals and the subsequent strain measurements are shown instead. In the next subsections, the damage propagation in the stiffened panel will be discussed based on the AE and strain data and the defined cycle periods. 0–100,000 Cycles. The strain measurements on the stiffener feet at 500 and 99,500 cycles are displayed in Fig. 4a and b, while the localized AE events for a similar cycle period are shown in Fig. 2a. Both measurements do not show indications of damage growth during this cycle period. Moreover, the cumulative energy of the AE hits has only shown a minimal increase from 0 to 100,000 cycles. The latter, together with the identification of a minimal strain reduction along the lengths of both stiffener feet, can be an indication that only stiffness degradation took place under the fatigue loading rather than disbond growth. 100,000–210,000 Cycles. Figure 4a and b show comparisons between the strain measurements on the stiffener feet at 100,500 and 210,500 cycles. The compressive strain in both feet has increased at 100,500 with respect to 99,500 cycles due to the increased maximum load (−50 kN to −60 kN). Comparing the

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Fig. 2. Localized acoustic emission hits for specified cycle periods. The initial disbond location is indicated by a blue square and the AE sensors by blue circles.

Fig. 3. AE cumulative energy versus number of cycles, with bins of 5000 cycles.

strain measurements at 100,500 and 210,500 cycles, little change is detected on the left foot, while the right foot shows a larger reduction in compressive strain. However, this reduction in strain does not occur with a similar rate at

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(a) Left stiffener foot

(b) Right stiffener foot

Fig. 4. Surface strain measurements along the stiffener feet. The disbond is located in the right stiffener foot and its initial location is indicated in both graphs using black dashed lines. Legend is similar for both figures.

all locations: the bottom region below a height of 90 mm shows a smaller absolute compressive strain reduction than the region above 90 mm. Evaluating the localized AE events for this cycle period (Fig. 2b), it is seen that some hits are localized around the disbond region. Figure 3 shows a further increase in cumulative energy, although it is a slightly steeper increase than in the previous cycle period. These results are an indication that, besides stiffness degradation, activity is present near the disbond resulting in disbond growth. 210,000–300,000 Cycles. Between 210,000 and 300,000 cycles, many AE events are localized, which are centered in the initial disbond area and above it (Fig. 2c). Furthermore, the cumulative energy curve (Fig. 3) has an increased slope and increases continuously throughout the considered period. The distributed strain measurements along the stiffener feet for 210,500 and 300,500 cycles are shown in Fig. 4a and b. Similar to the previous cycle period, the left stiffener foot experiences only minimal changes in strain values while the right stiffener foot shows larger differences: a small strain reduction is seen between 80 mm and 110 mm, while the region above 110 mm displays larger reductions in strain. Based on the results from both SHM techniques, it can be concluded that the disbond has grown during the considered cycle period. In addition, the direction of disbond growth can be derived from the results and it is believed to have grown upward along the length of the right stiffener. This is based on the concentration of AE events in the upper region and above the initial disbond area, as well as the minimal strain reduction on the right stiffener foot between

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80 mm and 110 mm. Lastly, based on the strain reduction above the disbond region, it is believed that further stiffness degradation took place. 300,000–425,000 Cycles. As little AE events are localized in the vicinity of the disbond region and the strain distribution on the right stiffener foot shows a constant reduction in strain over its length, it is expected that the disbond did not further grow between 300,000 and 425,000 cycles. Yet further stiffness degradation occurred as indicated by the reduced compressive strain in the right stiffener foot. Nevertheless, it needs to be recognized that a large number of AE events is localized in the center height of the left skin. Moreover, the cumulative energy increases rapidly after 300,000 cycles. Evaluating the left stiffener foot, only a minimal compressive strain reduction is noted in the lower half, thereby providing little clarification on the cause of these AE events. Therefore, data from two additional SHM techniques was evaluated, namely the DIC system and LWDS. Neither provided indications of damage initiation and propagation. Given that a PZT of the LWDS is located at [x, y] = [25.0, 120.0] mm, it is therefore argued by the authors that these AE hits do not indicate a damage process but that these were instead caused by an external source. A further investigation is required to establish the actual cause; however, it is hypothesized to be either a faulty PZT or a mechanical process such as the cable of the PZT hitting the panel during fatigue loading. 425,000–438,000 Cycles. Evaluating results closer to final failure, it is seen that the strain distribution in the lower half of the right stiffener foot remains close to constant, while the region above 90 mm shows a further strain reduction. Simultaneously, the left stiffener shows a reduction in compressive strain between approximately 100 mm and 130 mm while the strain in the lower and upper regions remains close to constant. The latter might be caused by either a disbond growth upward along the right stiffener or by a disbond growth from the right stiffener foot to the left stiffener foot. This reasoning can be further substantiated when evaluating the localized AE events (Fig. 2e); events are localized both below and above the initial disbond region. Additionally, around a height of y = 60 mm, multiple AE events are localized left of the disbond area. The growth of the disbond across the length of the stiffener as well as along its width is believed to have resulted in the final failure of the panel. The panel failed after 438,000 cycles during QS loading.

4

Conclusions

The first steps towards the development of an SHM data fusion framework were presented by means of a case study consisting of a composite single-stiffener panel with an artificial disbond of 30 mm. It was shown that the disbond growth under fatigue loading can be monitored by fusing AE data with DOFS strain

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measurements on the stiffener feet. The combination of the two techniques allows for a damage assessment on multiple SHM levels at different moments throughout the loading cycle. The AE hits allowed for detection and localization of the damage in the panel. The strain measurements from the DOFS allowed for monitoring the disbond growth, as well as the identification of stiffness degradation. By fusing the different datasets, an improved assessment of disbond growth was obtained, which additionally allowed for indicating the disbond growth direction. Although this work shows the first advantages of fusing SHM data for damage diagnostics, it also displays that two SHM techniques might not be sufficient to monitor the health of a stiffened skin panel under fatigue loading; additional techniques, namely digital image correlation (DIC) and LW measurements, were needed to investigate the source of localized AE events in the skin. Moreover, it was not yet possible to obtain a quantitative estimate of the disbond growth rate using just two techniques. The latter will require further investigation, for example with the assistance of additional SHM techniques or by training a sizing algorithm. Though several areas require further investigation, the presented study shows the benefits of fusing SHM data in a data-driven approach, thereby resulting in an improved damage diagnostic on multiple SHM levels. Acknowledgements. We would like to acknowledge Embraer for the design of the SSCs, Optimal Structural Solutions for the manufacturing of the SSCs, Smartec for the ´ SMARTapes procurement, Cedrat Technologies for the LW sensing equipment, Ecole Nationale Sup´erieure d’Arts et M´etiers for the LW data acquisition software, and our colleagues at the labs of TU Delft and University of Patras for their technical support. This work was financially supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 769288.

References 1. Baxter, M.G., Pullin, R., Holford, K.M., Evans, S.L.: Delta T source location for acoustic emission. Mech. Syst. Signal Process. 21, 1512–1520 (2007) 2. Ciampa, F., Meo, M.: A new algorithm for acoustic emission localization and flexural group velocity determination in anisotropic structures. Compos. Part A: Appl. Sci. Manuf. 41, 1777–1786 (2010) 3. Eaton, M.J., Pullin, R., Holford, K.M.: Acoustic emission source location in composite materials using Delta T mapping. Compos. Part A: Appl. Sci. Manuf. 43, 856–863 (2012) 4. Eaton, M.J., Pullin, R., Holford, K.M.: Towards improved damage location using acoustic emission. J. Mech. Eng. Sci. 226, 2141–2153 (2012) 5. Ge, M.: Analysis of source location algorithms. Part II: iterative methods. J. Acoust. Emission 21, 29–51 (2003) 6. de Groot, P.J., Wijnen, P.A.M., Janssen, R.B.F.: Real-time frequency determination of acoustic emission for different fracture mechanisms in carbon/epoxy composites. Compos. Sci. Technol. 55, 405–412 (1995) 7. Inaudi, D., Glisic, B.: Development of distributed strain and temperature sensing cables. In: Voet, M., Willsch, R., Ecke, W., Jones, J., Culshaw, B. (eds.) 17th International Conference on Optical Fibre Sensors, vol. 5855, pp. 222–225. International Society for Optics and Photonics, SPIE (2005)

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8. Kolanu, N.R., Raju, G., Ramji, M.: Experimental and numerical studies on the buckling and post-buckling behavior of single blade-stiffened CFRP panels. Compos. Struct. 196, 135–154 (2018) 9. Kundu, T., Nakatani, H., Takeda, N.: Acoustic source localization in anisotropic plates. Ultrasonics 52, 740–746 (2012) 10. Luna ODiSI-B: Optical Distributed Sensor Interrogator Model ODiSI-B: User’s Guide. Luna Inc. (2017) 11. Pearson, M.R., Eaton, M.J., Featherston, C.A., Pullin, R., Holford, K.M.: Improved acoustic emission source location during fatigue and impact events in metallic and composite structures. Struct. Health Monit. 16, 382–399 (2017) 12. Raju, Azmi, A.I., Prusty, B.G.: Acoustic emission techniques for failure characterisation in composite top-hat stiffeners. J. Reinf. Plast. Compos. 31, 495–516 (2012) 13. Ribeiro, F.N., Martinez, M., Rans, C.: Evaluation of mode II fatigue disbonding using Central Cut Plies specimen and distributed strain sensing technology. J. Adhes. 95, 259–285 (2019) 14. Tur, M., Bosboom, M.B., Evenblij, R., Michaelides, P., Gorbatov, N., Bergman, A., Ben-Simon, U., Kressel, I., Kontis, N., Koimtzoglou, C.: Fiber-optic based HUMS concept for large aircraft structure based on both point and distributed strain sensing. In: 8th European Workshop on Structural Health Monitoring (2016)

A Strain-Based Health Indicator for the SHM of Skin-to-Stringer Disbond Growth of Composite Stiffened Panels in Fatigue Dimitrios Milanoski1(&), Georgios Galanopoulos1, Agnes Broer2, Dimitrios Zarouchas2, and Theodoros Loutas1 1

Applied Mechanics Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Rio, Greece [email protected] 2 Structural Integrity and Composites Group, Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629HS Delft, The Netherlands

Abstract. Real-time Structural Health Monitoring (SHM) of aeronautical structural components is a technology persistently investigated the last years by researchers and engineers to potentially reduce the cost and/or implementation of scheduled maintenance tasks. To this end, various types of sensors have been proposed to serve this role, e.g. piezoelectric, acoustic emission, and strain sensors. In the present paper, a strain-based SHM methodology is proposed for skin/stringer disbond propagation health monitoring. Fiber-optic strain sensors with engraved Bragg gratings are utilized in order to evaluate the propagation of artificially-induced disbonds at single-stringered composite panels. The specimens are subjected to a block loading compression-compression fatigue spectrum. Longitudinal static strains are periodically acquired during quasi-static loadings every 500 cycles. A Health Indicator (HI), based on strains received from the stringer’s feet, is proposed and utilized to monitor the disbond growth. The evolution of this indicator is experimentally monitored throughout the lifespan of the specimens. The present paper verifies and consolidates via actual fatigue experiments the potential of the proposed static-strain based HI developed from numerical data in our previous work. Keywords: Strain-based SHM
Post-buckling
Composite stiffened panels
Skin-to-stringer disbond growth
Fiber Bragg grating sensors
Fatigue testing

1 Introduction Composite stiffened panels represent a significant proportion of modern airframe structures. Generally, the skin-to-stringer interfaces of stiffened panels are prone to the development of unseen damages as they form regions of intense stress concentrations at their edges. Moreover, unforeseen events such as foreign object impacts (e.g. tool drops) may also jeopardize the integrity of these critical areas. To this end, researchers develop Structural Health Monitoring (SHM) methodologies in an attempt to identify, localize and quantify the severity of such structural defects [1]. Recently, a tendency is being intensified towards applying the aforementioned SHM levels during operational © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 626–635, 2021. https://doi.org/10.1007/978-3-030-64594-6_61

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conditions. Condition-based maintenance (CBM) aims to substitute the current periodic ground inspections with a real-time SHM evaluation via a permanently installed network of sensors [2]. An emerging category of sensors in this direction is fiber Bragg grating sensors (FBGs), a specific subcategory of fiber optic sensors (FOS), capable of measuring static and dynamic strains. In the literature, limited studies have been developed on monitoring fatigue damage propagation, based on strain readings, oriented towards aeronautical/aerospace applications. Studies of various structural complexities can be found, from coupon level [3] or structural elements [4] to larger scale of structural components [5, 6]. A prominent experimental evaluation of the interfacial skin/stringer fatigue disbond progression can be found in Ref. [7], for the case of a hat-stiffened composite panel. A plethora of nondestructive testing (NDT) techniques was utilized, such as in-situ ultrasonic inspection (UT), passive thermography, and digital image correlation (DIC) in order to continuously monitor the disbond growth. Feng et al. [8] investigated the effect of fatigue loading on impact-induced damages of composite multi-stiffened panels. Furthermore, several works provide a deeper insight on various aspects which affect the behavior of composite stiffened panels subjected to post-buckling axial compression [9–14]. The present work implements a strain-based SHM methodology that exploits static strains acquired via surface mounted FBGs. In order to demonstrate more realistic operational conditions, and at the same time achieve gradual propagation of structural damage, we go beyond static testing and subject a composite single-stiffened panel to block loading compression-compression (C-C) fatigue. The present strain-based methodology leverages on static strains acquired during quasi-static (QS) test intervals throughout the test span. The limitations that arise due to the intrinsic dependency of strains on load, are tackled using a specially designed Health Indicator (HI) [15]. The robustness of the specific HI is investigated during the testing procedure and its sensitivity to disbond propagation is checked. Moreover, evidence of the damage extent is acquired from in-situ NDT, using a phased-array ultrasound camera. The promising results of this work empower the consolidation of a reliable SHM system operating with real-time influx of data.

2 Test Article and Experimental Campaign 2.1

Specimen Fabrication

For the purposes of this work, composite single-stringer flat panels were fabricated by OPTIMAL Structural Solutions Lda (Portugal). The skin is co-cured with a T-section stringer as depicted in Fig. 1b. Both skin and stringer are made of a graphite-epoxy continuum fiber-reinforced prepreg IM7/8552; the skin consists of 14 layers with ply orientation [45/−45/0/45/90/−45/0]s and the stringer is formed with a 10-layer layup [45/−45/0/45/−45]s. In order to ensure uniform transmission of the loads to the panel, two 30 mm-thick cast-tabs, made of an epoxy casting resin EPO 5019 of Axson Technologies®, were used at the two ends of the panel as shown in Fig. 1b. The free length of the panel, i.e. between the casted regions of the tabs, is 240 mm. In total, two

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defected specimens which contain a 30-mm artificial disbond at the skin/stringer interface were fabricated, by inserting Teflon amidst the two parts prior to curing process. 2.2

Experimental Campaign

The composite stiffened panels were subjected to blocks of constant amplitude C-C fatigue load with an R-ratio of 10 and testing frequency f = 2 Hz. The load amplitudes sequence and the rest of the operational characteristics are analytically presented in Table 1. Acquisition of the static strains, for L1_22, was enabled during a QS interval test every 500 fatigue cycles (Nf) as it is schematically depicted in Fig. 1a. The load range of each QS test was initiating from −0,5 kN up to maximum compressive load (Pmax) with a constant displacement rate of 0.5 mm/min. Specimen L1_22 was tested in the facilities of University of Patras – Applied Mechanics Lab using a servohydraulic Instron 8802 test machine whilst L1_23 in TU Delft – Aerospace Structures and Materials Lab using an MTS test machine, with load capacities ±250 kN and ±500 kN respectively. Moreover, every QS interval for L1_23 was conducted in a load range starting from minimum (Pmin) compressive load up to the maximum.

Fig. 1. a) Schematic representation of the test machine’s programme, b) fully-sensorized specimen during QS compression and positions of disbond & FBGs for c) L1_22 and d) L1_23.

The specimens were sensorized in both stringer feet with a SMARTape® (see Fig. 1b), i.e. a commercial glass-epoxy tape provided by SMARTEC SA (Switzerland) which accommodates two FOS. One optical fiber operates under the principles of distributed sensing technique while the second is an FBG-based fiber having five (5) gratings along its length, with a 20-mm spacing to each other. The sensor tapes were mounted to the surface of the stringer’s feet via a secondary bonding technique. A Griltex® copolyimide flexible adhesive was used to adhere the tape to the test article. Acquisition of the FBG strains was achieved using a 2-channel Micron Optics Inc. SM130 industrial grade dynamic interrogator with recording capabilities up to 1 kHz. During the QS tests, the acquisition frequency was set equal to 5 Hz. The interrogation serves the operational principle of FBGs and it is based on acquiring the reflected spectrum by the inscribed gratings. Based on the characteristics of the grating (e.g.

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grating period KB) the reflected light travels with the Bragg wavelength according to the Bragg equation: kB ¼ 2neff KB

ð1Þ

where neff is the effective refractive index and KB the grating period. Whenever the optical fiber is stretched or compressed due to mechanical deformation of the monitored host material, leads to modification of the effective refractive index and the Bragg period, and subsequently to a shift on the wavelength as follows: DkB =kB ¼ ð1  pÞe11

ð2Þ

where p is the photo-elastic coefficient and e11 is the induced longitudinal strain. Supplementary, there is also a thermal term in Eq. (2) associated to the thermal elongation of the fiber, but it was not accounted for in the current case study as the temperature did not change during the test. In Fig. 1b piezoelectric and acoustic emission sensors are also visible, which are not incorporated in the framework of the present strain-oriented methodology. Table 1. Specifications of the fatigue test and the defected specimens. R-ratio f Failure cycles Specimen Damage locationa Max. load L1_22 ¾ of stringer foot −35 kN (10k cycles) 10 2 Hz 345k −39 kN (10k cycles) −45 kN (10k cycles) −50 kN (170k cycles) −55 kN (85k cycles) −60 kN (60k cycles) L1_23 ¾ of stringer foot −50 kN (100k cycles) 10 2 Hz 438k −60 kN (338k cycles) a Measuring from top tab.

3 Assessment of Skin-to-Stringer Growth 3.1

Static Strains Acquisition

The strain histories throughout the test span are presented in Fig. 2. Particularly, the FBG strains recorded at Pmax of every QS test are depicted. For the L1_22 test article, sensor R5 was persistently recording low strains and we deduce that its bonding to the surface has been compromised. During the initial 30 k fatigue cycles, the relevant strains acquired at three different Pmax levels, i.e. −35, −39 and −45 kN, present minor deviation as the fatigue progresses. A clear indication of the load effect (i.e. the increase of Pmax in each block) is the stepped behavior of the induced static strains (Fig. 2a). When Pmax is raised to −50 kN, an observed variation in the strain readings is developed, intensified in sensor R1 which is located in the vicinity of the disbond. This

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observation can be attributed to a possibly increase of the disbonded area, as the newly developed surfaces would affect the sensor in their vicinity and in turn its readings. Only sensor R1 was directly above the disbonded region whilst R2 was lying outside the disbond. L1_23 initiated to operate with a nominal Pmax = −50 kN. Here, we can observe a rapid monotonic drop of the measured (maximum) strain of each QS interval. This mutual evidence from both specimens led to the notion that, within the range of loads [−50, −45) kN, the 30-mm disbond has initiated growing, which resulted to the strain variations with respect to the evolution of the cycles. Both specimens buckled following the pattern of the 1st buckling mode shape, forming a single half-wave along their skin sides (see Fig. 1b) but with an opposite way to each other. This means that the induced curvature along each skin side of L1_22 is formed by an inverted way with respect to L1_23. Besides of the direct visual validation, this can be indirectly estimated if we notice that the central FBGs, i.e. R2, R3 and R4, present higher postbuckling strains from those of R1 and R5 on L1_23 whilst the opposite observation can be made for the case of L1_22. Maximum strains belonging to the left foot are not shown, but it should be noted that they presented almost negligible deviation to their values (per Pmax level). This is a rather obvious observation, as they seem to be unaffected by the artificially-induced damage. Finally, since we do not have indirect validation of the disbond propagation for the case of L1_23, the strains are presented only for the first block, namely up to Nf = 1  105.

a)

b)

Fig. 2. Maximum FBG strains for a) L1_22 and b) L1_23 along the disbonded foot. (Similar legend for both figures)

3.2

In-Situ C-Scan Inspection

In order to enhance the SHM approach with in-situ evidence of the defect extent, a phased-array ultrasound system, i.e. DolphiCam, dedicated for NDT inspections on Carbon Fibre Reinforced Polymers (CFRP) materials, was utilized. DolphiCam supports a variety of inspection techniques such as A-, B- and C-Scans. In our test, C-Scan measurements were made utilizing the Time-of-Flight (ToF) feature. The testing operation was paused in several moments in order to perform measurements with the DolphiCam. In Fig. 3a several measurements are presented which progressively capture the fatigue disbond growth of the initial 30-mm rectangle disbond. The disbonded area is graphically enclosed within the black dashed line; the camera measurements

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have been made from the skin side of the panel (see Fig. 3b). From the inspection evidence we verify that only after the specimen enters the fatigue loading regime with Pmax = −50 kN, the disbond initiates its propagation along the longitudinal direction. The phased-array inspection was only conducted for specimen L1_22.

Fig. 3. a) Disbond growth of L1_22 as measured with DolphiCam using ToF feature and b) schematic illustration of the scanned region.

3.3

Strain-Based Health Indicator (HI)

To develop a strain-based HI we face the issue that absolute static strains are strongly dependent on load fluctuations and apparently load does not remain constant during service (see abrupt jumps in Fig. 2a). This means that the strain field might be more influenced by the load than by the defect itself, the latter which is the actual target of the majority of SHM systems. To this end, we proposed in our previous work [15] the following normalization scheme of the FBG strains: ei ðei Þj ¼ 1 P n

ei

ð3Þ

where, n is the number of FBGs per foot, i = 1, …, n and j is the index that defines the foot, i.e. RF: right foot and LF: left foot (j = RF, LF). We have studied the behavior of this normalization scheme in a numerical modeling framework in [15], and observed its dependency, on both load as well as various disbond configurations amidst the skin/stringer interface. The results showed, that prior to buckling this normalization feature presented a stable behavior, whilst when the structure underwent to the postbuckling regime, it deviated from its initial values. This could lead to confusion, as it is likely that the change to the indicator’s value is solely attributed to varying loads and not to the actual increase of the disbond. Hence, leveraging on the anti-symmetrical buckling mode shape of the specific test article, the authors proposed the following HI: HIi ¼

ðei ÞLF þ ðei ÞRF 2

ð4Þ

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where i stands for the FBG per foot. This indicator now utilizes strains acquired from a pair of sensors that belong to a corresponding position at the two feet of the stiffened panel. The evolution of this HI as well as the normalization schemes are presented in Fig. 4, based on strains acquired from L1_23 at the first QS test, i.e. before the initiation of the fatigue loading, coming from a wide load range [−50, −5] kN. The abscissa of the graphs is expressed in terms of sample points during the QS loading. However, we can clearly identify when buckling occurs by the separation between the two normalized features.

Sample points

Fig. 4. HIs and normalized strains for all FBGs of L1_23 during the 1st QS loading.

As it can be noticed from Fig. 4, the HI provenly presents a quite stable behavior in an extended load range. So, out of a plethora of values, that correspond to various load magnitudes, we will utilize the arithmetic mean of the HI samples as the monitoring feature. Due to potentially erroneous measurements of sensor R5 in L1_22, the 5th pair of FBGs was excluded from the calculation of the HIs. Out of the acquired data during the QS test, i.e. strains in load range up to Pmax, a representative sample was created by randomly picking elements from the whole population of strain readings (NS). Here, the sampling region (NSR) of the randomly selected data will be consisting of 75% of the total sample points (NS) as highlighted between the two dashed lines in Fig. 5a. In Fig. 5b and Fig. 6 the mean values of the HI samples are presented throughout the test span for specimens L1_23 and L1_22 respectively. Generally, we can observe monotonic trends on the indicators which is a desirable behavior from a diagnostics as well as a prognostics point of view [16, 17]. However, as the growth of the disbond progressively affects the adjacent sensors, some of the indicators reverse their gradient (also noticed in [15]), like HI2. This can be attributed to the gradient change of the absolute strain, as for example in sensors R2 of LP_22; when the structure operated with Pmax = −55 kN, its readings presented consecutive decrease but when Pmax shifted to −60 kN, in combination with further disbond propagation, the readings of this sensor suddenly increased till final failure. Moreover, two more potential reasons associated with the maximum strain deviation and by extension with the HIs’ behavior, can be pointed. Namely, degradation of the overall stiffness of the structure and the FOS’s bonding interface, would inevitably affect the strains readings and lead to variation on the values of the HIs during testing.

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0,25·NS

a)

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NS

b)

Fig. 5. a) Indicative samples of FBG strain readings and test machine’s compressive load, b) evolution of HIs for L1_23.

a)

b)

c)

d)

Fig. 6. Evolution of a) HI1, b) HI2, c) HI3 and d) HI4 for L1_22; black dashed lines represent the change of the block loading limits whilst the red ones indicate the time when the enclosed phased-array scan was made.

4 Conclusions A strain-based SHM methodology is presented in the current work. Two CFRP singlestiffened panels have been subjected to block loading C-C fatigue. The specimens contained a rectangular artificial disbond in the skin/stringer interface which was

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progressively growing due to the fatigue loading. Strain sensing, in terms of surface mounted FBGs, is utilized towards capturing the phenomenon of disbond propagation. NDT inspection, using an ultrasound phased-array camera, provided evidence regarding the morphology of the disbond growth. The methodology is based on acquiring static strains from the top surface of the stringers’ feet. To this direction, a special HI is proposed and assessed as a monitoring feature during the test span. By using the specific HI, we have seemingly achieved to eliminate the abrupt jumps of the absolute strains due to their inherent dependency on load. Moreover, strain data from an extensive load range have been utilized. The HIs’ sensitivity to the disbond increase is captured making it a considerable candidate for real-time strain-based feature. Specifically, on specimen L1_22 we report a maximum 25.9%, 8.9% and 15.8% modification on HI1 (the most affected from the disbond), HI2 and HI3 respectively, in comparison to their initial values. HI4, reflecting on a region where the disbond did not affect the 4th sensor pair, presented a minor modification of 4.6% if compared to its initial value. For L1_23, the most affected indicator, namely HI5, showed a modification to its values equal to 25.3%, whilst none of the rest HIs presented an overall deviation larger than 7.2%. Also, the present HI proved its ability to maintain its efficiency in cases where some sensors should not be considered, like for the case of sensor R5 of specimen L1_22. In the future we aim to give further insight in the behavior of this HI, which will be evaluated in an updated test campaign of a random spectrum fatigue excitation assisted by strain sensing during testing. Also, preliminary studies have shown that the HI consistently retains its behavior for various sizes of NSR but an investigation on its proper size will be conducted. Finally, the applicability of this HI to alternative sources of damages, such as impact-induced delaminations, will be investigated. Acknowledgements. The work was financially supported by the European Union’s Horizon 2020 research and innovation programme ReMAP (Grant Agreement Number: 769288).

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7. Dávila, C.G., Bisagni, C.: Fatigue life and damage tolerance of postbuckled composite stiffened structures with initial delamination. Compos. Struct. 161, 73–84 (2017) 8. Feng, Y., et al.: Effect of fatigue loading on impact damage and buckling/post-buckling behaviors of stiffened composite panels under axial compression. Compos. Struct. 164, 248– 262 (2017) 9. Falzon, B.G., Stevens, K.A., Davies, G.O.: Postbucking behaviour of a blade-stiffened composite panel loaded in uniaxial compression. Compos. Part A Appl. Sci. Manuf. 31, 459–468 (2000) 10. Yap, J.W.H., Scott, M.L., Thomson, R.S., Hachenberg, D.: The analysis of skin-to-stiffener debonding in composite aerospace structures. Compos. Struct. 57, 425–435 (2002) 11. Meeks, C., Greenhalgh, E., Falzon, B.G.: Stiffener debonding mechanisms in post-buckled CFRP aerospace panels. Compos. Part A Appl. Sci. Manuf. 36, 934–946 (2005) 12. Takeda, S., Aoki, Y., Nagao, Y.: Damage monitoring of CFRP stiffened panels under compressive load using FBG sensors. Compos. Struct. 94, 813–819 (2012) 13. Kolanu, N.R., Raju, G., Ramji, M.: Experimental and numerical studies on the buckling and post-buckling behavior of single blade-stiffened CFRP panels. Compos. Struct. 196, 135– 154 (2018) 14. van den Akker, B.P.H., et al.: The influence of hygrothermal aging on the fatigue behavior and residual strength of post-buckled co-bonded stiffened panels subjected to compressive loading. Compos. Part B Eng. 194, 108023 (2020) 15. Milanoski, D.P., Loutas, T.H.: Strain-based health indicators for the structural health monitoring of stiffened composite panels. J. Intell. Mater. Syst. Struct. (2020). https://doi. org/10.1177/1045389X20924822 16. Eleftheroglou, N., Loutas, T.: Fatigue damage diagnostics and prognostics of composites utilizing structural health monitoring data and stochastic processes. Struct. Heal. Monit. 15, 473–488 (2016) 17. Eleftheroglou, N., Zarouchas, D., Loutas, T., Alderliesten, R., Benedictus, R.: Structural health monitoring data fusion for in-situ life prognosis of composite structures. Reliab. Eng. Syst. Saf. 178, 40–54 (2018)

An Impact Monitoring System for Aeronautical Structures Alessio Beligni1(&), Kamil Kowalczyk2, Claudio Sbarufatti1, and Marco Giglio1 1

2

Mechanical Department, Politecnico di Milano, Via La Masa 1, 20156 Milan, Italy {alessio.beligni,claudio.sbarufatti, marco.giglio}@polimi.it Instytut Techniczny Wojsk Lotniczych, Księcia Bolesława 6, 01-494 Warsaw, Poland [email protected]

Abstract. Direct or indirect effects provoked by foreign object impacts on aeronautical structures, represent a major concern for military and civil aviation. The problem potentially intensifies with the adoption of composite materials, especially if Barely Visible Impact Damages (BVID) are generated in the structure. The knowledge of whether an impact event has happened and if it has produced a damage, is highly desirable allowing maintenance improvements and the management of risky situations. This can be achieved developing an Impact Monitoring (IM) system, eventually integrable with other monitoring systems for the implementation of a Predictive Maintenance (PM) philosophy. This work deals with the problem of the development of the conceptual scheme of an Impact Monitoring system; it can be considered composed of two parts: (i) a passive impact monitoring part and (ii) an active damage monitoring part. The former part is dedicated to the diagnosis of Low Velocity Impact (LVI) events, meaning the detection, localization and reconstruction of the force exerted on the structure by the foreign object. The latter part is dedicated to the diagnosis of an impact damage, meaning the detection of the damage presence and its qualitative estimation. The IM system is then applied to LVIs on composite structures, typical of aeronautical applications. Keywords: Impact monitoring
Strain waves
PZT
Lamb waves
Damage index

1 Introduction In the aviation field of application, safety and financial aspects have always had primary importance, where often the loss of safety has economic consequences in terms of unreliability, inefficiency and unpredicted maintenance, as well as life losses [1, 2]. Several are the causes could hamper the flight safety, not only during the flight itself but throughout all the vehicle operation phases [3, 4]. Each kind of impact event could lead to critical accidents, depending on the damage severity produced to the vehicle and the ability to adopt the most suitable countermeasures. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 636–646, 2021. https://doi.org/10.1007/978-3-030-64594-6_62

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The problem of impact damages could be even greater if composite structures are considered, instead of classic metallic ones [5]. If a damage is produced as effect of an impact event, it can be classified as Visible Impact Damage (VID) or Barely Visible Impact Damage (BVID), depending on the possibility to detect it during the vehicle maintenance procedures or only using Non-Destructive Inspection (NDI) techniques. This poses major problems in the health assessment of structures, especially considering that composite usage spread over different fields of application [6]. In addition, during the years Remotely Piloted Aircrafts (RPA) took a relevant role in the army strategies, as well as their use for scientific, environmental and securityrelated purposes in non-segregated areas has become always more attractive [7]. The pilot absence is the main key-point which allows a performance-devoted design of the vehicle and risk-free missions, in terms of human life. However, it falls into the lack of situation awareness, without any assessment of dangerous phenomena, like impact events, occurring during flights or operational maneuvers. An appealing solution to the problem is the development of a Structural Health Monitoring (SHM) strategy to determine structural changes due to impact events, specifically in the form of an Impact Monitoring (IM) system. It leverages on the presence of sensors installed on the structure and algorithms, developed to extract impact related features, for its characterization. Moreover, actuators inject ultrasonic diagnostic waves in the structure, to check the presence of a damage as consequence of the impact event. The information can then be used for the implementation of a Predictive Maintenance (PM) strategy and in the vehicle mission management process. The aim of this work is to define the conceptual scheme of the IM system, defining the main functions the system must accomplish. Then, it is applied to a simplified component representative of a real structure, executing non damaging and damaging impacts on a Carbon Fiber Reinforced Plastic (CFRP) panel. Circular Piezoelectric (PZT) elements are installed and algorithms are developed in MatLab® environment. The PZTs are used both to acquire the impact induced strain waves and to generate/acquire Ultrasonic waves. The objective is to show how the information passes through all the IM system phases, highlighting how they affects the results of the entire system. The structure of the paper is as follows: the conceptual IM architecture design is reported in Sect. 2. The theoretical background about the techniques used to develop the algorithms is briefly summarized in Sect. 3. Then, the experimental setup is described in Sect. 4. Finally, the results are analyzed in Sect. 5 and conclusions are given in Sect. 6.

2 Impact Monitoring System Architecture As far as interactions between foreign objects and aeronautical structures produce a response in the latter, a SHM strategy could monitor the structural changes eventually produced. Sensor networks acquire the structural response, while algorithms extract specific impact-related information, to understand if an impact event provoked a structural change. The complete IM system is thus considered composed of two parts: (i) a Passive Impact Monitoring and (ii) an Active Damage Monitoring. The passive

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part constantly monitors the structure with real-time capabilities for the impact event characterization. Then, if the event is considered critical, an alarm is given to the active part of the system, that inspects the structure to identify the damage. 2.1

Passive Impact Monitoring

The objective of the Passive Impact Monitoring part of the system is to identify the impact event, meaning: 1. Impact Detection: it is aimed at defining the impact occurrence. If a sensor gives to the observer an indication, then an impact event has occurred. 2. Impact Localization: it is aimed at defining the impact position. Using multiple sensors, it is possible to gather the information to obtain an estimation of the impact position. 3. Force estimation: it is aimed at the reconstruction of the impact force. Again, using the information coming from multiple sensors, it is possible to recover the impulsive force. If the force features are supposed to be critical, then the Active Damage Monitoring can be activated. In Fig. 1 the flowchart of the Passive Impact Monitoring part of the complete system.

Fig. 1. Passive impact monitoring flowchart. LVI stands for low velocity impact, while HVI stands for high velocity impact.

2.2

Active Damage Monitoring

The objective of the Active Damage Monitoring part of the system is to identify the damage produced by the impact event, meaning: 1. Damage Detection: it is aimed at defining the damage presence. Using multiple sensors/actuators, it is possible to build a map of the structure to detect damage induced perturbations. 2. Damage Localization: it is aimed at qualitatively locate the damage. Leveraging on the results of the damage detection and tomography techniques, it is possible to estimate the damage position. In Fig. 2 the flowchart of the Active Damage Monitoring part of the complete system.

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Fig. 2. Active damage monitoring flowchart.

3 Algorithms Theoretical Background Signal processing techniques are selected to accomplish each task of the complete IM system. Regarding the Passive Impact Monitoring: 1. Impact Detection: the threshold technique detects the Time of Arrival (TOA) of the impact induced strain wave at the sensor position. The threshold limits are chosen studying the typical sensor noise, as: thup ¼ n  maxðsensor noiseÞ thlow ¼ n  minðsensor noiseÞ

ð1Þ

where n is a multiplier chosen equal to 1.5. 2. Impact Localization: exploiting the TOAs, the triangulation technique is used to locate the impact position. The Genetic Algorithm (GA) optimization tool allows to obtain a reliable result, but requires the definition of an objective function: Fðx0 ; y0 Þ ¼ min

N X

! e1i

ð2Þ

i¼2

where N is the number of sensors while the error e1i is calculated in the form of distance differences, considering the position ðx1 ; y1 Þ of the first sensor reached by the impact-induced strain wave, assumed as reference sensor, the position ðxi ; yi Þ of the i-th sensor, the experimental TOAs, the unknown strain wave velocity v and the unknown impact position ðx0 ; y0 Þ: e1i ¼ vðTOAi  TOA1 Þ 

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðxi  x0 Þ2 þ ðyi  y0 Þ2 þ ðx1  x0 Þ2 þ ðy1  y0 Þ2 ð3Þ

The TOA difference is called Time Difference of Arrival (TDOA). 3. Force estimation: it exploits the Frequency Response Function (FRF) technique to recover the force time history and its peak value, to assess the dangerousness of the impact event. Depending on the position estimated by the localization step the correct FRF, obtained during the experimental activities, is selected and applied to the actual signals. For the correct application of the technique, the actual signal eðtÞ must be multiplied by an exponential window ect . The force is then recovered through a time-frequency-time domain conversion, as reported in Fig. 3.

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Fig. 3. Force recovering process using the FRF technique.

Passing to the Active Damage Monitoring, it relies on the ability of PZT elements to vibrate, if electrically excited. This allows to inject ultrasonic diagnostic waves into the structure, to scan it and obtain a picture of the actual structure health state: 1. Damage Detection: a baseline-based ultrasonic technique is used to detect the damage presence. A baseline database was collected during the experimental activities, first. Then, the actual signals fgs are compared with the baseline fgs;b calculating a feature, the Damage Index (DI), that measures the intensity of the wave scattering because of the damage, on a specific actuator-sensor path. If a damage is not present on the path, the DI would be low, while higher DIs for the paths that pass through the damage. In this work the adopted DI is:    maxf  gs;b     ð4Þ DI ¼ log  max fgs  2. Damage location: a tomography technique exploits the DI values calculated in the Damage Detection, to draw a colored map representative of the damage position. A damage probability distribution I ð pÞ is assigned to each point p of the structure, considering the intensity of each DI value calculated on each path and a parameter Rgs ð pÞ, for the distribution shape definition on the path: I ð pÞ ¼

X

DI ðg; sÞRgs ð pÞ

ð5Þ

g!s

4 Experimental Setup The experimental setup comprises the specimen, the sensor network, the acquisition system and the dynamometric hammer, for the Passive Impact Monitoring. Then, the specimen was damaged using a drop-weight tower and the damage checked with manual Non-Destructive Inspection (NDI) technique. Finally, a generation/acquisition system is used to actuate the PZTs for the Active Damage Monitoring.

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Setup for Passive Impact Monitoring

Specimen. The specimen is a CFRP flat panel made of commercial aeronautical carbon-fiber prepreg layers. It has 400  270 mm dimension and is composed of 18 plies, to obtain a thickness of 3.5 mm. Sensor Network. It is composed of 8 simple cylindrical elements produced by Phisyk Instrumente GmbH, with diameter of 5 mm, thickness of 1 mm and made of PIC255 material. The sensors are placed in an octagonal configuration, covering an area of 110  240 mm, and glued with Z70 cyanoacrylic glue produced by HBM GmbH. Acquisition System. PZT elements are wired and plugged into the oscilloscope with BNC connectors. The PicoScope 4824 produced by Picotech acquires the PZT sensor output voltage at a sampling frequency of 50 kHz. Dynamometric Hammer. It is the model 086C03 produced by PCB Piezotronics and equipped with a steel tip. The load cell signal is conditioned using a model 480E09 produced by PCB Piezotronics and acquired with a NI9239 DAQ. 4.2

Low Velocity Damage Generation

A drop-weight tower device is used to hit the panel in the center. The mass weights 2.67 kg and the height is equal to 1.35 m, to produce a total energy of around 36 J. The load cell charge is conditioned using a 5011B charge amplifier produced by Kistler and acquired at a frequency of 50 kHz with a NI9239 DAQ. The panel is clamped at its hedges using a rectangular metallic mask, closed with bolts, to keep the panel in position, as recommended by the ASTM D7136 standard. Finally, the damage is checked manually using the Olympus Epoch 600 with a 5 MHz scope, obtaining a dimension of around 500 mm2. 4.3

Setup for Active Damage Monitoring

The specimen and sensor network remain the same, what changes is the adoption of a unique device for ultrasonic waves generation/acquisition. The NI PXIe-1082 Chassis with the 5172, 5413 and 8840 Quad-core PXIe modules allows to generate the signal and acquire the PZTs response at a frequency of 2.5 MHz. The generated signal is a 3sine tone burst at a frequency of 250 kHz and 180 V peak, reached using a voltage amplifier.

5 Results During the experimental activities, the panel was hit 36 times in random positions for impact detection and localization training, while the system was validated impacting other 8 positions. Regarding the FRF, it was trained impacting the 8 detection/localization validation positions 10 times each and then hitting again the same positions another time, to validate the force reconstruction. Finally, the LVI

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impact with the falling mass was executed in the proximity of one of the FRF training positions (i.e. the center of the plate). In the following, the results will be shown following the IM system flow, highlighting the findings and the criticalities, especially if wrong information are passed from one step to the other of the system. 5.1

Impact Detection

During the training procedure, the threshold defined in Eq. (1) is applied to each sensor and for all the 36 impacts. As far as the impact positions are known, it is possible to calculate the distance difference Dd similarly to the TDOA, introduced in Sect. 3, as the differences between the impact position, the first sensor reached by the strain wave and the i-th sensor currently considered. The TDOAij  Ddij couples, where i is the sensor number and j the impact number, are graphed as depicted in Fig. 4. The figure on the left, reports the results obtained during the training procedure, where the cloud of data can be synthetically represented by its linear regression and the confidence interval, placed at 3r. As can be noticed, only one element of the entire training dataset is placed outside the confidence interval. The same can be done detecting the impacts in the validation positions, this time keeping the linear regression and confidence interval previously calculated. Figure 4 right reports the results, where it is possible to state that the linear regression gives a true picture of the phenomenon under investigation and that the detection procedure can be considered reliable, as it is defined. Finally, the time taken by the algorithm to detect the impact is of few splits of second only, meaning it has real time operation.

Fig. 4. Detected TDOAij  Ddij couples, linear regression and confidence interval obtained during the training procedure (left) and the validation data (right).

5.2

Impact Localization

The localization procedure does not require any training, but it can be applied exploiting the TOAs and the definition of some boundary conditions, for the GA operation. The median error obtained over all the validation positions is equal to 12.3 mm. This is a good result, especially considering that the impacts are manually

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executed, meaning that an intrinsic error between the drawn impact position and the point actually hit differs at least for few millimeters. In Fig. 5 a graphical comparison of the locations obtained with the Impact Localization procedure and the actual impact positions.

Fig. 5. Graphical representation of the localized positions (red crosses) and the real impact points (blue circles). The green circles are the PZT sensors.

In addition, another result can be obtained from the LVI executed with the dropweight tower. In this case the error made in localizing the impact position is of 36.8 mm. Again, the result is in line with what obtained with the hammer and in general the error can be considered small enough if compared to the plate dimensions. However, even a small error could produce great errors in the next phase of the system (i.e. Force Estimation), considering that the correct force recover is greatly influenced by the selection of the structure position. Finally, the GA is solved using the MatLab gamultiobj function, guarantying a processing time of only 14.4 s and thus meaning that this step of the procedure is in near real time. 5.3

Force Estimation

The last step is the Force Estimation implemented using the FRF technique, experimentally evaluated during the training. As already mentioned before, the impact location information greatly influences the force estimation results, through the selection of the correct FRF to be used for the force recover. For this reason, a first validation is made impacting again the training positions with the hammer. In Fig. 6 (left) an example of comparison between the force time history recorded by the load cell and the force recovered by the system. The error made in recovering the force, Fig. 6(right), is evaluated measuring the percentage difference between the recorded and recovered force peak.

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Fig. 6. A comparison between recorded (blue) and recovered force (green) (left) and the resume of the errors in calculating the force peak (right).

In addition, Fig. 7 depicts the force recovered during the LVI. Again, the error made on the peak value is equal to 1%, that is in line with the ones obtained with the hammer. In the same way, calculating the percentage error in the Full Width Half Magnitude (FWHM) of the force signal, the error is equal to 18%. Finally, the total time taken to recover the force time history is of only few seconds, making the Force Estimation step able to operate in real time.

Fig. 7. A comparison between recorded (blue) and recovered force (green) during the LVI.

5.4

Damage Detection and Location

Once the force is recovered, the Active Damage Monitoring can be operated. The training phase of the system is dedicated to the baseline signal acquisition with the structure in pristine condition, for each generator-sensor path. As far as ultrasonic waves are influenced by operational conditions (e.g. temperature, boundary conditions, applied loads, etc.) an initial comparison between two baselines is made, in Fig. 8(left).

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Fig. 8. DI maps with the panel in pristine conditions and considering all the environmental variabilities (left) and with the panel damaged (right).

The result is representative of all the variations the panel suffered during the experimental activities, before the LVI, and it is taken as the DI threshold to be crossed for the Damage Detection. Figure 8(right) is the map of the DI once the LVI has been executed, thus damaging the component. It is the representation of the Damage Detection, as in some positions the DI is higher than the threshold, but also the Damage Location, as some concentrated points have the highest DI value. These points indicate the most probable positions where the damage is supposed to be present. Finally, the Active Damage Monitoring procedure took about 5 min, where the signal processing time is of few decades of seconds only. The most time-consuming part of the method is the generation of diagnostic waves using all the PZTs as actuators.

6 Conclusions This work deals with the problem of the development of an architecture for an Impact Monitoring system. The integration of monitoring technologies in vehicles is highly desirable nowadays, for the increase of situation awareness, availability and costs reduction they can bring. The Impact Monitoring system architecture has been divided into two parts: (i) Passive Impact monitoring and (ii) Active Damage Monitoring. The experimental activities have been executed to explore the operation of each part of the system and the main outcomes are listed in the following: 1. Impact Detection: the system can correctly detect the occurrence of an impact event, evaluating a feature called TOA in real time. 2. Impact Localization: the system exploits the TOA information to correctly locate the impact position in near real time. Only a small error is made, if compared to the structure dimension. 3. Force Estimation: the system exploits the position information to select the proper FRF and recover the impact force, in real time. The error is low enough to make the reconstructed peaks indicative of the force order of magnitude.

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4. Damage Detection and Localization: ultrasonic waves are generated in a small amount of time using each PZT element, while a colored map representation gives awareness about the detection and position of the damage. Further detailed studies are required for compensating the environmental influences, defining the uncertainties and propagate them, making the system suitable for real applications. Acknowledgments. This work has been developed based on the results from SAMAS project (SHM application to Remotely Piloted Aircraft Systems), a Cat.-B project coordinated by the European Defense Agency (EDA) and financed by two nations, Italy and Poland. The project consortium includes the following parties: Italy (Politecnico di Milano, Leonardo S.p.A) and Poland (Instytut Techniczny Wojsk Lotniczych – AFIT, Military Aviation Works No. 1).

References 1. 2. 3. 4.

Allianze Global Corporate: Global aviation safety study, p. 63 (2014) IATA: Aviation benefits 2017. International Air Transport Association, p. 68 (2017) A.S. Network. https://aviation-safety.net/database/databases.php Wenner, C.A., Drury, C.G.: Analyzing human error in aircraft ground damage incidents. Int. J. Ind. Ergon. 26(2), 177–199 (2000) 5. Soutis, C.: Carbon fiber reinforced plastics in aircraft construction. Mater. Sci. Eng., A 412(1– 2), 171–176 (2005) 6. JEC. http://www.jeccomposites.com/ 7. European Defense Agency: European Commission UAS Panel 5th Workshop on Research and Development (2012)

Toward Composite Damage Classification Using in Situ Wavenumber-Frequency Modal Decomposition of Acoustic Emissions Cédric Rosalie1(&), Nik Rajic1, Stephen van der Velden1, L. R. Francis Rose1, Joel Smithard1, and Wing Kong Chiu2 1

2

Aerospace Division, Defence Science and Technology Group, Fishermans Bend, VIC 3207, Australia [email protected] Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3168, Australia

Abstract. A multi-element piezoelectric sensing capability is applied to in situ wavenumber-frequency modal decomposition of acoustic emissions (AE) generated by low-velocity impact on a fibre-reinforced polymer composite panel. The modal signatures of the impact AE are shown to be dominated by a lowfrequency antisymmetric (A0) Lamb wave, with little discernible difference in signature observed between non-damaging and damaging impacts. An artificial delamination induced mid-thickness in the same panel under quasi-static loading is also considered, for comparison. For this case, the modal signature was found to be dominated by a symmetric (S0) Lamb wave. The prospects and challenges for characterizing impacts based on modal decomposition of AE are briefly discussed in light of these results. Keywords: Composites
Acoustic emission
Distributed sensing Polyvinylidene difluoride
Impact damage
Delamination




1 Introduction The relatively high stiffness-to-weight ratio of fibre-reinforced polymer composite materials is attractive for aircraft construction. However, relative to metals, composites are more susceptible to low-velocity impact damage from hailstones, runway debris, tool drop and bird strike [1]. These impact events can result in Barely Visible Impact Damage (BVID), which is difficult to detect visually but can cause significant strength reduction, which can potentially lead to structural failure. Therefore, the detection of BVID and the impacts that produce such damage is important. Composites can experience different modes of failure including fibre breakage, matrix microcracking, fibre separation (debonding), and delamination. Damaging impacts typically produce a combination of these failure modes [2]. A previous review of non-destructive testing methods applicable to composite materials [3] identified Acoustic Emission (AE) and Acousto-Ultrasonics (AU) as prime candidates for rapid wide-area inspection. As AE allows for detection of the © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 647–657, 2021. https://doi.org/10.1007/978-3-030-64594-6_63

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damaging event itself, it is better-suited for early detection of damage in composites than AU. However, because the wavefield generated by an impact in a plate-like structure typically consists of multiple dispersive Lamb modes, the analysis of AE signals can be challenging [4–7]. Grid scanning of a wavefield followed by a two-dimensional fast Fourier transform (2D-FFT) [8] to produce a frequency-wavenumber modal decomposition is a powerful analysis approach which is applied routinely in AU studies using laser scanning vibrometry (LSV). Modal signatures thus obtained can be filtered to isolate modes of interest [9, 10], and to remove boundary reflections [11] and other extraneous components including random noise. However, as LSV systems are relatively large and cumbersome, they are not suitable for in situ application. A modal decomposition capability suitable for in situ structural health monitoring (SHM) was introduced in [12]. This capability used a high-density multi-element fibre Bragg grating (FBG) array. Although effective, each FBG in the array must be interrogated sequentially, which limits its use to applications in which the acoustic source is repeatable. That limitation was overcome recently with the development of a multielement piezoelectric sensor and complementary interrogation hardware, allowing for the first time a frequency-wavenumber modal decomposition of AE [13, 14]. In the present paper, this new modal decomposition capability is used to investigate AE in laminates subject to non-damaging and damaging low-velocity impact, as well as delamination damage induced by quasi-static loading. By enabling in situ modal decomposition of AE, this capability potentially opens new opportunities in damage classification and quantification based on characteristic modal signatures or “fingerprints” [15].

2 A Linear Array for Modal Decomposition and Analysis The modal decomposition capability relies on a high-density multi-element polyvinylidene difluoride (PVDF) sensor array, referred to by the acronym LAMDA for Linear Array for Modal Decomposition and Analysis, coupled to a modular, lowpower, low-noise, high-bandwidth data-acquisition instrument suitable for aircraft implementation. Salient aspects of the layout and construction of the sensor are illustrated in Fig. 1(a) and (b), with further construction details presented in [14]. The complementary data-acquisition instrument, referred to as the AUSAM+ for AcoustoUltrasonic for Structural health monitoring Array Module [16], is pictured in Fig. 1(c). This device has a bandwidth of 50 kHz–5 MHz and accommodates 4 channels; however several units can be networked via an optical link to provide as many as 248 independent channels. In the present work, eight devices were networked to provide the 32 channels required to interrogate the 32-element sensor shown in Fig. 1(a).

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Fig. 1. (a) LAMDA; (b) Through-thickness section of LAMDA; (c) AUSAM+.

Figure 2 depicts schematically a plane wave with wave vector, k, representing an idealisation of the AE from a sufficiently distant source. When this plane wave strikes the LAMDA array sensor, the time signals from all 32 sensor positions are recorded simultaneously by the AUSAM+ module. This set of N simultaneously acquired time signals (corresponding to N sensor elements), each discretised into M samples, is then processed using a 2D-FFT, resulting in a discrete wavenumber spectrum given by Hk þ 1;f þ 1 ¼

N X M X

un;m e2piðn1Þk=N e2piðm1Þf =M

ð1Þ

n¼1 m¼1

Here, k and f are wavenumber and frequency indices respectively and u is the voltage signal produced by a sensor element. For the sensor dimensions given in Fig. 1, the wave resolution and Nyquist limit are 331 rad/m and 2474 rad/m, respectively [17].

Fig. 2. Plane wave with wave vector k and amplitude Aðk Þ striking a linear array of N sensor elements.

3 Experimental Study An experimental study was conducted on a square-shaped carbon-epoxy composite panel with a side length of 560 mm. The panel was manufactured from 16 layers of unidirectional IM7/5250-4 pre-preg in a [90, −45, 45, 0, 0, 45, −45, 90]s layup

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resulting in a cured laminate thickness of 2.1 mm. A LAMDA was bonded to the centre of the composite panel using cyanoacrylate adhesive, with the sensor arms aligned with the 0o/90o ply directions. As previously mentioned, eight AUSAM+ units were networked to accommodate the 32-channel sensor. To acquire an AE signal, the system is placed in “stand-by” mode, in which all channels are continuously recorded to a cyclic buffer until a prescribed threshold signal level is exceeded on any of the channels, which is taken to signify the arrival of an AE. The buffered signals are then sent to a computer for modal decomposition and analysis. In the present study, the acoustic source was generated using an instrumented dropweight impact rig. The key components of the experimental test set-up are shown in Fig. 3. Impacts were made at distances of 50, 100 and 150 mm from the sensor origin, aligned with the sensor axes, as indicated in Fig. 2. Non-damaging impacts were made using an impactor with a 25 mm diameter hemispherical tup and a mass of 485 g. Damaging impacts were made by increasing the impactor mass to 1450 g or 2458 g. The impactor was held in place via a hooking mechanism (shown in Fig. 3) which was released manually to initiate the drop. The panel was clamped to an 18 mm thick aluminium base plate containing a 125  75 mm rectangular aperture in the middle, concentric to the point of impact, in accordance with the ASTM–D7136 standard for impact damage testing. The impactor was released at a height of 400 mm for all tests, thus maintaining an approximately constant impact velocity which was measured using a “flag” attached to the impactor passing through a pair of optical sensors separated by a known distance. In these experiments, the impact velocity varied in the range 2.74–2.76 m/s.

Fig. 3. Key components of the experimental set-up.

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4 Impact Tests Figure 4 shows a typical AE signal and modal signature corresponding to a nondamaging impact of the 485 g mass at a distance 50 mm from the sensor origin. The time signal contains a frequency down-chirp which is characteristic of the A0 mode. The time-history recorded at all 32 sensor locations is shown in Fig. 4(b); it is recalled that the sensor consists of two arms, with 16 elements each (Fig. 2). A 2D-FFT applied to the time signals corresponding to the 0o ply direction (i.e. elements 1 to 16), results in the wavenumber spectrum shown in Fig. 4(c) which also includes the theoretical dispersion curves obtained from Rayleigh-Lamb theory for a laminate [18], using the mechanical properties listed in [19] for the individual plies. This spectrum confirms the presence of a dominant A0 mode.

Fig. 4. (a) Time signal recorded at arbitrary sensor position; (b) wavefield for an impact aligned with the sensor; (c) wavenumber dispersion spectrum corresponding to wavefield across sensor elements 1 to 16, versus dispersion curves obtained from Rayleigh-Lamb theory.

The panel was impacted at the three aforementioned locations in a two-step approach consisting of a non-damaging impact with the 485 g tup followed by a damaging impact with the heavier 1450 g tup. The resulting wavenumber-frequency signatures obtained from the AE recordings are shown in Fig. 5. While there is a noticeable difference between the non-damaging and damaging impact signatures, the trends are inconsistent, e.g. for the modal signature corresponding to the 50 mm distance, the damaging impact produces a narrower bandwidth signature relative to the non-damaging case, while for the impact at 100 mm (middle row) the opposite is true. In addition to recording AE measurements, Phased-Array Ultrasonic Testing (PAUT) was applied after each impact to determine whether damage had occurred. The PAUT scans, which comprised cross-sectional B-scans with A-scans on the left of the image, are shown in Fig. 6 for impacts at the 50 mm location. The results confirmed that a mass of 485 g produced no internal damage whilst a mass of 1450 g caused significant damage. Visual inspection of the panel after impact of the 1450 g mass revealed slight indentations in the top surface and bulging at the bottom surface corresponding to the impact.

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Fig. 6. PAUT scans after 485 g drop (left); and after 1450 g drop (right).

The modal signatures obtained from the non-damaging impacts were observed to vary significantly, raising questions about the repeatability of the testing. The panel support arrangement was investigated as a potential cause for this variability. A second composite panel was demarcated into four quadrants and a LAMDA was bonded in each quadrant, as shown in Fig. 7, using cyanoacrylate adhesive, with the sensor in the same orientation relative to the layup as before. The panel was initially clamped directly onto the aluminium base plate, as was done for the first panel. A non-damaging 485 g mass was dropped ten consecutive times on the panel at a distance of 75 mm

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from the sensor origin. The total energy in the A0 signature was calculated for each impact event and compared. This comparison, shown in Fig. 8(a), illustrates a relatively large variation in energy. The boundary condition at the interface of the composite panel and the aluminium base plate was examined as a possible factor. For the next set of impacts, a layer of bubble-wrap was placed between the composite panel and the base plate. As before, the 485 g mass was dropped ten times on the panel at the same location. The results, shown in Fig. 8(b), are far more consistent across impacts, with significantly more energy observed in the A0 mode relative to the original support arrangement. The previous testing was repeated using this new set-up. In addition to the 1450 g mass tup used in the previous round of testing, a heavier 2458 g tup was also used.

Fig. 7. Test panel configuration for additional impact testing. 1.4

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Figure 9 shows a comparison between modal signatures for the non-damaging (i.e. 485 g mass) and two damaging impacts (i.e. 1450 g and 2458 g mass) using the new test set-up. The signatures are remarkably similar, suggesting that the AEs corresponding to

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the failure processes in this laminate are small relative to the AE caused by the impact event itself. Further investigation of the modal signature of damaging impacts will be conducted after completing experimental rig modifications aimed at improving the repeatability of the non-damaging AE.

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5 Delamination Test As pointed out in the introduction, one of the failure mechanisms contributing to impact damage is delamination. The results presented above suggest that the contribution of delamination to the modal signature of a damaging impact cannot be easily distinguished from the signature of the impact itself. An experiment was devised to determine the signature of a delamination in the same laminate using the LAMDA. A delamination was generated by means of quasi-static point loading, thus eliminating the contribution of the impact AE, which was dominant in the previous testing. Blind holes 3 mm in diameter and *1 mm deep were drilled into a laminate of the same construction as before, at the locations shown in Fig. 10. The panel was rested on an annular support concentric to the blind hole. The drill bit was then replaced with a steel rod of the same diameter and lowered into the hole using the drill press until contact was made with the bottom of the hole. The AUSAM+ system was set to “stand

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by” mode and pressure was then applied to the rod to initiate a delamination. This pressure was halted when the AUSAM+ system was triggered by an AE.

Fig. 10. Positions of blind holes relative to LAMDA sensor on the composite panel for the artificial delamination tests.

Figure 11 shows results corresponding to a delamination source 50 mm from the sensor origin (the location is circled in Fig. 10). The modal signature contains a dominant S0 contribution, which is to be expected as the delamination was introduced mid-thickness in the panel, which is a plane of symmetry. It is interesting to compare the time histories in Figs. 11 and 4. The signal strengths are comparable. This suggests that the artificial delamination generated by this quasi-static loading process produces a much stronger AE than that caused by delamination produced by impact. Further investigation of this result is underway.

Fig. 11. Time signal at recorded at a representative sensor element 50 mm away from the source (left); modal signature for an artificial delamination (right).

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6 Conclusion and Future Work A new capability for in situ frequency-wavenumber modal decomposition has been applied to investigate acoustic emissions in composite panels generated by lowvelocity impacts, as well as delamination induced by quasi-static loading. It was found that modal signatures produced by damaging impacts were indistinguishable from those produced by non-damaging impacts, with both dominated by a strong lowfrequency A0 contribution associated with the impact itself. This comparative analysis was confounded by a relatively high variability in the signature of notionally identical non-damaging impacts. Work is being undertaken to reduce this variability through appropriate modification of the experimental impact apparatus. The AE modal signature of an artificial delamination generated mid-thickness in the panel by means of quasi-static point loading was also studied. This signature comprised a dominant S0 contribution with a strength comparable to that of the A0 produced in the damaging impact experiments. The absence of any significant S0 contribution in the damaging impact signature is being investigated in light of this result. Acknowledgement. The authors would like to acknowledge the contribution of Mr. Daniel Bitton for the manufacturing of the composite panels used for the experimental study presented in this paper and for modification of the test rig to accommodate large composite panels and also for his assistance during the impact testing phase of the work.

References 1. Ali, M., Joshi, S.C., Sultan, M.T.H.: Palliatives for low velocity impact damage in composite laminates. Adv. Mater. Sci. Eng. (2017) 2. Okoli, O.I., Smith, G.F.: Failure modes of fibre reinforced composites: the effects of strain rate and fibre content. J. Mater. Sci. 33, 5415–5422 (1998) 3. Gholizadeh, S.: A review of non-destructive testing methods of composite materials. Procedia Struct. Integrity 1, 50–57 (2016) 4. Giurgiutiu, V., Zagrai, A., Bao, J.J.: Piezoelectric wafer embedded active sensors for aging aircraft structural health monitoring. Struct. Health Monit. 1(1), 41–61 (2002) 5. Chandarana, N., Martinez-Sanchez, D., Soutis, C., Gresil, M.: Early damage detection in composites by distributed strain and acoustic event monitoring. Procedia Eng. 188, 88–95 (2017) 6. Ni, Q.-Q., Iwamoto, M.: Wavelet transform of acoustic emission signals in failure of model composites. Eng. Fract. Mech. 69, 717–728 (2002) 7. Prosser, W.H., Jackson, K.E., Kellas, S., Smith, B.T., McKeon, J., Friedman, A.: Advanced, waveform based acoustic emission detection of matrix cracking in composites. Mater. Eval. 53(9), 1052–1058 (1995) 8. Alleyne, D.N., Cawley, P.: The interaction of lamb waves with defects. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 39(3), 381–397 (1992) 9. Ruzzene, M.: Frequency-wavenumber domain filtering for improved damage evaluation. Smart Mater. Struct. 16, 2116 (2017) 10. Tian, Z., Yu, L.Y.: Lamb wave propagation study using frequency-wavenumber analysis. In: Proceedings of the ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Stone Mountain, Georgia, USA (2012)

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11. Michaels, T.E., Michaels, J.E., Ruzzene, M.: Frequency-wavenumber domain analysis of guided wavefields. Ultrasonics 51(4), 452–466 (2011) 12. Rajic, N., Davis, C., Thomson, A.: Acoustic wave-mode separation using a distributed bragg grating sensor. Smart Mater. Struct. 18(12), 125005 (2009) 13. Rajic, N., Rosalie, C., van der Velden, S., Rose, L.R.F., Smithard, J., Chiu, W.K.: A novel high density piezoelectric sensing capability for in situ modal decomposition of acoustic emissions. In: Proceedings of the 9th European Workshop on Structural Health Monitoring, Manchester, United Kingdom, 10–12 July (2018) 14. Rajic, N., Rosalie, C., Vien, B., van der Velden, S., Rose, L.R.F., Smithard, J., Chiu, W.K.: In situ wavenumber-frequency modal decomposition of acoustic emissions. Struct. Health Monit. 19, 1–18 (2020) 15. Rajic, N., Rosalie, C., Davis, C., Norman, P.: A distributed sensing capability for in situ time-domain separation of lamb waves. In: Proceedings of IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (2013) 16. Smithard, J., Rajic, N., van der Velden, S., Norman, P., Rosalie, C., Galea, S., Mei, H., Lin, B., Giurgiutiu, V.: An advanced multi-sensor acousto-ultrasonic structural health monitoring system: development and aerospace demonstration. Materials 10(7), 832 (2017) 17. Oppenheim, A.V., Schafer, R.W.: Digital Signal Processing Pearson. Prentice Hall Inc., Englewood (1975) 18. Pavlakovic, B., Lowe, M.: DISPERSE version 2.0.11d (2001) 19. Solvay, Cycom 5250-4 prepreg system (2019)

Vehicle-Based Indirect SHM for Infrastructure

Identification of the Elastic Modulus of Simply-Supported Girders from Dynamic Tests: Method and in Situ Validation Angelo Aloisio(B) , Elena Antonacci, Riccardo Cirella, Dante Galeota, Rocco Alaggio , and Massimo Fragiacomo Dipartimento di Ingegneria Civile Edile-Architettura ed Ambientale, Universit` a degli Studi dell’Aquila, Piazzale Pontieri, Monteluco di Roio, 67100 L’Aquila, AQ, Italy [email protected]

Abstract. Dynamic measurements under known moving loads yield a novel procedure for the elastic modulus assessment of existing concrete bridges. The bridge deck is modelled as a single-span, simply supported Euler–Bernoulli beam excited by a travelling force. The elastic modulus assessment derives from an Ordinary Least Square procedure with a Bayesian uncertainty estimation, obtained by approximating the known term of the governing equations due to the travelling force with a square wave signal. The authors validated the procedure on six full-scale concrete girders of the A24 motorway in Italy and compared the results to the values obtained via in situ static load tests and further tests on concrete specimens. The procedure represents a straightforward test devised for supporting the drafting of quality control plans and the prioritization of the interventions.

Keywords: Structural health monitoring Dynamic identification

1

· Prestressed RC beams ·

Introduction

Several developed countries are handling the problem of ageing infrastructures and a limited budget on their maintenance. Maintenance and repair needs of existing structures require quick and non-destructive test methods [1–3]. Many methods may rely on the identification of the dynamic properties of the structure in its in-situ condition [4]. In the current work, the authors deliver a straightforward procedure for the elastic modulus identification of simply supported girders based on an elementary inverse problem formulation. The authors identified the elastic modulus of six Prestressed Concrete (PSC) girders and validated the procedure by comparing the results with the outcomes of static load tests. c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 661–673, 2021. https://doi.org/10.1007/978-3-030-64594-6_64

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Forward Problem

The equation representative of a bridge deck of length L, modelled as a singlespan simply supported Euler–Bernoulli beam [5] subjected to an external force P (t) travelling with velocity c can be written as: ρA

∂ 4 y(x, t) ∂y(x, t) ∂ 2 y(x, t) + EI + d = P (t)δ(x − ct) ∂t2 ∂t ∂x4

(1)

where: ρ is the mass per unit length of the beam, A is the cross-sectional area, d is the damping of the beam, E is the Young modulus, I the moment of inertia of the beam cross-section, y(x, t) is the transverse displacement function of the beam and δ(.) is the Dirac delta function. The Young modulus E is assumed constant along the whole beam. The dynamic deflection of the beam y(x, t) is assumed as a linear combination of mode shape functions Yi (x) multiplied by modal amplitude functions qi (t) [6]. Multiplying both terms of Eq. (1) by the generic mode shape function Yi (x) and integrating them with respect to x between 0 and L, given modal orthogonality, the generic ith modal equation can be written as: ∂qi (x, t) 1 ∂ 2 qi (x, t) + ωi2 qi (x, t) = + 2ξωi fi (t) (2) 2 ∂t ∂t mi   2 iπ where ωi = EI , ξi and mi are the reduced modal frequency, the damping ρA L

L ratio and the modal mass of the ith mode, fi (t) = P (t)δ (x − ct) Yi (x)dx is the 0

modal force. For a simply supported beam, assuming the following normalization L ρAYi (x)2 dx = 1

mi =

(3)

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the beam mode shapes are Yi (x) = matrix form can be written as:



2/(ρAL)sin ((iπ/L)x). Equation (2) in

{¨ q } + [C]{q} ˙ + [K]{q} = {f }

(4)

where {q} = {q1 (t), . . . , qn (t)} collects the n modal amplitude functions, {¨ q} indicates the second derivative with respect to time, {f }  = {f1 (t), . . . , f2 (t)}  iπ 4 are the collects the n modal forces, [C] = diag(2ξωi ) [K] = diag EI ρA L damping and stiffness matrices.

3

Inverse Problem

The authors estimated the bending stiffness from measured accelerations, which may be expressed as q }n×1 (5) {¨ y }nm ×1 = [Y ]nm ×n {¨

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where {¨ y } is the vector of accelerations at nm measurement locations, and n is the number of measured modes in the response. The modal accelerations can be written using the least-squares pseudo-inverse [7,8] −1  {¨ q }n×1 = [Y ]Tnm ×n [Y ]nm ×n [Y ]Tnm ×n

(6)

The modal displacement and velocity of the beam responses can be obtained from Eq. (6) by numerical integration. 3.1

Estimate of Moving Load Excitation

Indirect methods for moving load identification are classified by Wu and Law [9] into two categories: (i) methods based on a the formulation of a continuous model and a modal superposition technique to decouple the equation of motion (e.g. time domain method [10], interpretive methods [11], frequency–time domains methods [12], methods based on genetic algorithms [13]); and (ii) methods based on a discrete finite element model formulation [14–17]. The same Wu and Law [9] proposed a statistical moving load identification method, overcoming the deterministic methods hitherto prevalent. In the current paper an alternative and possibly straightforward approach is proposed based on the approximation of the modal load excitation vector into a square wave under the following assumption: a moving load with known weight P and unknown velocity c is moving back and forth on the bridge deck. A moving load can be represented by the following equation f (t) = P δ(x − ct)

(7)

where c is the moving load velocity, x the abscissa spanning the bridge length and t the time. f (t) function, projected on the first mode shape, assuming  π The  x , can be written as [18,19] Yi (x) = sin L π  L f (t) = P δ (x − ct) sin L x dx 0  πc  = P sin L t

(8)

The moving load projected on the first mode can be then estimated by (i) computing {f (t)} assuming the pulsation value ω1 and the modal damping factor ξ1 estimated from Operational Modal Analysis (OMA) Eq. (9), (ii) approximating the obtained vector with a square wave having absolute maxima equal to P . f (t) = {¨ q } + 2ξω{q} ˙ + ω 2 {q} 3.2

(9)

Identification of the Flexural Rigidity

To identify the flexural rigidity EI of the beam cross-section and the prestressing force T, Law and Lu [1] formulated an inverse problem to be solved at each time step using n modal equations.

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Their formulation may still have some critical issues, despite being conditioned by a Tikhonov regularization technique to further constrain the resulting solution [7,20]: – The authors [1] did not provide any convergence criterion: the identified prestressing and bending rigidities never asymptotically converge towards the estimand solution; – The prestressing force and flexural rigidities calculated at each time step have no relation with the one estimated in the previous time interval (Kalman filter e.g.); – Law and Lu did not attempt to estimate the uncertainty of the proposed linear regression model, neither a criterion to extrapolate a single value of the prestressing force and flexural rigidity, which are indeed time-independent physical quantities in the adopted Euler-Bernoulli beam model. In the current paper, an OLS linear regression model is proposed for the estimate of the bending stiffness from the first mode shape response. The OLS problem is not ill-posed as the bending stiffness is estimated using all measured samples ns , (ns >> 1): [X]ns ×1 {β}1×1 = {y}ns ×1

(10)

where {β} = {EI0 } is the unknown vector, {f } Eq. (11) is the note vector and [X] the coefficient matrix Eq. (12) {y}ns ×1 = {f } − {¨ q } − [C]{q} ˙ 

 π 4 1 {q} [X]ns ×1 = L ρA

(11) (12)

is computed from Eq. (10) The estimand of interest {β}   1×1 = [X]T [X]n ×1 −1 [X]T {y}n ×1 {β} ns ×1 ns ×1 s s

(13)

The OLS in Eq. (13) assumes as independent the observation errors with equal variance σ 2 [21]. The Bayesian uncertainty associated to the OLS model in Eq. (12) is estimated by means of the convenient noninformative prior distribution in Eq. (14), currently adopted for normal regression models: p(β, σ 2 |X) ∝ σ −2

(14)

As stated by Gelman et al. [21,22], the adopted noninformative prior distribution gives acceptable results when there are many data points (ns ) and a few parameters (dim({β}) = 2 × 1). The marginal posterior distribution of σ 2 has a scaled inverse-χ2 form σ 2 |y = Inv-χ2 (n − k, s2 ) (15)

Identification of the Elastic Modulus of Simply-Supported Girders

where n − k = the model degrees of freedom and T  1  {y} − [X]{β} {y} − [X]{β} s2 = ns − 1

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(16)

The marginal posterior distribution of β|y, averaging over σ, is the multivariate t-distribution with ns − 1 degrees of freedom.

4

Case Study

The University of L’Aquila carried out static and dynamic tests on the A24 motorway, see Fig. 1. The entire experimental campaign lasted from February to June 2019. The Autostrada A24 or “Parks Motorway”, in Abruzzo (Italy),

Fig. 1. (a) View of a part of the A24 motorway and (b) cross-section of the tested girders.

connects Rome to the Adriatic Sea. Starting from GRA and ending to Teramo, the A24 created a new historical linkage between Rome and the Apennines mountains. The A24 motorway has a consistent number of viaducts due to the complex orography. Many of them consist of single-span simply supported PSC beams. The girders have a trapezoidal cross-section, 2.3 m high with two cantilevered wings 3.85 m wide, prestressed by bonded post-tensioned tendons. The design modulus of elasticity Ed is 35000 MPa, while the inertia is approximately 4.338 m4 . A pair of piers, whose centre distance is about 40 m, sustains each bridge span. Since 2009, the demand for seismic safety arose as an almost emotional tide after the 2009 earthquake in L’Aquila [23–25], which caused damages to some girders: they slid more than the length’s support. This event induced the managing body to install an anti-sliding device to all girders with rack and rollers supports: it consists of a steel frame around the original supports 2–5 mm below the underside of the deck. The authors assume that, under the testing conditions, the support does not transfer significant moments. Hence the structural beam type may be considered as simply supported. The concrete piers have a hollow cross-section, reinforced at their corners.

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Experimental Setup

Ten Force Balance Accelerometers (FBA) determined the deck’s response to the excitation, see Fig. 2. The accelerometers were arranged into two measure-

Fig. 2. View (a) and sketch (b) of the experimental setup.

ment chains, each one driven by a master recording unit connected to a Wi-Fi access point and synchronized by GPS sensors [26,27]. The authors conducted the dynamic tests on the considered six bridge spans in February under similar environmental and weather conditions: the temperature and humidity generated likely negligible effects on the modal parameters. Unfortunately, it is not possible to assess any correlation between temperature and the outcomes of the tests. The time series is about 20 min long. A car weighing 1750 Kg excited the bridge by moving back and forth on the deck at speed between 30 and 60 Km/h. The modal parameters are determined from Output-Only Experimental Modal Analysis (EMA) using the Covariance-driven Stochastic Subspace Identification [28]. 4.2

Output-Only Dynamic Identification

The data were sampled at a rate 250 Hz. The cut-off frequency of the anti-aliasing filter was set 40 Hz. The preprocessed data were used for SSI [29] and subsequent modal analysis, resulting in eigenfrequencies, damping ratios, mode shapes and covariances of these modal parameters for each setup. The parameters used for the identification are i = 7, n = 20 and nb = 70 [30]. Table 1 shows the first identified eigenfrequencies and damping ratios and their estimated uncertainty bounds 2σ. The sole first mode shapes are sufficiently stable modes. A moving load forced the structure with a trajectory almost coincident with the longitudinal symmetry axis: the vehicle did not significantly excite the torsional modes. The low contribution of the second flexural mode to the overall response could be due to the proximity of the excitation frequency (f = c/2L Eq. (8)) to the first natural frequency.

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The frequencies of the first modes are quite different between each other, although the bridge spans descend from the same design. There are diverse causes why the bridge spans behave differently: (i) different boundary conditions; (ii) interactions soil-piles-structures; (iii) interactions with adjacent girders; Specifically, (i) the modified boundary condition could be due to the inhomogeneous distribution of roller and rack bearings and elastomeric ones. (ii) The soil-pilesstructures interaction changes from one bridge span to another, given the different heights of the piles. (iii) The influence of adjacent girders may lead to nonlinear interactions from the reciprocal contact between beams induced by the horizontal friction component of the moving load itself. The main finding of the dynamic identification for the current purposes is that the first bending mode principally drives the bridge response. 4.3

Results

The authors itemized the procedure followed for the estimate of the bending stiffness: – Purge the recordings from the torsional component. The PSC girder has a significant torsional rigidity: the authors computed the mean of the responses of each pair of accelerometers at the same distance from the supports. The five resulting time histories, almost corresponding to the sole flexural response of the beam, yield the estimate of the elastic modulus; – Equation (6) leads to the estimate of the modal responses; the velocity and displacement time histories derive from simple integration; – A sequence of square waves approximate the modal excitation vector {f }. Figure 3 reports the resulting elastic modulus for each PSC girder under test; – The Bayesian variance (± σ) of the associated t-distribution, Eq. (16), indicates the confidence intervals. The authors assumed ρ = 25 KN /m3 as the specific weight of concrete; Oscillations in the range 24–26 do not sensibly affect the final results. Table 1. Eigenfrequencies and damping ratios of the identified first modes: μf 1 is the mean of the identified frequencies and σ ˆf 1 the estimated variance, μξ1 is the mean of the identified damping ratios and σ ˆf 1 the estimated variance. μf 1 Biselli

2ˆ σf 1

μξ1

2ˆ σξ1

2.6524 0.0004 2.12% 0.12%

Cerchiara 4 2.2474 0.0043 2.00% 0.30% Cerchiara 7 2.6380 0.0003 2.00% 0.02% Cretara

3.4138 0.0003 2.00% 0.10%

San Nicola

2.6933 0.0070 3.34% 0.14%

Temperino

2.4832 0.0001 2.22% 0.25%

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The modal responses from Eq. (6) are the focus of the procedure. The natural frequencies obtained by picking all maximum peaks coincide with the ones from Output Only identification, despite in the Cerchiara 4 and Cretara the results are not convincing since other tantamount peaks compete with the main one. It could mean that the approximation of the first mode shape may be considered improper: other static schemes may better fit the one selected in the current analysis. Figure 3 and Table 2 report the identified elastic moduli. The results closely match with the outcomes of static tests. Excluding the Cerchiara 4 span, the even percentage error is 5%, which is quite satisfying for an almost rudimentary identification method. The Cerchiara 4 evidence low values of the elastic modulus: the procedure gets close to the value from static tests, but yield a higher error: this is due to the low modulus which, being at the denominator, produce more top percentage errors. The hiatuses between the elastic modulus from static tests and dynamic identification are within 1000–3000 MPa. Tests on concrete specimens extracted from the girder nearly confirm the values exhibited by Cerchiara 4 and 7. Table 3 reports the averaged results of nine concrete specimens extracted from each bridge span under test. Despite the discrepancies, the Cerchiara 4 has shallow values of the elastic modulus, possibly due to a low-quality cement paste (high water-cement ratio). Table 2. Identified parameters with their estimated σ confidence bounds. Name

Static tests E [MPa] Dynamic tests E [MPa] Variance Error [%]

Biselli

24900

25830

13.69

3.73%

Cerchiara 4 15000

18563

11.16

23.75%

Cerchiara 7 23700

25576

12.8

7.92%

Cretara

26661

13.23

2.54%

26000

San Nicola

26700

26658

12.24

−0.16%

Temperino

35900

32800

13.87

−8.64%

Fig. 3. Elastic moduli from static and dynamic tests.

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Table 3. Tests on cylindrical concrete specimens. Ecm0 indicates the mean secant elastic modulus; Ecm denotes the so-called stabilized elastic modulus. Name

Ecm0 [Mpa] Ecm [Mpa]

Biselli

–

–

Cerchiara 4 19361

21149

Cerchiara 7 23299

25886

Cretara

26416

28027

San Nicola

29978

32846

Temperino

–

–

The confidence bounds are a small fraction of the identified parameters: this aspect strengthens the reliability of the procedure. The proposed approach provides a sort of low-cost scanning of the state of simply supported girders, anticipating further specific and time-consuming analysis.

5

Discussion

The authorities, in charge with the structural management and maintenance of motorways, should produce a Quality Control (QC) plan, based on performance indicators [22,31–33], which should not only define the goals but identify the investment needs and priorities based on a life cycle cost criteria [34]. Visual inspections, non-destructive tests or monitoring systems yield qualitative or quantitative performance indicators. The so-called prioritization of interventions could profit from dynamic test methods, which may provide a global scanning of the structural state. The bridge spans, which are likely to manifest an unsatisfactory performance, should be further examined via static tests and possible extraction of concrete specimens. Unparalleled visual inspections cannot provide the assessment of the elastic modulus of concrete, which is a crucial parameter for the evaluation of its condition. Besides, static tests are quite expensive to be extended to all motorway bridge spans. Dynamic tests could be a meaningful compromise between reliability and saving. Static load tests are the most reliable; they assess nearly deterministically the state of a bridge. Conversely, several factors may impair the quality and the outcome of the presented procedure: quality of the measurements, road roughness and irregularities, vehicle-bridge interaction, influence between adjacent girders and piles, boundary condition effects, temperature and humidity factors. Assuming negligible the measurement error due to the accelerometers and the quality of the acquisition unit, several circumstances imputable to the operator could undermine the dynamic measurements: a twist of the cables, planarity defects of the accelerometers, fixing of the accelerometers; The quality of the experimental setup is imperative. The dynamic model of the bridge, considered in this paper, does not account the

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Deflection [m]

Left bearing

0.00055

Mid-span

0.0212

Right bearing 5×10−5

road roughness and irregularity, nor the vehicle-bridge interaction. However, the presence of road irregularities is readily detectable, and the vehicle-bridge interaction yield negligible dynamic effects [35]. The interaction between girders and piles, as well as boundary condition effects, are partially accounted by using the experimental mode shapes in deriving the modal response of the bridge span. Table 4 reports the static test results of a sample girder of the A24 motorway. The deflection of the bearings is less than 2% of the mid-span deflection. Consequently, the bearing deflection yields a less than 2% effect over the Young modulus estimation. It is reasonable that the impact of the bearings over the dynamic tests is also negligible. However, additional numerical analysis should be carried out to verify the influence of the bearings, which may be characterized by complex hysteretic behaviour [36]: the notable match between the outcomes of the considered procedure and the static load tests is likely to prove the absence of significant effects in the test cases. Unfortunately, the authors could not assess any correlation with ambient parameters. Yet, if the operators repeat the tests in the same environmental conditions, these effects can be disregarded. The presented identification method belongs to the class of parametric identification from OMA. These methods yield reliable results but have a critical disadvantage: the infrastructure must close to install the accelerometers. Recently, there are progressive techniques which may yield the same results, like in [37], based on the use of the so-called instrumented vehicle. The authors will aim at testing such identification methods on these girders and compare the two methodologies.

6

Conclusions

The authors present an elementary and straightforward method for the identification of the elastic modulus of simply-supported girders under known moving loads. It descends from an ordinary least square operator for the identification of the parameters and a Bayesian estimate of the uncertainty: (1) the modal displacements derive from the measured accelerations and the outcomes of Operational Modal Analysis; (2) the modal force vector is then approximated by a square wave with known amplitude; (3) then the unknown parameters are estimated. The authors validated the procedure on six of full-scale PSC box girders: the results closely match with the outcomes of static load tests and experimental tests on concrete specimens.

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The ageing of concrete structures urges civil engineering, as no straightforward strategies lead to the identification of the bending stiffness with low-cost and non-destructive tests. Elementary procedures, easily implementable by the Stakeholders in charge with the structural management and maintenance, are needed, for the sake of safety and economy to draft an accurate schedule for the replacement of damaged structures. The proposed method provides the current estimate of the Bayesian uncertainty, which could anticipate further investigations for the evaluation of the structural reliability from probabilistic limit state function formulations.

References 1. Law, S., Lu, Z.: Time domain responses of a prestressed beam and prestress identification. J. Sound Vib. 288(4–5), 1011–1025 (2005) 2. Pantoli, L., Muttillo, M., Ferri, G., Stornelli, V., Alaggio, R., Vettori, D., Chinzari, L., Chinzari, F.: Electronic system for structural and environmental building monitoring. In: Convegno Nazionale Sensori, pp. 481–488. Springer (2018) 3. Antonacci, E., Aloisio, A., Galeota, D., Alaggio, R.: The S. Maria di Collemaggio basilica: from vulnerability assessment to first results of SHM. J. Archit. Eng. 26(3), 05020007 (2020). https://doi.org/10.1061/(ASCE)AE.1943-5568.0000426 4. Saiidi, M., Douglas, B., Feng, S.: Prestress force effect on vibration frequency of concrete bridges. J. Struct. Eng. 120(7), 2233–2241 (1994) 5. Luongo, A., Zulli, D.: Mathematical Models of Beams and Cables. Wiley, Hoboken (2013) 6. Meirovitch, L.: Elements of Vibration Analysis. Engineering & Mathematics. McGraw-Hill Science, New York (1975) 7. Aloisio, A., Alaggio, R., Fragiacomo, M.: Dynamic identification of a masonry fa¸cade from seismic response data based on an elementary ordinary least squares approach. Eng. Struct. 197, 109415 (2019). https://doi.org/10.1016/j.engstruct. 2019.109415 8. Aloisio, A., Di Battista, L., Alaggio, R., Fragiacomo, M.: Analysis of the forced dynamics of a masonry facade by means of input-output techniques and a linear regression model. In: 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, COMPDYN 2019 (2019) 9. Wu, S., Law, S.: Statistical moving load identification including uncertainty. Probab. Eng. Mech. 29, 70–78 (2012) 10. Law, S.-S., Chan, T.H., Zeng, Q.: Moving force identification: a time domain method. J. Sound Vib. 201(1), 1–22 (1997) 11. Chan, T.H., Law, S., Yung, T., Yuan, X.: An interpretive method for moving force identification. J. Sound Vib. 219(3), 503–524 (1999) 12. Yu, L., Chan, T.H.: Moving force identification based on the frequency-time domain method. J. Sound Vib. 261(2), 329–349 (2003) 13. Jiang, R., Au, F., Cheung, Y.: Identification of masses moving on multi-span beams based on a genetic algorithm. Comput. Struct. 81(22–23), 2137–2148 (2003) 14. Law, S.S., Fang, Y.: Moving force identification: optimal state estimation approach. J. Sound Vib. 239(2), 233–254 (2001) 15. Law, S., Bu, J., Zhu, X., Chan, S.: Vehicle axle loads identification using finite element method. Eng. Struct. 26(8), 1143–1153 (2004)

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16. Pinkaew, T.: Identification of vehicle axle loads from bridge responses using updated static component technique. Eng. Struct. 28(11), 1599–1608 (2006) 17. Law, S., Wu, S., Shi, Z.: Moving load and prestress identification using waveletbased method. J. Appl. Mech. 75(2), 021014 (2008) 18. Fr` yba, L.: Vibration of Solids and Structures Under Moving Loads, vol. 1. Springer, Heidelberg (2013) 19. Ferretti, M., Piccardo, G.: Dynamic modeling of taut strings carrying a traveling mass. Continuum Mech. Thermodyn. 25(2–4), 469–488 (2013) 20. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67(2), 301–320 (2005) 21. Gelman, A., Stern, H.S., Carlin, J.B., Dunson, D.B., Vehtari, A., Rubin, D.B.: Bayesian Data Analysis. Chapman and Hall/CRC, Boca Raton (2013) 22. Aloisio, A., Battista, L.D., Alaggio, R., Antonacci, E., Fragiacomo, M.: Assessment of structural interventions using Bayesian updating and subspace-based fault detection methods: the case study of S. Maria di Collemaggio basilica, L’Aquila, Italy. Struct. Infrastruct. Eng. 1–15 (2020). https://doi.org/10.1080/15732479. 2020.1731559 23. Aloisio, A., Fragiacomo, M., D’Al` o, G.: Traditional TF masonries in the city centre of L’Aquila-the baraccato Aquilano. Int. J. Archit. Herit. 1–18 (2019). https://doi. org/10.1080/15583058.2019.1624874 24. Aloisio, A.: The timber-framed (TF) masonries in L’Aquila: the baraccato Aquilano. Heritage 3, 306–317 (2020). https://doi.org/10.3390/heritage3020018 25. Aloisio, A., Fragiacomo, M., D’Al` o, G.: The 18th-century baraccato of L’Aquila. Int. J. Archit. Herit. 14, 1–15 (2019). https://doi.org/10.1080/15583058.2019. 1570390 26. Aloisio, A., Alaggio, R., Fragiacomo, M.: Dynamic identification and model updating of full-scale concrete box girders based on the experimental torsional response. Constr. Build. Mater. 264, 120146 (2020) 27. Aloisio, A., Antonacci, E., Fragiacomo, M., Alaggio, R.: The recorded seismic response of the Santa Maria di Collemaggio basilica to low-intensity earthquakes. Int. J. Archit. Herit. (2020). https://doi.org/10.1080/15583058.2020.1802533 28. Peeters, B., De Roeck, G.: Stochastic system identification for operational modal analysis: a review. J. Dyn. Syst. Meas. Control 123(4), 659–667 (2001) 29. Aloisio, A., Pasca, D., Tomasi, R., Fragiacomo, M.: Dynamic identification and model updating of an eight-storey CLT building. Eng. Struct. 213, 110593 (2020). https://doi.org/10.1016/j.engstruct.2020.110593 30. Reynders, E., Pintelon, R., De Roeck, G.: Uncertainty bounds on modal parameters obtained from stochastic subspace identification. Mech. Syst. Signal Process. 22(4), 948–969 (2008) 31. Aloisio, A., Di Battista, L., Alaggio, R., Fragiacomo, M.: Sensitivity analysis of subspace-based damage indicators under changes in ambient excitation covariance, severity and location of damage. Eng. Struct. 208, 110235 (2020). https://doi.org/ 10.1016/j.engstruct.2020.110235 32. Aloisio, A., Alaggio, R., Fragiacomo, M.: Fragility functions and behavior factors estimation of multi-story cross-laminated timber structures characterized by an energy-dependent hysteretic model. Earthq. Spectra (2020). 8755293020936696. https://doi.org/10.1177/8755293020936696 33. Aloisio, A., Pasca, D., Alaggio, R., Fragiacomo, M.: Bayesian estimate of the elastic modulus of concrete box girders from dynamic identification: a statistical framework for the A24 motorway in Italy. Struct. Infrastruct. Eng. (2020). https://doi. org/10.1080/15732479.2020.1819343

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Free Vibration Selection Method in Acceleration Responses for Bridge Health Monitoring Murtuza Petladwala1(&), Shohei Kinoshita1(&), Shigeru Kasai1(&), and Satoshi Himoto2(&) 1

Data Science Research Laboratories, NEC Corporation, Kawasaki City, Japan {murtuza,kinoshita_jz,kasaishigeru}@nec.com 2 East Nippon Expressway Company Limited, Sendai City, Japan [email protected]

Abstract. This paper proposes a free vibration region selection method in acceleration signals for bridge health monitoring systems. Recent development in these systems, vehicle-bridge interaction based approaches are widely used for bridge damage detection including techniques like vibration modal analysis. Theoretically, these analysis techniques require the analysis region that is forced or free vibration for further calculations. With application to damage detection, selection of free vibration region, that is, after the vehicle has passed from bridge is crucial in modal analysis. In conventional systems, free vibration is selected based on amplitude-thresholding techniques on the signal. However, in real system deployments, these threshold based methods are sensitive to vehicle types and bridge structures, which requires manual calibrations during system installation. The performance of these threshold-based methods also degrades when a vehicle is followed by another vehicle. We propose an efficient unsupervised method to select free vibration region after vehicle-bridge interaction, considering the passage of multiple vehicles over a bridge. Our proposed method consist of two parts, first is vehicle detection, which includes nonparametric Bayesian modelling of transformed acceleration responses to automatically detect passing vehicle. Second part is region selection, which includes vehicle’s rear axle detection for determining start of free region boundary. In our real experiments on 2 continuous-span bridge at expressway in Japan, the proposed method precisely selected the free vibration region without using any additional sensor information even in multiple vehicle passage. Keywords: Acceleration
Forced & free vibration
Vehicle-bridge interaction
Vehicle detection
Non-parametric Bayesian model
Unsupervised clustering

1 Introduction Over the time, usage and deterioration of bridge infrastructure has led to an increasing demand for development of advanced bridge health monitoring systems. Recently, in these systems, Vehicle-Bridge Interaction (VBI) [1, 2] based approach for road vehicles © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 674–682, 2021. https://doi.org/10.1007/978-3-030-64594-6_65

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on highway bridges is enhancing towards robust techniques and system developments. In direct-VBI, dynamic responses produced by the excitation from vehicle wheels passing over a bridge, are analyzed for various applications like vehicle load identification [3, 4] and bridge damage detection [5–7] including techniques like change of bridge frequencies [8] and modal parameter extraction [9]. The most popular bridge health monitoring systems are based on analyzing modal shapes, which are extracted from responses of sensors (accelerometers) installed at various locations on the bridge [10]. A change in modal shapes are used to identify bridge damage at particular position in the bridge, referring the nearest sensor location. For modal parameters extraction, free vibration region selection in acceleration responses is important to extract the modes that correspond to bridge conditions especially related to bridge damages. The free vibration region starts when the vehicle passing over a bridge crosses the specified exit point on a bridge. Conventional systems select these free vibration region by following two approaches a) Fusion of camera with bridge sensors [11] and b) Additional detection zone of sensor array [12, 13]. In a), time-stamp of camera and bridge sensors are synchronized, which makes it easier to select the free vibration region boundary by identifying vehicle’s bridge exit from the video. However, in aging infrastructure of developed countries like Japan, camera systems are difficult to locate near bridge structures, which requires additional cost, repairs & maintenances. In later approach b), vehicle is detected by installing additional sensors near entry and exit point of the bridge with pre-calibrated thresholds on signal’s amplitude. After vehicle detection, free vibrations are extracted from the synchronized bridge sensors. The vehicle identification and classification method explained in [13] is promising for identifying vehicle of interest in the traffic, and can be adjusted to relatively select free vibration region, however, it is difficult to identify presence of multiple types (trucks & cars) of vehicles in the traffic due to threshold sensitivity of individual vehicles. This paper focuses on free vibration region selection by first detecting traffic status on the bridge, e.g., single vehicle on the bridge or vehicle following another vehicle on the bridge. Each vehicle presence is detected using acceleration response measured at exit-point of the bridge structure. A Moving force identification based approach [14] is used, which includes feature extraction from impulsive response of vehicle axles and its unsupervised clustering to precisely detect the position in the time-series. The peak formation near axle responses is assumed as a Gaussian distribution for each vehicle responses and a time-repetition feature is extracted from signal spectrum. A DirichletProcess Gaussian Mixture Model [15] is applied on time-repetition feature vector to detect the number of Gaussian Mixtures i.e. number of vehicles present in the signal. The second part of the proposed method is region selection that includes the counting of number of axles in the vehicle using wavelet-based filtering approach. The timestamp of rear axle of the last vehicle is assumed as the start time of free region boundary. Our experimental results on a real bridge shows the proposed method precisely selected the free vibration region boundary even in the presence of multiple types (trucks & cars) of vehicles on the driving lane. This paper is organized as follows. Section 2 explains the proposed methodology and its sub-parts; Sect. 3 explains the experiment setup and evaluation results of the proposed method; Sect. 4 concludes the work in this paper.

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2 Proposed Methodology The proposed methodology, shown in Fig. 1, consists of two parts. First part is vehicle detection that includes non-parametric Bayesian modelling of transformed acceleration responses to automatically detect count of passing vehicles over a bridge. Second part is region selection, which includes vehicle’s rear axle detection to determine start time of free vibration region boundary. In vehicle following another vehicle traffic status, proposed method detects total number of vehicles and selects free vibration from the last vehicle’s exit. In addition, proposed method uses only single sensor response, where sensor is located at bottom-side of concrete slab at exit-point of the driving lane. The detailed description of proposed method is explained in following sub-sections.

Fig. 1. Block diagram of the proposed method

2.1

Vehicle Detection

The vehicle detection method is explained with reference to examples in Fig. 2, comprising of 3 steps. The acceleration response shown in Fig. 2a is an example of vehicle following another vehicle traffic status. First step in vehicle detection is feature extraction, a time-repetition feature is calculated from the sum of normalized frequency bins of acceleration response. Second step is cluster estimation, number of clusters (Gaussian mixtures) estimation in time-repetition feature, where we assume each cluster (Gaussian distribution) as a single vehicle. Third step is vehicle time-interval extraction, start and end time extraction of each vehicle from the estimated number of vehicles.

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Fig. 2. Proposed vehicle detection method, (a) acceleration response at exit-point, (b) framewise sum of normalized spectrum, (c) time-repetition feature, (d) estimated clusters around each vehicle, (e) first vehicle response, and (f) second vehicle response.

The feature extraction step applies short-term Fourier transform (STFT) method to calculate frequency spectrum of the acceleration signal. Here, we assume sum of amplitudes of each frequency bin to be a constant value in each event, for example, to obtain constant value of one, each frequency bin is divided by its total sum value. The sum of normalized amplitudes is calculated as shown in Fig. 2b, which produces a vector with peak formation at axle location of the vehicle. Finally, time-repetition

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feature shown in Fig. 2c is obtained that includes repetition of time by its scaled magnitude value of sum of normalized amplitudes. For example, in Fig. 2b, at time = 0, if scaled amplitude is 2, time-repetition feature starts with a time value 0 repeating 2 times. A non-parametric Bayesian approach is used in cluster estimation step, which initially assumes the presence of infinite mixtures in the dataset and estimates the best fit distributions accordingly. The infinite mixture model i.e., Dirichlet process Gaussian mixture model (DPGMM) is applied on time-repetition feature to estimate the number of Gaussian mixtures. From the estimated Gaussian mixtures, best fit distributions are selected that have Gaussian density greater than a threshold value. As shown in Fig. 2d, each estimated Gaussian distribution relates to presence of each individual vehicle. The vehicle time-interval extraction step assumes ±3r value from mean value of the Gaussian distribution in time scale, as start time and end time of a vehicle presence. Since, each vehicle is estimated as Gaussian distribution, start time and end time of each vehicle is obtained as shown in Fig. 2e and f. 2.2

Region Selection

The region selection method is explained with reference to examples in Fig. 3, comprising of 2 steps. The acceleration response shown in Fig. 3a is last vehicle response extracted from response shown in Fig. 2a. First step in region selection is axle detection, detection of number of axles in acceleration response of the last vehicle. Second step is free vibration region selection, selection of a fixed time-length region starting from the rear axle, assuming the vehicle’s exit on the bridge by its rear axle. The axle detection step detects axle response by searching a specific pattern like damping, assuming the passing of vehicle axle over sensor-point as a general vibration damping phenomena. Additionally, deflection around bridge edges is less as compared with center of the bridge, causing less noise in vertical vibration measurement at exitpoint of the bridge. As shown in Fig. 3b, wavelet based filtering sub-step, filters the acceleration response (Fig. 3a) by wavelet decomposition using Reverse Bioorthogonal wavelet, which is similar to damping response and reconstructs back to filtered signal after setting detailed coefficients to zero. Damping peak detection substep, detects number of peaks in absolute value of the filtered signal, if peaks are above a pre-defined threshold. Figure 3c illustrates 4 peaks (star mark), which correspond to 4-axle truck. The free vibration region selection step selects start time from axle-damping response of the rear axle. Figure 3d illustrates free vibration region (dashed-line rectangle), starting from time-stamp of the rear axle peak value.

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Fig. 3. Proposed region selection method, (a) acceleration response of last vehicle w/4-axles, (b) wavelet-based filtered signal, (c) damping peak detection w/4 peaks, (d) free vibration selection starting from rear axle.

3 Experiments 3.1

Setup

The experimental test was conducted at a real bridge shown in Fig. 4, located in expressway of Eastern Japan to evaluate the proposed method. The structure of bridge is 2 continuous-span steel girder whose intermediate point is supported by a portal pier with steel beam. In our evaluation, passage of vehicles over the bridge is defined as an

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event (usually 4 to 18 s), where each event is categorized by number of vehicles present on the bridge. In this paper, with help from visuals of recorded video files, we selected the traffic status events that consist of vehicles only on driving lane of the bridge and extracted true time-stamp of free vibration region in each event. The experiment setup is shown in Fig. 5a and b, plan and side view respectively. The sensors S1 is placed at bottom-side of exit-point of driving lane of the bridge’s concrete slab as explained in previous section.

Fig. 4. Experimental site: 2 continuous-span bridge at expressway in Eastern Japan

Fig. 5. Experimental setup, (a) plan view of traffic on bridge, (b) side view of traffic status with vehicle following another vehicle & sensor position

3.2

Results

The proposed method is arranged to output the time-stamp of rear axle of the last vehicle in each event. We evaluated the proposed method with two metrics, first metric is accuracy, i.e., the number of events correctly estimated time-stamp and second metric is mean absolute time, i.e., mean of absolute time-difference between actual and estimated time-stamp. To calculate accuracy, we selected positive events that have

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absolute time-difference between actual and estimated time-stamp less than 10 ms and calculated mean absolute time between positive events. We relatively compared proposed method by vehicle-wise (vehicle detection) & axle-wise (region selection) count from each event. In vehicle-wise count (Table 1), all events are categorized by number of vehicles present in the event (Table 1, first column) and in axle-wise count, all events are categorized by number of axles of the last vehicle (Table 2, first column) & finally calculated two metrics for each category. Table 1. Results of vehicle-wise categorized events Vehicle count in each event 1 2 3 4

Number of measured events 467 119 27 7 Total: 620

Accuracy (Mean absolute time [ms]) 97.0 (0.73) 92.4 (0.77) 85.1 (0.80) 100.0 (0.96) Micro-average: 95.7 (0.74)

Table 2. Results of axle-wise categorized events Last vehicle axle count in each event 2 3 4

Number of measured events 433 87 100 Total: 620

Accuracy (Mean absolute time [ms]) 94.2 (0.89) 100.0 (0.54) 98.0 (0.32) Micro-average 95.7 (0.74)

Table 1 and Table 2 illustrates the results of vehicle-wise and axle-wise count respectively. It can be seen that in total of 620 events, the proposed method selected approximately 96% events with mean absolute time of 0.74 ms. Though, it is trivial to comment about each event category, we summarized that: – accuracy degrades with increase in number of vehicles in the event; considering imbalance in number of measured events in our dataset and, – last vehicle with 2-axles shows less accuracy as compared to more than 2-axles vehicles, meaning 2-axles vehicles following another vehicles are less detectable. In some events, 2-axle vehicles are Japanese kei-cars, which has relatively low vibration amplitude than trucks, leading to vehicle misdetection because of incorrect cluster estimation.

4 Conclusion A free vibration selection method in acceleration responses for bridge health monitoring has been proposed. The proposed method used only a single accelerometer located at bottom side of concrete slab of the bridge structure to detect the passing

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vehicle and precisely selected the free vibration regions. The proposed method has been evaluated on a real bridge structure and can be used in applications like vehicle counting & Bridge-Weigh-in-motion systems. This paper mainly focused on single lane of the bridge with single and serial vehicle traffic events. In future we plan to expand the proposed method to multiple lanes of the bridge.

References 1. Kim, C.W., Kawatani, M.: Challenge for a drive-by bridge inspection. In: Proceedings of the 10th International Conference on Structural Safety and Reliability (ICOSSAR 2009), pp. 758–765 (2009) 2. Yu, D., Wang, B., Li, Y., Zhang, Y., Zhang, W.: Road vehicle-bridge interaction considering varied vehicle speed based on convenient combination of simulink and ANSYS. Shock Vibr., Article ID 1389628 (2018) 3. Wu, S.Q., Law, S.S.: Vehicle axle load identification on bridge deck with irregular road surface profile. Eng. Struct. 33, 591–601 (2011) 4. Wang, H., Nagayama, T., Su, D.: Vehicle parameter identification through particle filter using bridge responses and estimated profile. In: Proceedings of the 6th Asia Pacific Workshop on Structural Health Monitoring (2016) 5. Doebling, S.W., Farrar, C.R., Prime, M.B.: A summary review of vibration-based damage identification methods. Shock Vibr. 30(2), 91–105 (1998) 6. Fan, W., Qiao, P.: Vibration-based damage identification methods: a review and comparative study. Struct. Health Monit. 10(1), 83–111 (2010) 7. Cantero, D., González, A.: Bridge damage detection using weigh-in-motion technology. ASCE J. Bridge Eng. 20(5) (2017) 8. Cantero, D., McGetrick, P., Kim, C.W., Obrien, E.: Experimental monitoring of bridge frequency evolution during the passage of vehicles with different suspension properties. Eng. Struct. 187, 209–219 (2019) 9. Liua, Y., Macdonalda, J., Maio, D.D.: Identification of modal parameters based on moving load excitation. In: Proceedings of the X International Conference on Structural Dynamics, EURODYN (2017) 10. Gomeza, H.C., Fanning, P.J., Feng, M.Q., Lee, S.: Testing and long-term monitoring of a curved concrete box girder bridge. Eng. Struct. 33, 2861–2869 (2011) 11. Basharat, A., Catbas, N., Shah, M.: A framework for intelligent sensor network with video camera for structural health monitoring of bridges. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications, pp. 385–389 (2005) 12. Sekiya, H., Kimura, K., Miki, C.: Technique for determining bridge displacement response using MEMS accelerometers. Sensors 16(2), 257 (2016) 13. Bhachu, K.S., Baldwin, J.D., Mish, K.D.: Method for vehicle identification and classification for bridge response monitoring. In: Proceedings of the IMAC-XXVIII, Society for Experimental Mechanics Inc. (2010) 14. Law, S.S., Chan, T.H.T., Zeng, Q.H.: Moving force identification: a frequency and time domains analysis method. Trans. ASME 121, 394–401 (1999) 15. Blei, D.M., Jordan, M.I.: Variational inference for Dirichlet process mixtures. Int. Soc. Bayesian Anal. 1(1), 121–144 (2006)

Deployment of Contact-Based Ultrasonic Thickness Measurements Using Over-Actuated UAVs Robert J. Watson1, S. Gareth Pierce1(&), Mina Kamel2, Dayi Zhang1, Charles N. MacLeod1, Gordon Dobie1, Gary Bolton3, Tariq Dawood4, and Juan Nieto5 1

Centre for Ultrasonic Engineering, University of Strathclyde, Glasgow, UK {robert.j.watson,s.g.pierce,dayi.zhang, charles.macleod,gordon.dobie}@strath.ac.uk 2 Voliro AG, Zurich, Switzerland [email protected] 3 National Nuclear Laboratory Ltd., Warrington, UK [email protected] 4 EDF Energy R&D UK Centre, Croydon, UK [email protected] 5 Autonomous Systems Lab, ETH Zurich, Zurich, Switzerland [email protected]

Abstract. Unmanned Aerial Vehicles (UAVs) are increasingly being utilized for the structural health assessment of on and off-shore structures. Visual inspection is the usual methodology for acquiring data from these structures, but there is often a need for contact based structural measurements, for example to assess local thickness on corroding structures. Conventional UAV platform dynamics have not traditionally allowed for such contact measurements. The limited dynamic control afforded by fixed plane rotor UAVs means that forward thrust (to apply contact forces for surface transduction) is only possible by tilting the whole platform, thus taking the UAV into a non-stationary state and limiting positional accuracy. An over-actuated UAV platform (with fully vectored thrust capability) may provide the required contact force for such thickness measurements whilst maintaining stable hovering next to the structure. The authors herein present a contact based ultrasonic thickness measurement technique, whereby an ultrasonic wheel probe deployed from a UAV was used to make single point and scanned measurements across a surface to provide a set of local thickness measurements. A 5 MHz, dry-coupled, dual-element, ultrasonic wheel probe is used to measure the thickness of an aluminum sample plate with thicknesses of 8.2 mm, 4.5 mm and 3.2 mm, and a precision stepped calibration block with size from 31.5 mm to 17.5 mm in steps of 1 mm, then steps of 0.1 mm down to 16.5 mm over a total length of 500 mm. The thickness resolution obtainable from the ultrasonic wheel probe was typically 0.1 mm, and the positional accuracy attained from the over-actuated deployment platform was 16.6 mm when performing single point measurements.

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 683–694, 2021. https://doi.org/10.1007/978-3-030-64594-6_66

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R. J. Watson et al. Keywords: Novel techniques
UAV inspection measurement
Automation
Remote access


Ultrasound
Thickness

1 Introduction Structural Health Monitoring (SHM) is instrumental in the continued efficient operation and management of civil, aerospace, energy generation, and industrial infrastructure. It provides a quantitative assessment of functionality and residual useful life. Through active monitoring of asset attributes such as mechanical vibration, strain or displacement at periodic intervals, a profile of the initial “damage free” structure is created. Degradation through fatigue, or spontaneous damage, is then quickly detectable by statistical pattern recognition using current data. A current structure status, combined with accurate lifetime modelling, provides the opportunity to depart from time-based maintenance in favor of condition-based strategies. As such, repair activity can be scheduled when most cost effective. This can be during planned down-time, safely in advance of when usage measurement and residual life estimation predict failure. Contemporary SHM takes many forms. The simplest techniques are based on visual inspection, but monitoring increasingly employs an array of permanently installed sensors distributed throughout the structure. Modal analysis techniques using arrays of accelerometers, strain sensors and global positioning systems detect and quantify changes to structural vibration modes brought on by damage [1]. Embracing the Internet of Things (IoT) paradigm, [2] details a scalable architecture for networking multiple low-cost, standardized transducers with the existing automated systems of an offshore platform. Use of a distributed sensor network has also been shown to detect changes to stimulated acoustic emissions indicative of crack formation in wind turbine blade edges [3]. Another example measured strain throughout wind turbine blades via embedded Fiber Bragg Grating (FBG) sensor system [4], detecting faults in the blades, tower and other components via their changes to their coupled resonances. Common across multiple industrial sectors is corrosion monitoring. It is particularly prevalent in the oil and gas industry, seeing detailed ultrasonic corrosion mapping of pipelines and storage tanks [5]. Ultrasonic thickness measurement is similarly deployed in the nuclear industry to monitor of flow accelerated corrosion of pipes, with feasibility recently demonstrated for low power sensors mounted under insulation that are individually polled by an inductive wand [6]. However, it is not always practical to embed a full suite of SHM sensors within a structure. Instead, periodic inspection may be conducted in person or with mobile robotic assistance. Surface crawler and UAV solutions are widely adopted to gather repeatable measurements in locales with time consuming or otherwise hazardous physical entry requirements [7]. SHM applications supported by UAV typically take the form of remote visual inspection. A pertinent example is found in the case of wind turbines, where UAV captured images are a primary means of assessing the state of the composite blade material, prone to weather based leading edge erosion and structural fatigue due to cyclical loading [8, 9]. As edge erosion is a surface exposed damage mode, UAVs are readily able to detect this and other similar modes visually, screening turbines for

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significant degradation requiring immediate remediation and precluding some instances of manned access. More advanced applications address the loss of context in discrete images via photogrammetric reconstruction [10], producing a 3D model and tracking geometry changes through periodic inspection. Similar processes have been deployed for the inspection of bridges [11, 12], topographic surveys [13], nuclear storage cannisters [14] and industrial chimneys [15]. However, when assessing internal structural status, visual inspection is limited to indirect qualitative inference using features like staining or surface breaking cracks. To provide subsurface health status, most SHM methods require physical contact with the structure. This has previously presented a problem to aerial assessment methods. Aerodynamic features such as near surface turbulence and vortices downwind of tall structures require highly dynamic corrective action by a UAV agent. Challenges are exacerbated by the need to maneuver the multirotor to reject such disturbance while simultaneously applying force sufficient to hold a sensor in stationary contact as readings are taken. Combined with the variations in orientation for access around the complex surface geometries of civil and industrial structures, aerial interaction for SHM is a non-trivial challenge. Addressing such, presented herein is a multirotor UAV capable of conducting remote, aerial, ultrasonic thickness measurement with a dry-coupling wheel probe. This may be deployed in sequential discrete point measurement and linear scanning modalities. Demonstrations are made of this capability, supported by quantitative assessment of the impact of the floating platform on the ultrasonic thickness measurement accuracy and the strategy used to localize these measurements relative to the target. The remainder of this paper is structured as follows: Section 2 examines the methodology for remote ultrasonic SHM by an aerial multirotor agent, and Sect. 3 details the results of laboratory flight testing using this system. Finally, potential future work and conclusions drawn from these findings are outlined in Sect. 4 and 5, respectively.

2 Inspection System Overview 2.1

Voliro Multirotor UAV

The Voliro aerial manipulation platform [16, 17] of Fig. 1 grants a unique capability to address the challenges of aerial SHM. As an over-actuated system, it has more actuators than dimensions of its aerial pose state. These effectors take the form of two main propeller pairs on arms extending from the craft center of mass and a reversible thrust tail rotor. As indicated, both propeller pairs may be independently oriented in two rotational degrees of freedom (DoF) during flight. This allows arbitrary wrench generation and force and torque control across all translational and rotational axes, permitting stable flight at non-horizontal attitudes. The Voliro thus benefits from an enhanced dynamic freedom over conventional multirotor platforms with unidirectional thrust which must re-orient the entire craft to change their net thrust vector. Additionally, the Voliro may maintain body orientation while altering its thrust to exert a

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maximum of 3 kg in all directions. This permits efficient rejection of near-surface aerodynamic disturbances during stationary surface contact. As proximity to large structures can deny signals from Global Navigation Satellite Systems (GNSS) via radio wave blocking and introduce multi-path errors, the Voliro may operate with several positioning technologies. Within the indoor laboratory environment translation and rotational 6 DoF pose estimation is provided by a Vicon motion tracker system [18]. This may be purposefully degraded to have accuracy similar to a typical GNSS system, allowing representative performance during testing. In flight the world frame positioning data is supplied to the UAV controller over Wi-Fi via a ground station PC linked to an onboard intel 17 NUC, both running the Robotic Operating System (ROS). Pose data (and other telemetry) is then shared with a Pixhawk Flight controller via a bi-directional serial link. The Pixhawk fuses pose data with measurements from the onboard Inertia Measurement Unit (IMU) to derive an accurate position estimate, as with GNSS data. It also regulates UAV position and orientation in accordance with pilot input. Power is supplied to the UAV from an onboard battery, but for convenience and extended flight duration, may alternately be provided from a domestic electrical outlet via a high-voltage DC tether.

Fig. 1. The over-actuated Voliro multirotor UAV. Annotations show the additional degrees of freedom about which the main propeller pairs may be independently rotated.

2.2

Ultrasonic Instrumentation

Lightweight ultrasonic instrumentation was embedded aboard the UAV in support of contact based SHM per Fig. 2. This proprietary ultrasonic pulse generator and receiver was developed for integration within small form factor mobile robots [19] and drove a 25 mm diameter, dry-coupling, dual-element, 5 MHz wheel probe [20]. The dedicated transmit and receive, dual-element configuration is recommended for corrosive measurement per international standards [21, 22], reducing sensitivity to surface roughness and dead-zone issues in thin samples. Further, the acoustic impedance and low

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attenuation of the dry-coupling rubberized tire enabled ultrasonic coupling without an onboard reservoir of the conventional liquid gel. This reduced instrumentation payload and precluded issues of changing center of mass as the gel was dispensed. A total mass of 132 g and bounding dimensions of 136 mm  61 mm  33 mm made this combination well suited to UAV platform constraints.

Fig. 2. Ultrasonic inspection system diagram. A cross-sectional view of the wheel probe shows the propagation route of the electrical and ultrasonic measurement signals through its dual piezoelectric elements and the sample under test.

Ultrasonic pulse transmission was initiated when the Field Programmable Gate Array (FPGA) received a command from the onboard PC. This caused the Field Effect Transistor (FET) to deliver a 180 V DC/DC boost converted single pulse excitation to one of the piezo elements within the wheel probe axel, and generating ultrasonic wave packets with 5 MHz center frequency. In the receiving pathway, ultrasonic signals reflected by the sample were converted to electrical pulses by the second piezo element. After passing through a variable gain transimpedance amplifier, the full A-scan waveform was captured by an 8-bit Analog to Digital Converter (ADC), operating at 100 Msps. This was stored in a 8192 byte buffer in FPGA memory, granting a maximum signal length of just under 82 ls duration at a variable delay following transmission. A-scans were transferred to the onboard PC via USB 2 connection and conditioned by a 60 dB band-pass filter attenuating noise outside ±2 MHz of the transducer center frequency. There, a custom interface node distributed them to the UAV ROS network. This node enabled logging and real-time inspection signal visualization from the ground station. It also remotely triggered the transceiver at a 100 Hz pulse repetition frequency. Other ROS modules aided aggregation of measurements with UAV pose data, encoding their position relative to the target structure to enhance inspection context. 2.3

Experimental Setup

Prior to adoption, any novel SHM approach must be quantitatively profiled for accuracy. Thus, the aerial ultrasonic inspection system has been assessed within a controlled

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laboratory environment, wherein aspects of performance can be independently tested. Empirical trials are conducted against the rolled aluminum samples of Fig. 3, having known geometry and containing features representative of corrosive thickness loss. The flat plate sample had two pockets milled from its rear surface representative of large scale corrosion. It was used to test positioning repeatability and measurement performance in thin samples approaching the accepted practical minimum thickness limit of one wavelength [21] (under 1.3 mm in aluminum). The bar sample was precision machined to give a thickness staircase decreasing from 31.5 mm in 1 mm steps until a thickness of 17.5 mm, then in 100 lm steps to 16.5 mm. It was used to test the ability of the system to perform linear scans. Owing to manufacturing tolerances, the dimensions of both samples vary slightly from the nominal dimensions of Fig. 3. Consequently, the “as built” dimensions, determined by micron caliper measurements taken at regular intervals across the samples, were used as the reference for all considerations of ultrasonic thickness measurement accuracy. To assess the effects capability on the inspection process of non-horizontal, over-actuated flights, the samples were mounted vertically or on the underside of a 45° overhang. This served as an analog to an inspection scenario amid complex industrial structures.

Fig. 3. Photographs of the rear surface of the aluminum plate (a) and bar (b) samples. Annotations give nominal geometry and cross-section profile. All dimensions are in millimeters.

3 Results 3.1

Multidirectional Structure Inspection

To obtain ultrasonic measurements with the dry-coupling probe, the UAV must apply pressure to deform the rubber tire and expunge the air layer at its sample interface. By experimentation, this tire material and surface roughness combination required a minimum of around 20 N. Further, the probe orientation must be within 10° of surface normal to align its piezo elements with the acoustically coupled region of the tire. As depicted in Fig. 4a and b, the over-actuated UAV was successfully re-orientated about its pitch axis to match the inspection surface normal by actuation of the propeller arms in mid-flight. It then generated additional force into the surface to meet the

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coupling criteria and capture the ultrasonic A-scans of Fig. 4c and d while in stable contact, here sustained for over 2.5 s. The A-scan amplitudes varied with minor changes in coupling, but have sufficient signal to noise ratio (SNR) for thickness measurement.

Fig. 4. Inspections are performed against an aluminum test piece in (a) vertical and (b) overhanging orientations. Normalized ultrasonic A-scan signals captured at the center of the sample in each of these poses are given in (c) and (d) respectively.

3.2

Ultrasonic Inspection Accuracy

Thickness, d, may be derived from an ultrasonic A-scan by Eq. (1). d ¼ vt=n

ð1Þ

Where: v is the material speed of sound, t is the signal time of flight, and n is the number of transits made across the thickness of interest. The time of flight between subsequent back-wall echoes corresponds to two transits of the sample thickness and is computed via auto-correlation. This yields a symmetrical waveform immune to changes in the first-echo position caused by variation in tire deformation force. Applying the Hilbert Transform then demodulates the signal, removing the 5 MHz piezo center frequency and yielding an amplitude envelope. The time of flight corresponds to the time of the first local maximum with positive lag, found by simple peak detection algorithm. The speed of sound for each sample completes Eq. (1) and was evaluated by recording the time of flight across a known thickness. From the average of 2000 readings of the plate and bar samples, the measured speeds of sound were 6417 m/s and 6405 m/s, respectively, agreeing with the standard range for rolled aluminum [23].

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Using the recorded ultrasound data from repeated contact inspections across multiple flight trials, system accuracy is quantified by the error between the numerical thickness and the “as built” sample reference geometry. The measurement error distribution from the vertical and overhanging plate sample inspections is described in Table 1. Table 1. Error distribution statistics in ultrasonic thickness versus reference geometry. Sample orientation Vertical 45° Overhang

Number of samples 3868 1482

Mean error [mm] −0.048 −0.054

Mean absolute error [mm] 0.099 0.092

5th percentile error [mm] −0.218 −0.225

95th percentile error [mm] 0.102 0.160

The error metrics for both orientations were broadly similar, indicating performance was maintained across interaction orientations, though 5th to 95th percentile range indicates a slight increase in variability in the overhanging case. With the mean absolute error close to 0.1 mm, the single ultrasonic measurement accuracy was comparable to typical hand-held, commercial thickness gauges [24, 25]. Further improvement is attainable via averaging of multiple readings. In both trials, mean error approached the theoretical thickness resolution limit of 0.032 mm given by the instrumentation sampling rate and speed of sound in aluminum. Accuracy was within the ±0.5 mm reported for typical in situ manual thickness gauging [26, 27]. 3.3

Positioning Encoding Uncertainty

For this assessment, a total of 11 measurement interactions were completed with the vertical plate sample, each holding stationary contact for at least 2 s. Null ultrasonic readings acquired out of contact with the surface were discarded by evaluation of their maximum amplitude relative to a noise threshold. The position of the remaining signals on the sample were then derived by projection from the UAV pose at the time of measurement, using the direction of the probe arm, to the point of intersection with the sample face. The uncertainty associated with the position encoding of these measurements when considering the UAV internal pose estimate was then evaluated by considering their distribution. As the probe position was constant during contact, taking the linear displacement of each ultrasonic reading from the mean of those in the same interaction allowed comparison across multiple contact instances for a total of 3874 samples. The resultant distribution given in Table 2 provided a measure for confidence in the position of any single reading within the context of the SHM process. Table 2. Position uncertainty distribution statistics. Number of samples 3874

Mean [mm] 5.13

90th percentile [mm] 9.49

95th percentile [mm] 11.01

Maximum [mm] 16.55

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From the worst case position encoding deviation of 16.55 mm recorded for any one measurement, it may be concluded that the internal position estimate of the system is sufficient for localization of large-scale, flat pitting, uniform, or mesa-type corrosive thickness loss features to within an acceptable margin. However, if the application entails detailed corrosion mapping of features at a scale much below 10 mm, then additional measurements should be taken to increase positional certainty. 3.4

Scanning Acquisition

Leveraging the Voliro capability for dynamic environmental interactions, scanning measurements were conducted. Rolling the wheel probe in continuous contact along the surface of the stepped bar sample, and combining the recorded A-scans, the crosssectional B-scan view depicted in Fig. 5 was produced.

Fig. 5. A B-scan cross-sectional view captured by rolling the wheel probe along the stepped bar sample surface. This shows variation in sample thickness with time along the UAV flightpath over two passes across the sample, from the thinnest end, to the thickest and back again in under 65 s. Derived numerical thickness is also plotted.

Within Fig. 5, the relative thickness of the part under inspection is clearly visible in the time difference between the front-wall echo (received after around 12 µs, with slight variation due to changes in tire compression) and the first back-wall echo. It is also observed in the time between subsequent backwall echoes. All thickness features within the swept path along the bar sample were successfully captured in multiple data points, however the scale of the 0.1 mm steps relative to the ultrasonic measurement accuracy means they appear more as a linear gradient. Asymmetry in the B-scan image arises owing to variation in the UAV rolling speed between the outward and return motions. This was a due to a sudden reduction in rolling resistance at the transition from the static to dynamic mode. The resultant burst of motion was challenging to

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sustain within the acoustic coupling criteria. In spite of this the integrated system maintained the acoustic coupling conditions with sufficient consistency to resolve thickness features at the surface parallel pitch under test over two passes.

4 Future Work In light of these findings, possible avenues for future work are identified. Development of the scanning process to improve the speed and stability of surface traversal would permit the time efficient acquisition of densely sampled corrosion maps. Additional surface-relative position sensors could improve position encoding precision, augmenting resolution of small features and repeatability between scans. Additional possibilities may be found in the incorporation of sensing modalities beyond the ultrasonic testing used here. Such developments increase applicability to SHM scenarios where time intensive and hazardous inspection methods would conventionally be deployed.

5 Conclusions In summary, a prototype for an aerial contact based inspection system to support remote structural monitoring has been presented. Challenges of stability and repeatability within the context of contact-based ultrasonic inspection from aboard a UAV have been addressed. Employing an over-actuated aerial manipulator platform in conjunction with a dry-coupling ultrasonic wheel probe, sustained interaction supportive of remote ultrasonic thickness measurement has been demonstrated in multiple flight orientations, successfully interacting with vertical, inclined, and overhanging structural features. These scenarios are supportive of corrosion monitoring activity in a range of SHM applications. Further, this article has presented a quantitative characterization of aerial ultrasonic thickness measurement accuracy and the ability of the system to locate these data relative to the inspection target. Lastly, the ability of the integrated UAV system to perform rolling, time-encoded, cross-sectional B-scan profiling has been documented and suggestions made for further development in future work. Acknowledgements. This work was supported by EPSRC funding under the iCASE program, Ref: EP/R512114/1, in conjunction with EDF Energy and the National Nuclear Laboratory.

References 1. Deraemaeker, A.: Vibration based structural health monitoring using large sensor arrays: overview of instrumentation and feature extraction based on modal filters. In: Deraemaeker, A., Worden, K. (eds.) New Trends in Vibration Based Structural Health Monitoring, pp. 19– 54. Springer, Vienna (2010) 2. Sayed, M., Nemitz, M., Aracri, S., McConnell, A., McKenzie, R., Stokes, A.: The Limpet: a ROS-enabled multi-sensing platform for the ORCA Hub. Sensors 18(10), 3487 (2018). https://doi.org/10.3390/s18103487

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3. Beale, C., Niezrecki, C., Inalpolat, M.: An adaptive wavelet packet denoising algorithm for enhanced active acoustic damage detection from wind turbine blades. Mech. Syst. Sig. Process. 142, 106754 (2020). https://doi.org/10.1016/j.ymssp.2020.106754 4. Arsenault, T.J., Achuthan, A., Marzocca, P., Grappasonni, C., Coppotelli, G.: Development of a FBG based distributed strain sensor system for wind turbine structural health monitoring. Smart Mater. Struct. 22(7), 075027 (2013). https://doi.org/10.1088/0964-1726/ 22/7/075027 5. Britton, C.F.: 4.36 corrosion monitoring and inspection. In: Cottis, B., Graham, M., Lindsay, R., Lyon, S., Richardson, T., Scantlebury, D., Stott, H. (eds.) Shreir’s Corrosion, vol. 4, p. 50. Elsevier (2010) 6. Tamura, A., et al.: A non-contact ultrasonic sensor for pipe-wall thinning inspection of nuclear power plants. In: 45th Annual Review of Progress in Quantitative Nondestructive Evaluation, Portland, OR, USA, May 2019, vol. 2102, p. 6 (2019). https://doi.org/10.1063/1. 5099799 7. Lattanzi, D., Miller, G.: Review of robotic infrastructure inspection systems. J. Infrastruct. Syst. 23(3), 04017004 (2017). https://doi.org/10.1061/(ASCE)IS.1943-555X.0000353 8. Morgenthal, G., Hallermann, N.: Quality assessment of unmanned aerial vehicle (UAV) based visual inspection of structures. Adv. Struct. Eng. 17(3), 289–302 (2014). https://doi.org/10.1260/1369-4332.17.3.289 9. Wang, L., Zhang, Z.: Automatic detection of wind turbine blade surface cracks based on UAV-taken images. IEEE Trans. Ind. Electron. 64(9), 7293–7303 (2017). https://doi.org/10. 1109/TIE.2017.2682037 10. Zhang, D., Watson, R., Dobie, G., MacLeod, C., Khan, A., Pierce, G.: Quantifying impacts on remote photogrammetric inspection using unmanned aerial vehicles. Eng. Struct. 209, 109940 (2020). https://doi.org/10.1016/j.engstruct.2019.109940 11. Seo, J., Duque, L., Wacker, J.: Drone-enabled bridge inspection methodology and application. Autom. Constr. 94, 112–126 (2018). https://doi.org/10.1016/j.autcon.2018.06. 006 12. Khaloo, A., Lattanzi, D., Cunningham, K., Dell’Andrea, R., Riley, M.: Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling. Struct. Infrastruct. Eng. 14(1), 124–136 (2018). https://doi.org/10.1080/15732479.2017. 1330891 13. James, M.R., Robson, S., d’Oleire-Oltmanns, S., Niethammer, U.: Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment. Geomorphology 280, 51–66 (2017). https://doi.org/10.1016/j. geomorph.2016.11.021 14. Clark, R.A., et al.: Autonomous and scalable control for remote inspection with multiple aerial vehicles. Robot. Auton. Syst. 87, 258–268 (2017). https://doi.org/10.1016/j.robot. 2016.10.012 15. Quenzel, J., Nieuwenhuisen, M., Droeschel, D., Beul, M., Houben, S., Behnke, S.: Autonomous MAV-based indoor chimney inspection with 3D laser localization and textured surface reconstruction. J. Intell. Robot. Syst. (May 2018). https://doi.org/10.1007/s10846018-0791-y 16. Kamel, M., et al.: The Voliro Omniorientational Hexacopter: an agile and maneuverable tiltable-rotor aerial vehicle. IEEE Robot. Autom. Mag. 25(4), 34–44 (2018). https://doi.org/ 10.1109/MRA.2018.2866758 17. Voliro Airborne Robotics. https://www.voliro.com/. Accessed 29 June 2020 18. Vicon: Tracker Motion Capture Software for VR and Object Tracking. http://www.vicon. com/products/software/tracker. Accessed 30 June 2020

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19. Macleod, C.N., Dobie, G., Pierce, S.G., Summan, R., Morozov, M.: Machining-based coverage path planning for automated structural inspection. IEEE Trans. Autom. Sci. Eng. 15(1), 202–213 (2018). https://doi.org/10.1109/TASE.2016.2601880 20. Home | Eddyfi. https://www.eddyfi.com. Accessed 31 October 2019 21. Technical Committee ISO/TC 135: “Non-destructive testing – Ultrasonic thickness measurement (ISO 16809:2017),” International Standards Organisation, Geneva, Switzerland, November 2017. https://www.iso.org/standard/72430.html. Accessed 1 November 2019 22. E07 Committee: E797: Standard Practice for Measuring Thickness by Manual Ultrasonic PulseEcho Contact Method. ASTM International. https://doi.org/10.1520/e0797_e0797m-15 23. Dwight, E.: Gray, American Institute of Physics Handbook, 3rd edn. McGraw-Hill, New York (1972) 24. 27MG Ultrasonic Thickness Gage. https://www.olympus-ims.com/en/27mg/#!cms[tab]=% 2F27mg%2Fspecifications. Accessed 29 June 2020 25. Ultrasonic Thickness Tester PCE-TG 50 | PCE Instruments. https://www.pce-instruments. com/english/measuring-instruments/test-meters/ultrasonic-tester-ultrasonic-testing-pceinstruments-ultrasonic-thickness-tester-pce-tg-50-det_5233277.htm. Accessed 29 June 2020 26. Drury, J.C.: Corrosion monitoring and thickness measurement - what are we doing wrong? Insight Non-Destr. Test. Cond. Monit. 39(1), 17–20 (1997) 27. Weier, D.R., Pardini, A.F.: Evaluation of UT wall thickness measurements and measurement methodology. Pacific Northwest National Laboratory, Richland, Washington 99352, US, PNNL-16828 Rev. 0, 1035013, October 2007. https://doi.org/10.2172/1035013

Drive-by Bridge Health Monitoring Using Multiple Passes and Machine Learning Abdollah Malekjafarian1(&), Callum Moloney2, and Fatemeh Golpayegani3 1

3

School of Civil Engineering, University College Dublin, Dublin, Ireland [email protected] 2 AECOM Bridges and Structures, Dublin, Ireland School of Computer Science, University College Dublin, Dublin, Ireland

Abstract. This paper studies a machine learning algorithm for bridge damage detection using the responses measured on a passing vehicle. A finite element (FE) model of vehicle bridge interaction (VBI) is employed for simulating the vehicle responses. Several vehicle passes are simulated over a healthy bridge using random vehicle speeds. An artificial neural network (ANN) is trained using the frequency spectrum of the responses measured on multiple vehicle passes over a healthy bridge where the vehicle speed is available. The ANN can predict the frequency spectrum of any passes using the vehicle speed. The prediction error is then calculated using the differences between the predicated and measured spectrums for each passage. Finally, a damage indicator is defined using the changes in the distribution of the prediction errors versus vehicle speeds. It is shown that the distribution of the prediction errors is low when the bridge condition is healthy. However, in presence of a damage on the bridge, a recognisable change in the distribution will be observed. Several data sets are generated using the healthy and damaged bridges to evaluate the performance of the algorithm in presence of road roughness profile and measurement noise. In addition, the impacts of the training set size and frequency range to the performance of the algorithm are investigated. Keywords: Bridge


Damage detection
Machine learning
ANN

1 Introduction Bridges are integral parts of the transport networks worldwide. Globally, bridges are used by a large percentage of the world’s population daily where an unexpected closure or collapse of such a structure would cause serious disruptions to the networks. It is obvious that having an understanding of the current structural condition of bridges is of major importance to networks’ owners worldwide. As bridges age, damage and deterioration become more present in the structure. However, bridges are most commonly assessed by carrying out a visual inspection. These inspections can give good results when it comes to the immediate structural appearance of the bridge but are oblivious to any internal defects that may be present.

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 695–703, 2021. https://doi.org/10.1007/978-3-030-64594-6_67

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In recent years, more focus has been put on the use of Structural Health Monitoring (SHM) methods for bridge condition assessment. These methods use vibration data gathered from the bridge to extract information relating to the bridge’s modal parameters. These parameters consist of the natural frequencies, mode shapes and modal damping which are unique to each bridge. If there is a defect present in the structure, then it should result in a detectable change in the information gathered in these parameters [1]. Currently, the majority of SHM methods applicable to bridges are the approaches that directly instrument bridges. These methods generally require many vibration detection sensors to be installed at intervals all along the span of the bridge [2]. Due to the cost and time required for installation of these sensors, the direct methods are limited to use on larger bridges, and their implementation across whole networks is unfeasible [3]. This is a major constraint to the widespread implementation of direct detection methods, as the majority of bridges globally are of short or medium span. As a result of this, attention has been turned to developing an indirect method for the damage detection of bridges. The use of an indirect method would eliminate the need to install anything directly on the bridge. Measurements and data would be collected by the sensors attached to a vehicle which passes over the bridge. The passing vehicle would be far more efficient to implement across a range of bridges and could be used to cover an entire road/rail network [3]. Yang et al. [4] first proposed the idea of using vehicle measurement for the purpose of bridge monitoring. To date, finding the bridge modal parameters, such as natural frequencies [4], damping ratios [5] and mode shapes [6], has been the main focus of drive-by methods. In addition, some of the methods do not rely on the modal identification and directly process the measured vehicle responses to assess the bridge health condition. For example, Wavelet spectrum of the vehicle response has been employed in a few studies [7, 8]. Many obstacles still remain in the way of the completion of an indirect damage detection method that can be applied consistently under real-life conditions. An approach that is robust to the calculation noise produced by a combination of operational and environmental variables is yet to be produced. These variables include such things as the road surface profile, vehicle speed, and temperature. Road profile with an uneven roughness will result in high amplitude vibrations being introduced into the response spectrum which can mask the changes in natural frequencies of the bridge. As vehicle speed increases, the road profile effects are amplified, the vehicle bridge interaction (VBI) time reduces, and the resolution of the acceleration signal is significantly depleted. Temperature and other environmental effects have been shown to cause a shift in frequency amplitudes due to the associated change in the stress and strain levels in the bridge that accompany climatic changes [9]. A few studies have suggested that the use of multiple runs is a promising approach to tackle these issues [10–13]. Malekjafarian et al. [12] propose a new bridge-damage detection approach using machine learning techniques, combining an Artificial Neural Network model (ANN) and a Gaussian process, to identify healthy bridge condition from unhealthy ones. In this paper, the indirect damage detection algorithm has been proposed by the authors in [12], is further investigated. A numerical finite element model of a bridge is

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created, and a set number of vehicle crossings of the bridge are simulated at various damage levels. The time domain signal of the VBI is recorded for each crossing. Following this, the signal for each run is used to extract the frequency amplitudes. Fast Fourier Transform (FFT) is performed to extract the frequency amplitudes from the time domain signal. Next, the damage detection algorithm is formulated in two stages. An ANN is trained using a training dataset of velocities and the desired range of frequency amplitudes. Once trained, the ANN is then used to predict the frequency amplitudes when given the vehicle velocity. In the second stage, a Gaussian process is employed to create a damage index which assesses the sensitivity of the algorithm to increasing levels of damage. The predicted data points along the frequency signal are compared to the measured values and a prediction error is found. A larger prediction error in the results corresponds to increased damage being present in the bridge. The network configuration is adjusted, and the results are compared with the aim of finding the optimum arrangement of the network for damage detection. Finally, the VBI response signals are polluted by noise, to represent real-life unpredictable environmental and operational conditions, and the robustness of the network is checked.

2 Finite Element Modeling The VBI shown in Fig. 1 is numerically modelled using finite element (FE). The model that used here is adopt the properties used by Malekjafarian et al. [12] and is a coupled system consisting of a quarter-car model representing the vehicle, and a simplysupported beam model to represent the bridge. The quarter-car system containing two sprung masses and its properties are given in Table 1.

Fig. 1. The VBI.

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A. Malekjafarian et al. Table 1. The vehicle properties. Properties Body mass Axle mass Suspension stiffness Tyre stiffness Suspension damping

Symbol ms ma ks kt cs

Value 9300 700 4  105 1.75  106 103

Unit kg kg N/m N/m Ns/m

The bridge is modelled as a succession of a number of beam finite elements. The two nodes of each beam element each have a translational and rotational degree of freedom. The bridge properties are given in Table 2. Table 2. The bridge properties. Properties Length Depth Width Mass per unit length Modulus of elasticity Second moment of area First natural frequency Second natural frequency Damping ratio

Symbol L db b mb E I f1 f2 n

Value 15 0.75 10 28,125 35,000 0.35156 4.62 18.47 3

Unit m m m kg/m N/mm2 m4 Hz Hz %

In reality, road surfaces are never entirely smooth due to requirements for surface friction capabilities, varying aggregate sizes and deterioration. Road profile has also been seen to excite the response signal recorded by the drive-by vehicle resulting in a polluted spectrum from which the bridge frequencies are undetectable. For this reason, a Class A road surface profile has been applied to the FE model based on ISO 8608 to enable the algorithm to be tested in a more realistic environment.

3 The Damage Detection Algorithm Figure 2 shows the design of the damage detection algorithm. It uses the fast Fourier transform (FFT) amplitudes of the accelerations measured at the axle to discover the presence of damage in the structure. It consists of two stages. In the first stage, an ANN is trained to predict the frequency amplitudes during the passage of a vehicle over the bridge. In the second stage, the measured FFT frequency amplitudes are compared to the predicted values from the network and the error is used to estimate the presence of damage. The training set is comprised of two inputs and a single target output. The target output is the frequency amplitudes, these are acquired from FFT performed on the acceleration signal of the vehicle. The two inputs are the velocity of the vehicle for a given crossing of the bridge, and a set frequency range to correspond to the target amplitudes.

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Fig. 2. The damage detection algorithm.

4 Numerical Results 4.1

Data Generation and Damage Detection

The numerical VBI model is simulated in MATLAB and is used to gather data for a range of damage levels in the bridge. A training set collected in the healthy condition and monitoring sets for damage levels from 0% damage to 30% damage are formed. Each monitoring set will contain 100 crossings of the vehicle across the bridge with a set damage level. For each crossing, the vehicle speed, between 10 and 15 m/s, and the frequency amplitudes from FFT performed on the acceleration signal are recorded. There is a total of 7 monitoring sets with the corresponding damage level increasing in increments of 5% for each set. The damage is modelled as a crack imposed on the 7th element of the bridge.

Fig. 3. (a) The prediction error and (b) the damage index (DI).

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Figure 3(a) shows the prediction errors calculated for the monitoring sets. It can be seen that as the damage level increases, the prediction error also increases. This is reflected in the damage index (DI) in Fig. 3(b). 4.2

Impact of the Training Set Size

The size of training data set is an important attribute that contributes to the prediction power of the network. The ideal network can predict the exact frequency amplitudes over the given frequency range for any velocity. The training set is initially made up from the vibration data gathered from 100 runs across the bridge. The monitoring data sets are also made up of signals from another 100 random velocities between 10 and 15 m/s. As the vehicle speeds in the new healthy and damaged sets differ from the training set, the prediction errors will vary for different iterations of the crossing within the same damage level scenario. When predicting the signal for the velocities in the monitoring sets the network is required to interpolate from the signals related to the velocities it has been trained from. The more runs that are included in the training set, the more velocities that the training set will be accustomed to. This in turn results in a shorter interpolation required to predict for the velocity sets of the monitoring sets. It can then be deduced that by increasing the size of the training set the ambiguity in the prediction should decrease. In this section, the network is trained using 150 and 200 runs. Figure 4 shows the DI for the different numbers of runs in the training part. It can be seen that there is an obvious issue with reliability of the network. The algorithm shows a general sensitivity to an increasing level of damage but for each damage scenario there are a regular number of outliers where the damage index has jumped away from the mean of that set. The effect of such a regular miscalculation for the application of a damage detection algorithm can be of major cost to an economy. An incorrect classification of the deterioration of a bridge, could lead to repairs being planned when in fact the bridge is healthy or, with more serious consequences, vice versa. From Fig. 4, it can be derived that for the training set of 200 runs the number of divergent runs is greatly reduced and the major outliers are completely eliminated. 200 runs will be used as the standard size of the training set for further testing.

Fig. 4. The damage index for (a) 100 runs, (b) 150 runs and (c) 200 runs.

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Impact of the Frequency Range

The frequency range to be analysed is a very important factor when designing an algorithm to detect damage once a road profile is introduced. The vibrations imparted on the recording vehicle by the road profile result in a substantial increase in the amount of frequency peaks present in the signal. With an increased number of peaks, and less consistency between the response signals recorded at different speeds, difficulty in associating frequency magnitude shifts to damage rises dramatically. In this section, the frequency range to be analysed by the damage detection algorithm will be adjusted to find the range that gives the optimal sensitivity to damage. The current frequency range being used by the algorithm is the 0–8 Hz range. This range was chosen based on the vehicle axle hop frequency of approximately 8 Hz. Figure 5 plots the prediction error recorded from 10 to 15 m/s for each damage scenario for the frequency ranges of 0–5 Hz, 0–8 Hz and 0–20 Hz. It can be seen that the road roughness appears to still have a major influence over the prediction errors adjudged to be present in the monitoring sets in the 0–20 Hz range. In contrast to this, the 0–5 Hz range shows an obvious positive relationship between the presence of damage and the prediction error recorded. From plotting the damage indexes, it can be deduced that the 0–20 Hz range is unsuitable for damage detection and is highly sensitive to the road profile. Whereas the 0–5 Hz range offers a highly sensitive solution to an increasing damage level.

Fig. 5. The damage index when the frequency range used is (a) 0–5 Hz, (b) 0–8 Hz and (c) 0– 20 Hz.

4.4

Impact of Measurement Error

A significant obstacle to the formation of a reliable damage detection algorithm previously, has been the changes to the dynamic behaviour of the structure caused by environmental conditions. In this study, the effects of environmental and operational effects, such as the presence of traffic, are characterised by the presence of white noise. The performance of the algorithm is checked when the recorded acceleration signal has been polluted by a range of noise levels. The acceleration response of the quarter-car with the addition of Gaussian random white noise (GRWN). The robustness of the current network configuration was tested against three levels of measurement noise,

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3%, 5%, 10%. The sensitivity of the network to damage and robustness to noise was checked carrying out 100 crossings at each damage scenario, from 0% damage to 30%, and plotting the error on a damage index. The prediction error recorded for every crossing under the increasing noise conditions is plotted in Fig. 6.

Fig. 6. The damage index when the added noise is (a) 3%, (b) 5% and (c) 10%.

The damage indexes plotted in Fig. 6 confirm that the current algorithm maintains a high sensitivity to damage even when measurement error up to 5% has been added to the VBI response. As the noise level is increased, the variability of the damage index for each monitoring set increases.

5 Conclusion This paper studies the feasibility of machine learning for indirect damage detection. An FE model is created and the response of the VBI is recorded. FFT is performed on the time-domain signal and the frequency domain response of the VBI to each crossing of the quarter-car is recorded. The ANN is trained using a set of data recorded for the healthy state of the bridge. The presence of damage is then detected by comparing the measured response for a damaged state with the networks predicted response in the healthy condition and evaluating the level of error present. The sensitivity of the current network configuration to increasing levels of damage is evaluated by the formulation of a damage index. A Gaussian process is adopted to convert the prediction error into the damage index. The size of the training set and the frequency range to be assessed are all adjusted to find the network configuration that gives the optimum performance for damage detection. The performance of the ANN is evaluated measuring the response from a quarter-car model crossing a bridge of length 15 m at random speeds of 10– 15 m/s, with a low roughness surface profile. It is found that the network shows the most sensitivity to damage while remaining robust to the effects of the surface profile when a training set of 200 runs and the frequency range was limited to 0–5 Hz. Once the optimal network configuration is chosen, the network is initially examined when 3%, 3% and 10% noise added to the responses. Under these conditions, the algorithm continued to show good sensitivity to the presence of damage up to 5% noise. The

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variance within the monitoring sets saw a substantial increase due to noise, but the healthy condition always remained well defined and separated from the damaged scenarios. It can be concluded that the ANN shows a good robustness to measurement noise, and the future ability to be trained to differentiate.

References 1. Brownjohn, J.M.: Structural health monitoring of civil infrastructure. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 365, 589–622 (2007) 2. Carey, C., OBrien, E.J., Malekjafarian, A., Lydon, M., Taylor, S.: Direct field measurement of the dynamic amplification in a bridge. Mech. Syst. Sig. Process. 85, 601–609 (2017) 3. Malekjafarian, A., OBrien, E.J., Golpayegani, F.: Indirect monitoring of critical transport infrastructure: data analytics and signal processing. In: Data Analytics for Smart Cities, pp. 157–176. Auerbach Publications (2018) 4. Yang, Y.-B., Lin, C., Yau, J.: Extracting bridge frequencies from the dynamic response of a passing vehicle. J. Sound Vib. 272, 471–493 (2004) 5. González, A., OBrien, E.J., McGetrick, P.: Identification of damping in a bridge using a moving instrumented vehicle. J. Sound Vib. 331, 4115–4131 (2012) 6. Malekjafarian, A., OBrien, E.J.: On the use of a passing vehicle for the estimation of bridge mode shapes. J. Sound Vib. 397, 77–91 (2017) 7. Fitzgerald, P.C., Malekjafarian, A., Cantero, D., OBrien, E.J., Prendergast, L.J.: Drive-by scour monitoring of railway bridges using a wavelet-based approach. Eng. Struct. 191, 1–11 (2019) 8. Hester, D., González, A.: A discussion on the merits and limitations of using drive-by monitoring to detect localised damage in a bridge. Mech. Syst. Sig. Process. 90, 234–253 (2017) 9. Locke, W., Sybrandt, J., Redmond, L., Safro, I., Atamturktur, S.: Using drive-by health monitoring to detect bridge damage considering environmental and operational effects. J. Sound Vib. 468, 115088 (2020) 10. Mei, Q., Gül, M.: A crowdsourcing-based methodology using smartphones for bridge health monitoring. Struct. Health Monit. 18, 1602–1619 (2019) 11. Mei, Q., Gül, M., Boay, M.: Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis. Mech. Syst. Sig. Process. 119, 523– 546 (2019) 12. Malekjafarian, A., Golpayegani, F., Moloney, C., Clarke, S.: A machine learning approach to bridge-damage detection using responses measured on a passing vehicle. Sensors 19, 4035 (2019) 13. Martinez, D., Malekjafarian, A., OBrien, E.: Bridge flexural rigidity calculation using measured drive-by deflections. J. Civ. Struct. Health Monit., 1–12 (2020)

Guided Waves in Structures for SHM

Vectorization of the Code for Guided Wave Propagation Problems Pawel Kudela(B)

and Piotr Fiborek

Institute of Fluid-Flow Machinery, Polish Academy of Sciences, 80-231 Gdansk, Poland [email protected]

Abstract. Vectorization of the code for simulation of guided wave propagation problems based on the spectral element method is presented. In the code, flat shell spectral elements are utilized for spatial domain representation. The implementation is realised by using Matlab Parallel Computing Toolbox and optimized for Graphics Processing Unit (GPU) computation. In this way, considerable computation speed-up can be achieved in comparison to computation on conventional processors. The implementation includes an interpolation of wave-field on a uniform grid. The method was tested on experimental full wave-field data measured by scanning laser Doppler vibrometer. Good agreement between numerical and experimental results was achieved. Due to relatively short computation time, large data sets can be generated by using the proposed implementation. The large data sets are especially useful for deep neural network training or other soft computing methods opening up new possibilities in health monitoring of metallic and composite structures.

Keywords: Spectral element method vectorization · GPU computation

1

· Guided waves · Code

Introduction

The motivation of this work was the need for the development of large data set to be used for Machine Learning purposes. It consists of 475 examples of various delamination sizes and locations in a composite plate of dimensions 500×500 mm. For each example simulation of guided wave propagation and interaction with delamination for selected excitation signal is needed. Guided wave propagation problems are computationally demanding. Usually, a very dense mesh is necessary to model short wavelengths (at least five nodes per wavelength are required to represent the shape of wave). To date, there is no commercial software available which could be used for efficient wave propagation simulation. This fact is confirmed by studies conducted by Leckey et al. [8]. They investigated four numerical simulation tools: custom implementation of the 3D Elastodynamic Finite Integration Technique (EFIT) [14] along with three widely used commercial finite element codes: COMSOL, ABAQUS, and ANSYS. The c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 707–715, 2021. https://doi.org/10.1007/978-3-030-64594-6_68

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investigated example was related to the interaction of propagating Lamb waves with delaminations in cross-ply laminates. The laminate was modelled by a fine mesh of 3D solid elements. The numerical results were compared with experiments in terms of the wave-field showing quite good agreement in case of COMSOL, ABAQUS Implicit and ANSYS implicit. Unfortunately, despite the simulations were performed on a workstation equipped with 16 cores, the efficiency of each investigated methods is so low that it is prohibitive to perform any parametric study or generate large data sets (the shortest simulation run time was for the case of COMSOL i.e. 19.5 h followed by ABAQUS implicit i.e. 40 h). The guided wave propagation modelling problem has been tackled by using various methods over the past few decades. The following methods can be included: analytic methods [4], semi–analytic methods [1,5], analytical and higher order finite element hybrid approach for 2D analysis [17], the frequency domain spectral finite element method [2,13], the wavelet spectral finite element [10,19], the time domain spectral element method [9,12,15], the spectral cell method [3] and the Local Interaction Simulation Approach [6]. Some of these methods have been recently implemented for the use on Graphics Processing Units (GPU) in order to decrease computation time [6,7,11,16]. The advantage of such approach is staggering computation speedup in comparison to the use of CPU. The method presented in this paper for solving guided wave propagation problems combines the high order time domain spectral element method (SEM) with the Compute Unified Device Architecture (CUDA), through Matlab Parallel Computing Toolbox. The presented concept of parallel implementation of SEM is similar to the parallel implementation developed previously [7] but it is applied to flat shell spectral elements instead of 3D solid elements. Therefore, the computation can be performed faster than in case of utilisation of 3D solid spectral elements.

2

Code Vectorization Concept

In classic finite element approach elemental matrices are assembled to form equation of motion: ¨ + CU ˙ + KU = F, MU

(1)

where M is the global mass (inertia) matrix, K is the global stiffness matrix, C is the global damping matrix, U is the vector of global degrees of freedom and F is the vector of the time-dependent excitation (in this particular case the vector of equivalent piezoelectric forces). The most efficient way to solve the (1) is by using explicit integration scheme. Assuming the central difference method: ¨  1 (ut+Δt − 2 ut + ut−Δt ) , U Δt2 ˙  ut+Δt − ut−Δt U 2Δt and substituting (2)–(3) into (1) leads to:

(2) (3)

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     2 1 1 1 1 M+ M ut + − 2 M + C ut+Δt = Ft −(Kut ) + C ut−Δt . Δt2 2Δt Δt2 Δt 2Δt             i F 

M0

M1

M2

(4) It should be underlined that due to the orthogonality of shape functions and application of the Gauss-Lobatto-Legendre (GLL) integration rule the mass matrix is diagonal. It has been shown that for the Lamb wave attenuation modelling it is possible to assume that the damping matrix is proportional to mass matrix [18]. In such case calculation of displacements at the time step t + Δt is straightforward and does not require costly matrix inversion. However, the term Fi = Kut related to internal forces at the time step t is still computationally intensive. Moreover, assembly of the stiffness matrix is troublesome because it requires a lot of memory and limits the size of wave propagation problems which can be simulated. In order to alleviate these deficiencies, a parallel code is proposed in which assembly is performed at the internal force vector level without the necessity of stiffness matrix assembly. The proposed approach is very similar to the parallel implementation given in [7]. The current method differs in the calculation of elemental forces which depend on the contribution of the extensional stiffness, the flexural stiffness, bending-stretching coupling, twisting-stretching along with bending-shearing coupling, stretching-shearing coupling and bending-twisting coupling instead of the matrix of elastic constants assigned to each layer of a composite laminate. Hence, the proposed method is more suitable for wave propagation modelling in multilayer composite laminates because it leads to a much lower number of degrees of freedom. Essentially, the term Kut is expanded for each element by using sparse matrices of shape function derivatives Neξ , Neη , components of the inverse of Jacobian matrix (J−1 )eij , elastic constants Qeij integrated over the thickness, integration ˆ e0 . These matriweights We and nodal displacement vector at the element level u ces and vectors are combined together in the form corresponding to disjoint spectral elements as: ⎡ e=1 ⎡ e=1 ⎤ ⎤ ˆ0 u N,ξ 0 ... 0 e=2 ⎥ ⎢ 0 N,e=2 . . . 0 ⎥ ⎢u ξ ⎢ ⎢ ˆ0 ⎥ ⎥ ⎢ ⎢ ⎥ , Ux = ⎢ . ⎥ (5) N,ξ = ⎢ . .. .. ⎥. . 0 ⎥ . ⎣ .. ⎣ .. ⎦ ⎦ ˆ e=n 0 0 0 N,e=n u 0 ξ Therefore, at the final step, it is necessary to assemble the global force vector. It can be performed according to mesh colouring algorithm proposed in [7]. The algorithm uniformly divides the nodes of spectral elements within the whole mesh into 12 sets. The sets are of the same size so the computation can be perfectly balanced between workers or resources can be uniformly divided within one graphics card. The 36-node spectral elements are used in the current approach.

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It guarantees that the mesh can be divided into 12 equal sets because the result of operation 36/12 = 3 is integer. Once internal forces Fi are calculated and substituted into (4), the displacements at time step t + Δt can be explicitly obtained from a perfectly vectorized code:  (6) ut+Δt = 1./M0 . ∗ Ft − Fi + M1 . ∗ ut + M2 . ∗ ut−Δt , in which terms M0 , M1 and M2 are stored as vectors and ./ is element-wise division and .∗ denotes element-wise operation known as Hadamard product (the same symbol for element-wise operation is used in Matlab). In particular, all components in (6) are implemented in Matlab Parallel Computing Toolbox as gpuArray. In this way, the implementation is simple whereas CUDA GPU computation is transparent to the user. Depending on the size of the problem, calculations are about 5–12 times faster on GPU than on a single CPU.

3

Results

The numerical results were validated by experimental wave-field data acquired by scanning laser Doppler vibrometer. The investigated specimen was made out of unidirectional CFRP laminate with an orientation angle of reinforcing fibres 90◦ . Material properties of single-layer CFRP laminate are given in Table 1. The mass density was 1574.1 kg/m3 . The dimensions of the specimen were 1200 × 1200 mm and the thickness was 2.85 mm. A piezoelectric transducer of diameter 10 mm was placed at the centre of the plate. Three excitation frequencies were considered 16.5 kHz, 50 kHz and 100 kHz. The signal had a form of sinusoid modulated by Hann window (5 cycles). The measurements were taken on a lower left quarter of the composite laminate on the opposite side with respect to the piezoelectric transducer. Measurements were acquired at a regular grid of 491 × 491 points. Numerical simulations were carried out with the same parameters as in the experiment. The wave-field data at a quarter of the plate was interpolated on a regular grid of points of the same size as in the experimental data. It should be added that damping was not included in the numerical simulations. Table 1. Material properties of the investigated unidirectional CFRP laminate; Units: GPa. Q11 Q12 Q22 Q44 Q55 Q66 120 5.6

12.7 3.1

5.3

4.5

Comparative results are presented in Figs. 1, 2 and 3. It can be seen that qualitative agreement between numerical and experimental wave-fields is very good especially for A0 mode which has the greatest amplitude.

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(a) Numerical

(b) Experimental

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Fig. 1. Wave-field of propagating guided waves for the excitation frequency 16.5 kHz at the time instances: 0.25 (a)–(b), 0.5 (c)–(d) and 0.75 (e)–(f) ms.

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(a) Numerical

(b) Experimental

(c) Numerical

(d) Experimental

(e) Numerical

(f) Experimental

Fig. 2. Wave-field of propagating guided waves for the excitation frequency 50 kHz at the time instances: 0.25 (a)–(b), 0.5 (c)–(d) and 0.75 (e)–(f) ms.

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Fig. 3. Wave-field of propagating guided waves for the excitation frequency 100 kHz at the time instances: 0.2 (a)–(b), 0.3 (c)–(d) and 0.4 (e)–(f) ms.

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In experimental wave-fields at 50 kHz additional low-amplitude waves can be observed which correspond to the faster S0 mode (see Fig. 2a). Unfortunately, S0 mode is not visible in numerical simulations. However, this problem can be alleviated by tuning in-plane and out-of-plane damping matrix components. When 100 kHz excitation signals are applied, more guided wave modes can be observed in experimental signals (Figs. 3b, d and f). The model is not able to properly simulate higher guided wave modes because it is based on the firstorder shear deformation theory. It has not enough degrees of freedom per node in order to properly model through-thickness guided wave behaviour at higher frequencies.

4

Conclusions

A novel vectorized code for guided wave propagation problems was developed. It is based on the time domain spectral element method in which flat shell elements are utilized. The proposed code is implemented for the use on GPU which results in 5–12 times computation speed-up in comparison to computations on CPU. Qualitative results in terms of full wave-filed data are satisfactory. The model is limited to the modelling of fundamental guided wave modes. Therefore, discrepancies between numerical and experimental results at higher frequencies are expected. Further studies are needed in relation to the optimisation of damping parameters and quantitative estimation of differences between numerical and experimental signals. Acknowledgements. The research was funded by the Polish National Science Center under grant agreement no 2018/31/B/ST8/00454. P. Kudela would like to acknowledge the Polish National Agency for Academic Exchange for the support in the frame of the Bekker Programme (PPN/BEK/2018/1/00014/DEC/1). Authors are also grateful to Task-CI for allowing the use of Matlab and Parallel Computing Toolbox licences.

References 1. Bartoli, I., Marzani, A., di Scalea, F.L., Viola, E.: Modeling wave propagation in damped waveguides of arbitrary cross-section. J. Sound Vib. 295(3–5), 685–707 (2006) 2. Doyle, J.F.: Wave Propagation in Structures. Springer, New York (1989) 3. Duczek, S., Joulaian, M., D¨ uster, A., Gabbert, U.: Numerical analysis of Lamb waves using the finite and spectral cell methods. Int. J. Numer. Methods Eng. 99(1), 26–53 (2014) 4. Giurgiutiu, V.: Structural Health Monitoring with Piezoelectric Wafer Active Sensors. Academic Press, Cambridge (2014) 5. Gravenkamp, H., Bause, F., Song, C.: On the computation of dispersion curves for axisymmetric elastic waveguides using the Scaled Boundary Finite Element Method. Comput. Struct. 131, 46–55 (2014)

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6. Kijanka, P., Radecki, R., Packo, P., Staszewski, W.J., Uhl, T.: GPU-based local interaction simulation approach for simplified temperature effect modelling in Lamb wave propagation used for damage detection. Smart Mater. Struct. 22(3), 035014 (2013) 7. Kudela, P.: Parallel implementation of spectral element method for Lamb wave propagation modeling. Int. J. Numer. Methods Eng. 106(6), 413–429 (2016) 8. Leckey, C.A., Wheeler, K.R., Hafiychuk, V.N., Hafiychuk, H., Timu¸cin, D.A.: Simulation of guided-wave ultrasound propagation in composite laminates: benchmark comparisons of numerical codes and experiment. Ultrasonics 84, 187–200 (2018) 9. Lonkar, K., Chang, F.K.: Modeling of piezo-induced ultrasonic wave propagation in composite structures using layered solid spectral element. Struct. Health Monit. 13(1), 50–67 (2013) 10. Mitra, M., Gopalakrishnan, S.: Wave propagation analysis in anisotropic plate using wavelet spectral element approach. J. Appl. Mech. 75(1), 0145041–0145046 (2008) 11. Mossaiby, F., Joulaian, M., D¨ uster, A.: The spectral cell method for wave propagation in heterogeneous materials simulated on multiple GPUs and CPUs. Comput. Mech. 63(5), 805–819 (2019) 12. Ostachowicz, W., Kudela, P., Krawczuk, M., Zak, A.: Guided Waves in Structures for SHM: The Time-Domain Spectral Element Method. Wiley, Hoboken (2012) 13. Roy Mahapatra, D., Gopalakrishnan, S.: A spectral finite element model for analysis of axial–flexural–shear coupled wave propagation in laminated composite beams. Compos. Struct. 59(1), 67–88 (2003) 14. Schubert, F., Peiffer, A., K¨ ohler, B., Sanderson, T.: The elastodynamic finite integration technique for waves in cylindrical geometries. J. Acoust. Soc. Am. 104(5), 2604–2614 (1998) 15. Schulte, R.T., Fritzen, C.P.P., Moll, J.: Spectral element modelling of wave propagation in isotropic and anisotropic shell-structures including different types of damage. IOP Conf. Ser. Mater. Sci. Eng. 10(1), 012065 (2014) 16. Shen, Y., Cesnik, C.E.S.: Local interaction simulation approach for efficient modeling of linear and nonlinear ultrasonic guided wave active sensing of complex structures. J. Nondestr. Eval. Diagn. Progn. Eng. Syst. 1(1), 011008 (2017) 17. Vivar-Perez, J.M., Duczek, S., Gabbert, U.: Analytical and higher order finite element hybrid approach for an efficient simulation of ultrasonic guided waves I: 2D-analysis. Smart Struct. Syst. 13(4), 587–614 (2014) 18. Wandowski, T., Kudela, P., Malinowski, P., Ostachowicz, W.: Guided wave attenuation in composite materials. In: Kundu, T. (ed.) Health Monitoring of Structural and Biological Systems 2017, p. 101701D (2017) 19. Yang, Z.B., Chen, X.F., Xie, Y., Zuo, H., Miao, H.H., Zhang, X.W.: Wave motion analysis and modeling of membrane structures using the wavelet finite element method. Appl. Math. Model. 40(3), 2407–2420 (2016)

In-situ Strain Monitoring Performance of Flexible Nylon/Ag Conductive Fiber in Composites Subjected to Cyclic Tensile Loading Yumna Qureshi1(&), Mostapha Tarfaoui1, Khalil K. Lafdi2, and Khalid Lafdi2,3 1

ENSTA Bretagne, IRDL - UMR CNRS 6027, 29200 Brest, France [email protected], [email protected] 2 Nanomaterials Laboratory, University of Dayton, Dayton, OH 45469-0168, USA [email protected], [email protected] 3 Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne, UK

Abstract. Although smart textile materials have significant importance because of their advanced technology, they haven’t replaced the conventional electronics completely. Nevertheless, these smart textile materials are now developed into the fabrication of in-situ structural health monitoring systems for structures and wearable technologies. The objective of this study was to develop a flexible microscale conductive fiber for in-situ strain monitoring applications by depositing uniform coating film of silver (Ag) nanoparticles on the surface each filament of nylon yarn by electroless plating process without jeopardizing the integrity of each material. The sensitivity of this Nylon/Ag conductive fiber was calculated experimentally, and the gauge factor (GF) was found to be in the range of 21–25 which showed a high sensitivity of the conductive fiber to the applied strain. Then, Nylon/Ag conductive fiber was fractured under tensile loading and a good agreement between the electromechanical response of the conductive fiber was found with repeatability of the results. Then, this Nylon/Ag conductive fiber was inserted in composite specimens at four different directions i.e. 0°, +45°, and 90° respectively in each ply and specimen was machined in a star shape in which each leg represented the direction of the individual position of the Nylon/Ag conductive fiber. The composite star specimen was then subjected to tensile cyclic loading and results showed excellent reproducibility in the mechanical behavior of composite specimens and electrical signals of each conductive fiber although, the conductive fiber in each position showed distinct response because of their respective direction. The increase or decrease in the resistance of the fiber sensor signified the presence of tensile or compressive strain respectively and the intensity of the signal quantified the amount of deformation. The results demonstrated the way to design a cost-effective microscale smart textile for strain monitoring. This Nylon/Ag conductive fiber

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 716–726, 2021. https://doi.org/10.1007/978-3-030-64594-6_69

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can then be used in in-situ structural health monitoring even in high strain applications because of their good sensitivity, flexibility, and stability. Keywords: Smart textile
Conductive polymer yarn response
Composites
In-situ strain monitoring


Electro-mechanical

1 Introduction Composites have substituted traditional materials in almost every engineering and structural application because of their extraordinary mechanical performance and costeffectiveness however, even they have limitations and are prone to damage [1]. So, it is essential to examine and monitor their behavior during working conditions to avoid sudden failure [2]. Structural health monitoring (SHM) is a well-known technique widely used to study and monitor the performance of the composites and other materials in working conditions to ensure safe and reliable structures [3]. These monitoring systems and sensors were established progressively over time from nondestructive methods to in-situ monitoring of materials [4, 5]. In-situ monitoring systems had been frequently designed for detecting various types of failures in structural components to ensure their durable service life [6, 7]. Similarly, numerous studies examined the strain deformation and failure sensing of the composites using different SHM methods under tensile elongation however, very little or no information was available about the influence of the location of the sensor on their sensitivity and damage detection [8]. Furthermore, it was vital to understand the sensing response of the sensor to differentiate between the types of damage during tensile deformation. Flexible smart textiles were considered to be a favorable alternative for the SHM of structural composites because after insertion, they could not only monitor the deformation of the structure but also act as reinforcement [9, 10]. Conductive polymers were first used for real-time monitoring of composites however, their conductivity was less as compared to nanoparticles and they were unstable under environmental effects [11, 12]. Likewise, inserting or coating conductive nanoparticles on polymeric yarns were also considered for in-situ SHM of structures [13]. CNTs based fibers were used numerous times for in-situ structural health monitoring of composites because of their outstanding performance however, their sensitivity could be affected after insertion into the composite structures because of their porous network and tunneling effect [14–17]. In addition, reduced graphene oxide (RGO) based sensors also showed good flexibility, sensitivity with good stability in in-situ monitoring of high strain applications and did not show any resin penetration because of their surface and geometry conformability [14]. However, RGO is toxic in nature and had stability issues when exposed to air [18, 19]. Furthermore, metal nanoparticles were also used for in-situ SHM application but among all, silver (Ag) had great potential as a coating material on flexible substrates because of its stability in the air, competitive price, good conductivity and mechanical

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performance [20–23]. However, no or very little information is available regarding the use of flexible sensor wires for multi-mode detection of strain deformation in composites subjected to tensile deformation for application in larger and unapproachable structural areas [24]. Accordingly, an experimental study was conducted to examine the in-situ monitoring capability of the Nylon/Ag fiber sensor for the deformation of composite material during repeated tensile loading. In addition, the response of the fiber sensor was also studied to distinguish between different types of deformations by placing them in different directions i.e. 0°, +45°, 90°, −45° regarding the loading axis and fiber sensor in individual positions were separated by the single-ply in all three specimens. The Nylon/Ag fiber sensor was fabricated by electroless plating and then integrated within the composite samples at their respective position and direction during the fabrication process. Afterward, samples were tested experimentally on INSTRON-50 under cyclic tensile loading one by one and their strain deformation was monitored by correlating its mechanical behavior with the response of each fiber sensor. The results presented interesting behaviors and indicated that the fiber sensor did not only monitored the deformation in each cycle but also demonstrated that the location and direction of the sensor played an important part in differentiating between different types of damage and in quantifying them.

2 Methodology 2.1

Fabrication Process

Nylon/Ag fiber sensors were developed by depositing Ag nanoparticles as a uniform continuous film on every filament of Nylon by using an electroless plating process [22, 23], (see Fig. 1). Then the fiber sensor was inserted between the plies of chopped glass fibers in their respective position and direction during the fabrication of the composite specimen. The chopped fiber mat ensured isotropic mechanical behavior with poor conductivity and electrical isolation for each fiber sensor. Nylon/Ag fiber sensors were inserted in the specimen in their particular direction such that sensor A was in 0° between plies 1 and 2, sensor B was in 45° between plies 2 and 3, sensor C was in 90° between plies 3 and 4 and sensor D was in −45° between plies 4 and 5 from bottom to top. After the mixture of resin and hardener was added into the mold and after the curation process of 48 h at room temperature, full insertion of fiber sensors was achieved in each specimen. The specimens were machined using CNC (Computer numerical control) machine in a star shape in which each leg represented the direction and placement of the fiber sensor, (see Fig. 2).

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Fig. 1. SEM of Nylon/Ag fiber sensor after fabrication [25] wire

(a)

(b)

Fig. 2. (a) The composite samples turned transparent after the fabrication and embedded Nylon/Ag fiber sensors were visible in each leg. (b) Geometric Parameters and dimensions of the star specimen

2.2

Experimentation

The star specimen was placed between the fixtures in the INSTRON-50 machine in such a manner that sensor A was along the loading axis and the data acquisition system was attached to the electrode wires connected to the Nylon/Ag fiber sensors, (see Fig. 3). It was vital to ensure that the sample was positioned properly among the fixtures and the electrical connections were isolated from any metallic part of the machine to avoid any external influence on the electrical response of the fiber sensors. Then, the test was performed at a low strain rate i.e. 5 mm/min up to 15 kN to avoid any permanent deformation and each test was performed for 10 cycles. The mechanical response of the specimen with the electrical profile of all four fiber sensors was obtained.

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Fig. 3. Setup to examine the in-situ monitoring performance of the Nylon/Ag fiber sensor when integrated within the composite sample during a tensile test.

3 Results and Discussion 3.1

Mechanical Behavior of Composite Star Specimen

First, it is important to understand the strain deformation of the composite under cyclic tensile loading to apprehend the strain detection by all three sensor systems, (see Fig. 4). One leg of the star specimen was fixed between the fixtures of the machine and the other legs were free. The loading axis was considered as the reference and sensor place in this direction was at 0° and labeled as sensor A. When the specimen was loaded, tensile stresses were produced in 0° and compression stresses were produced in 90° i.e. transverse direction. In addition, it was understood that the combined effect of tensile and compression stresses is generated in oblique direction i.e. +45°. However, in test 1 and 2, samples were placed between the fixtures in such manner that the leg of the star sample consisting of sensor A was along the loading axis i.e. in 0° and in test 3,

In-situ Strain Monitoring Performance

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the sample was placed in a way that the leg of the composite sample consisting of sensor C was along the loading axis i.e. in 0°, sensor A in 90° and sensor B & D interchanged their position for all three sensor systems, (see Fig. 5). The step to interchange the positions of the sensor in test 3 was conducted to examine the load sensitivity of each sensor system and it didn’t affect the comparison of the mechanical performance of the composite samples. Three composite specimens were tested for each sensor system successfully, and mechanical behavior was plotted as elastic modulus and overall initial stress-strain curve which showed good repeatability in the behavior. Figure 6 shows a comparison of three samples and results confirmed that the mechanical behavior of all composite samples was similar irrespective of the choice of the loaded leg or the sensor system and was isotropic because of the use of the chopped glass fiber mat. The presence of any sensor system in different directions and positions did not affect the structure’s integrity. Moreover, the mechanical behavior of all the three star-samples was similar to each other regardless of the placement of the specimen. This further confirmed that the placement of fiber sensors at different positions [21] and directions did not influence the mechanical behavior and integrity of the composite sample and its isotropic nature.

Fig. 4. Star Specimen with (a)–(b) Geometrical illustration of the placement of sensor systems in their individual sample i.e. in individual leg and through-thickness (section view) correspondingly.

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(a) Case I

(b)Case II

Fig. 5. Position of the composite star samples between the fixture for tensile loqding: (a) Sample placement in tests 1 and 2, (b) Sample placement in a test.

(a)

(b)

Fig. 6. Mechanical behavior of all three star-samples. (a) Elastic modulus (b) Overall tensile stress-strain behavior

3.2

In-situ Monitoring and Identification of Strain Deformation by Nylon/Ag Fiber Sensor

Nylon/Ag strain sensor wire showed good electrical signal response during all three mechanical tests of the composite star specimen. The resistance was changing in each case with the gradual increase of the load and the fiber sensor showed a similar response in all 4 directions i.e. 0°, +45°, and 90°. The electrical response of each Nylon/Ag fiber sensor showed a change of resistance with an increase of strain in the specimen, however, during deformation the strain sensor wire within the specimen showed different behavior because of its position and direction regarding the loading axis. This showed that it not only monitored the deformation but, also identified it as to whether it was compressive, tensile, or both. Test 1 and test 2 were performed by placing the specimen in such a way that sensor A was in the loading direction and sensor C was in the transverse direction while in test 3, the specimen was placed in such a way that

In-situ Strain Monitoring Performance

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sensor C was in the loading direction and sensor A was in the transverse direction. The cyclic tensile test further confirmed the reproducibility of electrical response and the real-time strain monitoring behavior of the Nylon/Ag fiber sensor under the 10 cycles of tensile load. This showed that the Nylon/Ag fiber sensor also had stability in the detection response and long-term response cycle. This also verified that this fiber sensor can be reused unless it is fractured even then; the divided part of the sensor wire could be used as a sensor for damage detection. During the applied cyclic strain as high as between 1–2% and for 10 cycles, the Nylon/Ag fiber sensor perfectly correlated with the applied strain in each cycle. This confirmed the durability and stability of the sensor. • Test 1 and Test 2 confirmed the reproducibility of electrical response and the real-time strain monitoring behavior of the Nylon/Ag fiber sensor. All sensors A, B, C, and D showed changed in resistance during the deformation and correlated perfectly in both tests, (see Fig. 7). Moreover, it was observed that the maximum increase in resistance was recorded by sensor A which confirmed maximum tensile deformation occurred in the loading direction. However, sensor C showed a decrease in resistance and this negative behavior confirmed the presence of compressive stress and deformation which established the Poisson’s effect during the deformation of the structure. The compression strains are dominant in the transverse direction while very less tensile strain present. That is why, the sensor in the C leg showed decrease in resistance detecting the presence of compressive strains. The minimum change in resistance was recorded by sensors B and D and both sensors showed identical responses. This identical response of sensors B and D was because in isotropic material, these two directions are a mirror of each other regarding the loading axis. However, slight diminution with less than 1% was recorded for the sensor A in comparison with the sensor B, C, and D. This reduction was negligible in comparison to the overall behavior during the cyclic loading. Nevertheless, the reason behind this behavior of sensor A was because sensor A was placed in the loading direction and was experiencing the maximum effect of the applied strain. Moreover, the applied cyclic strain was applied between 1–2% which is within the plastic deformation regime. Sensor A might experience minute permanent deformation during cyclic tensile and compressive strain because of the Poisson’s effect during the loading and unloading of the cyclic load. • Test 3 was performed and compared with Test 1 to check the sensitivity of the Nylon/Ag fiber sensor with respect to the loading axis, (see Fig. 8). This comparison was carried out to not only confirm the strain detection response of the Nylon/Ag fiber sensor but also showed its sensitivity to the applied load or loading direction. Sensor C recorded the maximum increase in resistance in test 3 because it was placed in the loading direction while sensor A showed a decrease in resistance because it was in a transverse direction regarding the loading axis. However, sensors B and D showed similar behavior in both tests because of their identical response in both directions i.e. +45°. Moreover, it was observed that the change in resistance was the same in each direction in both tests irrespective of the sensor. For example, sensor A in test 1 and sensor C in test 3 showed a similar change in resistance because both placed along the loading axis. This confirmed that the sensitivity of the sensor was dependent on its position and direction of the applied load otherwise the response of each sensor A, B, C, and D can be similar, and, in every case, the strongest signal was recorded along the loading direction.

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In each specimen, the sensor did not only detect the deformation but also distinguished between the type of deformation whether it was tensile or compression.

Fig. 7. Real-time strain (ST) monitoring by Nylon/Ag fiber sensor in the composite star specimen during cyclic tensile loading. In both tests 1 (T1) and 2 (T2), sensor A (SA) was along the loading axis, sensor B (SB) at 45, sensor C (SC) at 90° and sensor D (SD) in −45°.

Fig. 8. Real-time strain monitoring by Nylon/Ag fiber sensor during cyclic tensile strain. In test1 (T1), sensor A (SA) was along the loading axis, sensor B (SB) at 45, sensor C (SC) at 90° and sensor D (SD) in −45° while in test-3 (T3) the specimen was placed transversely with respect to the specimen 1 and sensor C (SC) was along the loading axis, sensor D (SD) at 45, sensor A (SA) at 90° and sensor B (SB) in −45°.

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4 Conclusion An experimental study was performed in this research to examine and understand the application of a Nylon/Ag fiber sensor in in-situ monitoring and identification of strain deformation in composites under cyclic tensile deformation. Nylon/Ag fiber sensor was integrated within the composite specimen at specific direction and position to demonstrate the strain detection behavior of the Nylon/Ag fiber sensor and identification of different types of deformation which occurred during tensile deflection. The experimental results showed good repeatability in the mechanical performance of the composite structures and response of the fiber sensor in the monitoring of the deformation. Each fiber sensor showed individual response signals during the deformation of the composite specimen because of their specific position and direction. This distinct behavior of each fiber sensor confirmed the detection of different types of damage i.e. tensile or compression during the deformation and different intensity or magnitude of the signals quantified the amount of damage induced. Thus, each fiber sensor not only showed the detection of different types of deformation but also quantified the deformation. The Nylon/Ag fiber sensor demonstrated good potential as a flexible reinforcement in composite materials for in-situ monitoring of strain because the applied strain was up to 1–2% for 10 cycles and the Nylon/Ag fiber showed the perfect correlation of its signal with the applied strain in each cycle. This verified the stability and durability of this fiber sensor.

References 1. Tarfaoui, M., Nachtane, M., El Moumen, A.: Energy dissipation of stitched and unstitched woven composite materials during dynamic compression test. Compos. Part B Eng. 167, 487–496 (2019) 2. Pang, J.W.C., Bond, I.P.: A hollow fibre reinforced polymer composite encompassing selfhealing and enhanced damage visibility. Compos. Sci. Technol. 65, 1791–1799 (2005) 3. Ihn, J.-B., Chang, F.-K.: Pitch-catch active sensing methods in structural health monitoring for aircraft structures. Struct. Heal. Monit. 7, 5–9 (2008) 4. Loayssa, A.: Optical fiber sensors for structural health monitoring. In: Mukhopadhyay, S.C. (ed.) New Developments in Sensing Technology for Structural Health Monitoring, pp. 335– 358. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21099-0_14 5. Raghavan, A.C., Cesnik, C.: Review of guided-wave structural health monitoring. Shock Vib. Dig. 39, 91–114 (2007) 6. Sassi, S., Tarfaoui, M., Yahia., H.B.: In-situ heat dissipation monitoring in adhesively bonded composite joints under dynamic compression loading using SHPB. Compos. Part B Eng. 54, 64–76 (2018) 7. Tarfaoui, M., Moumen, A.E. Yahia., H.B.: Damage detection versus heat dissipation in EGlass/Epoxy laminated composites under dynamic compression at high strain rate. Compos. Struct. 186, 50–61 (2018) 8. Azhari, F., Banthia, N.: Cement-based sensors with carbon fibers and carbon nanotubes for piezoresistive sensing. Cem. Concr. Compos. 34, 866–873 (2012)

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9. Trifigny, N., et al.: PEDOT: PSS-based piezo-resistive sensors applied to reinforcement glass fibres for in situ measurement during the composite material weaving process. Sensors 13, 10749–10764 (2013) 10. Atalay, O., Kennon, W.R.: Knitted strain sensors: impact of design parameters on sensing properties. Sensors (2014) 11. Seyedin, S., et al.: Knitted strain sensor textiles of highly conductive all-polymeric fibers. ACS Appl. Mater. Interfaces 7, 21150–21158 (2015) 12. Jerkovic, I., Koncar, V., Grancaric, A.: New textile sensors for in situ structural health monitoring of textile reinforced thermoplastic composites based on the conductive poly(3,4ethylenedioxythiophene)-poly(styrenesulfonate) polymer complex. Sensors 17 (2017) 13. Nauman, S., Cristian, I., Boussu, F., Koncar, V.: Smart Sensors for Industrial Applications. Part V Piezoresistive, Wireless, and Electrical Sensors (2013). Iniewski, K. (ed.) 14. Cai, G., et al.: Flexible and wearable strain sensing fabrics. Chem. Eng. J. 325, 396–403 (2017) 15. Nag-Chowdhury, S., et al.: Crossed investigation of damage in composites with embedded quantum resistive strain sensors (sQRS), acoustic emission (AE) and digital image correlation (DIC). Compos. Sci. Technol. 160, 79–85 (2018) 16. Alamusi, et al.: Piezoresistive strain sensors made from carbon nanotubes based polymer nanocomposites. Sensors 11, 10691–10723 (2011) 17. Wang, G., et al.: Structure dependent properties of carbon nanomaterials enabled fiber sensors for in situ monitoring of composites. Compos. Struct. 195, 36–44 (2018) 18. Barnard, A.S.: Modelling of the reactivity and stability of carbon nanotubes under environmentally relevant conditions. Phys. Chem. Chem. Phys. 14 (2012) 19. Murray, A.R., et al.: Oxidative stress and inflammatory response in dermal toxicity of singlewalled carbon nanotubes. Toxicology 257, 161–171 (2009) 20. Kim, K.-S., et al.: Revisiting the thickness reduction approach for near-foldable capacitive touch sensors based on a single layer of Ag nanowire-polymer composite structure. Compos. Sci. Technol. 165, 58–65 (2018) 21. Qureshi, Y., Tarfaoui, M., Lafdi, K.K., Lafdi, K.: Development of microscale flexible nylon/Ag strain sensor wire for real-time monitoring and damage detection in composite structures subjected to three-point bend test. Compos. Sci. Technol. 181, 107693 (2019) 22. Qureshi, Y., Tarfaoui, M., Lafdi, K.K., Lafdi, K.: Real-time strain monitoring performance of flexible Nylon/Ag conductive fiber. Sens. Actuators A Phys. 295, 612–622 (2019) 23. Qureshi, Y., Tarfaoui, M., Lafdi, K.K., Lafdi, K.: Real-time strain monitoring and damage detection of composites in different directions of the applied load using a microscale flexible Nylon/Ag strain sensor. Struct. Heal. Monit. 19, 885–901 (2019) 24. Qureshi, Y., Tarfaoui, M., Lafdi, K.K., Lafdi, K.: Nanotechnology and development of strain sensor for damage detection. In: Advances in Structural Health Monitoring. InTech Open (2019) 25. Qureshi, Y., Tarfaoui, M., Lafdi, K.K., Lafdi, K.: In-situ monitoring, identification and quantification of strain deformation in composites under cyclic flexural loading using Nylon/Ag fiber sensor. IEEE Sens. J. 20, 1 (2020)

Guided Waves Dispersion Analysis in Composite Pipe Using the SAFE Method Zhengyan Yang(&) and Zhanjun Wu State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, People’s Republic of China [email protected]

Abstract. Guided wave propagation in composite pipe has multi-modal and dispersive characteristics. In this paper, guided wave propagation in composite pipe is solved by a semi-analytical finite element (SAFE) method. The theoretical framework is formulated using finite element method (FEM) to describe the displacement fields in the waveguide cross-section, while displacement fields in the wave propagation direction are assumed analytical solutions. The dispersive solutions are obtained in terms of phase velocity and group velocity. Knowledge of guided wave propagation properties in composite pipe is beneficial for practical nondestructive testing and structural health monitoring. The SAFE method is validated by comparison with numerical results by ABAQUS. Also, experimental results from group velocity measurement on a composite pipe are presented, showing the feasibility of this SAFE method. Keywords: Guided waves
Composite pipe
SAFE method
Group velocity

1 Introduction Guided wave plays an important role in the field of structural health monitoring (SHM). SHM based guided wave is suitable for the detection of long distance structure, like rail, stringer and pipe. Due to anisotropic behavior and complicated guided-wave features, the composite pipe in aerospace structures has proposed new challenges in SHM field. An accurate knowledge of dispersive characteristics in composite pipe is necessary, but still a challenging task. Several efficient techniques are available for analytically modeling guided waves propagation in pipes. Gazis developed propagation of free harmonic waves along a hollow circular cylinder of infinite extent with the framework of the linear theory of elasticity [1] and discussed the dispersive characteristics [2]. Herrmann presented approximations for guided waves in axisymmetric cylinders using shell theory analysis [3]. Two analytical methods that have been developed to allow dispersion curves to be calculated for multi-layered structures are the transfer matrix technique [4] and the global matrix technique [5]. Rose [5] calculated the guided wave propagation in multilayered viscoelastic cylinders by global matrix technique. However, the analytical techniques have limitation, such as time consuming and sometimes unstable. Semi-analytical finite element (SAFE) solution provide a suitable and effective way to calculate guided wave propagation in composite pipes. SAFE © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 727–738, 2021. https://doi.org/10.1007/978-3-030-64594-6_70

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method has been applied to calculate dispersion curves in some complex waveguides, such as rail [6], stringer [7], composite plate [8] and pipe [9]. SAFE method is also utilized to analyze the guided wave propagation in viscoelastic pipe elbow [10]. The high computational efficiency is main advantage of SAFE approach. This paper studies the guided wave dispersion analysis in multiple-layer composite pipe. First, in Sect. 2, the formulation of semi-analytical finite element method of guided wave propagation in pipe is studied. In Sect. 3, isotropic aluminum pipe and anisotropic composite pipe are considered. Guided wave propagation is solved by proposed method. The example in isotropic aluminum pipe is chosen to examine the validity of SAFE method. Then in Sect. 4, the dispersion curves in multiple-layer composite pipe are validated using FE numerical simulation and experimental investigation. It’s show that the proposed SAFE method achieves good agreements with FE and experimental results.

2 SAFE Formulation 2.1

Problem Definition

The SAFE method for modeling guided wave propagation in composite pipe is investigated. In this research, the solution einh in the circumferential direction is considered. The guided waves propagate along x-direction in composite pipe, so we can use exact representation in both h and x direction. The finite element approximation reduces to only one dimension r to discretize the cross-section. The SAFE model in a composite pipe is shown in Fig. 1. The guided waves propagate along x-direction with a wavenumber k and a frequency x. And the pipe cross-section lies on the r-h plane.

Fig. 1. The SAFE model of wave propagation in composite pipes.

The displacement field is defined by three orthogonal components (ux, ur, uh), and the strains in cylindrical frame are, ur @ur 1 @uh 1 @ur x ex ¼ @u @x ; eh ¼ r þ r @h ; er ¼ @r ; ehr ¼ r @h þ @ux @uh @ur 1 @ux erx ¼ @x þ @r ; exh ¼ r @h þ @x

@uh @r

 urh

ð1Þ

Guided Waves Dispersion Analysis

2.2

729

Governing Equation

The governing equation of wave motion is obtained by inserting the kinetic and the potential energies into Hamilton equation. The variation form of Hamiltonian is, Z dH ¼

t2

dðU  KÞdt ¼ 0

ð2Þ

t1

where U is the strain energy and K is the kinetic energy. The strain energy and kinetic energy are expressed in, 1 U¼ 2 K¼

1 2

Z

~ eT CedV

ð3Þ

_ u_ T qudV

ð4Þ

V

Z V

where C, V and q are material stress-strain relationship matrix, volume and density, respectively. After performing the variation, the governing equation can be written as, Z

t2

t1

Z

~ dðeT ÞCedV þ

½ V

Z dðuT Þq€ udVdt ¼ 0

ð5Þ

V

In general, for a harmonic wave propagating in the z-axis, the displacement field can be described as, 2

3 2 3 Ux ðrÞ ux ðx; h; r; tÞ uðr; h; z; tÞ ¼ 4 uh ðx; h; r; tÞ 5 ¼ 4 Uh ðrÞ 5eiðkx þ nhxtÞ ur ðx; h; r; tÞ Ur ðrÞ

2.3

ð6Þ

Finite Element Method

Displacements within the element u(e) are approximated from their nodal displacements q(e) as, uðeÞ ðx; h; r; tÞ ¼ NðrÞqðeÞ eiðkx þ nhxtÞ

ð7Þ

where q(e) is the nodal displacement vector at eth element, N(r) is the displacement approximation function at the thickness direction r. For a three node element, N(r) is a 3  9 matrix. The shape function can be written as, 2

N1 NðrÞ ¼ 4 0 0 with,

0 N1 0

0 0 N1

N2 0 0

0 N2 0

0 0 N2

N3 0 0

0 N3 0

3 0 0 5 N3

ð8Þ

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N1 ¼

n2  n n2 þ n ; N2 ¼ 1  n2 ; N3 ¼ 2 2

ð9Þ

where −1  n  1 is the natural coordinates in the r direction. The Jacobian function J is given by J¼



dx ¼ n  12 2n dn

2 3 ux1  n þ 12 4 ux2 5 ux3

ð10Þ

The element strain can also be written in terms of nodal displacement and shape functions, eðeÞ ðx; h; r; tÞ¼ ðB1 þ ikB2 ÞqðeÞ eiðkx þ nhxtÞ 2 6 6 6 6 B1 ¼ 6 6 6 4

0 0 0 0

0 i nr N1 0 @N1 N1 @r  r 0 0

@N1 @r i nr N1

2

N1 6 0 6 6 0 B2 ¼ 6 6 0 6 4 0 0

0 0 0 0 0 N1

0 0 0 0 N1 0

0 N1 r @N1 @r i nr N1

0 0 N2 0 0 0 0 0

...

...

3

...

. . . N3

N2

... 0 0 0 0 0 N2

ð11Þ 7 7 7 7 7 ...7 7 5

ð12Þ

... 0 0 0 0 N2 0

N3 0 0 0 0 0

0 0 0 0 0 N3

3 0 0 7 7 0 7 7 0 7 7 N3 5 0

ð13Þ

The discretized version of the Hamiltonian leads to final form of the wave equation, 

 K1 þ inK2 þ n2 K3  x2 M U ¼ 0

ð14Þ

where U is the displacement vector. Where the stiffness matrices and mass matrix are given by,  R R R R  K1 ¼ Rr Rh BT1 CB1 rdrdh; K2 ¼ rR hR BT1 CB2  BT2 CB1 rdrdh K3 ¼ r h BT2 CB2 rdrdh; M ¼ q r h NT Nrdrdh

ð15Þ

The stiffness matrices and mass matrix can be written in terms of element stiffness,

Guided Waves Dispersion Analysis

K1 ¼

nelement [ e¼1

Z Xe

731

  nelement [ Z     ~ e B1 2pdXe ; K2 ¼ ~ e B2  BT C ~ e B1 2pdXe r BT1 C r BT1 C 2

K3 ¼

nelement [

e¼1

Z r



Xe

e¼1

~ e B2 BT2 C





Xe

2pdXe ; M ¼

nelement [ e¼1

Z Xe







r N qe N 2pdXe T

ð16Þ When the fiber direction h is nonzero, the stiffness matrix C in global coordinate is calculated form local stiffness matrix Clocal by the coordinate transform, ~ ¼ T1 C ~ local TT C

ð17Þ

where T is the transform matrix defined as, 2

m2 6 n2 6 6 0 T¼6 6 0 6 4 0 mn

n2 m2 0 0 0 mn

0 0 1 0 0 0

0 0 0 m n 0

0 0 0 n m 0

3 2mn 2mn 7 7 7 0 7 7 0 7 5 0 2 2 m n

ð18Þ

where m = cos(h), n = sin(h). We can define the stiffness matrix for each layer of lamina with a certain fiber direction. 2.4

Phase and Group Velocity

The Eq. (14) can be computed by first fixing the wavenumber k and solving for the frequency x. The phase velocity can be solved by, cp ¼

x k

ð19Þ

The wavenumber k is obtained from this eigenvalue results. The group velocity is defined as, Cg ¼

@x UTL ðiK2 þ 2nK3 ÞUR ¼ @n 2xUTL MUR

where UL is the left eigenvectors and UR is the right eigenvectors.

ð20Þ

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3 Numerical Results with SAFE Method 3.1

Isotropic Aluminum Pipe

In SAFE method, 1-D FEM mesh is used to discretize the pipe thickness direction. A case study of wave propagation is isotropic pipe, 10 mm outer diameter, 2 mm wall thickness. The material properties are listed in Table 1. The example in isotropic pipe is chosen because it’s studied by solving the analytical solution. Figure 2 shows the dispersion curve comparison between SAFE method and analytical solution. In the SAFE simulation, 30 elements are used across the pipe thickness. It can be show that the SAFE results match well with analytical solution. Table 1. Material constants for 6061-T6 aluminum. Material Density Young’s Modulus Poisson’s ratio 3 0.33 Al 2700 kg/m 71 GPa

Fig. 2. Dispersion curves for waves propagating in an aluminum pipe.

Figure 3 shows the wave field distribution of the axisymmetric mode (n = 0) and flexural modes (n = 1, 2, 3) in pipe along the circumferential direction. It can be seen that the displacement distribution of circumferential direction for axisymmetric wave modes pipe is uniform, so it is easier to be excited and received in the pipe than flexural

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733

wave. It’s important to investigate the excitation of non-axisymmetric guided waves in pipe by the characteristic of displacement distribution of flexural wave.

Fig. 3. Wave field distribution in the circumferential direction for wave modes with zero to the 3th order in pipes: (a) n = 0; (b) n = 1; (c) n = 2; (d) n = 3

The wave structures are the displacement distribution along the pipe thickness direction. The information provided by wave structure can be used for analyzing the behavior of a certain guided wave mode. Figure 4 shows the wave structures of four typical modes (L (0, 1), T (0, 1), L (0, 2) and F (1, 1)). The L and T modes are symmetric modes. The displacement of L (0, 1) mode concentrates on thickness direction. The wave structures indicate that the wave field of T (0, 1) is dominated in circumferential direction and the displacement of L (0, 2) is dominated by axial direction. F (1, 1) is the non-axisymmetric mode. The displacements of F (1, 1) in three directions cannot be ignored.

Fig. 4. Wave structures: (a) L(0,1); (b) T(0,1); (c)L(0,2); (d) F(1,1)

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Anisotropic Composite Pipe

A multilayered composite laminate with unidirectional laminae in a quasi-isotropic layup is computed in this section. The selected system consists of ten unidirectional M40J laminated composite plies with a stacking sequence of ±15/02/±45/02/±15. The material properties for each single lamina are listed in Table 2. In SAFE approach, 30 1D FEM meshes are used to discretize the composite pipe thickness direction. Figure 5 shows the dispersion curves of mentioned composite pipe. Table 2. Material constants for single M40J lamina E2 G12 m12 m23 Material Density q E1 M40J 1500 kg/m3 217.74 GPa 9.07 GPa 6.35 GPa 0.3 0.4232

Fig. 5. Dispersion curves of composite pipe.

Figure 6 shows the wave structures of four typical modes (L (0, 1), T (0, 1), L (0, 2) and F (1, 1)) in 10-layer composite pipe. The displacement of L (0, 1) mode concentrates on thickness direction. The wave structures indicate that the wave field of the displacement of L (0, 2) is dominated by axial direction. F (1, 2) is the non-axisymmetric mode. Compared with the wave structures of T (0, 1) and F (1, 2), it can be found that two wave structures are similar in 150 kHz. The displacements are all dominated in circumferential direction. F (1, 2) mode in 150 kHz has torsional characteristic.

Fig. 6. Wave structures of composite pipe: (a) L(0,1); (b) L(0,2); (c)T(0,1); (d) F(1,2).

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4 Validation 4.1

FE Numerical Simulations

A commercial finite element (FE) package (ABAQUS IMPLICIT) is used to validate the SAFE results of composite pipe. The FE model of a 1.08 m long composite pipe is established as shown in Fig. 7. The stacking sequence of ten unidirectional M40J laminated composite and material properties of each single lamina are the same as those listed in Sect. 3.2. The composite pipe is modeled as shell pipe. The element type is S4R and element size is 1 mm in circumferential direction and 3 mm in axial direction.

Fig. 7. The FE model of a 1.08 m long composite pipe.

The dynamic displacement excitation is applied to the left edge of the model. The input signals are 5-cycle Hanning windowed sinusoidal signals, as shown in Fig. 8. The stable time increment was set to 2.5  10−7s.

Fig. 8. 5-cycle Hanning windowed sinusoidal signal.

As shown in Fig. 6, the displacement of L (0, 2) mode concentrates on longitudinal displacement. It is difficult to achieve single mode excitation, but propagating modes can be controlled with different excitation patterns. For getting selected L (0, 2) modes, longitudinal displacement signals of all nodes of the left boundary are added. From Fig. 9, it can also be observed that the L (0, 2) mode is the main propagating mode

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under this excitation pattern at 140 kHz. The guided waves propagating along the pipe are collected at the right boundary.

Fig. 9. Guided wave propagation at 140 kHz.

Table 3 gives the group velocity of L (0, 2) mode directivity using our SAFE method and FEM method by ABAQUS. The relative errors between two methods are bigger as the increase of frequency, but all below 5%. A good match between the FEM and SAFE results is achieved. Table 3. The group velocity of L (0, 2) mode. Frequency 140 kHz 160 kHz 180 kHz 200 kHz

4.2

Cg_SAFE 10807 m/s 10823 m/s 10832 m/s 10840 m/s

Cg_ABAQUS 10573 m/s 10524 m/s 10449 m/s 10385 m/s

Relative error 2.16% 2.76% 3.53% 4.11%

Experimental Investigation

The experiment is conducted on a 1.08 m length composite pipe, as same as mentioned pipe in Sect. 3.2. Several piezoelectric lead zirconate titanate (PZT) transducers were placed in a pitch-catch configuration, as shown in Fig. 10. 12 Exciting transducers and 12 receiving transducers are placed exactly at the same position but on the other side of composite pipe, as shown in Fig. Using this exciting model, only the symmetric mode waves can be excited. The exciting transducers are excited by a ten-cycle Hanning window sinusoidal excitation signal at 200 kHz.

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Fig. 10. The unwrapped plane of experimental pipe.

In Fig. 11, the guided wave signals simulated and experimentally measured from composite pipe are shown. This case is the intact pristine condition without damage. As expected, the good match between experiment and numerical simulation is achieved.

Fig. 11. The guided wave signals simulated and experimentally measured from composite pipe.

5 Conclusion This paper presents a SAFE method to analysis the guided wave dispersion characteristics in composite pipe, including dispersion curves and wave structures. First, the mathematical derivations of SAFE method are proposed. Next, isotropic aluminum pipe and anisotropic composite pipe are considered and guided wave propagation is solved by this method. Finally, numerical simulations by ABAQUS and experimental validations are performed on a unidirectional CFRP composite pipe. These results show the validity of SAFE method.

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References 1. Gazis, D.C.: J. Acoust. Soc. Am. 31 (5), 568 (1959) 2. Gazis, D.C.: J. Acoust. Soc. Am. 31 (5), 573 (1959) 3. Herrmann, G., Mirsky, I.: Three dimensional and shell theory analysis of axially-symmetric motions of cylinder. J. Appl. Mech. 23(4) (1956) 4. Thomson, W.T.: J. Appl. Physic. 21(2), 89 (1950) 5. Barshinger, J.N., Rose, J.L.: IEEE Trans. Ultrason. Ferroelectr. Freq. control 51 (11), 1547 (2004) 6. Loveday, P.W.: Ultrasonics 49(3), 298 (2009) 7. Yang, Z., Wu, Z., Zhang, J., Liu, K., Jiang, Y., Zhou, K.: Smart Mater. Struct. 28(4), 045013 (2019) 8. Mei, H., Giurgiutiu, V.: Presented at the Health Monitoring of Structural and Biological Systems XII (2018, unpublished) 9. Mu, J., Rose, J.L.: J. Acoust. Soc. Am. 124(2), 866 (2008) 10. Hayashi, T., Kawashima, K., Sun, Z., et al.: Guided wave propagation mechanics across a pipe elbow. J. Press. Vessel Technol. 125(3) (2003)

Machine Learning Algorithms for Health Monitoring of Timber Utility Poles Using Stress Wave Propagation S. Bandara(&), P. Rajeev, and E. Gad Department of Civil and Construction Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia [email protected]

Abstract. Stress wave propagation (SWP) technique is a simple and costeffective non-destructive testing technique which can be effectively employed for the health monitoring of timber utility poles. In this paper, Artificial Neural Network (ANN) pattern recognition algorithm is used for the classification of stress wave responses obtained from testing in-service timber poles. Thirty inservice timber poles in Victoria, Australia are tested which belong to different timber species and varying geometric parameters. The tested poles are uprooted and subjected to full scale bending tests in order to determine the failure moments. Health status of each pole is defined based on the ratio between the failure moment and the design moment capacity. 252 stress wave responses are obtained from the field testings by the application of different impacts. An ANN model is developed to classify these signals based on the defined target groups according to the health status. The mobility spectrum of the pole responses in the low frequency region and the pole diameters are selected as the inputs to the ANN model. The performance of the developed ANN model is evaluated by calculating some performance parameters. Further, Support Vector Machine (SVM) and k-nearest neighbors (k-NN) algorithms are also applied to the same data set for classification. The performance of each technique is compared to select the best performing method. Results of this study showed that the developed ANN model outperforms the other techniques for the condition assessment of timber poles using the stress wave propagation technique. Keywords: Health monitoring Timber poles


Stress wave propagation
Neural networks


1 Introduction Timber utility poles are widely used in power distribution and telecommunication sectors due to the relative advantages of timber in comparison with other alternative pole types such as steel, concrete and composite poles. There is an estimated amount of about 5 million timber poles in Australia which comprise of 80% of the total utility pole population [1]. In addition, there are between 120 to 200 million in-service treated timber poles in United States and a large amount of money is annually spent for the maintenance and management of these assets [2]. Sounding, drilling and visual © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 739–748, 2021. https://doi.org/10.1007/978-3-030-64594-6_71

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inspection are the commonly practiced pole inspection techniques in Australia. Nevertheless, these methods are subjective techniques and the reliability of these practices are in question. These inspection methods can overlook serious damages which result in pole failures or can result in premature pole replacements due to misdiagnosed defects [3]. Stress wave propagation (SWP) technique has shown great potential for the condition assessment of timber poles as a non-destructive testing technique [e.g. 4, 5]. However, complexities exist in the wave behaviour with in a timber pole due the orthotropic material property, presence of natural imperfections and the soil-pole interaction. Moreover, the wave dispersion complicates the analysis and interpretation of the SWP in a timber pole. In order to address these issues, multi-sensing systems, data mining and machine learning techniques are adopted. Additionally, advanced signal processing techniques are employed in combination with the SWP in timber poles [e.g. 6, 7]. Application of a longitudinal impact at the top of a pole is not practical due to the difficulties in reaching the top of a pole. Besides, the presence of overhead cables and other attachments raise safety concerns in reaching the pole top. Thus, transverse SWP has to be adopted for timber poles by the application of a lateral impact close to the ground level of the pole. This paper investigates the use of artificial neural networks (ANNs) in combination with transverse SWP for the health monitoring of timber utility poles. Transverse SWP is carried out in 30 in-service poles in Victoria, Australia. Tested poles are uprooted and subjected to full scale bending tests to determine the failure moments. In-service conditions of the poles are determined by defining a ratio between the failure moment and the design moment. Recorded stress wave patterns from these tests are used to develop an ANN pattern recognition algorithm to classify the signals based on the health status of the poles. Inputs for the ANN model is defined based on the features of the recorded stress wave patterns. Pole diameter and the values of the mobility spectrum of the pole responses in the low frequency region are selected as the inputs. Three output groups are defined in the model considering the health status of the poles. The performance of the developed ANN model is evaluated by calculating several performance evaluation parameters. Further, support vector machine (SVM) and k-nearest neighbors algorithms are also employed for classifying the signals from the same data set. The optimum learning algorithm for the health monitoring of timber poles is selected by comparing the performance of the developed classifiers.

2 Learning Algorithms for Signal Classification Both the supervised and unsupervised learning algorithms can be employed for signal classification. In supervised learning, the algorithm trains from a set of labelled data and the trained algorithm predicts the outputs for unseen data. On the other hand, unsupervised learning algorithms learn with un-labelled data and works on its own to determine the structure of the underlying data. ANN, SVM and k-NN algorithms are implemented in this paper as supervised learning algorithms for health monitoring of timber poles.

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ANN Pattern Recognition Algorithm

In the context of SWP, most of the defect identification methods use mathematical models to determine relationships between signal features and the defect characteristics. However, when the relationships between defect characteristic and signal features are complex, analysis and interpretation using typical mathematical models become complicated. Neural network technique is a type of machine learning which imitates the mechanisms of human brain to learn by trial and error while determining the relationships between the underlying data. ANN models can predict complex nonlinear relationships while demonstrating excellent parallel processing capabilities, robustness and tolerance. ANNs have broad range of applications in pattern recognition, classification, function approximation, optimisation and prediction. Defect identification using stress wave propagation is a pattern recognition problem where features of the reflected stress wave are attributed to certain characteristics of defects. Therefore, pattern recognition capability of ANNs can be effectively used as a tool for the health monitoring of timber poles using the SWP. The architecture of the ANN model has to be selected depending on the problem at hand. Multi-layer backpropagation neural network is used in this study and Bayesian regularization back-propagation algorithm is used for training the network. A single hidden layer is employed for the network due to the simplicity of the model which is only involving 3 output classes. A sigmoid tangent function is used as the activation function and 5 hidden neurons are selected for the hidden layer. Preliminary studies showed that 5 hidden neurons are sufficient to properly model the problem ensuring the generalisation. 252 stress wave responses are generated from the field testings and these are randomly divided in to 85% training data and 15% testing data. A validation data set is not required in Bayesian regularization back-propagation. 2.2

SVM Classifier

SVM is a supervised machine learning technique which can be used for data classification and regression analysis. SVM is developed based on the statistical learning theory and in the case of a data classification problem, the classifier tries to solve for the decision function. For a binary data classification, SVM technique find the separating plane between the two data classes maximising the margin between classes which is a constrained optimisation problem. When there are more than three input features, the SVM classifier finds a hyperplane to separate the data sets. In this paper, a non-linear SVM classifier is developed employing radial basis function as the kernel function to map the data. The grid search methodology is adopted to carry out the parameter setting. 2.3

k-Nearest Neighbors Algorithm

k- nearest neighbors algorithm (k-NN) is a non-parametric supervised machine learning algorithm which can be used for classification and regression analysis. Neighbors based classification does not attempt to construct a general internal model. However, the instances of the training data are stored. When the target needs to be determined for a

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new data point, k-NN algorithm goes through the entire data set to find the k-nearest instances to the new data point and outputs the most frequent class for a classification problem. Nevertheless, the value of k is user defined. This technique is simple to implement, robust to noisy training data and computationally less time consuming in comparison with the other learning algorithms.

3 Field Testing and Results Thirty in-service timber poles in Victoria, Australia are tested which belong to different timber species and varying geometric parameters. Tested poles belong to Messmate stringybark, Mountain Grey-gum and Grey ironbark timber species. 252 stress wave responses are obtained by the application of impacts separately, to obtain the pole responses. Transverse impacts are generated in these poles close to the ground levels and the reflected wave pattern is recorded by placing a geophone at the same height of the pole. A handheld hammer instrumented with a load cell is used to generate the impact. Applied impulse generate upward and downward travelling stress waves which are reflected from the boundaries. Figure 1 shows the testing method and the expected wave reflection pattern. The points 1, 2 and 3 in Fig. 1 correspond to the bottom reflection, tip reflection and the total reflection respectively. Total reflection is where the stress wave travels a full cycle with in the pole. When there are other imperfections present in a pole, there will be intermediate reflections from these defects other than the above-mentioned reflections. The defect features are contained in the recorded pole response.

Fig. 1. Testing method and the expected transverse wave reflection pattern

The stress wave response of each pole depends on the applied excitation. Therefore, the pole response is normalized by obtaining the value of the pole velocity at maximum force for each frequency increment. This is termed as the mobility and the variations in mobility spectrum can be used as an effective parameter in determining the condition of

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timber utility poles. Previous research has shown that the features related to the stress wave reflections are contained in the low frequency region of the pole response [7]. Thus, the mobility values extending up to 500 Hz are used as the input parameters to the ANN model. Mobility spectrum of a pole varies with the pole diameter and hence, pole diameter has to be considered in the input features. Therefore, diameter of the pole at the ground level is also taken as another input parameter. Figure 2 shows an example of the recorded data and the calculated mobility spectrum of the pole response. Figure 2(a) shows the applied excitation in the time domain where the magnitude of the impact is about 8 kN and the duration of the impact is about 2 ms. The frequency content of the excitation is illustrated in Fig. 2(b). As observed in the figure, the applied impulse is a broadband excitation containing a range of frequencies extending up to about 1 kHz. Captured stress wave response is shown in Fig. 2(c) and its frequency content is illustrated in Fig. 2(d). The mobility spectrum of the response is shown in Fig. 2(e), which is obtained by normalising the amplitude of the velocity in frequency domain (Fig. 2(d)) by the applied impact in frequency domain (Fig. 2(b)). The initial part of the mobility spectrum contains all the features related to the stress wave reflections. Moreover, the slope of the low frequency linear portion of the mobility spectrum from the origin to the first peak provides the dynamic stiffness of the pole around the location of the impact. Impact is generated close to the ground level of the poles and thus, a representative value for the dynamic stiffness at the ground level of the pole can be obtained using the mobility spectrum. The ground level of a timber pole is the most critical section, since a pole acts as a cantilever and the bending moment will be highest at the ground level. Each tested pole is uprooted and subjected to three-point bending test and the failure moment of the poles are recorded. Most of the poles failed around the ground level, whereas few poles failed above the ground level. A health index is defined as follows to determine the output class for the ANN model. Overall health index ¼

Failure moment Moment capacity

ð1Þ

Failure moment is determined from the bending tests and the moment capacity is calculated using the measured diameter and the fibre strength of the poles. Moment capacity can be defined as the product of section modulus and the fibre strength as shown in the following equation. The section modulus is determined from the measured diameter and fibre strength is obtained from the previous research [e.g., 8]. Moment capacity ¼ fb z ¼ fb pD3 =32

ð2Þ

where fb is the fibre strength, z is the section modulus and D is the ground line diameter of the pole. Fibre strengths of commonly used Australian timber species are obtained from bending tests of full-scale pole specimens in previous researches.

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(a)

(b)

(c)

(d)

(e) Fig. 2. (a) Applied force in time domain (b) Frequency content of the force (c) Stress wave response in time domain (d) Frequency content of the stress wave response (e) Mobility spectrum

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Three output classes are defined according to the health index where the index below 0.4 is considered as defective poles belonging to group 3. The common Australian industry practice is to condemn poles which have less than 50% of the original pole moment capacity [8]. Group 2 is defined as being in intermediate health status, where the health index ranged between 0.4 to 0.8. Group 1 is for the intact poles where the health index is above 0.8. Altogether there are 35 input features including the pole diameters and mobility values of the frequencies extending up to 500 Hz. Figure 3 shows the confusion matrix of the developed ANN model. Subplots in this figure are for the training and testing phases. A validation data set is not required in Bayesian regularization back-propagation algorithm. The ANN model has an overall success percentage of 95.2% with higher values of coefficient of determination for both the training and testing phases. There are only a small number of misclassifications in the training and testing phases. Results of the ANN model suggests that the mobility spectrum of the stress wave responses with pole diameter can better represent the condition of the timber poles. Developed ANN model is more reliable since the output classes are defined based on the actual health condition of the poles determined using the bending tests.

Fig. 3. Confusion matrix of the ANN model

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The SVM and k-NN algorithms are also applied to the same input features and output groups to check the performance of these techniques. Instead of using a large number of input features, principal component analysis is carried out to reduce the dimensionality. Figure 4 shows the contribution of the first 30 principal components for the variance. Figure 4(a) and 4(b) are for the individual and cumulative variance contribution respectively. The cumulative variance contribution reaches close to 100% with the sum of individual variance contribution of the first 15 principal components. Therefore, the first 15 PCs are considered when developing the SVM classifier and kNN algorithm. Projection of the data into first two PCs are shown in Fig. 5 and clear data clusters cannot be observed in this figure for the three output classes sine the first two PCs do not contribute to an adequate variance. A non-linear SVM classifier is developed employing radial basis function as the mapping function and a grid search is carried out to determine the optimum parameters for the classifier. A performance comparison of the developed ANN model, SVM classifier and kNN algorithm is carried out by evaluating some performance parameters. Coefficient of determination (R2), mean absolute error (MAE), relative absolute error (RAE), root relative square error (RRSE) and root mean square error (RMSE) are evaluated as shown in following equations. P R ¼1 2

MAE ¼

i ðt i

P

 oi Þ 2

i ð oi Þ

!

2

1X j t i  oi j n i

ð3Þ ð4Þ

P jti oi j RAE ¼ P i 1 P  i jti n i ti

ð5Þ

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u P u ðti  oi Þ2 RRSE ¼ tP  i P 2 1 i ti  n i ti

ð6Þ

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1X RMSE ¼ ð t i  oi Þ 2 n i

ð7Þ

where, ti is the target parameter and oi is the output parameter and n is the number of data points. Table 1 shows a summary of the evaluated performance parameters for the developed classifiers. It can be noticed that the SVM classifier and k-NN algorithm do not provide better accuracies when compared with the ANN model. Moreover, the sum of errors in these two techniques are significantly larger than that of the ANN model. The coefficient of variation in the ANN model is high for both the training and testing phases and these similar values of R2confirm that overfitting is avoided in the ANN model.

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(a)

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(b)

Fig. 4. Contribution of the first 30 principal components (a) Individual variance contribution (b) Cumulative variance contribution

Fig. 5. Projection of data into the first two principal components Table 1. Performance comparison of ANN, SVM and k-means clustering techniques Control parameter ANN Training 0.9912 R2 MAE 0.0043 RAE 0.0079 RRSE 0.1201 RMSE 0.0676 Sum of errors 0.1999 Success % 95.8

Testing 0.9871 0.0251 0.0081 0.1231 0.1602 0.3165 92.1

SVM

k-NN algorithm

0.9758 0.0581 0.0867 0.3692 0.2792 0.7932 91.3

0.8534 0.1595 0.3712 0.6157 0.4351 1.5815 81.4

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4 Conclusion This paper investigated the use of ANN, SVM and k-NN algorithms for the condition assessment of timber poles via transverse stress wave propagation. Thirty in-service poles are subjected to SWP and the tested poles are uprooted to perform bending tests to determine the failure moments. Health status of each pole is determined using the ratio between the failure moment and the design moment. Health status of the poles are used as the outputs to the learning algorithms. Input features are extracted from the recorded stress wave responses. Developed ANN pattern recognition model showed around 95% success having higher coefficient of determination for both the training and testing phases. The proposed SVM classifier and k-NN algorithm only had a success of 91% and 81% respectively. Further, the SVM and k-NN techniques resulted in higher errors in classifying the signals. Thus, among these three techniques, ANN model showed the best performance for signal classification in timber poles. Accuracy of the ANN model show that it has a great potential to be used as a condition assessment technique for timber poles using SWP.

References 1. Francis, L., Norton, J.: Australian timber pole resources for energy networks. a review (2006) 2. Bolin, C.A., Smith, S.T.: Life cycle assessment of pentachlorophenol-treated wooden utility poles with comparisons to steel and concrete utility poles. Renew. Sustain. Energy Rev. 15(5), 2475–2486 (2011) 3. Li, J., Subhani, M., Samali, B.: Determination of embedment depth of timber poles and piles using wavelet transform. Adv. Struct. Eng. 15(5), 759–770 (2012) 4. Mudiyanselage, S., Rajeev, P., Gad, E., Sriskantharajah, B., Flatley, I.: Application of stress wave propagation technique for condition assessment of timber poles. Struct. Infrastruct. Eng. 15(9), 1234–1246 (2019) 5. Sriskantharajah, B., Gad, E., Bandara, S., Rajeev, P., Flatley, I.: Condition assessment tool for timber utility poles using stress wave propagation technique. Nondestruct. Test. Eval., 1–21 (2020) 6. Bandara, S., Rajeev, P., Gad, E., Sriskantharajah, B., Flatley, I.: Damage detection of inservice timber poles using Hilbert-Huang transform. NDT and E Int. 107, 102141 (2019) 7. Bandara, S., Rajeev, P., Gad, E., Sriskantharajah, B.: Damage severity estimation of timber poles using stress wave propagation and wavelet entropy evolution. J. Nondestruct. Eval. Diagn. Prognostics Eng. Syst. 4(1) (2020) 8. Ryan, P.C., Stewart, M.G., Spencer, N., Li, Y.: Reliability assessment of power pole infrastructure incorporating deterioration and network maintenance. Reliab. Eng. Syst. Saf. 132, 261–273 (2014)

Selective Actuation of Antisymmetric Lamb Waves Using Internal d15 Transducers for SHM Hussain Altammar1 , Parry Carrison2, and Nathan P. Salowitz2(&) 1

2

University of Jamestown, Jamestown, ND 58405, USA University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA [email protected]

Abstract. Advanced capabilities of ultrasonic SHM in thin plates, like identification of damage type and compensation for environmental effects, regularly depend on the knowledge and analysis of specific wave propagation modes. Antisymmetric wave propagation modes have been identified as being particularly useful for these purposes because of their relatively slow propagation velocity and associated short wavelength at these frequencies. Recent studies have found that location of shear deforming (d15) piezoelectric actuators and sensors at the neutral axis of a beam or plate-like structure exclusively actuate and sense antisymmetric wave propagation modes, rejecting symmetric modes. This paper presents results from recent investigations into the properties of ultrasonic wave generation and detection using d15 piezoelectric transducers internally embedded within structures, including effects of transducer placement through the structure’s thickness, off the neutral axis. Experimentally validated simulations found that locating a d15 actuator inside a structure, but off of the neutral axis increases deflections indicative of symmetric waves but did not diminish antisymmetric deflections. While the overall trends were similar, the specific results varied with frequency. Simulations and experiments were also performed to investigate the ability of systems employing d15 transducers to detect bond line defects in laminate beams. Keywords: Ultrasonic
Shear deformation
Actuator
Sensor
Transducer d15
Mode selection
Antisymmetric
Bond line damage




1 Introduction Embedded ultrasonic inspection systems enable some of the most advanced capabilities in structural health monitoring (SHM). Detection, location, characterization, and quantification of damage is possible through detailed analysis of changes in the elastic waveforms propagated through a structure.

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Background

Typical ultrasonic (SHM) systems are composed of lead zirconate titanate (PZT) piezoelectric transducers permanently mounted on a structure to actuate and sense elastic waves. It is common to use piezoelectric wafer active sensors (PWASs) employing the d31 piezoelectric property or interdigitated transducers (IDTs) employing the d33 piezoelectric property mounted on the surface or embedded within a structure [1–3]. The signals produced by these systems mounted on thin beam and plate like structures are complex and hard to interpret because of the existence and coupling to multiple Lamb wave propagation modes. Lamb waves include symmetric modes, with in plane particle displacements in the direction of propagation that are symmetric across the midline of the plate or beam, and antisymmetric modes, with antisymmetric particle displacements across the midline of the structure [1, 4]. These different propagation modes interact differently with various forms of damage. Additionally, the different propagation modes have different propagation velocities that are dependent on the material properties, structural thickness, and frequency, and therefore are dispersive. As a result, signals with a spectrum of frequency content, e.g. due to modulation, distort as they propagate. At low frequency only the fundamental symmetric mode (S0) and antisymmetric mode (A0) can exist and the S0 mode propagates with a higher group and phase velocities than the A0 mode. Signals are further complicated by reflections, refractions, and mode conversions that can occur when Lamb waves interact with structural boundaries and heterogeneities. Output signals of basic SHM systems, that couple to both modes, are complicated by multiple signals overlapping, causing constructive and destructive interference. Advanced capabilities of ultrasonic SHM are dependent on analysis of the individual wave propagation modes. Additionally, analysis of the A0 mode has been identified as being particularly useful in compensation for environmental effects [5]. Multiple approaches have been pursued to enable analysis of the various wave propagation modes including signal design, truncation of the signal, modeling, and modification of the actuators and sensors. Hanning windows are commonly used for modulation in ultrasonic SHM because they limit the frequency bandwidth introduced while creating a time-limited signal. Another one of the most common methods to simplify the signals produced by Lamb waves is to only look at the first wave packet arrival. This wave packet must be the fastest propagating mode traveling the shortest distance (direct path). With a long enough propagation path, discrete time signal, and differences in propagation velocities of the different modes it is simple to acquire a clean first wave packet with no other interference. At relatively low frequencies the S0 mode propagates faster than the A0 mode, therefore this is an effective method to identify S0 mode. Unfortunately this method cannot acquire a clean A0 mode when the S0 mode exists because the A0 mode travels slower and the information is often discarded. Modeling of the wave propagation is also common using tools like the program DISPERSE based on the Lamb equations or finite element (FE) methods [6]. This approach is very powerful but is limited by the fact that results can only be as accurate as the input material and geometric properties. Further, if an anomaly is observed in an

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experimental signal there is the inverse problem of identifying the anomaly’s source using simulations, often with non-unique solutions. Actuation and sensing techniques have also been developed to exclusively couple to specific Lamb wave propagation modes. Mode selection can be achieved by sizing or angling transducers based on the wavelength or frequency of the mode desired, known as mode tuning and wedge transducers. However, this selective coupling is only functional at one specific frequency. A pair of transducers located opposite each other on a structure can be used to identify wave modes based on in-phase (symmetric) or 180°-out-of-phase (antisymmetric) motion, unfortunately this approach requires precise placement of the transducers and cumbersome wiring to both sides of the structure. Single surface mounted devices have been developed to couple to the A0 mode by only pressing perpendicularly against the structural surface and negating shear parallel to the surface. This has been achieved by introducing a lubricant [7] or a selectively transmitting porous silicon carbide interlayer [5] between the transducer and structure. Unfortunately this results in weak transduction (actuating against the transducer’s mass or an added mass and lost energy) and potential introduction of a contaminant. Recent research has found that integration of a shear deforming PZT transducer, employing the d15 piezoelectric property, located at the neutral axis of a beam or plate like structure exclusively couples to antisymmetric waveforms for both actuation and sensing purposes [3, 8–15]. This paper presents the results of recent research into the effect of off neutral axis location of the transducer on mode selectivity to identify optimal transducer placement followed by a study of inspection of a laminate beam using d15 PZTs undergoing 3-point bending to introduce bond line damage.

2 Approach A combination of finite element modeling and experimental tests were performed to inspect the strain and electrical signals produced by d15 transducers embedded within plate and beam like structures. Matching FE models and experimental specimens were created and results compared to validate the model. Then, a parametric study was performed using FE to evaluate the effects of moving the d15 actuator through the thickness of the structure on the mode selectivity. Finally, a laminate beam specimen with an internally embedded d15 PZT actuator and sensor, in a pitch-catch configuration, was subjected to a 3 point bending test to introduce bondline damage and inspected using the d15 PZTs. An additional d31 PWAS was mounted on the surface of the specimen adjacent to the sensing d15 PZT to enable signal comparison.

3 Methods Experimental beam specimens were created composed of two layers of 1 mm thick 6061-T6 Aluminum. Shear deforming d15 PZT transducers composed of APC-850 were placed between the aluminum sheets with appropriate wiring, attached with CircuitWorks 2400 conductive epoxy [16], oriented so that the polarization

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directions of the PZTs were co-linear in the global direction and the electrodes aligned with the global direction, as shown in Fig. 1 [2]. Finally the aluminum sheets were bonded together with the PZTs between them using Loctite EA 9394 epoxy [17]. Because the PZTs were 1 mm thick, this resulted in a 1 mm ± 0.2 mm thick bond-line (confirmed by measurement with calipers) [14]. This resulted in a highly symmetric structure with the d-15 PZTs located at the mid-plane neutral axis. Similar FE models were created in ANSYS using multiphysics elements to represent the PZTs, published material properties [2, 17, 18], and the idealized geometry, neglecting wiring and adhesive between the transducers and aluminum. The FE models simulated from electrical input to the actuator to electrical output from the sensor. a)

15 mm

b)

PZT-1 Wire

PZT-2 Wire







PZT-1

Al 9394 Al

3 mm

PZT-2

Fig. 1. Geometry of a 6061-T6 aluminum beam. a) Picture of the top of the beam with d15 PZTs bonded in place. b) Graphic of the complete beam cross-section (side view). Local (PZT), and global coordinate systems are shown.

3.1

Model Validation

2D simulations were performed because the PZTs spanned the full beam so there was no geometric variation in the width direction as shown in Fig. 1. Simulations were also performed using DISPERSE to calculate the propagation velocities of the various Lamb wave modes, producing the dispersion curves shown in Fig. 2 [6]. Experiments and models of the pristine structure were actuated with 5-peak Hann windowed tone bursts at various frequencies and showed negligible difference between experimental signal and simulation output validating the simulation. Simulation results were closely inspected to understand the wave propagation. The time of flight of the signals observed in both experimental data and FE simulation closely matched to the A0 propagation velocity calculated with DISPERSE. Further, the displacement profile shown in Fig. 3 was observed to be purely antisymmetric across the midline of the structure supporting the conclusion that actuation exclusively produced antisymmetric waves.

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Vgr (mm/ms)

S0

Al

A0 A1

Epoxy



Al Frequency (MHz) Fig. 2. Group velocity dispersion curves for the aluminum Epoxy structure calculated in DISPERSE [6].

3.2

Fig. 3. Typical displacement profile along the propagation path from the PZT-1 actuator to PZT-2sensor observed in both plate and beam simulations [3]. [14]

Parametric Study of Actuator Location Effect on Mode-Selectivity

A series of FE simulations were performed using ANSYS based on the validated model where the d15 actuator was moved off of the neutral axis to inspect if or how an offset would affect exclusive coupling to the A0 Lamb wave mode. While the simulation was based on the validated work, the epoxy was replaced with aluminum properties to circumvent issues with a-symmetries due to a bondline being off the neutral axis. Simulations were performed at multiple offsets and actuation frequencies. 3.3

Damage Detection During a Three-Point Bending Test

A three-point bending test was performed with symmetric cylindrical supports 50 mm apart, a cylindrical loading head, and the structure centered in the fixture. Testing was performed with an Instron 3360 Series Universal testing system [19]. Data was acquired in a no-load condition after successively increased applied deflections. Each cycle involved subjecting the specimen to a mid-point deflection 0.1 mm greater than the prior cycle, then releasing it, and actuating d15 PZT-1 with a ±100 V Hann windowed 5-peak tone burst centered at 30 kHz using a KEYSIGHT 33500B Series Waveform Generator amplified through a Krohn-Hite 7602 M Wideband Amplifier [20, 21]. Signals were recorded with a Tektronix MDO3014 Mixed Domain Oscilloscope connected to the actuation channel, output of d15 PZT-2, and output of an auxiliary d31 PWAS 6 mm in diameter and 0.25 mm thick mounted on the surface adjacent to PZT-2 for comparison [22].

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4 Results and Discussion This section presents the results from each step of the work performed. 4.1

Model Validation

As previously reported, results from experimental data, FE simulation, and modeling with DISPERSE produced very similar results within 8% of the experimental result as shown in Table 1. Variation was attributed to idealization of material properties and geometry. This demonstrated that the model results and experimental results were reasonably aligned. Table 1. Time of flight and group velocity of first arrivals [3] Time of flight (ls) Group velocity (m/s) Experiment 110.1 1181 Finite element 119.2 1090 4862 DISPERSE S0 -NADISPERSE A0 -NA1154

4.2

Parametric Study of Actuator Location Effect on Mode Selectivity

Simulations were performed multiple times, actuating with Hann windowed 5-peak tone bursts at a variety of center frequencies, and with the actuator placed at, and various distances offset from the neutral axis of the beam. The maximum and displacements observed along the entire neutral axis in the direct propagation were recorded and compared with displacement indicating symmetric modes and displacement indicating antisymmetric modes. As can be seen in Fig. 4a and b, when the transducer is located at the neutral axis, 0 offset, there was negligible motion and significant motion at all frequencies inspected indicating negligible symmetric motion and strong antisymmetric motion. Frequency had nominal effects on the magnitude of symmetric motion, but strong effects on the antisymmetric motion. Conversely, increasing offset introduced significant symmetric motion, but had negligible effects on antisymmetric motion.

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Fig. 4. Maximum displacement of particles on the neutral axis in the direction (a) and direction (b) as a function of transducer position relative to the neutral axis and frequency [3].

Combined this demonstrated that offsetting the d15 actuator away from the neutral axis did not have a significant effect on the strength of antisymmetric waves generated, though frequency did effect the strength. However, offsetting the actuator did introduce symmetric waveforms, reducing the mode purity of the system. Therefore, to couple exclusively to antisymmetric modes, a d15 transducer must be located at the neutral axis. This matches theory coupling transverse shear to bending in beams and plates to bending [23–28]. 4.3

Damage Detection During a Three-Point Bending Test

A three point bending test was performed on a specimen with d15 PZTs at the optimal location for actuating, and sensing, at the neutral axis (based on the parametric study and reversal theory) in a pitch catch configuration with an additional d31 sensor for comparison. The specimen was loaded to a predetermined deflection, unloaded and tested, and then deformed again to a 0.1 mm greater deflection. 33 cycles were performed with a final mid-point deflection of 3.3 mm. Damage was first observed at the 1.0 mm deflection cycle as shown in Fig. 5a. This damage occurred at roughly the loading point and consisted of cracks in the epoxy bond line at roughly a 45° angle to the plane of the beam. Damage initiation was also observed by a drop in beam stiffness, observed in the measured load deflection curve at 1.0 mm deflection. As deflections were continually increased, damage grew to include cohesive failure near and parallel to the epoxy-aluminum interface as shown in Fig. 5b at 3.3 mm deflection.

Fig. 5. Damage growth in the epoxy layer of a laminate beam subjected to three-point bending. 1) initial observed damage at 1.0 mm deflection. b) fully developed damage at 3.3 mm deflection [13]

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As can be seen in the data shown in Fig. 6, the d15 sensor showed a greater change in signal strength than the d31 sensor, especially for the 3.3 mm deflection. This was also evident in the calculation of damage indices. The Pearson correlation coefficient (PCC) and normalized signal energy (NSE) were calculated based on the data [13]. The PCC showed an interesting trend with both sets of data in that it peaked at the first damage, at 1.0 mm deflection, reduced to 2.3 mm deflection, and then rose again. However, both damage indices were generally larger for the d15 to d15 combination than for the d15 to d31 system. 3.3 mm deflection

1.0 mm deflection b)

c)

d)

d15 to d31

d15 to d15

a)

Fig. 6. Raw signals from d15 (a & b) and d31 (c & d) sensors at 1.0 mm (a & c) and 3.3 mm (b & d) displacements [13]

5 Conclusions and Future Work A series of simulations and experiments were performed that demonstrated that use of d15 actuators and sensors located at the neutral axis of a beam would exclusively produce antisymmetric Lamb waves. A parametric study was performed using an experimentally validated FE model that indicated that positioning the actuator off the neutral axis would not diminish the strength of antisymmetric waves but would introduce symmetric waves, diminishing mode purity. The simulation results were independent of frequency. Finally a 3 point bending test was performed that compared the ability to detect bondline damage using a d15 PZT actuator at the neutral axis of a beam, and comparing the outputs of a d15 PZT sensor at the neutral axis to that of a d31 PZT located on the structural surface. It was found that the changes in the output signal

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were generally greater for the d15 shear deforming PZT sensor, confirmed by calculation of PCC and NSE damage indices. Future work to further understand the benefits and limitations of SHM systems based on d15 shear deforming PZTs include; 1) An investigation into signal strengths in both the forms of strain and voltage output relative to d31 PWAS based systems and 2) investigation into the effects of thinning d15 shear deforming transducers to reduce parasitic effects on host structures. Acknowledgement. This work was supported by funds from the University of WisconsinMilwaukee Department of Mechanical Engineering.

References 1. Giurgiutiu, V.: Structural Health Monitoring with Piezoelectric Wafer Active Sensors, 2nd edn. Elsevier, Boston (2014) 2. APC International Ltd. Physical and piezoelectric properties of APC materials, July 2020. https://www.americanpiezo.com/apc-materials/physical-piezoelectric-properties.html 3. Carrison, P., Altammar, H., Salowitz, N.: Selective actuation and sensing of antisymmetric ultrasonic waves using shear-deforming piezoelectric transducers. Struct. Health Monit. (2020) (Accepted) 4. Lamb, H.: On waves in an elastic plate. Proc. Roy. Soc. Lond. Ser. A Contain. Papers Math. Phys. Charact. 93, 114–128 (1917) 5. Clarke, T., Simonetti, F., Rohklin, S., Cawley, P.: Development of a low-frequency high purity A0 mode transducer for shm applications. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56(7), 1457–1468 (2009) 6. Imperial College London. Non-Destructive Evaluation: Products and Services: Disperse (2016). http://www.imperial.ac.uk/non-destructive-evaluation/products-and-services/disperse/ 7. Ning, H., Shimomukai, T., Fukunaga, H., Zhongqing, S.: Damage identification of metallic structures using A0 mode of lamb waves. Struct. Health Monit. 7(3), 271–285 (2008) 8. Altammar, H., Dhingra, A., Salowitz, N.: Ultrasonic sensing and actuation in laminate structures using bondline-embedded d35 piezoelectric sensors. Sensors 18(11) (2018) 9. Altammar, H., Salowitz, N.: Shear actuation of piezoelectric transducers embedded within laminate structures for damage detection. In: Proceedings of the 7th Asia Pacific Workshop on Structural Health Monitoring, Hong Kong SAR, P.R. China (2018) 10. Altammar, H., Salowitz, N.: Ultrasonic inspection of bonded metal laminates using internal shear-mode piezoelectric transducers. In: Proceedings of the European Workshop on Structural Health Monitoring, Manchester, England (2018) 11. Altammar, H.: Structural health monitoring of laminate structures using shear-mode piezoelectric sensors, Ph.D. thesis ed. Milwaukee, Wisconsin, U.S.A.: University of Wisconsin - Milwaukee (2019) 12. Altammar, H., Salowitz, N.: Selective actuation and sensing of antisymmetric waves using shear-mode piezoelectric transducers. In: Structural Health Monitoring 2019, Stanford, CA (2019) 13. Altammar, H., Dhingra, A., Salowitz, N.: Damage detection using d15 Piezoelectric sensors in a laminate beam undergoing three-point bending. Actuators 8(70) (2019) 14. Altammar, H., Dhingra, A., Salowitz, N.: Initial study of internally embedded shear-mode piezoelectric transducers for the detection of joint defects in laminate structures. J. Intell. Mater. Syst. Struct. 30(15) (2019)

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15. Altammar, H., Dhingra, A., Salowitz, N.: Investigating the feasibility of ultrasonic shear actuation for evaluation of adhesive joints in multilayered structures: FE simulation. In: American Society of Nondestructive Testing Annual Conference 2017, Nashville, TN (2017) 16. AllSpec Industries, “CW2400 - CircuitWorks Conductive Epoxy,” WILMINGTON, NC, Datasheet (2012) 17. Corporation, H.: LOCTITE EA 9394 AERO Data Sheet. Rocky Hill, Connecticut (2014) 18. U.S. Department of Defense, “METALLIC MATERIALS AND ELEMENTS FOR AEROSPACE VEHICEL STRUCTURES,” (2003) 19. Instron. 3360 Series Universal Testing Systems, June 2020. https://www.instron.us/products/ testing-systems/universal-testing-systems/electromechanical/3300/3360-dual-column 20. Keysight Technologies, “33500B Serise Waveform Generators,” Data Sheet (2017) 21. Krohn-Hite, “Model 7600 M/7602 M Wideband Power Amplifiers,” Data Sheet (2018) 22. Tektronix, MDO 3000 Series Mixed Domain Oscilloscopes User Manual (2016) 23. Budynas, R.G., Nisbett, J.K.: Shigley’s Mechanical Engineering Design. McGraw Hill, New York (2015) 24. Benjeddou, A., Deü, J.F.: Piezoelectric transverse shear actuation and sensing of plates, part 1: a three-dimensional mixed state space formulation. J. Intell. Mater. Syst. Struct. 12(7), 435–449 (2001) 25. Benjeddou, A., Trindade, M.A., Ohayon, R.: A unified beam finite element model for extension and shear piezoelectric mechanisms. J. Intell. Mater. Syst. Struct. 8(12), 1012– 1025 (1997) 26. Benjeddou, A., Trindade, M.A., Ohayon, R.: New shear actuated smart structure beam finite element. AIAA J. 37(3), 378–383 (1999) 27. Benjeddou, A., Trindade, M.A., Ohayon, R.: Piezoelectric actuation mechanisms for intelligent sandwich structures. Smart Mater. Struct. 9, 328–335 (2000) 28. Benjeddou, A.: Shear-mode piezoceramic advanced materials and structures: a state of the art. Mech. Adv. Mater. Struct. 14, 263–275 (2007) 29. Leo, D.J.: Engineering Analysis of Smart Materials Systems. Wiley, Hoboken (2007)

Sensitivity of Ultrasonic Guided Waves to Elastic Constants: A Numerical Study Jannis Bulling(B) , Georg Franosch , Yevgeniya Lugovtsova(B) , and Jens Prager Bundesanstalt f¨ ur Materialforschung und -pr¨ ufung (BAM), Berlin, Germany {Jannis.Bulling,Yevgeniya.Lugovtsova}@Bam.de

Abstract. The dispersive properties of Lamb waves can be utilised for material characterisation because the frequency-wavenumberrelationship, as well as the group velocity, depend on material parameters. These dependencies make a non-destructive estimation of an elastic constant possible. This preliminary study investigates the sensitivity of dispersion curves caused by a change in elastic constants. The Scaled Boundary Finite Element Method is used to compute special dispersion curves, which show the sensitivity value of the frequency and group velocity as a colour value. This representation allows for easy identification of patterns and local effects. Two sets of dispersion curves are presented, one set for a steel plate and the other set for a plate made of a carbon fibre reinforced polymer. In general, we notice that the sensitivity often increases with the frequency and that higher-order modes seem to be more suitable for material characterisation. Moreover, specific modes respond to material changes while others are relatively unaffected, which must be taken into consideration for material characterisation. Keywords: Scaled Boundary Finite Element Method · Lamb waves Dispersion curves · Carbon fibre reinforced polymer · Transverse isotropy

1

·

Introduction

A precise knowledge of the elastic constants is essential for modelling and evaluation of the mechanical behaviour of materials. Currently accepted methods for the assessment of the mechanical properties are dynamic mechanical analysis, which is mainly used for isotropic materials, and tensile testing, which is the only choice when it comes to anisotropic materials. Both methods require intricate sample preparation and the latter method is destructive. Thus, it is desirable to develop a non-destructive characterisation method which will allow assessment of effective elastic constants. In the context of Structural Health Monitoring, the estimation of effective elastic constants can be advantageous not only for establishing realistic models but also for monitoring the degradation of composites due to loads and environmental conditions. Here, ultrasonic guided waves come c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 759–768, 2021. https://doi.org/10.1007/978-3-030-64594-6_73

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into play. For thin structures, which are typical for laminates, the multimodal nature of Lamb waves can be utilised because of their sensitivity to different elastic constants. In this regard, various approaches for the reconstruction of elastic constants have been presented [1–7], with some studies dedicated to the sensitivity analysis of fundamental modes (A0 and S0 modes). However, it is not clear how many modes are needed to ensure a reliable reconstruction of all elastic constants, especially in the case of anisotropic materials. Therefore, this study aims at filling the gap in the literature by analysing the sensitivity of higher-order modes, so that a reliable reconstruction of all elastic constants based on ultrasonic guided waves can be ensured. In this contribution, we analyse the sensitivity of higher-order modes for two properties, the frequency-wavenumber-relationship and the group velocity, because both can be used for the material characterisation [1,8]. This paper aims to provide a graphic representation, which takes the multimodality and frequency-dependency into account. The frequency and the group velocity are computationally derived and presented as dispersion curves. For layered plates, the dispersion curves can be analytically derived by finding the roots of the characteristic function [9–12]. In general, semi-analytical methods are more versatile in modelling inhomogeneities, different layers or functionally graded materials. All of the following numerical methods must solve either a quadratic or a generalised eigenvalue problem. The so-called Thin-Layer Method was one of the first contributions to a generalised method to calculate dispersion curves numerically [13]. Another possibility is the use of the spectral collocation method to compute an eigenvalue problem [14] and thus solve for the dispersion curves. Three methods that directly use finite elements for the approximation are the Semi-Analytical Finite Element (Safe) [15,16], the Waveguide Finite Element [17] and the Scaled Boundary Finite Element Method (Sbfem) [18]. These methods have early predecessors in [19,20]. The Sbfem for waveguides is embedded in a general context for solving differential equations. The Sbfem is also used for static problems, especially linear fracture mechanics [21,22]. Here, we use Sbfem to calculate dispersion curves because of its general approach and its efficiency when using high-order elements. However, the results can be obtained with any method of the above if the model fits the problem. It is worth mentioning that sensitivity analysis in static Sbfem has led to new results in the context of fracture mechanics [23,24]. The work is structured as follows: The second section summarises the theory of the calculation of dispersion curves and leads to the definition of sensitivity. The third section presents the dispersion curves for an isotropic and a transversely isotropic material with the sensitivity as a colour value. The fourth and last section draws the conclusions for the material characterisation.

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Theory

Fig. 1. Mesh with two elements of degree p = 4, i.e., 5 nodes per element

This part summarises the theory and mathematical assumptions. The result is the definition of the sensitivity of dispersion curves. For the computation of dispersion curves, we assume an infinite waveguide as in Fig. 1 where the linear elastic wave equation under a plane strain assumption and traction free boundary conditions is valid. The x-direction is the direction of wave propagation.

The SBFEM. The Sbfem is a fully developed method to compute dispersion curves. For example, the method can be applied to problems with plates [18], curved waveguides [25], fluid-structure interaction [26] and 3D-problems with an arbitrary cross-section [27]. Here, we consider only plates with plane strain assumption. In what follows, the essential steps are summarised. A more detailed derivation can be found in the literature, for example, in [18,27,28]. A feature of this method is that only one line needs to be meshed for a plate as it is shown in Fig. 1 at x = 0. The Safe-method [16] shares this feature. For a given wavenumber k, the finite element mesh can be used to compute the following generalised eigenvalue problem −(ω )2 M0 u = Ek u ,

(1)

with the squared angular frequency as an eigenvalue (ω )2 and the nodal mode shape vector u as an eigenvector [14]. The matrices are the mass matrix M0 of the line x = 0 and a matrix associated with the stress Ek = −k 2 E0 + i k(ET 1 − E1 ) − E2 .

(2)

The derivation of matrices E0 , E1 , E2 and M0 of size N × N can be found in [18,27], where N is twice the number of nodes in the finite element mesh. For a single wavenumber, there are N angular frequencies that solve Eq. (1) each corresponding to a mode. For an isotropic plate, for example, the first index is associated with the A0 mode, the second with the S0 mode, the third with the A1 mode, the fourth with the S1 mode. See [9] for more details on the higher-order modes. In previous studies in the context of the Sbfem [18,27,28], the modes are computed by prescribing the angular frequency ω and solving the quadratic eigenvalue problem for the wavenumber k. To investigate the sensitivity, a parameterisation via the wavenumber seems to be more natural than a parameterisation via the frequency. The reason is that the curves change more in the frequency-direction than in wave-number-direction (see Fig. 2a). According to the mathematical definition, a function has a single output per input. The

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Group velocity (m/s)

Frequency (MHz)

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4 3 2

E = 200GPa direct E = 220GPa direct E = 220GPa approx.

1 0

0

2

4

6

8

6000 4000 2000 E = 200GPa direct E = 220GPa direct E = 220GPa approx.

0 -2000

0

Angular wavenumber (rad/mm)

1

2

3

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Frequency (MHz)

(a) wavenumber for two similar (b) group velocity for two similar materials materials

Fig. 2. Isotropic example with the density ρ = 7.85 g cm−1 , Young’s modulus E as to be seen in the legend and Poisson’s ratio ν = 0.3. The black markers are computed using β = 10 in Eq. (6).

definition is only fulfilled if the wavenumber parameterizes the wavenumberfrequency-relationship for one mode. The frequency f of each mode can simply be computed by f = |(ω )2 |/2π, and the group velocity cg by [28] cg =

q u − u q ∂ω =i  ∂k 4πf u M0 u

  with q = i kE0 + ET 1 u ,

(3)

where an over-line denotes the complex conjugate. Finally, we propose to compute the scaled sensitivity S of the frequency and group velocity as a function of the wavenumber at a given parameter α by a finite difference, i.e., Sf (k; α) =

α df α Δf α f (k; α + 0.5Δα) − f (k; α − 0.5Δα) (k) ≈ (k) = 100 dα 100 Δα 100 Δα

(4)

Scg (k; α) =

dcg

Δcg

α α α (k) ≈ (k) = 100 dα 100 Δα 100

cg (k; α

cg (k; α

+ 0.5Δα) − Δα

− 0.5Δα)

(5)

with a small parameter variation Δα. For example, in an isotropic plate, the parameter α can be the density ρ, Young’s modulus E or Poisson’s ratio ν. Since a material change by β can be approximated with the first Taylor term f (k; (1 + β/100) α) ≈ f (k; α) + β · Sf (α) cg (k; (1 + β/100) α) ≈ cg (k; α) + β · Scg (α)

,

(6)

this definition of sensitivity measures the movement of the mode-curves for 1% of a material parameter change. The sensitivity is proportional to the vertical difference of the red and black markers for each frequency and group velocity in Fig. 2a and b, respectively.

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Mode Sorting. For most eigenvalue solvers, the default sorting of the eigenvectors is according to their eigenvalues. When two mode-curves cross each other, the default sorting switches up the index of the modes which can lead to errors in the finite differences. For the smallest wavenumber, we sort the eigenvectors in increasing order according to the eigenvalues. Then for a bigger, current wavenumber, an eigenvector is assigned to the index, which maximises the scalar-product between the current eigenvector and eigenvectors from the previ˜ j (k) be the unsorted eigenvectors from the ously calculated wavenumber. Let u current wavenumber and u (k − Δk) the sorted eigenvectors from the previous wavenumber, then the index is defined such that u (k) = max |˜ uj (k) · u (k − Δk)|. j=1,...,n

(7)

In our experience, this process leads to a unique sorting if the step size between the wavenumbers is small enough and if the smallest wavenumber is larger than zero.

3

Results

This section presents two sets of dispersion curves. One set is derived for an isotropic material to show the simplest case. The second example is relevant for current material characterisation because material characterisation based on Lamb waves is mainly proposed for more complicated materials, for example, transversely isotropic materials. Note that too small parameter variations can lead to significant round-off errors for finite differences. In both examples, the parameter variation Δα is chosen to be 2−7 α. The choice is made by investigating the differences between steps of the type 2−m α. A 1 mm steel plate is considered for the isotropic example. The material parameters for this example can be found in Table 1a. There are three material parameters to be examined: the Young’s modulus, Poisson’s ratio ν and density ρ. Here, we present only the first two because the density and Young’s modulus have a very similar effect on the dispersion curves and their sensitivities. While Young’s modulus scales the right-hand side of Eq. (1), the density scales the left-hand side. Both material parameters lead to a linearly changing sensitivity Sf with increasing frequency. Figure 3a and b show the effect of Young’s modulus on the frequency and group velocity dispersion curves, respectively. The typical dispersion diagrams have been extended by a colour value that corresponds to the sensitivity. The scaled sensitivity Sf can be used to estimate the sampling rate required to measure a material change of 1%. In Fig. 3a, all sensitivity values are positive. Therefore, all curves would move up with a positive change in Young’s modulus. The diagram for the density is identical apart from a changed sign and is therefore omitted.

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(a) Sf (k; E)

(c)

f

(k; ν)

(b) Scg (k; E)

(d)

cg (k; ν)

Fig. 3. Isotropic example with the material parameters from the Table 1b

There is a non-linear relationship between the Poisson’s ratio and the righthand side of Eq. (1). This non-linearity leads to local changes in sensitivity for both the frequency and group velocity (see Fig. 3c and d, respectively). There are negative and positive sensitivity values. Consequently, some parts of the modecurves would move downwards, whereas others would move upwards. The fourth mode, the S1 mode, shows a high value of sensitivity Sf . As a second example, a plate with transversely isotropic material properties is examined. The material properties of the IM7/8552, which is a carbon fibre reinforced polymer, are obtained from the literature [29] (Table 1b). For simplicity, a unidirectional composite is considered. However, any other lay-up can be analysed with a few modifications. The direction of propagation and the direction of the longitudinal Young’s modulus E1 are lined up. The direction of the longitudinal Young’s modulus is the fibre direction, while the direction of the transverse Young’s modulus E2 is perpendicular to the fibres. Figure 4 shows that the sensitivity of each material parameter leads to different patterns. Similar to the isotropic material case, positive changes in the Poisson’s ratio ν12 lead to both positive as well as negative movements of the curves (see Fig. 4i). Again, we omitted the figures for the density because the sensitivity Sf (k, ρ) is proportional to the frequency. The proportionality is similar to Fig. 3a.

Sensitivity of Ultrasonic Guided Waves to Elastic Constants: A Numerical Study

(a) Sf (k; E1 )

(b) Scg (k; E1 )

(c) Sf (k; E2 )

(d) Scg (k; E2 )

(e) Sf (k; G12 )

(f) Scg (k; G12 )

(g) Sf (k; G23 )

(h) Scg (k; G23 )

(i)

f

(k; ν12 )

(j)

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cg (k; ν12 )

Fig. 4. Transversely isotropic example with the material parameters from the Table 1a

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(a) Structural steel

(b) Carbon fiber reinforced polymer [29]

Isotropic material

Transversely isotropic material

E = 200 GPa ν = 0.3

E1 = 171 GPa G12 = 5.3 GPa ν12 = 0.32

ρ = 7.85 g cm−3

4

E2 = 9 GPa

G23 = 2.8 GPa ρ = 1.57 g cm−3

Conclusion

We examined the influence of the material properties on the dispersion curves of Lamb waves. For the investigation, the sensitivities of the frequencywavenumber-relationship and group velocity were introduced. The sensitivity is displayed as a colour value in the dispersion curves. The resulting images allow an easy identification of curve parts that would change if the material properties changed. Without a change in the frequency or the group velocity material characterisation based on Lamb waves is not possible. We expect that a more significant change will improve the quality of the material characterisation because the method is less affected by measurement errors. In general, we noticed that the sensitivity often increases with the frequency. This confirms the observations in [8]. For isotropic materials, the sensitivity Sf due to changes in the Young’s modulus or density is a linear function of the frequency. As a result, both parameters cannot be determined at the same time. Typically, the density can be measured separately and does not have to be estimated using Lamb waves. For the Poisson’s ratio, some modes have a significantly different sensitivity. In particular, the results suggest that the fourth mode, the S1 mode, should be excited to determine the Poisson’s ratio. The two fundamental modes (A0 and S0) have a very low sensitivity to the Poisson’s ratio in the low-frequency range. Therefore, it may be hard to estimate the Poisson’s ratio only using these modes. For transversely isotropic materials, the influence of each material parameter is distinguishable from the others. Here, a targeted excitation would be a way to optimise specific parameters. If all parameters are to be characterised at the same time, a broadband signal is recommended. In general, the results suggest that the two fundamental modes are not as well suited for material characterisation as the higher-order modes. Acknowledgment. The authors are grateful for the funding by the German Research Foundation (Deutsche Forschungsgemeinschaft (DFG) - project: 428590437).

References 1. Sale, M., Rizzo, P., Marzani, A.: Semi-analytical formulation for the guided wavesbased reconstruction of elastic moduli. Mech. Syst. Sig. Process. 25(6), 2241–2256 (2011)

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2. Bochud, N., Laurent, J., Bruno, F., Royer, D., Prada, C.: Towards real-time assessment of anisotropic plate properties using elastic guided waves. J. Acoust. Soc. Am. 143(2), 1138–1147 (2018) 3. Vishnuvardhan, J., Krishnamurthy, C.V., Balasubramaniam, K.: Genetic algorithm reconstruction of orthotropic composite plate elastic constants from a single nonsymmetric plane ultrasonic velocity data. Compos. B Eng. 38(2), 216–227 (2007) 4. Eremin, A.A., Glushkov, E.V., Glushkova, N.V., Lammering, R.: Evaluation of effective elastic properties of layered composite fiber-reinforced plastic plates by piezoelectrically induced guided waves and laser Doppler vibrometry. Compos. Struct. 125, 449–458 (2015) 5. Webersen, M., Johannesmann, S., D¨ uchting, J., Claes, L., Henning, B.: Guided ultrasonic waves for determining effective orthotropic material parameters of continuous-fiber reinforced thermoplastic plates. Ultrasonics 84, 53–62 (2018) 6. Dreiling, D., Feldmann, N., Henning, B., Itner, D., Gravenkamp, H.: Increasing the sensitivity in the determination of material parameters by using arbitrary loads in ultrasonic transmission measurements. In: SMSI 2020, vol. 53, no. 7, pp. 261–262 (2020) 7. Claes, L., Meyer, T., Bause, F., Rautenberg, J., Henning, B.: Determination of the material properties of polymers using laser-generated broadband ultrasound. J. Sens. Sens. Syst. 5, 187–196 (2016) 8. Kudela, P., Fiborek, P., Radzienski, M., Wandowski, T.: Parametric studies of composite material properties influence on dispersion curves of lamb waves. In: Health Monitoring of Structural and Biological Systems IX, vol. 11381, p. 113810U. International Society for Optics and Photonics (2020) 9. Rose, J.L.: Ultrasonic Guided Waves in Solid Media. Cambridge University Press, Cambridge (2014) 10. Thomson, W.T.: Transmission of elastic waves through a stratified solid medium. J. Appl. Phys. 21(2), 89–93 (1950) 11. Haskell, N.A.: The dispersion of surface waves on multilayered media. Bull. Seismol. Soc. Am. 43(1), 17–34 (1953) 12. Knopoff, L.: A matrix method for elastic wave problems. Bull. Seismol. Soc. Am. 54(1), 431–438 (1964) 13. Kausel, E.: Thin-layer method: formulation in the time domain. Int. J. Numer. Meth. Eng. 37(6), 927–941 (1994) 14. Adamou, A.T.I., Craster, R.V.: Spectral methods for modelling guided waves in elastic media. J. Acoust. Soc. Am. 116(3), 1524–1535 (2004) 15. Hayashi, T., Song, W., Rose, J.L.: Guided wave dispersion curves for a bar with an arbitrary cross-section, a rod and rail example. Ultrasonics 41(3), 175–183 (2003) 16. Marzani, A., Viola, E., Bartoli, I., Di Scalea, F.L., Rizzo, P.: A semi-analytical finite element formulation for modeling stress wave propagation in axisymmetric damped waveguides. J. Sound Vib. 318(3), 488–505 (2008) 17. Finnveden, S., Fraggstedt, M.: Waveguide finite elements for curved structures. J. Sound Vib. 312(4–5), 644–671 (2008) 18. Gravenkamp, H., Song, C., Prager, J.: A numerical approach for the computation of dispersion relations for plate structures using the scaled boundary finite element method. J. Sound Vib. 331(11), 2543–2557 (2012) 19. Nelson, R.B., Dong, S.B., Kalra, R.D.: Vibrations and waves in laminated orthotropic circular cylinders. J. Sound Vib. 18(3), 429–444 (1971) 20. Aalami, B.: Waves in prismatic guides of arbitrary cross section (1973) 21. Song, C.: The Scaled Boundary Finite Element Method: Introduction to Theory and Implementation. Wiley, Hoboken (2018)

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22. Bulling, J., Gravenkamp, H., Birk, C.: A high-order finite element technique with automatic treatment of stress singularities by semi-analytical enrichment. Comput. Methods Appl. Mech. Eng. 355, 135–156 (2019) 23. Chowdhury, M.S., Song, C., Gao, W.: Shape sensitivity analysis of stress intensity factors by the scaled boundary finite element method. Eng. Fract. Mech. 116, 13–30 (2014) 24. Chongshuai, W., Yiqian, H., Haitian, Y.: A SBFEM and sensitivity analysis based algorithm for solving inverse viscoelastic problems. Eng. Anal. Bound. Elem. 106, 588–598 (2019) 25. Krome, F., Gravenkamp, H.: A semi-analytical curved element for linear elasticity based on the scaled boundary finite element method. Int. J. Numer. Meth. Eng. 109(6), 790–808 (2017) 26. Wasmer, P., Krome, F., Bulling, J., Prager, J.: A fluid model for the simulation of fluid-structure interaction in the scaled boundary finite element method for prismatic structures. PAMM 18(1), e201800139 (2018) 27. Gravenkamp, H., Man, H., Song, C., Prager, J.: The computation of dispersion relations for three-dimensional elastic waveguides using the scaled boundary finite element method. J. Sound Vib. 332(15), 3756–3771 (2013) 28. Krome, F., Gravenkamp, H.: Analyzing modal behavior of guided waves using high order eigenvalue derivatives. Ultrasonics 71, 75–85 (2016) 29. Rogge, M.D., Leckey, C.A.C.: Characterization of impact damage in composite laminates using guided wavefield imaging and local wavenumber domain analysis. Ultrasonics 53(7), 1217–1226 (2013)

A Structural-Aware Frequency Division Multiplexing Technique for Acoustic Data Communication in SHM Applications Federica Zonzini1(B) , Luca De Marchi2 , Nicola Testoni1 , Christian Kexel3 , and Jochen Moll3 1

2

Advanced Research Center on Electronic Systems (ARCES), University of Bologna, Bologna, Italy [email protected] Department of Electronical, Electronic and Information Engineering (DEI), University of Bologna, Bologna, Italy 3 Department of Physics, Goethe University of Frankfurt am Main, Frankfurt am Main, Germany

Abstract. The technological advancements in the sensor design and fabrication process brought about a new generation of smart sensor nodes to be used for Structural Health Monitoring (SHM) purposes, which are concurrently capable of data sensing and processing in situ. This is the case of GWs-based monitoring applications, where the capability of the state-of-the-art transducers to generate custom signals inspired new potentials for acoustic data communications without the need for external cabling. Thus, information about the structural integrity might be transferred between sensor nodes permanently attached to the structure and exchanged across the monitored mechanical waveguide as a numerical damage indicator. Here, a combination of square-wave excitation sequences and frequency-division multiplexing (FDM) is explored for simultaneous communication with multiple nodes. In detail, the problem of selecting the most appropriate carrier frequencies is specifically tackled, by proposing two different strategies for structural aware SHM data communication systems. A Multiple-in Multiple-out (MIMO) miniaturized smart sensor network, consisting of low-power and low-cost sensor nodes, was deployed to prove the effectiveness of the advanced solutions. Transducers were positioned in a spatially distributed and permanently installed network. Cable-free exchange of encoded information across a square metallic plate as well as on a stiffened carbon-fiber reinforced plastics (CFRP) panel is achieved. Keywords: Guided waves · Intelligent structures · Acoustic data communications · Frequency Division Multiplexing

c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 769–778, 2021. https://doi.org/10.1007/978-3-030-64594-6_74

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Introduction

Structural Health Monitoring (SHM) systems provide a tangible response to the constantly increasing demand for more robust and resilient structures either against man-made or built-in degradation processes. To this end, Lamb waves represent a specific class of Guided Waves (GWs) particularly apt for the nondestructive inspection of thin-walled structures thanks to their peculiar sensitivity to multiple types of damages (e.g. cracks, impact, delamination). From a purely mechanical standpoint, they have an additional advantage consisting of the relatively low dispersive nature, therefore travelling comparatively long distances with minimal attenuation and/or dispersion. Nonetheless, some factors still concur to render Lamb waves’ propagation behavior quite complex, such as multi-modal response subject to multi-path fading, mode conversion and wave speed variability as a function of exciting stimulus, and material properties [7].

Fig. 1. An example of GWs-based SHM system integrating structural monitoring and data communication functionalities.

Furthermore, the combined advancements in the information, electronic and structural research communities promoted the design of a new generation of structures, which are intelligent in that they integrate a network of smart sensors concurrently capable to perform structural monitoring and to share the results of the inspection in form of damage indicators. As exemplary drawn in Fig. 1, the main idea at the basis of GWs-based communication systems exploits the mechanical structure itself as the waveguide, i.e. the physical layer for data communication, whereas the real information content is encoded in the elastic waves travelling on the structure. Noteworthy, such a hybrid inspection-communication paradigm may be essential in those scenarios where the classical electromagnetic medium is prone to fail, i.e. underground, underwater or special lightweight environments [5].

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Despite representing a relatively new research field, effective data transfer based on GWs has been already demonstrated in several works reaching a transfer rate of up to hundreds of kbits/s. This range is expected to be sufficient for the prospective application scenarios, which may depend on the transfer of a numeric damage indicator. In the very last few years, authors demonstrated that classical modulation techniques employed in conventional wireless systems can be fairly revisited under an acoustic data communication perspective. Among the most representative examples, a code-division multiplexing mechanism combined with dispersion compensation, was presented in [11] in the case of a metallic beam. Moreover, a correlation-based digital on-off keying (OOK) strategy founded its success in the combined inspection/communication of technically involved structures, such as a sandwich panel and a helicopter rotor blade piece [8], cylindertype structure [1], subsea oil rig [6] and water-bearing pipes [4]. Another scientific work on GWs-based data transmission explored the adoption of a pulse-position modulation (PPM) scheme, where the channel reciprocity condition can be particularly exploited to automatically gain dispersion suppression. Very recently, even the transmission of images with ultrasonic elastic waves on a metallic pipe was achieved, by resorting to the amplitude shift keying (ASFK) protocol [3].

2

Theoretical Background

Frequency Division Multiplexing (FDM) is a data modulation technique with which a specific frequency band is assigned to each emitter of the network for communicating in parallel at multiple carrier frequencies over the whole transmission period. As far as GWs-based digital communication systems are considered, elastic waves are inherently subjected to a dispersive propagation pattern, which makes the proper selection of the carrier frequencies of the utmost importance. This requirement becomes even more stringent in case of Multiple-Input Multiple-Output scenarios, where the plurality of active transmitting nodes introduces undesired inter-transmitter disturbances. 2.1

Carrier Frequency Selection

Resorting to the classical square-wave excitation technique, the FDM approach proposed here assumes carrier-related information to be sent in the form of rectangular digital pulses. This actuation mechanism has been preferred over more sophisticated techniques to prompt its readily on-sensor deployment given the available processing resources of the employed SHM network. However, such an approach implies odd-numbered harmonics besides the fundamental one to be unavoidably excited. According with this observation, a golden rule for effective data communication applies, which implies the highest carrier frequency to lie below the second harmonic of the lowest carrier component. Hence, a carrier frequency selection procedure has been specifically suggested to maximize the energetic content of each transducer-related message.

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Two different approaches were found to be compatible with energy aware acoustic data communication, which will be referred to as model-assisted and empirical approach, respectively. Model-Assisted Approach. The model-assisted carrier selection method encompasses the exploitation of numerical or analytical models to predict the eigenmodes of the mechanical waveguides of the structure. As one may easily understand, this solution should be endorsed whenever the structural, mechanical, and geometrical properties are very well known and easy to be modeled via software. Let’s assume N transmission channels are active, then each n-th communication link is associated with an excitation sequence length Ln defined by the number of times one rectangular pulse of duration tn is repeated. Assuming that waveforms are produced with a constant duty cycle τ , the total excitationtn · Ln . sequence duration is Tn = τ Then, the problem is further specialised by adding two fundamental hypotheses, namely: 1. two distinct sequences Lh , Lk possess identical duration T , a crucial condition to be guaranteed for communication scenarios where a global clock or synchronization mechanism is desired; 2. the distance between two successive carrier frequencies is constant, which is necessary to prevent the solving system to be overdetermined. F Hence, fn = Ln holds, where F is the sample rate. Since F and T are constant T quantities common to all the communication links, it can be deduced that the carrier frequency value is uniquely derived once Ln is determined. To this end, the optimal set of excitation-sequence length can be estimated as those values corresponding to the minimum sequence duration T concurrently allowing for the minimization of the maximal sequence length LN related to the highest-order input harmonic. Finally, by imposing the pulse width tn such that the corresponding carrier frequencies are proximal to the numerically estimated mechanical eigenmodes, the transmission channels can be configured. Empirical Approach. A more versatile alternative was also considered, which only requires empirical data gathered at the network start up phase, thereby it is thought to be executed directly in-field. Consequently, the above-mentioned procedure may become providential in real use-cases whose general complexity makes the structural design impracticable.

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The proposed procedure only requires a couple of TX-RX nodes. More specifically, by exploiting the advantageous capability of the current state-of-the-art transducers to generate custom signals, pseudorandom digital sequences can be repeatedly transmitted from one active node and then recorded by a passive transducer. It follows that the frequency spectra of the injected signals are approximately white, i.e. constant value over the whole frequency scale. Under this reasonable assumption, the spectral content hidden within the received signals tends to coincide with the transfer function of the structure and hence the carriers to be selected correspond to its the most energetic peak-related frequencies. 2.2

Spectrum-Driven Bit Reconstruction

In compliance with the exploited excitation mechanism, a one-to-one correspondence exists between a single frequency carrier and one delivered bit: transmitting at a certain carrier frequency encodes the binary value ‘1’, whereas omitting transmission at this frequency represents the binary value ‘0’. Thus, the complete digital message a node can send at a time is given by the linear superposition of several single-carrier excitations. Correspondingly, the decoding procedure at the receiving node can be simply accomplished by probing for the absence or presence of a particular carrier frequency within the spectral content of the acquired signals. If the intensity in the surrounding of the carrier is higher than the noise level, then the value ‘1’ is considered, otherwise the binary value ‘0’ is assumed.

3 3.1

Experimental Validation Materials

Two geometrically identical square plates (1000 mm wide and 3 mm thick), one is a metallic structure made of Aluminum (Al, Fig. 2(a)) and the other a carbonfiber reinforced plastics panel (CFRP) (Fig. 2(b)), were instrumented with a small MIMO communication network comprising a couple of transmitter nodes (TX1 and TX2) and two receiver nodes (RX1 and RX2) organized in a d = 10 cm squared configuration. Nodes are connected in a daisy chain configuration by means of a Sensor Area Network (SAN) bus exploiting data-over-power communication that can be accessed from a remotely connected host with a USBconnected network gateway interface. The final goal of the experimental campaign was to assess how the selection of the carrier frequencies may impact on rather different communication media. Indeed, if the metallic structure exhibits a relatively deterministic propagation behavior, this is not the case of the CFRP plate, where the anisotropic structure implies a strong attenuation, mode conversion, and consistent reverberation. Additionally, as evidenced with a black bold line in Fig. 2(b), the communication path on the CFRP structure presents an additional structural complexity given by the presence of a stiffener element in the midway of the sensor installation scheme.

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(a) Aluminum plate setup

(b) CFRP plate setup

Fig. 2. Experimental setup and relative communication distances: (a) aluminum plate; (b) CFRP plate, with highlighted stiffener element.

Focusing on the pure electronic equipment, each communication node comprises a multi-channel lead zirconate titanate (PZT) transducer with three active regions [2] and an electronic board hosting all the circuitry necessary for the low-voltage excitation and acquisition of the electric signals generated by the PZT sensing unit. A key feature of these prototype sensor nodes concerns their integrated Digital Signal Processing functionalities, namely the capability to onsensor process data, in strict proximity were the information is actually sensed by the PZT discs, without the need for external bulky instruments were the analysis is usually carried out. In this way, a sensor-near monitoring paradigm is fostered in order to reduce the time and the cost of the inspection process, concurrently minimizing the possible electromagnetic noise. In addition, striving to reduce the electrical and geometrical impingement of the SHM network, the hardware of the communication node was thought to be compliant with low weight (less than 5 g), small size (30 mm × 23 mm) and low current consumption (44.8 mA @ 5 V external voltage reference). The complete description of the monitoring architecture is provided in [9]. 3.2

Methods

The digital excitations of two carrier frequencies per transmitter node were allocated to two out of three different channels of the same PZT disc. Particular attention was given to the selection of the best set of carrier frequencies according to the structure under consideration. In particular, if the implementation of a structural numerical model is quite a straightforward task in case of isotropic structures, such as the Al panel here considered, modeling with sufficient precision the material complexity of the CFRP plate is much more than a concern. Thus, a pure model-assisted approach was adhered to for the computation of the eigenmodes of the Al structure by leveraging the COMSOL software package. This simulation finally returned with λ1 = 9.8 kHz,

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λ2 = 14.3 kHz, λ3 = 18.1 kHz as first three mechanical wavemode. Hence, obeying to the model-assisted derivation of burst-excitation sequence lengths, Ln = {10, 12, 15, 18} cycles were estimated and the practical set of carrier frequenA A A = 9.89 kHz, f1,2 = 14.82 kHz, f2,1 = 11.85 kHz, cies was computed equal to f1,1 A f2,2 = 17.78 kHz. In the above notation, the subscript n, j stands for transmitting node n ∈ {TX1,TX2} and channel related carrier frequencies, respectively. Contrariwise, moving to the CFRP structure, an empirical carrier selection procedure was resorted to as previously discussed in [10], leading to C C C C = 10.70 kHz, f1,2 = 12.84 kHz, f2,1 = 16.00 kHz, f2,2 = 19.25 kHz. Before conf1,1 figuring the CFRP communication network on the estimated empirical-driven carriers, some tests were run maintaining the transmission side tuned on the best set of carrier values originally derived for the Al setup. In this way, it was specifically possible to investigate how communication performances may deteriorate as a consequence of mischosen transmission channels. For the sake of clarity, the caption ‘Non Opt’ and ‘Opt’ will be employed in the result section to indicate the non-optimized and optimized set of frequencies, respectively. At the receiving side, RX nodes were programmed to simultaneously acquire data at a sample rate fs = 500 kSps to grant quite a precise frequency resolution for the adopted transmission components. Linear superposition of effects was leveraged to inject up to four different signals into the structure at a time, spanning 24 = 16 different bit combinations (e.g. 16 different damage-indicating messages). Each test was repeated 10 times to assess the robustness against noise and jitter. The complete GWs-based communication system so far designed includes the following three phases: (1) actuation, performed by TX nodes which send a specific digital message encapsulated in form of elastic waves, (2) propagation, i.e. the excited GWs travel along the mechanical waveguide and (3) decoding, charged to the RX nodes which passively collect and analyse the input signals by sensing its spectral content.

4

Results and Discussion

Rigorous quantification of the bit reconstruction procedure was accomplished by proposing an ad-hoc spectral-based metric rn =

SnIA , SnA

n ∈ 1, . . . N

(1)

which measures the ratio between the spectral intensities occurring at the carrier peak values when the n-th channel is inactive (SnIA ) over the active (SnA ) configuration. The lower the rn , the higher the carrier identification is and thus the quality of the reconstructed bit value.

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0.2

0.2

0.1

0.1

[V]

[V]

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0 -0.1

-0.1

-0.2

-0.2 0

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Fig. 3. Exemplary signals for damage code indicator 1111 sent through the CFRP panel in presence of non-optimal carrier frequencies: time and frequency signal representation domain are presented in the bottom and lower panels, respectively. Each column refers to the signal received at one of the two receiving nodes.

Obtained rn values are reported in Table 1, which compares the spectral intensity accumulated in a frequency bin of 2 kHz around each of the carriers at both receivers. As a general remark, the analysis illustrates that sharp discrimination can be achieved between active and inactive communication status if and only if a structural aware set of carrier frequencies is selected. This is demonstrated by very well pronounced spectral amplitudes characterized by a low inactive-active ratio below 0.5 both for the Al and the CFRP plate configured on their relative best set of carrier harmonics. Additionally, the impact of potential geometrical discontinuities should be underlined, since it may hamper the direct communication path. Prove is the fact that an order of magnitude in the ratio rn exists among line-of-sight (TX1-RX1, TX2-RX2) and obstructed (TX1-RX2 and TX2RX1) communication links. Moreover, in Fig. 3 the time-frequency behavior of acquired signals transmitted through the CFRP panel under non optimal carrier frequencies for bit configuration 1111 is reported, where it is evident the filtering effect due to the stiffener. Similar experimental evidence does not apply to the Al plate because of its homogeneous mechanical features.

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Table 1. Comparison of normalized spectral intensity that is accumulated in a bin (width 2 kHz) around the carrier frequencies at both receivers. Al CFRP non opt CFRP opt f1,1 f2,1 f1,2 f2,2 f1,1 f2,1 f1,2 f2,2 f1,1 f2,1 f1,2 f2,2 S IA 0.09 0.08 0.12 0.07 0.04 0.05 0.08 0.23 0.01 0.02 0.03 0.01 S A 0.63 0.55 0.43 1.00 0.07 0.06 0.14 0.52 0.06 0.16 0.41 0.21 r

5

0.14 0.15 0.29 0.07 0.52 0.76 0.57 0.43 0.17 0.02 0.07 0.05

Conclusions

This paper showed the successful employment acoustic data communication using multiple transmitters and receivers at the same time. Two different carrier frequency selection procedures are presented to better match the mechanical propagation behavior of the energy efficient solutions. The experimental validation of this structural-aware MIMO approach was demonstrated on a metallic as well as on a composite material with complex geometry using special miniaturized sensor nodes. Acknowledgements. C.K. and J.M. acknowledge support by the German Research Foundation DFG (grant number 349435502).

References 1. Chakraborty, S., Saulnier, G.J., Wilt, K.W., Curt, E., Scarton, H.A., Litman, R.B.: Low-power, low-rate ultrasonic communications system transmitting axially along a cylindrical pipe using transverse waves. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 62(10), 1788–1796 (2015). https://doi.org/10.1109/TUFFC.2015.007078, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7296567 2. De Marchi, L., Testoni, N., Marzani, A.: A novel shaped piezoelectric sensor for impact localization in plate structures. In: 2015 XVIII AISEM Annual Conference, pp. 1–4. IEEE (2015) 3. Heifetz, A., Shribak, D., Huang, X., Wang, B., Saniie, J., Young, J., Bakhtiari, S., Vilim, R.B.: Transmission of images with ultrasonic elastic shear waves on a metallic pipe using amplitude shift keying protocol. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67(6), 1192–1200 (2020) 4. Joseph, K.M., Watteyne, T., Kerkez, B.: Awa: using water distribution systems to transmit data. Trans. Emerg. Telecommun. Technol. 29(1), e3219 (2018) 5. M¨ alzer, M., Kexel, C., Maetz, T., Moll, J.: Combined inspection and data communication network for lamb-wave structural health monitoring. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 66(10), 1625–1633 (2019) 6. Mijarez, R., Gaydecki, P.: Automatic guided wave ppm communication system for potential SHM of flooding members in sub-sea oilrigs. Smart Mater. Struct. 22(5), 055031 (2013) 7. Mitra, M., Gopalakrishnan, S.: Guided wave based structural health monitoring: a review. Smart Mater. Struct. 25(5), 053001 (2016)

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8. Moll, J., Kexel, C., M¨ azler, M.: Complex intelligent structures with data communication capabilities. In: 9th European Workshop on Structural Health Monitoring, Manchester, UK, pp. 1–7 (2018) 9. Testoni, N., De Marchi, L., Marzani, A.: A stamp size, 40 ma, 5 grams sensor node for impact detection and location. In: European Workshop on SHM, pp. 1–8 (2016) 10. Zonzini, F., De Marchi, L., Testoni, N., Kexel, C., Moll, J.: Guided-wave MIMO communication on a composite panel for SHM applications. In: Health Monitoring of Structural and Biological Systems IX, vol. 11381, p. 1138136. International Society for Optics and Photonics (2020) 11. Zonzini, F., De Marchi, L., Testoni, N., Marzani, A.: Direct spread spectrum modulation and dispersion compensation for guided wave-based communication systems. In: 2019 IEEE International Ultrasonics Symposium (IUS), pp. 2500–2503. IEEE (2019)

Strategies for Identification of Elastic Constants in Highly Anisotropic Materials Using Lamb Waves Maciej Radzieński(&) , Paweł Kudela , Tomasz Wandowski and Wiesław Ostachowicz

,

Institute of Fluid-Flow Machinery Polish Academy of Sciences, Gdańsk, Poland [email protected]

Abstract. Information about exact material properties may be of great importance in many areas where CAD/CAE software is used. It is also a key component of properly operating model-based SHM systems. Unfortunately, composite laminates producers are not providing sufficient and/or precise enough materials data sheets to meet such requirements. This is the reason why material properties identification techniques are attracting considerable interest. This paper presents a new, non-destructive elastic constants identification technique based on Lamb wave phenomenon. Experimental dispersion curves are obtained by 3D Fourier transform of full wavefield time responses registered in a tested sample by scanning laser Doppler vibrometer. Numerical dispersion curves, generated by a semi-analytical element model, are optimized to match experimental dispersion curves. By minimizing the discrepancies between two sets of data, the elastic constants are identified. Two approaches are tested, where the Genetic Algorithm is used to fit dispersion curves in the wavenumber-frequency domain for chosen propagation angles or angular profiles in the wavenumber-angle domain for chosen frequencies. The direct approach was used in which C-tensor components where optimized. Keywords: Lamb waves techniques


Elastic constants identification
Optimization

1 Introduction Elastic constants are of great importance in the process of designing a structure. For isotropic materials, they have been assessed by destructive testing methods [1] or by minimizing the differences between the theoretical and experimental natural frequencies [2]. For anisotropic materials, destructive testing requires specials cube-cutting to determine all elastic constants [3] which makes it more complex and expensive. Ultrasonic methods for the determination of elastic constants of composite laminates have been recently enhanced by the utilization of the ultrasonic polar scan method [4]. An alternative approach is based on signals of propagating Lamb waves. Ong et al. [5] proposed a method in which experimental and numerical signals acquired along lines corresponding to selected angles of propagation are used. Measurements are taken © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 779–787, 2021. https://doi.org/10.1007/978-3-030-64594-6_75

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on the upper and bottom surface of the plate so that symmetric and antisymmetric modes can be separated. Signals are processed by using a 2D Fourier transform in order to obtain dispersion curve patterns. Correlation between numerical and experimental dispersion curve patterns is considered in the objective function. However, measurements taken along a line may cause problems of the contribution of reflected waves from the boundaries of the plate. Therefore, we propose to utilize the full wavefield of propagating waves in the construction of the objective function as in paper [6]. Full wavefield data is transformed with 3D Fourier transform to the wavenumber-frequency domain where it is sliced at chosen propagation angles or at chosen frequencies to create dispersion curves or angular profiles correspondingly. Elastic constants are estimated by minimizing the error between experimental and numerical data.

2 Semi-analytical Spectral Element Method Dispersion curves were calculated using the semi-analytical spectral element (SASE) method, which is a modification of the semi-analytical finite element (SAFE) method proposed by Bartoli et al. [7]. The modification includes the application of spectral elements instead of classic finite elements through the thickness of a laminate, preserving wave equation in the propagation direction. Moreover, dispersion curves equations are defined in a way that allows for solving at any propagation angle b. The wavevector k is defined as [8]: k ¼ k cosðbÞ^x  k sinðbÞ^ y;

ð1Þ

where ^x and y^ are unit vectors. The general wave equation has a form of eigenvalue problem:


 A  x2 M U ¼ 0;

ð2Þ

where x is the angular frequency, M is the mass matrix, U is the nodal displacement vector. Matrix A is defined as: A ¼ k2 ðs2 K22 þ c2 K33  csK23  csK32 Þ þ ikTT ðcK13  sK21 þ sK12 þ cK31 ÞT þ K11 ;

ð3Þ

where s ¼ sin b, c ¼ cos b, and b is the Lamb wave propagation angle. Stiffness matrices Kmn depend on elastic constants of a specimen and the relations between displacements and strains. For element (eÞ these matrices are defined as follows

Strategies for Identification of Elastic Constants

Kemn ¼

Z

ðeÞ

781

ð4Þ

BTm Ceh Bn dz;

where B is the matrix relating displacements and strains, Ceh is the elastic tensor. In order to solve Eq. (2) two various numerical approaches may be used: • solving standard eigenvalue problem xðkÞ • solving second-order polynomial eigenvalue problem kðxÞ The matrix of elastic constants C of an orthotropic linear elastic material with known layer orientation may be defined as: 2

C11 6 C12 6 6 C13 C¼6 6 0 6 4 0 0

C12 C22 C23 0 0 0

C13 C23 C33 0 0 0

0 0 0 C44 0 0

0 0 0 0 C55 0

3 0 0 7 7 0 7 7: 0 7 7 0 5 C66

ð5Þ

This gives 9 independent coefficients to be determined in the optimization process.

3 Experimental Measurements 3.1

Specimen

To verify the proposed elastic constants identification technique a 1200  1200  2.85 mm3 CFRP plated was tested. This specimen was composed of 40 layers of ThinPregTM NTPT 736LT prepregs stacked in one direction (90°). 3.2

Experimental Set-up

Lamb waves were excited by a 10 mm round piezoelectric disk (PZT) attached to the specimen’s back surface. Chirp signal with the frequency range 0–500 kHz lasting 200 µs was generated every 10 ms and applied to the PZT element through the signal amplifier. The specimen central area of 455  455 mm was measured in 499  499 points using a scanning laser Doppler vibrometer. In every measurement point, 1024 time samples were registered with a sampling frequency of 1.28 MHz. The response recording in every measurement point was repeated 20 times and averaged to improve the signal to noise ratio. The experimental set-up is shown in Fig. 1.

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Fig. 1. Experimental set-up.

4 Optimization with Genetic Algorithm To determine elastic constants (nine C-tensor components) of the tested CFRP plate, the genetic algorithm toolbox [9] was used to find the best fit between numerical and experimental dispersion curves or angular profiles. Numerical data have a form of discrete curves determined for specific propagation angle kb ðxÞ or specific frequency kx ðbÞ for a given set of material properties in which elastic constants C are used as tuning parameters. Experimental data from full wavefield measurement is in 3-dimensional form. To be able to assess agreement between numerical and experimental data, numerical dispersion curves, and angular profiles were transform into binary images by assigning 1 to the pixels containing any curve and zero otherwise. Experimental data was cut along particular propagation angles or particular frequency to create a set of 7 images. Taking this into account, the objective functions for those two optimization problems may be defined as: min j

min j

  ð jÞ SASE k x; C  kbEXP ðxÞ; b b

ð6Þ

  ð jÞ SASE k b; C  kxEXP ðbÞ: x x

ð7Þ

X

X

In both cases, 70 generations were calculated with 100 individuals per population. Chromosomes of 10% of the best-fitted individuals were used to create the next generation. The convergence of GA was obtained after about 40 generations.

5 Results Registered full wavefield data (presented in Fig. 2) was transformed from the spacespace-time domain (x-y-t) into the wavenumber-wavenumber-frequency (kx-ky-f) using 3D Fourier transform.

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Fig. 2. Full wavefield data in the space-space-time domain for chosen time frames.

Fig. 3. Wavenumber-wavenumber-frequency data sliced at 45° and 90° of wave propagation angles.

Two approaches were used to project 3-dimensional experimental data into a set of 2D images: • Slicing 3D data at chosen propagation angles - creating a set of dispersion curves (Fig. 3). • Slicing 3D data at chosen frequencies - creating a set of angular profiles (Fig. 4).

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Fig. 4. Wavenumber-wavenumber-frequency data sliced at two chosen frequencies.

Fig. 5. Dispersion curves for chosen propagation angles.

For dispersion curves optimization process seven various propagation angles b were used, namely: 0°, 15°, 30°, 45° 60°, 75°, and 90°. Final dispersion curves drawn on experimental data with white lines for chosen propagation angles are presented in Fig. 5.

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Fig. 6. Angular profiles for chosen frequencies.

Very good agreement between experimental and numerical dispersion curves have been observed for all tested propagation angles. In the second proposed approach in which numerical angular profiles where optimized to match experimental data, seven various frequencies were used, namely: 48.85, 98.94, 149.04, 199.14, 249.24, 299.33, and 349.43 kHz. Final numerical angular profiles after the optimization process are drawn with white lines on experimental data and presented for chosen frequencies in Fig. 6. Very good agreement between numerical and experimental data has been achieved for all modes at all tested frequencies. Due to symmetry in the wave propagation, only the area corresponding to positive wavenumbers was used in both optimization processes. Elastic constants estimated for the same CFRP sample with two presented techniques are given in Table 1. Similar results were obtained for most elastic constants

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beside C33 and C66. The difference of around 10% is non-negligible, and without additional tests with other techniques, it is impossible to determine which results are closer to the real specimens elastic constants. Further verification tests are planned in future.

6 Conclusions In this work, a technique for elastic constants identification based on Lamb wave propagation measurement is presented. Two approaches for the optimization are proposed. SASE model and full wavefield measurements along with the genetic algorithm are used in the optimization process. In both cases, a very good agreement between numerical and experimental data has been reached. However, the optimization of dispersion curves was computationally more efficient than the optimization of angular profiles. The time needed for a single genetic algorithm run was about ten times shorter for the former. It should be noted that estimated elastic constants for the same specimen using both approaches have some discrepancies and additional testing with another technique should be used to determine which results were closer to the real values. Such studies are planned in the future. Acknowledgments. The research was funded by the Polish National Science Center under grant agreement no 2018/29/B/ST8/00045.

References 1. Wang, W., Kam, W.: Material characterization of laminated composite plates via static testing. Compos. Struct. 50, 347–352 (2000) 2. Wesolowski, M., Barkanov, E., Rucevskis, S., Chate, A., La Delfa, G.: Characterisation of elastic properties of laminated composites by nondestructive techniques. In: ICCM International Conferences on Composite Materials, Edinburgh (2009)

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3. Rose, J.L., Ditri, J.J., Huang, Y., Dandekar, D.P., Chou, S.C.: One-sided ultrasonic inspection technique for the elastic constant determination of advanced anisotropic materials. J. Nondestruct. Eval. 10, 159–166 (1991) 4. Martens, A., Kersemans, M., Daemen, J., Verboven, E., Van Paepegem, V., Delrue, S., Van Den Abeele, K.: Characterization of the orthotropic viscoelastic tensor of composites using the Ultrasonic Polar Scan. Compos. Struct. 230, 111499 (2019) 5. Ong, W., Rajic, N., Chiu, W., Rosalie, C.: Determination of the elastic properties of woven composite panels for Lamb wave studies. Compos. Struct. 141, 24–31 (2016) 6. Kudela, P., Radzienski, M., Fiborek, P., Wandowski, T.: Elastic constants identification of woven fabric reinforced composites by using guided wave dispersion curves and genetic algorithm. Compos. Struct. 249, 112569 (2020) 7. Bartoli, I., Marzani, A., Lanza di Scalea, F., Viola, E.: Modeling wave propagation in damped waveguides of arbitrary cross-section. J. Sound Vibrat. 295, 685–707 (2006) 8. Taupin, L., Lhemery, A., Inquiete, G.: A detailed study of guided wave propagation in a viscoelastic multilayered anisotropic plate. J. Phys: Conf. Ser. 269, 012002 (2011) 9. Chipperfield, A., Fleming, P., Pohlheim, H., Fonseca, M.: Genetic Algorithm Toolbox User’s Guide, Technical report, University of Shefield (1994)

Damage Detection with Ultrasonic Guided Waves Based on Broadband Random Excitation and Stochastic Signal Processing Jonas Brettschneider1(&), Peter Kraemer1, Pawel Kudela2, and Jochen Moll3 1

Chair of Mechanics/Structural Health Monitoring, University of Siegen, Siegen, Germany [email protected] 2 Institute of Fluid-Flow Machinery, Polish Academy of Sciences, Warsaw, Poland 3 Institute of Physics, Goethe University of Frankfurt am Main, Frankfurt, Germany

Abstract. In the last decades, ultrasonic guided waves have proven to be a promising tool for structural health monitoring (SHM). For a number of reasons, narrowband burst signals are widely used to excite structures in order to reduce the impact of multimodal wave propagation and dispersion. This paper addresses a different approach using broadband random excitation signals. While burst signals are advantageous for damage localization and compensation of environmental and operational conditions, the interference of stochastic waves resulting in a complex wavefield could be more sensitive to structural changes, including defects. Based on promising experimental results published recently, potentials and limitations resulting from random excitation are investigated in this paper. Sensor signals are simulated using the time domain spectral element method for a carbon fiber composite plate and twelve piezoelectric transducers. The simulated sensor signals are analyzed using different statistical methods, including the Nullspace-based Fault Detection algorithm known from vibration-based SHM, to compute damage indices for the intact and damaged states of the plate. Moreover, wavefield images computed by the root mean square (RMS) are presented. Detected defects and non-visible damage positions are compared and the results are discussed. Keywords: Structural Health Monitoring
Guided waves
Signal processing
Random excitation
Composites

1 Introduction There are various upsides using narrowband bursts signal for guided waves based SHM. One of the main benefits is the reduced complexity of wave propagation. Regarding guided waves in general, multimodal wave propagation and dispersion lead to complex wavefields and measured signals that are difficult to interpret, especially under changing environmental conditions. Narrowband tone burst excitation can be used to simplify the © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 788–797, 2021. https://doi.org/10.1007/978-3-030-64594-6_76

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resulting signals, especially when combined with wave tuning. Changes in the signals can then be identified in the simplest case by subtracting baseline signals from currently measured ones or by using additional signal processing [1]. A different approach using stochastic excitation or diffuse wavefields was used multiple times in vibration based SHM and was recently transferred to guided waves [2]. The reasons for this approach are related to possible advantages of the complex wavefields; due to the interfering nature they might be more sensitive to changes than narrowband tone bursts. This could lead to several benefits, either the detection of smaller damages or the application of less sensors. Another advantage could be the exploitation of ambient excitation so that potentially no active excitation would be required and therefore the weight of SHM-systems could be reduced. In recent examples different ways of excitation were used to generate diffuse wavefields, one example is to use high pressured air that is randomly sprayed over the tested plate [3]. Another research group worked with a pulsed Nd:yttrium-aluminumgarnet-laser enabling local excitation at various positions [4]. The analysis in both cases was based on Greens function. The transfer function between sensors was calculated and compared in different structural conditions. Due to the path-based analysis at least weak localization of damage was possible. The analysis in this paper is based on simulated data. This has on one hand several advantages, for example all unwanted influences, like temperature or humidity are excluded. In addition, different damage cases or sensor distributions can be tested without too much expenses later on. On the other hand, simulated data could be corrupted by different numerical errors and discrepancies between the model and reality. The simulated signals used for analysis later on are computed using the spectral elements method, known for excellent modeling of wave propagation [5]. The simulated model consists of a carbon fiber composite plate and twelve piezoelectric transducers. One transducer is always used in turn as an actuator while the others sense the simulated signals. Four different states of the model are simulated and the following methodology is used to identify damaged and undamaged states: The excitation signal for the actuator consists of white noise to generate a complex wavefield; the resulting signals at the sensors are then analyzed using different techniques especially by means of the Nullspace-based Fault Detection algorithm (NSFD). Damage is simulated in this model at different positions on the plate in form of delamination. The novelty of this approach is to simulate and analyze data of diffuse wavefields generated by piezoelectric transducers with stochastic input signals. Because the approach is not based on path related damage indicators, localization will be rather difficult and the approach will need to be combined with additional algorithms.

2 Numerical Simulations 2.1

The Time-Domain Spectral Element Method

The time-domain spectral element method [5] was used for numerical simulations of propagating elastic waves. A flat shell element was used which is based on Mindlin-

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Reissner first-order shear deformation theory. It has 36 nodes and 5 degrees of freedom at each node: displacement components along three axes and two independent rotations of cross-sections. Parallel implementation, similar to the one presented in Ref. [6], was used to speed up computations. The proposed approach differs in the calculation of elemental forces which depend on the contribution of the extensional stiffness, the flexural stiffness, bending-stretching coupling, twisting-stretching along with bending-shearing coupling, stretching-shearing coupling and bending-twisting coupling instead of the matrix of elastic constants assigned to each layer of a composite laminate. Hence, the proposed method is more suitable for wave propagation modelling in multilayer composite laminates because it leads to a much lower number of degrees of freedom (see [7] for more details). 2.2

The Numerical Model

The structure under investigation was quasi-isotropic carbon-fibre-reinforced polymer (CFRP). The assumed layup of the composite was [45/0/-45/90/-45/0/-45/0/45/90]. The dimensions were 500 mm  500 mm and the thickness 2 mm. The assumed material properties of unidirectional CFRP are given in Table 1. The assumed mass density was 1571 kg/m3.

Table 1. Material properties of unidirectional CFRP (Units: GPa), see [8] for more details. C11 C12 C22 C44 C55 C66 130 6.1 11.2 3.0 4.2 4.2

Piezoelectric transducers of the diameter 10 mm and the thickness 0.2 mm were taken into consideration in the model by changing local inertia and stiffness in corresponding spectral elements. The composite laminate was modelled by using one layer of spectral flat shell elements at its neutral plane. However, the contribution of all composite layers to the overall elemental stiffness was taken into account in the usual way. Three locations of delamination of diameter 10 mm were investigated. The delamination locations and arrangement of 12 piezoelectric transducers is presented in Fig. 1. Each delamination case was considered separately. Two layers of flat shell spectral elements were used at delamination location in order to mimic separation of composite layers. Furthermore, contribution to the stiffness is divided into upper and lower elements according to the position of the delamination in between composite layers (it was assumed that the delamination is between 4th and 5th composite layer). Other important parameters used in the numerical simulation are the total wave propagation time of 1.3 ms, the number of elements 9360 and number of nodes 234 612. It gives on average 1 mm spacing between nodes. It should be noted that the mesh is dense enough for wavelength up to about 5 mm. However, due to random excitation,

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Fig. 1. The geometry of the analyzed composite laminate, see [8].

higher frequencies and shorter wavelengths are generated which are not properly modelled. But the conceptual work presented here is not affected by that. The mesh density was selected to achieve reasonable computation time. The time integration step equal to 0.0106 ms was chosen to assure the stability of the solution. The random excitation was applied to the transducer T1 and signal voltages were collected at all 12 transducers. Additionally, transverse displacements at the top surface were computed on a uniform grid of 500  500 points (for full wavefield analysis). The simulation was repeated 5 times to have 5 different random excitations and corresponding responses. Next, the simulation was repeated in the same manner for transducer T2 and so on. The procedure was repeated 4 times which covers delamination locations #1, #2, #3 and reference state. The total number of simulations was 240.

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3 Data Analysis 3.1

Full Wavefield Signal Processing

The aim of full wavefield analysis is verification whether the randomly excited propagating waves contain damage-related information. Each frame of propagating waves was processed by using a median filter with kernel size 3  3. Next, the frames were combined by using weighted root mean square (WRMS). Essentially WRMS gives information about the spatial distribution of energy. The energy distribution is shown in Fig. 2. High values of the presented map can be seen at the excitation area and delamination area of corresponding delamination locations. Of course, for the reference case (Fig. 2d) high values occur only at transducer location. The energy distribution maps confirm that signals of propagating waves contain damage-related features. It is important to note that each delamination can be precisely localized although the excitation was stochastic. 3.2

Damage Detection Analyzing the Simulated Signals

Nullspace-Based Fault Detection Algorithm. The main tool to analyze the signals is the NSFD, known from vibrations based SHM. The algorithm is based on state-space models that are able to describe the behavior of the monitored structure. Using the covariance-driven approach the respective modelling is defined by system matrices derived from auto- and cross-correlation of measured signals in form of a Hankel matrix. Structural changes of the monitored structure result in changes in the respective Hankel matrix and can be detected using the nullspace of reference Hankel matrices. Further explanation regarding the algorithm can be found in various literature [9]. The vibration-based approach, of course, cannot be transferred one-to-one to guided wave-based SHM. In contrast to vibration-based structural monitoring, the NSFD cannot detect modal changes here. However, it is a promising mathematical tool to detect changes in wave fields as well. The two most important requirements are simultaneous recording of the signals and broadband stochastic excitation. On the following pages a number of figures will be presented, illustrating different kinds of damage indices (DIs). All of them are organized in a similar form. The first measurement of the undamaged state is chosen to be the reference state and no DI will be displayed due to fixed values. First four DIs will refer to the undamaged state, while indicators 6-10 refer to delamination at position 1 (see Fig. 1), 11–15 refer to position 2 and 16–20 to position 3. In addition, the average value for each structural state will be presented on top of respective bars. The following figure shows the DINSFD calculated using the NSFD for excitation at transducer six (T6) as an example. In general, the results can change due to the position of the excitation, but in this study the differences in the results, especially for delamination at position 2 and 3, are rather small and excitation at position 6 is presented as representative for all the others. The delamination at position one, however, is a lot harder to be observed and its detection does depend on the actuator position. In this example signals of eleven sensors have been analyzed to calculate the DINSFD (Fig. 3).

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(a) delamination #1

(b) delamination #2

(c) delamination #3

(d) reference

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Fig. 2. Wavefield energy for the case of excitation at transducer T1.

The next example shows the DINSFD computed under same excitation conditions but using only the signals of T9-12. This sensor configuration was chosen, since in this case there are no delamination on the direct path between sensors and actuator. Also, in this case the changes in the global wavefield can be well detected (Fig. 4). Again, delamination at position 2 and 3 are clearly detected while position 1 is barely visible. This leads to the observation that the influence on the wave field at this location is smaller than at the other positions and further investigation in the future might show why this is the case. In this paper, however, the focus will continue to be on the signal processing techniques that are able to detect the changes in signals due to delamination at different positions. Damage Detection Based on Auto- and Cross Correlation Vectors. There is a certain disadvantage using NSFD; the time needed to compute DIs is increasing drastically using multiple sensors and an increasing number of timesteps for the

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Fig. 3. Results of NSFD algorithm using all sensors; excitation at T6.

Fig. 4. Results of NSFD algorithm only using the sensors T9-12.

computation of the Hankel-Matrix. To make live monitoring more easily another algorithm was derived demanding less computational time. Here, the vectors of the auto- and cross-correlation functions are computed in the same way as for NSFD. The length of the resulting vectors corresponds to the number of selected time shifts, while the number of vectors corresponds to the number of possible sensor combinations. Since there are 11 sensors 66 differing combinations of sensors are possible and 15360 time shifts have shown to deliver good results. The resulting 66  15360 matrix can be computed for every measurement and another DI can be calculated by comparing these matrices. An easy way to do so is to calculate the correlation coefficient for corresponding columns and then average the 66 results to get the DIcorr shown in Fig. 5. Using the correlation coefficient of course means that the DIcorr should decrease if structural changes occur. The advantage is of this approach is decreased computation time because no singular value decomposition has to be calculated and accordingly more

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Fig. 5. Results using the algorithm based on correlation vectors; all sensors; excitation at T6.

timesteps can be used and live monitoring would be easier. The downside is reduced sensitivity and clarity of DIs. Further Signal Processing Based on Simple Spectra Analysis. The presented algorithms are designed to detect changes in the structural behavior and therefore in the resulting wavefield of the monitored object. Knowledge about the type of change is crucial for effective use of the algorithms. This part is focusing on further signal processing to investigate what type of change is detected in the signals. There are countless possibilities to analyze stochastic signals, but in this case frequency analysis delivers promising results. The first step to compare the different states of the simulated plate was to compute the power spectral density for one signal in every simulated measurement. In this case the signal received at T7 while excitation took place at T6 was picked as an example. Knowing that measurements 1–5, 6–10, 11–15 and 16–20 belong to the 4 different states they were averaged to get four respective spectra to compare with each other (Fig. 6).

Fig. 6. Example of PSD: Averaged for each structural state.

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A closer look at the spectra leads to the thesis that the main difference in the spectra are to be found between 1 MHz and 3,5 MHz, and again the changes for delamination two and three are much easier to identify. To test this the correlation coefficient of the spectra is computed in this area. The following figure shows the results for the correlation coefficient averaged for all received signals with excitation at T6 (Fig. 7).

Fig. 7. Correlation coefficients: Comparing spectra averaged for each structural state.

A gap between measurement 9 and 10 can be identified not only in this example with excitation at T6, but for all actuator-sensor combinations. This leads to the conclusion that mainly this change in the spectra of the signals is detected by the algorithms used in this paper. Of course, other signal processing techniques have been used on the simulated data. Techniques known from classic guided waves SHM based on burst signals couldn’t detect the damage due to the stochastic signals. The change in RMS- values also is not sufficient to detect delamination, even if it is calculated in the frequency range between 1 MHz and 3,5 MHz.

4 Conclusion and Outlook The NSFD as well as the algorithm based on auto- and cross-correlation vectors are able to detect the structural changes in the simulated plate resulting from delamination at position two and three while position one is hardly visible. Further signal processing leads to the conclusion that small shifts in the power spectral density of the signals lead to the detection of the delamination. The simulations providing the necessary signals to test the algorithms run stable and are a powerful tool to optimize various features of the SHM-system: For example, number and position of the transducers or signal range of excitations signals. Furthermore, different kinds and positions of damage can be simulated in the future and the sensibility of damage detection algorithms can be tested. Of course, further investigation has to be done regarding the mentioned discrepancies between simulation and reality, namely by testing the algorithm with experimental data

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containing information about delamination. In addition, it needs to be tested if ongoing stochastic excitation shortens the lifetime of respective transducers. Another downside, as already mentioned, is the high sensitivity of the NSFD for changes in the environmental conditions (EOCs). Possibilities to differentiate between damage and changes in EOCs need to be found or clustering might be one possibility. A promising approach is the combination of different approaches to compensate the lack for EOCcompensation and damage localization. Acknowledgments. The investigations took place within the framework of the R&D project KamoS and were kindly funded by the German Federal Ministry for Economic Affairs and Energy (project executing organization: German Aerospace Center, DLR) under the number 20Q1725D. Pawel Kudela would like to acknowledge the Polish National Agency for Academic Exchange for the support in the frame of the Bekker Programme (PPN/BEK/2018/1/00014/DEC/1). Jochen Moll acknowledges the supported by the Federal Ministry for Economic Affairs and Energy (grant no 03SX422B).

References 1. Moll, J., Kexel, C., Pötzsch, S., Rennoch, M., Herrmann, A.S.: Temperature affected guided wave propagation in a composite plate complementing the Open Guided Waves Platform. Sci. Data 6(1), 1–9 (2019). https://www.nature.com/articles/s41597-019-0208-1.pdf 2. Brettschneider, J., Kraemer, P., Moll, J.: Alternative excitation and data analysis techniques for damage detection in metallic plates. In: The 12th International Workshop on Structural Health Monitoring, Stanford, California, USA, 10–12 September 2019. DEStech Publications, Inc., Lancaster, Electronic product, pp. 766–774 (2019) 3. Chang, Y., Yuan, F.-G.: Damage detection and localization via cross-correlation on metallic panels under ambient loading. In: The 12th International Workshop on Structural Health Monitoring, Stanford, California, USA, 10–12 September 2019. DEStech Publications, Inc., Lancaster, Electronic product, pp. 1975–1983 (2019) 4. Sabra, K.G., Srivastava, A., Lanza di Scalea, F., Bartoli, I., Rizzo, P., et al.: Structural health monitoring by extraction of coherent guided waves from diffuse fields. J. Acoust. Soc. Am. 123(1) (2007). https://asa.scitation.org/doi/pdf/10.1121/1.2820800 5. Ostachowicz, W.M.: Guided waves in structures for SHM. The time-domain spectral element method. Wiley, Chichester, p. 337 (2012) 6. Kudela, P.: Parallel implementation of spectral element method for Lamb wave propagation modeling. Int. J. Numer. Methods Eng. 106(6), 413–429 (2016). https://doi.org/10.1002/nme. 5119 7. Kudel, P.: Vectorization of the code for guided wave propagation problems. In: EWSHM 2020, Palermo (2020) 8. Moll, J., Kathol, J., Fritzen, C.-P., Moix-Bonet, M., Rennoch, M., et al.: Open Guided Waves: online platform for ultrasonic guided wave measurements. Struct. Health Monit. 18(5–6), 1903–1914 (2019). https://doi.org/10.1177/1475921718817169 9. Basseville, M., Abdelghani, M., Benveniste, A.: Subspace-based fault detection algorithms for vibration monitoring. Automatica 36 (2000)

Structural Event and Damage Diagnosis in a Composite Fuselage Structure Alejandro S´ anchez S´anchez(B) , Santiago Guerrero V´ azquez, Patricia D´ıaz-Maroto Fern´ andez, Jaime Garc´ıa Alonso, Antonio Mu˜ noz Chamorro, Manuel Iglesias Vallejo, and Daniel I˜ nesta Gonz´alez SHM & Structural Integrity Department, Airbus Defence and Space S.A.U., 28906 Getafe, Spain [email protected]

Abstract. Nowadays the composite material is becoming essential for aerospace structures due to the weight reduction. The lightweight composite structures bring with some problems like accidental damage. The support of Structural Health Monitoring (SHM) systems in overall Structural Integrity (SI) management in those cases is in continuous growth. Airbus Defence and Space is developing a SHM System able to diagnose acci-dental events/damages during aircraft operation to provide additional information for maintenance program application. In this paper it is presented the evaluation of this SHM system for a composite fuselage cockpit structure in the diagnosis of mechanical impacts (events) and resulting damages. The system has been applied to composite complex and large structure using the background knowledge of previous studies on composite reinforced flat panels. An analytic elastic wave propagation 2D/3D model for composite material structures supports the event and damage diagnosis. The results indicate the particularities of SHM diagnosis for events and damages in a real composite structure.

Keywords: Event diagnosis Usage monitoring

1

· Damage diagnosis · Elastic waves ·

Introduction

Composite materials have been gradually introduced in aerospace sector and are gaining market as primary structure material, providing confidence and implying a structural weight reduction. Besides, composite material presents better fatigue and corrosion properties than e.g. aluminium alloys [2]. Structural degradation in composite material is generally induced by accidental damages produced by different events, impacts, overloads, buckling [9], overheats and lightning strikes. The loose of mechanical properties, delamination appearance and debonding of reinforcement elements are the result of these events on the material. c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 798–808, 2021. https://doi.org/10.1007/978-3-030-64594-6_77

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The aircraft structural integrity is assured taking into account the previous considerations, but usually deriving in conservative designs and therefore increasing the weight of the aeronautical structures. In order to optimize the aircraft weight, while maintaining the safety levels, Airbus Defence & Space (ADS) is developing an innovative SHM system which is able to diagnose events and damages regarding aircraft structural integrity. This SHM system will constantly be working, providing information and warnings about operational faults and pointing out the real integrity status condition of the structure. The SHM system is an evolution of the usage monitoring systems already in use in ADS products [6], which detects the hazardous situations using indirect measurements such as aircraft flight parameters and structural operational loads (fatigue monitoring, opera-tional monitoring [12]). This article gives an overview of the event and damage diagnosis methodology implemented in the SHM system. It is mainly supported by Elastic Waves (EW) 3D propagation analytical model applied to a fuselage cockpit structure and by means of piezoelectric sensors measured data (PZT sensors). The elastic wave propagation analytical model is developed for non-plane arbitrary geometries and supports the diagnosis path followed (detection, location, assessment and prognosis [13]) for each event or damage. The fuselage cockpit demonstrator in which the SHM system is installed is shown in Fig. 1.

Fig. 1. Fuselage cockpit demonstrator.

2

SHM System Description

The SHM system encompasses a series of global objectives cited subsequently but only the impact’s events and damages diagnosis methodologies are within the scope of this article. The main characteristics of the system are the following:

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– The SHM system have been tested on a hybrid fuselage cockpit demonstrator which is made of carbon fibre and aluminium, see Fig. 1. – SHM system goals within the scope of this article are impact’s events and damages diagnosis. The SHM system is intended to diagnose other events, such as: overload, buckling, overheat, lighting strike, etc. [4] but their description are out of the scope of the current paper. – The sensitive feature used in impact’s events and damages diagnosis comes from the generated elastic waves in the structure. They can be passively captured by the PZT sensors (after impact) or can be produced by the PZT sensor during active interrogations of the structure. – The SHM system is supported by a multi-type sensor network which has been installed on the cockpit structure. The multi-sensor network consists of: piezoelectric sensors, distributed fibre optic sensors, strain gauges, temperature sensors, accelerometers, etc. [1]. In impact’s events and damages diagnosis tasks, only the PZT sensors are used. – The data acquisition (DAQ) system is able to acquire the data from the multitype sensor network and also to produce the active interrogations. Moreover, the calibrated hammer data is also acquired by the same DAQ. – The SHM system analyses the acquired experimental data from the multitype sensor network and feed the elastic wave propagation model to cover the impact and damages diagnosis.

3

SHM Sensor Installation

The multi-type sensor network installed in the hybrid fuselage cockpit includes: – – – – –

Piezoelectric sensors (PZT sensors). Distributed Fibre Optics Sensors (DFOS). Accelerometers. Strain Gauges. Temperature Sensors.

The impact’s events and damages diagnosis is performed using the data by the PZT sensors which are distributed in the cockpit through a sparse (for impact event diagnosis) and dense PZT sensor network (for impact damage diagnosis). The following Fig. 2 shows the whole SHM sensors installation on the cockpit.

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Fig. 2. Multi-sensor network installed in the cockpit.

4

SHM System Testing

The impact’s event and damage diagnosis is tested through mechanical impacts of several energies. Two different kinds of impact are produced: – Very low energy impacts hit by a hammer aiming event diagnosis. The hammer is configured with three masses, carrying out the impacts with all of them and being recorded synchronously with the whole PZT sensor network. – Low energy impacts are developed through a Compression Air Mobile Impact Device (CAMID) aiming damage diagnosis. These impacts are performed in a specific area of the cockpit where a dense piezoelectric network is recording during the test. The structure is interrogated by the DAQ system –using the active mode– before and after the impact is carried out in order to get several state conditions of the structure.

5

Data Analysis

This Section explains the data treatment process from the raw data to the final event and damage diagnosis results. Including the following steps: – – – – – – –

Obtaining the data (raw data). Pre-processing raw data. Impact detection. Computing elastic waves’ speeds using impact detection data. Impact location. Damage detection. Damage location.

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Raw Data

The raw data comes from the acquired data of the mechanical impacts test devel-oped either by hammer or CAMID. Each test presents particularities and is explained separately. – Very low and low energy impact passive raw data: The hammer and piezoelectric signals are synchronously recorded. An example of the hammer and PZT sensor acquired signal is shown in the Fig. 3. Hammer & PZT Sensor Signal Raw Data

Amplitude [volts]

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PZT Sensor Signal Hammer Signal

3 2 1 0 2630.395

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2630.405

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Time [seconds]

Fig. 3. Very low energy impacts developed by hammer.

– Low energy impact damage active interrogation raw data comes from the different active interrogations completed with a combination set of PZT sensors. The active interrogation is developed through the pitch-catch scheme [3] in which one PZT sensor acts as an actuator while the others are listening to it. Then the data is clustered into the dif-ferent state conditions for each impact, PZT sensor paths and interrogation frequencies. The Fig. 4 shows the acquired data in two different state conditions for low energy impact derived damage.

Amplitude [volts]

Low energy impact test Reference Signal Damaged Signal

500

0

-500 5

6

7

8

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Fig. 4. Low energy impact test, two state conditions signal through a path.

5.2

Pre-processing Raw Data

The raw data must be pre-processed in order to adapt it to be used in detection and location algorithms. It includes errors detection and correction, filtering, resampling, synchronization, i.e. all modifications applied to the raw data in order to be ready and adapted for the analysis.

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The passive impact data is separated by an algorithm which detects each impact based on the hammer signal and clusters it based on the following constraints: cockpit zone, hammer mass, impact number and hammer and PZT sensor data. The PZT sensor and hammer passive data present variable noise levels that have been reduced applying a filter depending on the noise characteristics level as is shown in the Fig. 5a for hammer signal and Fig. 5b for PZT sensor signal: Hammer Signal Detection

Filter Applied to PZT sensor Acquired 0.08

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22.445

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123.35

123.4

123.45

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(a) Filtered Hammer Signal.

(b) Filtered PZT sensor Signal.

Fig. 5. Effects of filtering.

Moreover, the hammer and PZT sensor signals are corrected and conditioned, recon-structing the saturated zones and outliers values. The active interrogation raw data is already clustered by paths and frequencies when is acquired. However, the active interrogation data is normalized with the excitation signal sent by the actuators and therefore the data is able to be compared between paths. The Fig. 6 shows the data acquired by the sensor for a path, the cross-talk and the analysis window.

Fig. 6. Pre-processing active interrogation raw data.

5.3

Impact Detection

The impact detection is achieved when an event flag is warned. The impact detection algorithm finds sudden changes in the signals based on their variance.

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It is the key step of the event diagnosis since the following steps depends on the impact detection quality. The following Fig. 7 shows the impact detection in the PZT sensor signal.

Amplitude [volts]

PZT sensor Signal Detection PZT Sensor Signal Acquired Detected PZT Sensor Signal

0.1 0.05 0 -0.05 -0.1 2362.3

2362.35

2362.4

2362.45

2362.5

2362.55

2362.6

Time [seconds]

Fig. 7. PZT sensor signal acquired aiming impact detection.

5.4

Computing Elastic Waves Speeds

The elastic wave propagation characteristics differ depending on the directional properties, material properties, structure geometry and the elastic wave source characteristics (impact-passive or active interrogation). Using that information, a propagation model can be generated providing the waves speeds in a given specimen (the dispersion curves). The dispersion curves knowledge will allow tracing the evolution of the wave from the source point to a distance based on the propagation velocity. The source point can be an event or a piezoelectric sensor which is used either for impact location purposes or active interrogation (damage detection purposes). The dispersion curves are calculated for an anisotropic composite material structure using a computational method based on the Transfer Matrix Method [5]. In addition, a wave propagation analytical model is developed for a 3D arbitrary geometry applied to the fuselage cockpit as is shown in the following Fig. 8:

Fig. 8. Guided elastic waves propagation in fuselage cockpit demonstrator.

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This model is feed with the impact detection or damage detection obtained values from the PZT sensor network to support the impact/damage location. 5.5

Impact Location

The impact location is carried out thanks to the elastic wave’s propagation analytical model through a point cloud geometry loaded with an internally developed tool [1], from an arbitrary CATIA IGES file. The impact is located with a combination of 3 PZT sensors using the trilateration methodology. Hence, three elastic wave propagation models are launched in order to get the final location as can be seen in the following Fig. 9 through red points:

Fig. 9. Impact location through trilateration methodology.

5.6

Damage Detection

The damage detection is performed using Damage Index (DI) value for each damage path (the line between the PZT actuator and PZT sensor in an active interrogation) comparing the DI values in two different moments, the undamaged state condition against the damaged one (i.e. after impact warning). The DI reflects the signal degradation effects when the elastic waves cross a damaged area during the active interrogation. The Fig. 10a shows the DI values for a frequency sweep and the data fitting to a log-normal distribution which points out the damage detection warning is shown in the Fig. 10b. 5.7

Damage Location

After damage detection confirmation, the damage location algorithm is launched and locates the damage in the structure, using the DI values of each path, creating a DI map using contour level lines and considering the 3D wave propagation model in the specimen [8]. The low energy impacts are developed in certain small cockpit areas which can be considered as flat areas and therefore the 3D propagation model will give the same result as the 2D one. The Fig. 11 shows the low energy impact damage location.

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Fig. 10. Damage detection methodology.

Fig. 11. Damage location.

6

Results

The acquired signals with high noise levels must be filtered in order to ensure the whole event diagnosis methodology and not cause detection errors. The impact detection outcomes and elastic wave velocity calculations feed the elastic wave propagation model in the fuselage cockpit structure helping the impact location. Event diagnosis is the starting point to confirm the damage presence and damage diagnosis.

7

Conclusions and Way Forward

The pre-process of data is crucial in order to increase the reliability of the future results. The event and damage diagnosis results must be supported by additional model, whether analytical or FEM models [7,10].

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The environmental and operational conditions (EOC) can affect drastically to the propagation of the elastic waves and therefore could cause errors in event and damage diagnosis [11]. Moreover, these effects produce changes in the baseline geometry used by the 3D elastic wave propagation model. Thus, the EOC effects must be compensated in order to increase the reliability of the SHM system. The EOC effects are treated in [1]. Acknowledgements. Activities reported in this paper were developed in the frame of the European Com-munity Seventh Framework Program, where Airbus Defence and Space S.A.U. was partner of the Clean Sky Green Regional Aircraft Integrated Technology Demonstrator.

References 1. D´ıaz-Maroto Fern´ andez, P., Guerrero V´ azquez, S., Garc´ıa Alonso, J., Miguel Guiraldo, C., Iglesias Vallejo, M., I˜ nesta Gonz´ alez, D.: Dynamic distributed fiber optic sensing for environmental and operational aircraft monitoring. In: EWSHM 2020 (2020) 2. Holmes, M.: Aerospace looks to composite for solutions. Reinf. Plast. 61(4), 237– 241 (2017). Elsevier 3. Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning. Wiley, Hoboken (2013) 4. Cano, F., Garc´ıa Alonso, J., Men´endez, J., Tavares, H., Khamlichi, A., Ram´ırez, A. Fern´ andez, A., Aguilar, C.: Lightning test on a full-scale hybrid (composite-metal) demonstrator cockpit with integrated structural health monitoring systems. In: International Conference on Lightning and Static Electricity (ICOLSE), Wichita, Kansas, USA (2019) 5. Kamal, A.: Comparative study of several methods for the calculation of ultrasonic guided waves in composites. In: 54th Structural Dynamics and Materials Conference, Boston, Massachusetts, USA (2013) 6. G´ omez Escalonilla, J., Garc´ıa Alonso, J., Andr´es Sosa, M., Armijo Torres, J.: Strain predictions using artificial neural networks for a full-scale monitoring system. In: AIAC-13 Thirteenth Australian International Aerospace Congress (2013) 7. S´ anchez Iglesias, F., Tejerina, R., Garc´ıa Alonso, J., Caffyn, P., Iglesias Vallejo, M.: Elastic waves simulation on aircraft subcomponent for test correlation using piezoelectric sensors. In: 8th EWSHM, Bilbao, Spain (2016) 8. Lozano Mart´ın, A., Marcos Garc´ıa, R., Garc´ıa Alonso, J., Fern´ andez L´ opez, A., Iglesias Vallejo, M., Guemes, A.: Material elastic waves test exploitation in benefit of composite structure health monitoring. In: 8th EWSHM, Bilbao, Spain (2016) 9. D´ıaz-Maroto, P., Guemes, A.: Buckling detection of an omega-stiffened aircraft composite panel using distributed fibre optic sensors. Thin-Walled Struct. 132, 375–384 (2018) 10. Osl´e Dorremochea, E., S´ anchez Iglesias, F., Garc´ıa Alonso, J., Tejerina, R., Iglesias Vallejo, M., Lorente, L., Garc´ıa-Etxebarria, J., Alonso-Fern´ andez, D.: Numerical simulation and improved correlation techniques for low energy impact detection and damage characterization with elastic waves on composite material. In: NAFEMS World Congress, Stockholm, Sweden (2017)

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11. Garc´ıa Alonso, J., Fern´ andez L´ opez, A., Gonz´ alez Requena, I., Guemes, A.: Environmental effect compensation for damage detection in structures using artificial neural networks and chirplet transform. In: 8th EWSHM, Bilbao, Spain (2016) 12. Perspectives on Integrating Structural Health Monitoring Systems into Fixed-Wing Military Aircrafts. SAE International. AIR6245, Issued November 2018 13. Rytter, A.: Vibrational based inspection of civil engineering structures. Ph.D. thesis (1993)

The Potential of Ultrasonic Edge and Lamb Waves Propagating in Laminates to Detect Defects Near an Edge and Weakened Adhesion Zones Mikhail V. Golub1(B) , Maria Wilde2 , Artem Eremin1 , and Olga Doroshenko1 1

Kuban State University, Krasnodar, Russian Federation m [email protected], {eremin a 87,oldorosh}@mail.ru 2 Saratov State University, Saratov, Russian Federation mv [email protected]

Abstract. The application of guided waves for detection of partially debonded interfaces or zones of imperfect contact between sub-layers and defects near an edge (surface-breaking defect) in laminate thin-walled composite is considered here theoretically and experimentally. The effect of imperfect contact between sub-layers of a specimen on dispersion and amplitude properties of elastic edge and Lamb waves propagating in a laminated composite structure is analysed. Edge wave interaction with a surface-breaking defect at the edge of the plate has been experimentally and numerically investigated. It is demonstrated that the amplitudes of edge waves caused by reflection and scattering of guided waves are high enough to be employed in NDE and SHM.

Keywords: Guided waves contact · Scattering

1

· Edge waves · Delamination · Imperfect

Introduction

Ultrasonic elastic waves are widely employed for inspection of laminate thinwalled composite structures [10]. The accuracy of defects’ detection might be improved if the physics of wave scattering and diffraction by the damages is clarified beforehand [9]. In the current study, wave interaction with two kinds of defects is considered both theoretically and experimentally. The latter are partially debonded interfaces or zones of imperfect contact between composite sub-layers and defects near an edge, e.g. surface-breaking defect/crack. The effect of an imperfect contact on dispersion and amplitude properties of guided waves (GWs) propagating in a laminated composite structure is analysed. The distributed spring model is employed to simulate partially damaged interfaces. For this purpose, analytic frequency-dependent expressions for spring stiffnesses c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 809–818, 2021. https://doi.org/10.1007/978-3-030-64594-6_78

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derived in [2,3] using the ensemble average technique and the boundary integral equation method are used here. Several layered specimens with weakened adhesion at interfaces have been manufactured and the frequency-wavenumber analysis shows that the distributed spring model predicts similar changes in dispersion properties as estimated from the experimental data [4]. Edge waves (EWs) represent another type of GWs in thin-walled structures. Physically they are similar to Lamb waves but strongly localized in the vicinity of edges [12,13]. This feature allows them to reveal a near-surface defect. EWs interaction with a surface-breaking defect at the edge of the plate is experimentally and numerically investigated. It is demonstrated that the amplitudes of the waves caused by reflection and scattering of EWs are high enough to be employed in methods of non-destructive evaluation and structural health monitoring.

2

GWs in a Laminate with Damaged Interface

Let us consider a laminate composed of two dissimilar isotropic plates Vj , j = {1, 2} of thickness hj with Lame constants λj , μj and mass density ρj as shown in Fig. 1. Partially debonded interface can be modelled as a distribution of microcracks or via spring boundary conditions (SBCs) [2–4]. Normal (s = N ) and tangential (s = T ) spring stiffnesses, which define the severity of damage, can be represented as follows fs ic1s k1s c2s k2s fs . (1) − , fs = 2 c1s k1s + c2s k2s C(p s · u s )   Here cjT = μj /ρj and cjN = λj + 2μj /ρj (j = 1, 2) are the transverse and longitudinal wave velocities, kjs = ω/cjs are wavenumbers, ω = 2πf is the angular frequency, f is the frequency, C is a crack density, p s is a unit wave vector, and u s is the average of the crack opening displacement (COD). κs =

Plate (Material 1)

h2 x3 x2

h1

Plate (Material 2)

Transducer

Interface (damaged/perfect)

x2

x1

x3

Surface-breaking defect 100 mm 30 mm

Fig. 1. Geometry of the problem.

H x1

d mm

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811

Relying on asymptotic analysis in the frequency domain and assuming a certain shape of micro-cracks, approximate analytical representations for SBCs could be derived [2,3]. The asymptotic expressions for the CODs are specified in [2] and [3] for circular or square cracks respectively. The substitution of asymptotic average COD into (1) results in a closed-form relations for tangential κT and normal κN spring stiffnesses. In the case of a distribution of identical circular cracks with radius a, the spring stiffnesses can be expressed in terms of the circular frequency ω as follows (C)

κT (ω) = (C)

T 2 2 T 4 4 T 6 6 π bT 0 + b1 a ω + b2 a ω + b3 a ω , T T T 2 2 4 280Ca c0 + c1 a ω + c2 a ω 4

(2)

N 2 2 N 4 4 bN 1 0 + b1 a ω + b2 a ω . N N 2 240Caπ c0 + c1 a ω 2

(3)

κN (ω) =

Coefficients bsk and csm can be found in [2]. In the case of the distribution of identical square cracks of a side a, the following stiffnesses are obtained: (S)

 16 ξT  (1) (2) 2 (d , + d )G (1) + 2(t + t )G (1) · (aω) 1 2 1 2 T T Caπ 3.5  8 ξN  (S) (1) (2) 2 κN (ω) = d , G (1) + 2t G (1) · (aω) 2 4 L L Caπ 3.5

κT (ω) =

(k)

(4) (5)

where Meijer G-function Gs (x) and parameters dk and tm can be found in [3]. The spring stiffnesses for a distribution of identical square cracks possess the correction factors ξs arising due to the deviation of asymptotic COD from the numerical solution (it is smaller than 0.5% − 4% for the majority of material pairs). The correction factors are well approximated by regressions on the material constants of two media. In the case of the debonded interface between two identical media, the difference in spring stiffnesses for square and circular micro(C) (S) (C) (S) cracks is about 5%: κT (0)/κT (0) ≈ 0.950734, κN (0)/κN (0) ≈ 0.949235. For various heterogeneous media these ratios are bouncing near 5% discrepancy as well. Therefore, it is concluded that the basic micro-crack shape only slightly affects the values of the derived spring stiffnesses. The applicability of the SBCs for damage interface simulation was demonstrated experimentally in [4]. In the present paper, the dispersion properties of GWs propagating in a two-layered plate made of steel (λ1 = 112.5 GPa, μ1 = 75 GPa, ρ1 = 7900 kg/m3 ) and glass (λ2 = μ2 = 27.66 GPa, ρ2 = 2770 kg/m3 ) of the same thicknesses h1 = h2 = 4 mm are investigated. Partially damaged interface between the layers is simulated employing SBCs according to the theory presented above. Spring stiffnesses in normal and both tangential directions x1 and x3 are assumed to be equal and independent on the frequency. In the case under consideration, the 3D dispersion equation for modes of an infinite plate allows decomposition into two independent ones. The first one defines Lamb waves (LWs), and the second one is the dispersion equation for SH-waves.

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Slowness, s/m

=∞  = 500 GPa/mm  = 50 GPa/mm

0 0.0

1.6

3.2

4.8

Lamb waves

6.4

Edge waves

8.0

SH-waves

Frequency∙thickness, MHz∙mm

9.6

Fig. 2. Slownesses of GWs propagating in a two-layered plate (glass and steel of the same thicknesses) with a damaged interface simulated via the SBC.

The slownesses of LWs and SH-waves are shown in Fig. 2. One can see that the sensitivity of these waves to the interface damage varies depending on the frequency and the number of the mode. SH-waves split into two alternating sub-families. First of them is nearly insensitive to damage, the second one exhibits good sensitivity growing with the mode number. The behaviour of LWs is more complicated. Generally, higher modes are more sensitive to the damage, although there are frequency ranges where the sensitivity decreases. The fundamental modes in the low-frequency range (much lower than the first cut-off frequency) are practically insensitive to the weakened adhesion of considered severity, whereas the effect of the damage is pronounced at f H ∈ (1.5, 4), especially for the fundamental symmetric LW S0 . Two next LWs also show good sensitivity in this region. Since being a family of GWs in plates as well, EWs are potentially suitable for detection of weakened adhesion zones and delaminations in the border regions of the plate. In the case of a homogeneous plate, EWs are thoroughly studied. The existence of the EW-family containing two fundamental waves and infinitely many high-order waves is established theoretically and experimentally (see [12, 13] and references therein). The studies of EW in laminates are, to our best knowledge, restricted to the long-wave case and carried out on the basis of 2D plate theories, which reduce the laminate to a homogeneous plate with some effective stiffnesses [1,8]. In this work, the 3D theory of elastodynamics is used to study EWs in a semi-infinite two-layered plate of glass and steel with the parameters stated above. The statement of the problem and the method of the solution are analogous to those in [12]. The calculated slownesses of EWs are shown in Figs. 2 and 3a.

The Potential of Edge and Lamb Waves to Detect Edge Defects

a)

Slowness, s/m

380

=∞  = 50 GPa/mm

Attenuation, kHz

320 20

813

LW SH EA0 ES0 EA0.5 ES0.5

b)

0 0.0

3.2

6.4

9.6

12.8

Frequency, MHz

Fig. 3. Dispersion properties of four lowest EWs in a two-layered plate with damaged interface simulated via the SBC.

Comparison of dispersion curves for two-layered plate with those for an aluminium plate (see [12,13]) shows that their behaviour is similar in general. Thus, the notations EAl , ESl used for the homogeneous plate can be also applied here. The attenuation of EWs is shown in Fig. 3b. This effect is caused by the radiation of the energy transferred into the interior of the plate due to the coupling of EWs with propagating Lamb and SH-modes. The attenuation of the remaining EWs shown in Fig. 2 is of the order of that of ES0.5 or higher. It is typical for edge modes with attenuation that their dispersion curves split into branches because of the intersection with the cuts in the complex plane, associated with propagating Lamb and SH-modes. Some branches with high values of attenuation were omitted from Fig. 2, since they are of no practical importance. To analyze the eigenforms of EWs, the plots of normal displacement u2 on the surfaces are presented in Fig. 4. In Fig. 5, the absolute values of normal displacements u3 on the edge  are shown. In both figures, the eigenforms are normalized by max |u2 |x3 =0 . High-order edge modes ES0.5 , EAk , ESk (k = 1, 2, . . .) are highly sensitive to the weakening of the adhesion, but a strong attenuation makes their observation challenging. For the fundamental EWs, the main difference with the case of a homogeneous plate consists in the attenuation of the mode ES0 . It can be explained as an effect of bending-extension coupling in the unsymmetric laminate. Unlike its velocity, the attenuation of ES0 is quite sensitive to the damage of the interface (see Fig. 3b). The dispersion properties and the influence of the

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Fig. 4. Eigenforms of EWs: normal displacement on the surfaces.

interface damage for wave EA0 are analogous to those for fundamental antisymmetric Lamb wave A0 . Comparison of the wave forms in Fig. 4 and 5 shows that wave EA0 is better observed on the steel surface, whereas the observation of ES0 is preferable on glass. On the other hand, the amplitudes of the propagating wave coupled with ES0 is larger in steel and it is sensitive to interface damage. The most promising for the detection of an interface damage is wave EA0.5 , for which the considerable lowering of the attenuation at κ = 50 GPa/mm is revealed (see Fig. 3b). Moreover, this wave becomes non-attenuating at higher frequencies. Due to this property, the weakening of the adhesion will manifest itself as arising of a new wave in the observed wave-field. It should be also mentioned that the glass surface is more preferable for the observation of this EW. As the frequency grows, waves EA0 and ES0 tend to become wedge waves localized near the steel wedge (EA0 ) or the glass wedge (ES0 ) (see Fig. 5). This fact explains why the damage of the interface does not influence the velocity of these waves at higher frequencies (see Fig. 3a). But for wave EA0.5 the dispersion curves for perfect and weakened adhesion can be still distinguished at high frequencies, since its form tends to the one localized near the interface. This property is could be useful for detecting weakened adhesion zones and delaminations near the edge of a laminate.

The Potential of Edge and Lamb Waves to Detect Edge Defects =∞ 0.0

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 = 500 GPa/mm

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Fig. 5. Eigenforms of EWs: normal displacement on the edge.

3

Scattering of Edge Waves by a Surface-Breaking Crack

In the case of a homogeneous plate, the scattering of ES0 by a crack of the increasing length d breaking the edge surface x3 = 0 (see Fig. 1) was investigated experimentally in [5] for a plate-like aluminium sample of the thickness 4.85 mm. In the experiment, defect’s depth d ∈ { 0.0 mm, 0.5 mm, 1.0 mm, 1.9 mm, 3.0 mm}, while GW were excited by thin piezoelectric transducers adhered to a surface or an edge of the specimen. The actuator was driven with a transient voltage in the form of a narrow-band Hann-windowed five-cycle sine burst, which spectrum is concentrated near its central frequency f0 . Analysis of the data acquired by Laser Doppler vibrometry shows that the surface-breaking defect is distinguishable due to reflected EWs, whereas the intensity of reflected EW is increasing with frequency. Based on these data, one can make some theoretical predictions for a laminate. From the practical point of view, it is important to study the problem in the case of low frequency-thickness product, since for a thin plate it can be the only range available for the measurements. The theoretical model used here is based on the classical laminated plate theory [6]. The comparison with the numerical solution of the 3D problem shows that for wave ES0 in the low frequency range the bending-extension coupling may be neglected. Thus, the problem is reduced to 2D one for plate extension. In order to calculate the field of the EW excited by given edge load the model proposed in [7] is used. In the case of a short crack, it is natural to suppose that the incident wave induces only normal stress on the sides of the crack, uniformly distributed along its depth. The solution annihilating this residue stress is assumed to be a sum of EWs and a quasi-static component. The latter is obtained via the classical solution for the crack in an infinite plate under uniform static pressure [11]. This solution gives a residue stress on the edge of the plate. To annihilate the latter, EW field is determined, which provides the reflected wave and the difference between incident and transmitted waves. By equating the sum of the residues on the sides of the crack induced by the incident and reflected waves to

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the unknown pressure in the quasi-static problem, one can find the expression for the latter. This pressure defines both reflected and transmitted EW. Out-of-plane velocities, mm/s -0.6

0.6

Time, s

100 75 50 25

50

100

150

200

250

Distance, mm

50

100

150

200

250

Distance, mm

Fig. 6. Measured (left) and calculated (right) out-of-plane velocities excited in the aluminuim plate with 3.0 mm surface-breaking crack at f0 = 100 kHz.

In Fig. 6, the measured velocities are compared with theoretically predicted ones. One can see that even for such a relatively small defect (the ratio to the wavelength d/L ≈ 0.1) the reflected EW and the position of the crack can be distinguished in the experimental wave-field, and also that the theoretical model allows to predict the amplitude of the reflected wave. For SHM, it is important to investigate the possibility of detection of the defect by a sensor placed in a fixed point. Let the observation point be between the actuator and the crack, e.g. at x1 = 38 mm. In Fig. 7a, the spectra of experimental velocities measured in this point at f0 = 100 kHz are shown. For the spectra evaluation, only the interval of time between the moments of the passing of the incident wave and the arrival of the waves reflected by the other edges of the plate is used. The amplitudes are normalized by the maximal amplitude in the spectrum of the incident wave. Corresponding theoretical predictions are given by Laplace transform of the reflected wave. In the signal measured on the pristine plate, none of the frequencies can be particularly distinguished, so it represents the noise level. For the plate with the defect of the depth 1.9 mm (d/H = 0.4), the reflected wave still cannot be clearly distinguished. However, for the depth d = 3.0 mm (d/H = 0.6), the peak at a frequency near 100 kHz can be seen. It could be readily associated with the presence of a wave reflected by the crack. The height of the peak coincides well with the one predicted by the model. The calculations for deeper cracks (see, e.g., the result for d/H = 0.8) show that the amplitude of the reflected waves grows, so one may conclude that cracks of the depth above 0.6 H can be detected at this frequency. In Fig. 7b, the theoretical predictions at f0 = 60 kHz are shown for the two-layered plate considered in the previous section. Assuming the noise level to be the same as in Fig. 7a, we can expect that the surface-breaking crack

The Potential of Edge and Lamb Waves to Detect Edge Defects

a) aluminium plate

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Theoretical: d/H=0.4 d/H=0.6 d/H=0.8

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0.2

0.4

0.6

0.8

Frequency∙thickness, MHz∙mm

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Fig. 7. Spectra of the signals measured in the point x1 = 38 mm in the interval of time containing only waves reflected by the crack and theoretically predicted for the homogeneous plate (a); theoretical prediction of the same quantity for the two-layered plate (b).

of the depth above d = 0.6 H and even less can be detected in the two-layered plate by the analysis of the reflected waves.

4

Conclusion

Partially debonded interfaces or zones of imperfect contact between composite sub-layers and cracks near an edge (surface-breaking crack) are considered theoretically and experimentally. It is shown that GWs propagating in a laminate with a damaged interface change their dispersion properties. EWs, which are strongly localized in the vicinity of edges, are promising for revealing nearsurface defects and could be employed in ultrasonic-based NDE and SHM. The research was carried out with the financial support of the Russian Foundation for Basic Research (projects 20-01-00673 and 19-41-230012) and the Administration of Krasnodar Region (joint project 19-41-230012).

References 1. Fu, Y.B., Brookes, D.W.: Edge waves in asymmetrically laminated plates. J. Mech. Phys. Solids 54, 1–21 (2006) 2. Golub, M.V., Doroshenko, O.V.: Effective spring boundary conditions for modelling wave transmission through a composite with a random distribution of interface circular cracks. Int. J. Solids Struct. 165, 115–126 (2019) 3. Golub, M.V., Doroshenko, O.V.: Effective spring boundary conditions modelling wave scattering by an interface with a random distribution of aligned interface rectangular cracks. Eur. J. Mech. A. Solids 81, 103894 (2020)

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4. Golub, M.V., Doroshenko, O.V., Eremin, A.A., Shpak, A.N.: Wave propagation in laminated structures with imperfect contact between sublayers: spring boundary conditions and experimental validation. Proc. Meet. Acoust. 38, 065002 (2019) 5. Golub, M.V., Eremin, A.A., Wilde, M.V.: Experimental and theoretical investigation of edge waves propagation and scattering in a thick plate with surface-breaking crack-like defect. In: Proceedings of ECNDT 2018, 0295, NDT.NET, Gothenburg (2018) 6. Jones, R.M.: Mechanical of Composite Materials. Scripta Book Company, Washington (1975) 7. Kaplunov, J., Prikazchikov, D.: Asymptotic theory for Rayleigh and Rayleigh-type waves. Adv. Appl. Mech. 50, 1–106 (2017) ˘ 8. Kroupa, T., Cerv, J., Valeˇs, F.: Stress wave propagation in thin long-fiber carbon/epoxy composite panel. Numerical and experimental solutions. Appl. Comput. Mech. 1, 127–136 (2007) 9. Memmolo, V., Monaco, E., Boffa, N., Maio, L., Ricci, F.: Guided wave propagation and scattering for structural health monitoring of stiffened composites. Compos. Struct. 184, 568–580 (2018) 10. Moll, J., Kathol, J., Fritzen, C.P., Moix-Bonet, M., Rennoch, M., Koerdt, M., Herrmann, A.S., Sause, M.G.R., Bach, M.: Open guided waves: online platform for ultrasonic guided wave measurements. Struct. Health Monit. 18(5–6), 1903–1914 (2019) 11. Muskhelishvili, N.I.: Some Basic Problems of the Mathematical Theory of Elasticity. 3rd rev. and augmented edn., Groningen, Noordhoff (1953) 12. Wilde, M.V., Golub, M.V., Eremin, A.A.: Experimental and theoretical investigation of transient edge waves excited by a piezoelectric transducer bonded to the edge of a thick elastic plate. J. Sound Vib. 441, 26–49 (2019) 13. Wilde, M.V., Golub, M.V., Eremin, A.A.: Experimental observation of theoretically predicted spectrum of edge waves in a thick elastic plate with facets. Ultrasonics 98, 88–93 (2019)

Guided Wave Monitoring of Industrial Pipework – Improved Sensitivity System and Field Experience Thomas Vogt1, Sebastian Heinlein1, Josh Milewczyk1, Stefano Mariani2, Robin Jones1, and Peter Cawley2(&) 2

1 Guided Ultrasonics Ltd., Brentford TW8 8HQ, UK Department of Mechanical Engineering, Imperial College, London SW7 2AZ, UK [email protected]

Abstract. Low frequency guided wave inspection using the torsional, T(0,1), mode is routinely used in the petrochemical and other industries for the detection of corrosion patches, the detection threshold being typically around 5% cross section loss, though better sensitivity is obtained on simple pipe configurations not suffering from general corrosion. It has been shown in a blind trial that switching to a permanently installed system operating in SHM mode can improve the sensitivity to about 1% cross section loss and this is very attractive in corrosion monitoring applications. Later work has shown that the detection limit could be reduced to below 1% cross section loss if the compensation for environmental changes, particularly temperature, could be improved. This paper presents a new temperature compensation method involving both overall signal stretching, analogous to the well-known baseline stretch technique, and a further, location-by-location adjustment; this gives significant further improvements in performance. A commercial permanently installed monitoring system giving both local thickness measurements at the transducer location and longrange monitoring for corrosion over 10 s of metres from the transducer position is described. The system enables frequent measurements to be taken, the results being delivered to the operator via a wireless link. The benefits of the frequent readings enabled by the automatic data collection and transmission are discussed. Initial results presented here indicate that this enables defects as small as 0.1% cross section loss to be detected. Keywords: Guided waves Sensitivity


Pipes
Corrosion
Automated detection


1 Introduction Inspection systems based on guided waves are widely used to detect damage in structures found in numerous fields, such as aerospace, energy and oil & gas. The main advantage of these systems over conventional ultrasonic techniques is their ability to inspect large areas of the structure from a single sensor location [1]. A well-established application is the testing of pipes for the oil & gas industry by means of the first order © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 819–829, 2021. https://doi.org/10.1007/978-3-030-64594-6_79

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torsional wave mode using a pulse-echo configuration at frequencies in the order of tens of kHz [2]. This wave mode offers a virtually uniform coverage of the entire pipewall and a very low attenuation in steel, thus enabling inspection for tens of metres from the sensor location [3]. The drawback is a reduced sensitivity to changes in the pipe cross section, particularly when the guided wave sensors are used in a one-off inspection configuration. In this setting, the sensor is deployed on the structure and it is then removed after taking one (or a few) measurements. The sensor typically consists of two rings of transducers positioned roughly a quarter wavelength apart in the axial direction of the pipe to enable direction control [2]. The transducers, which can be either piezoelectric or EMAT-based, are evenly distributed around the ring and apply a tangential force to the pipe surface, so exciting the torsional mode. Unfortunately, in addition to the desired T(0,1) torsional wave mode, other signal components exist due to imperfect direction control [2] and to the excitation and reception of unwanted flexural modes [4]. Theoretically the generation of flexural modes can be prevented by ensuring that the number of elements in the ring is greater than k, where F(k,1) is the highest order flexural mode whose cut-off frequency is within the bandwidth of the excitation signal [5]. However, in practical systems, some non-uniform transduction sensitivity of the transducers around the circumference is inevitable and will break the desired axi-symmetry, hence generating (and receiving) flexural modes as well as enhancing the amplitude of circumferential modes [6]. Because these unwanted signal components are deterministic, they cannot be eliminated through averaging and hence they set a background noise level which is referred to as coherent noise. Defects must produce a reflection somewhat larger than this noise for reliable detection in a one-off inspection [6]. For this reason, the defect ‘call level’ is typically set to reflection amplitudes corresponding to anomalies (e.g. defects) that present approximately 5% change in the cross-sectional area of the pipe [7]. This value can vary significantly depending on the general condition of the inspected pipe, the position of the defect and the presence of other pipe features, such as welds, that also give reflections [8]. Recently, there has been strong interest in moving from the standard one-off inspection configuration to a permanently installed monitoring system (PIMS), which is particularly appealing when dealing with cases of high access costs (e.g. pipes buried underground), and which allows for frequent collection and interpretation of data [8]. The frequent monitoring potentially allows for the detection of damage at earlier stages, and the advantages of PIMS over one-off inspection systems are expected to be even greater for defects occurring in the vicinity of structural features that generate reflections even in the absence of defects. A further benefit enabled by the use of PIMS is that, after a positive detection, the progression of damage can easily be monitored, so that predictions on the remaining life of the structure can be attempted. In a PIMS setting, the data analysis typically involves comparing new measurements with baseline records, where any change in signals could represent a defect signature. Unfortunately, baseline subtraction is hindered by the effects of changing environmental and operational conditions (EOCs), primarily temperature, which are responsible for changes in the signals, so degrading the damage detection performance. There has been a great deal of work on temperature compensation methods and much progress has been made [6, 9–12].

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To date, the compensation methods treat the whole guided wave signal in one operation and so deal well with, for example, velocity [9] or velocity and phase [6] variations with temperature. However, there are other, less-studied, effects caused by temperature variations on the wave propagation. As the wave velocity changes with temperature, so does the wavelength, thus varying the efficacy of the direction control, which relies on the spacing between the two rings of transducers being a known fraction of the wavelength [2]. Also, some applications of guided wave-based monitoring systems are affected by strong signal attenuation, which is usually temperature dependent, for example monitoring of a pipe coated with a viscoelastic material such as bitumen. Importantly, any change to the balance of transduction around the pipe or to the transducer frequency responses is likely to alter the generation of unwanted flexural and circumferential modes and so modify the coherent noise in a way that is not corrected by previous temperature compensation methods. Specifically, at each temperature, the unique balance of excited unwanted modes which travel at different velocities, these velocities being in general a function of frequency (i.e. the modes are dispersive), will give constructive and destructive interference at different positions along the pipe, so the coherent noise is a spatially dependent function of temperature. Therefore, since the coherent noise is a function of both temperature and location along the pipe, it cannot be perfectly compensated by the ‘global’ compensation methods such as the BSS [9] or PSC [6] methods developed previously, and further, axial location dependent, compensation is required, where each axial location corresponds to one sample of the received signal. This is achieved with a novel procedure that will be called the location specific temperature compensation (LSTC) method and is presented in this paper and in more detail in [13]. Importantly, for its application there is no requirement for any prior knowledge of the exact source of noise at each temperature (i.e. which exact combination of unwanted modes is present), which greatly simplifies the problem.

2 Location Specific Temperature Compensation (LSTC) The LSTC method comprises two phases, namely a calibration phase (steps 1 to 3) and a monitoring operation phase (steps 4 to 7), as shown in Fig. 1. In the calibration phase, the guided wave instrumentation acquires n signals at different temperatures within a temperature range “Tlow – Thigh” (step 1), which will form the calibration data for the LSTC. The number n of measurements needs to be sufficient to define the function of temperature; this is expected to be smoothly varying, so a modest number of measurements distributed over the temperature range of interest is sufficient. A signal (e.g. the first available one, here denoted “S1”) is chosen as the signal to which all subsequent measurements will be compared for the compensation of the temperature-dependent T (0,1) wave speed, which can be accomplished either by using the PSC procedure [6] or the standard BSS [9] method (step 2). The next step is the computation of a “signal amplitude vs temperature” calibration curve for each axial location (i.e. each sample of the received signals) (step 3). This is achieved by fitting the set of RF signal amplitudes obtained at each axial location in the measurements forming the calibration data with an appropriate curve; order 4 polynomial fits were used in the results presented here.

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In the monitoring operation phase, the guided wave instrumentation acquires a new waveform “Si” at some temperature “Ti” (step 4), which is first compensated for the temperature-dependent wave speed (step 5, same as step 2). Ideally, the temperature “Ti” would lie in the range Tlow  Ti  Thigh. For “Ti” outside the calibration temperature range, the accuracy of the compensation procedure would depend on the accuracy of extrapolation of the fitting curves out of the range. At each axial location of “Si”, the value predicted by the curve computed for that location and for a temperature equal to “Ti” is subtracted from the current measured value at the given location (step 6). The result of this process is a newly formed residual signal, which can be interrogated to check whether there has been significant change in the structure at any location (step 7). In operation, each successive acquired signal is compensated according to steps 4 to 7 and any significant change at any location is identified. If direct temperature readings are not available, the entire procedure can be performed using an indirect temperature measurement such as the time of arrival of a reflector or the stretch factor computed by the BSS method. The latter has the advantage that the operator does not need to know a priori about the existence or the location of reflectors in the structure. Further details of the method are given in [13].

Fig. 1. Flow-chart description of the location specific temperature compensation method for assessing the integrity of a structure.

3 Application of LSTC to Experimental Data The new LSTC method described above was applied to a dataset of ultrasonic guided wave signals acquired by a Guided Ultrasonics Ltd gPIMS® sensor ring [14] shown in Fig. 2(a) set to use the T(0,1) wave mode. The sensor ring was attached to an 8 inch

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schedule 40 pipe whose layout is shown in Fig. 2(b) and which was fitted with heating blankets to control the temperature [15]. The excitation was an 8-cycle toneburst centered at 25.5 kHz. The pipe comprised 7 m and 2 m straight sections connected by a 90° elbow (with a bend radius of 1.5 times the outer diameter of the pipe), and the sensor was installed 4.5 m from the right hand end in the figure. In addition to the elbow welds, there was a girth weld in the longer straight section of the pipe. The measurements used to perform the analysis reported in this paper are the ones in the ‘forward’ direction as indicated in Fig. 2 where two artificial defects were introduced during the testing timeframe by a manual grinding process to produce wall losses with circular/oval area profiles at each defect location. The defects were then gradually deepened in multiple stages by increasing the extent of the wall loss, giving the cross sectional area (CSA) losses as a function of measurement number (time) shown in Fig. 3(a). Only defect 2 is discussed here; further results are given in [13]. The pipe was subjected to heating and cooling cycles, with the temperature fluctuating between about 13 °C and 38 °C. Figure 3(b) shows the temperature measured by a sensor installed on the pipe near the sensor ring. Figure 3(c) shows the first measurement, which was acquired at 19.1 °C prior to the introduction of any defect. The time domain ultrasonic signal is converted to distance in Fig. 3(c) using the known T(0,1) velocity. Each signal was normalized to the reflection from the end of the pipe and was compensated for the temperature-dependent wave speed and transducer phase shift using the PSC procedure [6], using the first measurement as the baseline. The compensated (i.e. phase-shifted and time-stretched) signals are expected to exhibit well-aligned RF peaks, as seen for example in Fig. 4(a) where two signals recorded at temperatures of 36 °C and 20 °C are shown. The plot is zoomed in a portion of the pipe around the weld and shows excellent phase alignment throughout. However, the different coherent noise existing in different measurements still produces non-zero residuals even for measurements both taken on the undamaged structure, as seen in Fig. 4(b) where the residual between the two signals in Fig. 4(a) is plotted, so making it more difficult to detect residuals due to the presence of a defect.

Fig. 2. (a) Guided Ultrasonics Ltd gPIMS® sensor ring; (b) geometry of the 8 inch schedule 40 test pipe. The 7 and 2 m pipe sections are connected by a 90° elbow with the bend radius being 1.5 times the outer diameter of the pipe. The defects introduced in the ‘forward’ direction are highlighted and numbered in red. The distances given of the defects and weld in the forward direction are measured from the middle of the sensor.

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Fig. 3. a) Cross sectional area losses due to the introduction of the defects. b) Temperature of the pipe during the testing timeframe. The calibration signal set was chosen to be from measurement 51 to 200 (which are the ones indicated by the green bar). (c) Measurement #1 acquired at 19.1 °C and used as baseline for the PSC temperature compensation procedure [6].

Fig. 4. (a) Signals acquired on undamaged pipe at 20 °C and 36 °C after PSC temperature compensation. (b) Residuals between the two measurements in (a). Note the different vertical scales in (a) and (b). (c) Residual signals obtained using PSC and the baseline subtraction method for measurements 2–252, i.e. before the first defect was introduced. (d) Compensation of measurements 1–252 using the new LSTC method performed by fitting 4th order polynomials on the calibration signal set including measurements 51 to 200.

4 Results The calibration signal set was chosen to be the cooling cycle from measurement 51 to 200 indicated by the green bar in Fig. 3(b). This choice was primarily motivated by the fact that the temperature range spanned by that cooling cycle is the only one sufficiently

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wide to cover the temperature range of all the other measurements. At each axial position a compensation curve of signal amplitude against temperature was computed by fitting the calibration data with 4th order polynomials, as shown below in Fig. 5(a, d). Figure 4(c) shows the residuals obtained by using the standard baseline subtraction method with PSC temperature compensation [6] when processing the first 252 defectfree measurements (i.e. signal 1 subtracted from the signal obtained by PSC compensation applied to each of signals 2–252), the signals being overlaid on top of each other. Very high values of residuals are seen at the pipe end; since both the baseline and ‘current’ signals contain large components of the pipe end reflection, baseline subtraction involves subtracting two large quantities and so is very sensitive to environmentally induced signal changes. These include frequency response changes of the transducers, such as ringing effects (e.g. the signal tail at 19.1 °C shown in Fig. 3c). For comparison, Fig. 4(d) shows the residual signals output by the LSTC method when processing the same 252 defect-free measurements. The residuals output by the LSTC method are lower than those obtained by the standard baseline subtraction, with more than an order of magnitude improvement at the pipe end. Clearly, the reduction of residuals enabled by the LSTC over the standard baseline subtraction method also depends on the range of temperatures over which the measurements are acquired, with the benefits expected to increase as the temperature range increases. The reduction in residuals produced by the new LSTC method to below 0.6% of the pipe end reflection at locations away from the pipe end means that defects removing around 1% of the cross sectional area would be detectable in a single test, with further improvements likely using multiple readings as discussed below. Even close to the pipe end it would be possible to detect a defect removing about 1.5% cross sectional area in a single test. Figure 5 shows the progression of residuals obtained over the whole set of 552 signals at a defect-free area of the pipe (a–c) and at the location of defect 2 (d–f). Figure 5(a,d) show the calibration curves for the two points derived from readings taken in the green region of Fig. 3a. Figure 5 (b,e) shows temperature readings (same plot as in Fig. 3(b)) alongside the residuals obtained using the baseline subtraction method with PSC temperature compensation. There is clearly strong correlation between the trends of the residuals and the temperature variation. The LSTC procedure produced the residuals shown in Fig. 5(c,f), which show no sign of temperature dependence and which are randomly distributed about zero when no defect is present (Fig. 5c) and track the defect growth in Fig. 5f. The LSTC residuals in the absence of a defect have been shown to be normally distributed [13] which means that it is possible to use change detection methods such as the GLR test [16] to automate the data processing and increase confidence in the defect call. The defects introduced in the blind trial [15] and shown in Fig. 3 were grown rapidly so few readings were taken at each defect condition. In order to demonstrate the power of the LSTC method in enabling the detection of small defects, Fig. 6 shows an example of the growth of a defect removing just 0.1% of the pipe cross sectional area. This was introduced synthetically using the procedure described in [17] and validated on the test pipe used in this paper in [18]. This process involves superposing the predicted reflection from a defect of a specified size obtained using finite element analysis onto experimental signals obtained on an undamaged structure, so including

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all the environmental effects that are present in signals obtained under different conditions. Two temperature cycles of data taken in the undamaged condition were used, as shown in Fig. 6a; these were taken from the data of Fig. 3 and re-numbered for convenience. Figure 6b shows all the A-scans obtained after PSC temperature compensation, the large signals being from the weld and pipe end shown in Fig. 2b; the defect signal appears between the two dotted lines but is too small to be seen above the noise. Figure 6c shows the profile of defect growth, while Fig. 6d shows all the residuals after LSTC processing. Figure 6e then shows the application of the GLR algorithm to the signals from the location where the synthetic defect is placed, the blue curve showing the case with no defect and the red curve the change produced when a defect removing 0.1% cross section loss is added. The GLR score rises rapidly after the defect is introduced reflecting increased confidence that there is a significant change as more readings are taken. Figure 6f then shows the maximum GLR score at all locations on the pipe, demonstrating that with appropriate threshold choice the defect removing 0.1% cross sectional area can be detected after 10 readings following defect initiation with no false calls from elsewhere on the pipe.

Fig. 5. Results at (a–c) point at 1.45 m (no defect growth); (d–e) point at 2.76 m (defect 2 maximum reflection). (a,d) Calibration data for LSTC method and best-fit curves; (b,e) Temperature readings and residuals obtained using the baseline subtraction method with PSC temperature compensation; (c,f) Residuals output by the LSTC method. The cross sectional area loss due to the defect also shown in (f).

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Fig. 6. Example results of GLR signal processing on synthetically introduced defect. (a) pipe temperature (green calibration data, black ‘test’ data; points re-numbered from cycles in Fig. 3b); (b) all signals after PSC temperature compensation. Dotted lines show region where defect signal is added; (c) synthetic defect growth profile; (d) residual signals after LSTC processing – note 0,1% defect too small to be evident; (e) GLR processing (blue – no defect, red – when synthetic defect added at same location). Possible threshold for defect call also shown; (f) Maximum GLR value at each location. Setting threshold as shown leads to 0.1% cross section loss defect being detected with no false calls after a modest number of readings in the defective state.

5 Conclusions The use of permanently installed monitoring systems for SHM of pipes (or other structures) has the potential to enable detection of damage at earlier stages than when using one-off inspection systems. In a typical permanently installed monitoring system (PIMS) setting, residuals are obtained by using the baseline subtraction method, where an earlier measurement is subtracted from the ‘current’ signal. The goal is to minimize the residuals in the absence of defects, so that, when a defect occurs, it can easily be identified by detecting high residual values due to partial signal reflections. Unfortunately, this procedure is hindered by the effects of changing environmental and operational conditions, for example temperature, which produce changes in the coherent noise affecting the signals. Many temperature compensation methods have been developed that typically involve stretching the whole signal to compensate for the variation of wave speed

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induced by a change in temperature. However, these ‘global’ methods cannot target spatially dependent signal changes. This paper introduced a location specific temperature compensation (LSTC) procedure that, after the application of a ‘global’ method such as BSS or PSC, is used to adjust the signal at each position so that in the absence of a defect the residual is reduced to close to zero. This has been shown to give substantial improvement on the standard method, essentially removing any evidence of temperature dependence from the residual signals obtained after baseline subtraction; further details are given in [13]. As the residuals are normally distributed about zero it is then possible to apply change detection methods such as the GLR algorithm to automate the defect detection process. Initial results presented here indicate that this enables defects as small as 0.1% cross section loss to be detected.

References 1. Cawley, P., Cegla, F., Stone, M.: Corrosion sements. J. Nondestruct. Eval. 32(2), 156–163 (2013) 2. Cawley, P., Lowe, M., Alleyne, D., Pavlakovic, B., Wilcox, P.: Practical long range guided wave inspection-applications to pipes and rail. Mater. Eval. 61, 66–74 (2003) 3. Alleyne, D.N., Cawley, P.: The excitation of Lamb waves in pipes using dry-coupled piezoelectric transducers. J. Nondestruct. Eval. 15(1), 11–20 (1996) 4. Gridin, D., Craster, R.V., Fong, J., Lowe, M.J.S., Beard, M.: The high-frequency asymptotic analysis of guided waves in a circular elastic annulus. Wave Motion 38(1), 67–90 (2003) 5. Alleyne, D.N., Lowe, M.J.S., Cawley, P.: The reflection of guided waves from circumferential notches in pipes. J. Appl. Mech. 65(3), 635 (1998) 6. Mariani, S., Heinlein, S., Cawley, P.: Compensation for temperature dependent phase and velocity of guided wave signals in baseline subtraction for structural health monitoring. Struct. Heal. Monit. (2019). https://doi.org/10.1177/1475921719835155 7. Nunez Ledesma, V.M., Perez Baruch, E., Demma, A., Lowe, M.J.S.: Guided wave testing of an immersed gas pipeline. Mater. Eval. 67, 102–115 (2009) 8. Cawley, P., Cegla, F., Galvagni, A.: Guided waves for NDT and permanently-installed monitoring. Insight - Non-Destr. Test. Cond. Monit. 54(11), 594–601 (2012) 9. Croxford, A.J., Wilcox, P.D., Drinkwater, B.W., Konstantinidis, G.: Strategies for guidedwave structural health monitoring. Proc. R. Soc. A Math. Phys. Eng. Sci. 463(2087), 2961– 2981 (2001) 10. Lu, Y., Michaels, J.E.: A methodology for structural health monitoring with diffuse ultrasonic waves in the presence of temperature variations. Ultrasonics 43(9), 717–731 (2005) 11. Croxford, A.J., Moll, J., Wilcox, P.D., Michaels, J.E.: Efficient temperature compensation strategies for guided wave structural health monitoring. Ultrasonics 50(4–5), 517–528 (2010) 12. Harley, J.B., Moura, J.M.F.: Scale transform signal processing for optimal ultrasonic temperature compensation. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 59(10), 2226– 2236 (2012) 13. Mariani, S., Heinlein, S., Cawley, P.: Location specific temperature compensation of guided wave signals in structural health monitoring. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67, 146–157 (2020) 14. http://www.guided-ultrasonics.com

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15. Heinlein, S., Cawley, P., Vogt, T., Burch, S.: Blind trial validation of a guided wave structural health monitoring system for pipework. Mater. Eval. 76, 1118–1126 (2018) 16. Willsky, A.S., Jones, H.L.: A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems. IEEE Trans. Autom. Control 21, 108–112 (1976) 17. Liu, C., Dobson, J., Cawley, P.: Efficient generation of receiver operating characteristics for the evaluation of damage detection in practical structural health monitoring applications. Proc R. Soc. A 473, 20160736 (2017) 18. Heinlein, S., Cawley, P., Vogt, T.: Validation of a procedure for the evaluation of the performance of an installed structural health monitoring system. Structural Health Monitoring (2019). https://doi.org/10.1177/1475921718798567

Composite Leading Edge Monitoring with a Guided Wave System Joseba Castillero, Gerardo Aranguren(&), Josu Etxaniz, and José M. Gil-Garcia Electronic Design Group, University of the Basque Country (UPV/EHU), Bilbao, Spain [email protected]

Abstract. Over the last two decades, a wide variety of metal and composite structural health monitoring techniques have been developed. Most of the tests on composite material reported in academia are run on flat and rectangular structures, but real-world parts are more complex than these simple structures. Usually, the physical features of real-world structures are complex. These parts are large, asymmetric, and non-flat structures. They are made out of several attached pieces and might include holes or fixing elements. The wave transmission does not only depend on the usual test conditions (type of sensor, frequency, and waveform) and composite material properties (anisotropic behaviour and high attenuation), but also on the physical features of the structure under test (irregular shape, curvatures, obstacles,…). As a result, the guided waves used for monitoring show a hard to predict behaviour that can be considered chaotic. This paper introduces the preparation and performance of the SHM laboratory tests carried out on an airplane’s leading edge made of composite. During the tests, specific equipment was used for the generation and acquisition of ultrasonic guided waves. The goal of the tests is to adapt the monitoring techniques applied on simple structures to real-world structures. The tests compare guided wave emission techniques with one and many piezoelectric transducers. The research shows the difficulties to monitor real-world specimens and points out the means and set-up to overcome them. Keywords: Leading edge monitoring Guided wave


Composite material
Beamforming


1 Introduction Composite materials have been successfully introduced in the airborne manufacturing industry due to their resistance and lightness, which are higher than the ones found in metallic materials employed until now [1]. At the same time, the need for safety and cost reduction are pushing the development of structural health monitoring (SHM) systems [2] that can monitor the structural integrity of a part during its life cycle. Ultrasonic guided wave testing (UGWT) is one of the most promising technologies to achieve this objective due to the capacity of the guided waves to reach all areas of the specimen under test and interact with the mounted elements in an active way over long distances [3]. This technology is based on the emission of guided or © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 830–837, 2021. https://doi.org/10.1007/978-3-030-64594-6_80

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Lamb waves that are generated by means of piezoelectric wafer active sensors (PWAS) permanently embedded in the structure [4]. Those waves propagate all over the structure and are reflected back by the edges and obstacles found on their way. The acquisition and processing of those signals are useful to detect defects that can appear in the structure like scratch, corrosion in metallic materials or delamination in composite materials [5, 6]. Some other advantages of this technology are its low cost, low weight, and low impact on the monitored structures [7]. The objective of this paper is to show the techniques to accomplish UGWT for SHM employing only PWAS installed on a real-world structure made of composite material. The study shares the same empirical methodology followed in preceding research works. The starting point is an actual structure and then, by means of UGWT techniques, massive test with hardware-processed data are carried out to analyze the possibility of its implementation on real-world systems.

2 Proposed Techniques Composite materials are heterogeneous and have an anisotropic and non-linear behavior regarding the transmission of sound [8]. This non-linearity, combined with the wide range of shapes of structures, the multi-mode wave propagation [9], the high attenuation [10], and the drift over time of the characteristic parameters make the study of the behavior of these waves available from the Chaos Theory point of view [11]. The use of a single PWAS to monitor a complex specimen lacks of enough energy for UGWT on composite structures. The simplest way to obtain more energy is to use more PWAS. The PWAS can be located at different positions of the structure to induce additive interferences among the waves in propagation. Another possibility is the emission of different signals to cause constructive interferences. Anyhow, both techniques produce a huge amount of data in acquisition. Any single acquired waveform in an UGWT requires about hundreds of kbytes. For example, the SHMUS used in the tests requires between 32 kbytes and 256 kbytes by signal. If we had to multiply this amount of memory space by the number of acquired signals in the UGWT, the number of sub-structures, and the frequency of the periodic UGWT itself, several gigabytes of raw data can be easily generated per structure and day. Thus, the processing time could exceed the response time. For this reason, a signal preprocessing system is proposed to drastically reduce the amount of data and the processing time. Therefore, the real time response of the whole process is improved. The goal of the algorithm used for damage detection is to reduce the system´s processing load. The methodology of the algorithm is based on the hypothesis that internal changes in the structure can be evidenced by Time-of-Flight (ToF) differences between consecutively measured maximum and minimum peaks [12]. These peaks are known as characteristic points (CP) and can be obtained by a signal pre-processing system. The following hardware processing diagram of acquired signals (Fig. 1) can be implemented in a SHM system to detect and store the CP.

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Fig. 1. Diagram of the proposed PC detection hardware processing.

A smoothing window is applied before the edge detection to filter out the digitalization noise of the ADC. Then, the CP detection window analyzes when the modulus of the amplitude of the acquired signal is over the defined threshold. When enough successive rising or falling edges happen, either a maximum or a minimum peak is detected. Then, the peak amplitude and ToF are stored in an internal FIFO. However, higher frequency components, such as noise, are filtered. Both the CP detection and smoothing window are sliding windows, processing the signal as the ADC acquires new samples. Thus, the CP are obtained during the UGWT reducing the duration of the test, which eases sending only the CP instead of the complete signal. Moreover, if the UGWT has multiple PWAS, then the hardware processing system in Fig. 1 can be replicated to process concurrently all the acquisition channels in a FPGA. As a result, the processing is faster and more efficient, the amount of produced data is reduced, and the processing and transmission times are shortened. The hardware processing system has been implemented in the Artix-7 FPGA of SHMUS with little resource cost increase (183 LUT, 16 LUTRAM, 202 Flip-Flops and 0.5 BRAM for 64 CP stored per channel or PWAS).

3 Tests Setup A leading edge constructed on a cross-ply composite material has been employed to evaluate the proposed technique (Fig. 2).The main dimensions of this part are 1600 x 590 x 360 mm. The leading edge has six vanes and seven ribs, dozens of holes for different functions, mounting brackets, and a highly curved shape.

Fig. 2. Tested leading edge employed to assess the proposed technique.

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Figure 3 shows the test setup. It includes eight PWAS, one Structural Health Monitoring Ultrasonic System (SHMUS) to run the test and a Personal Computer to process the recorded data. SHMUS is a self-developed specialized hardware device that generates the electric signals to drive the PWAS and to acquire the received waveform through the very same PWAS [13]. Waveforms propagate all over the structure from the generation to the acquisition while the edges and the obstacles reflect them and the waveforms interact with the structural defects. The acquired signals, properly processed, can be used to determine the structural state of the unit under test and to find defects that can arise over time.

Fig. 3. Setup schematic and photograph of the UGWT.

PWAS have been lined up and glued with epoxy in the central area of the leading edge keeping 10 mm distance between them. They are 7 mm diameter and 0.2 mm thickness model SMD07T02R412WL from Steminc. The SHMUS device was designed in a previous research project [13] that was specifically focused on UGWT and, therefore, has better features than generic commercial devices. For example, it has the capability to generate and acquire up to 18 different signals simultaneously. The excitation signals for the UGWT described in this paper consisted of four sine cycles of 340 kHz and 48 V peak to peak.

4 Tests Results The first set of UGWT was run applying the described signals to the PWAS#1 (located at the left end of the array of PWAS) and acquiring the propagated waveforms with the other PWAS in the array. Figure 4 shows the amplitude and the ToF of the acquired signals. Afterwards, the same UGWT is run but, this time, signals are acquired in “preprocessed mode”. The points obtained in such mode are depicted in Fig. 5. When preprocessed acquisition mode is selected, only the depicted points are obtained. An interpolation line connects the points to show the equivalence between Figs. 4 and 5.

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Fig. 4. Waveforms acquired by PWAS#2 to PWAS#8 when driving PWAS#1 during a UGWT.

Fig. 5. CP acquired by PWAS#2 to PWAS#8 after carrying out the same UGWT than in Fig. 4, but capturing data in “pre-processed mode”.

Figure 6 shows the signals acquired by PWAS#2 in both methods: the full waveform acquisition signal (blue) and the pre-processed acquisition signal once the FPGA process it (red). It can be noted that small amplitude peaks are considered as noise and are not reported in pre-processed mode. Next, the excitation signals were applied to all PWAS except to the one located at the right end of the array, namely PWAS#8. Figure 7 shows the comparison of the signals acquired by PWAS#8 when only PWAS#1 is excited (blue) and when all other PWAS (from PWAS#1 to PWAS#7) are driven by the SHMUS (red).

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Fig. 6. Comparison of signals acquired by PWAS#2 using full waveform acquisition (blue) and com-pressed acquisition (red).

Fig. 7. Waveforms acquired in PWAS#8 when driving PWAS#1 only (“Single” blue signal) and when driving PWAS#1 to PWAS#8 (“Multiple” red signal).

5 Discussion of Results Full waveform acquisition generates 128 Ksamples (256 kbytes) of raw data per acquisition channel in the SHMUS device. If the installation of a SHM system in a complex structure, e.g., an airplane, is considered, the amount of bytes measured per channel is multiplied by the number of measurement channels, the number of substructures, and the number of UGWT run per unit of time. This produces a huge amount of data. The amount of time needed to transfer each waveform obtained by the SHMUS used in the tests is 120 ms when using USB 2.0 at 480 Mbps. If we had to multiply that time by more channels, and structures and tests, a considerable amount of time would be required without considering the processing time of all that information.

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On the contrary, pre-processed acquisition mode only generates 64 samples (256 bytes) per channel, namely, 1000 times less than the full waveform acquisition mode. Therefore, the transfer time and memory required to process the data is reduced by the same factor. Moreover, the hardware of the SHMUS carries out the most tedious part of the processing and shortens the time required to post-process the data. The acoustic wave emission with only one PWAS provides with very low energy and the waves, after a certain distance, have very small amplitude, which are useless to monitor the structure. The emission with multiple PWAS delivers more energy in the UGWT.

6 Conclusions This paper introduces a set of initial tests to assess the ultrasonic guided wave technique to monitor a real-world leading edge of an airplane. An empirical methodology that led to draw some conclusions from the tests results has been followed. The first study has demonstrated the effectiveness of the proposed pre-processing in the UGWT signal acquisition. It has been shown that this pre-processing can save the amount of generated data by three orders of magnitude and, subsequently, the transfer time and the final data processing could be decreased by a similar proportion, facilitating real-time monitoring. Improvement in UGWT test results has also been demonstrated when more than a single PWAS is used to generate ultrasonic waves. The future work focuses on studying the amplitude improvement in UGWT when delayed multiple emission is used. Another aspect that will need further research is the comparison of the signals recorded from a pristine structure and a faulty structure when the proposed resource-saving pre-processing acquisition mode is used in UGWT.

References 1. Ohlsson, F.: Weight reduction by optimized reinforcement structures. In: Lightweight Composite Structures in Transport, pp. 191–215. Woodhead Publishing (2016) 2. Farrar, C.R., Worden, K.: An introduction to structural health monitoring. Philos. Trans. Roy. Soc. A: Math. Phys. Eng. Sci. 365(1851), 303–315 (2007) 3. Fateri, S., Lowe, P.S., Engineer, B., Boulgouris, N.V.: Investigation of ultrasonic guided waves interacting with piezoelectric transducers. IEEE Sens. J. 15(8), 4319–4328 (2015) 4. Giurgiutiu, V.: Structural health monitoring with piezoelectric wafer active sensors. Elsevier, Amsterdam (2007) 5. Rose, J.L.: Ultrasonic Guided Waves in Solid Media. Cambridge University Press, New York (2014) 6. Su, Z., Ye, L., Lu, Y.: Guided Lamb waves for identification of damage in composite structures: a review. J. Sound Vib. 295(3–5), 753–780 (2006) 7. Boukabache, H., Escriba, C., Fourniols, J.Y.: Toward smart aerospace structures: Design of a piezoelectric sensor and its analog interface for flaw detection. Sensors 14(11), 20543– 20561 (2014)

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8. Datta, S.K.: Elastic waves in composite media and structures: with applications to ultrasonic non-destructive evaluation. CRC Press, Boca Raton (2019) 9. Fateri, S., Boulgouris, N.V., Wilkinson, A., Balachandran, W., Gan, T.-H.: Frequencysweep examination for wave mode identification in multimodal ultrasonic guided wave signal. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61(9), 1515–1524 (2014) 10. Chan, C.W., Cawley, P.: Lamb waves in highly attenuative plastic plates. J. Acoust. Soc. Am. 104(2), 874–881 (1998) 11. Fernandez-Ramirez, K.I., Baltazar, A.: Beamforming of ultrasonic guided waves for defect search using chaos optimization. In: AIP Conference Proceedings, vol. 2102, issue 1, p. 050021. AIP Publishing, LLC (2019) 12. Aranguren, G., Etxaniz, J., Barrera, E., Ruiz, M., Olivares, M.A., Taboada, I., Urrutia, A., Melendez, R.: Structural health monitoring ultrasound system. In: 8th European Workshop on Structural Health Monitoring, EWSHM, vol. 4 (2016) 13. Cantero-Chinchilla, S., Chiachío-Ruano, J., ChiaChío-Ruano, M., Etxaniz, J., Aranguren, G., Jones, A., Essa, Y., Martin De La Escalera, F.: Lamb wave-based damage indicator for plate-like structures. In: European Conference of the Prognostics and Health Management Society (2018)

The Global-Local Approach for Damage Detection in Composite Structures and Rails Margherita Capriotti1, Francesco Lanza di Scalea2, and Antonino Spada3(&) 1

Ultrasound Research Laboratory, Department of Radiology, Mayo Clinic College of Medicine and Science, 200 First St SW, Rochester, MN 55905, USA [email protected] 2 NDE and SHM Laboratory, Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093-0085, USA [email protected] 3 Department of Engineering, University of Palermo, Viale Delle Scienze, Edificio 8, 90128 Palermo, Italy [email protected] Abstract. Structural components with waveguide geometry can be probed using guided elastic waves. Analytical solutions are prohibitive in complex geometries, especially in presence of structural discontinuities or defects. The Global-Local (GL) approach provides the solution by splitting the waveguide in “local” and “global” regions. The “local” region contains the part of the structure responsible for the complex scattering of an incident wave. What happens in this region cannot be reproduced analytically. The “global” region is regular and sufficiently far from the scatterer, in order to exploit known analytical wave propagation solutions. The proposed GL approach discretizes the local region by regular finite elements, and utilizes the efficient Semi-Analytical Finite Element (SAFE) method in the global region. Kinematic and mechanical constraints ensure the displacements and stresses continuity at the global-local interface. The evaluation of the energy of reflected and transmitted waves is used to check the before-after scattering energy balance. Numerical results are shown with regard to the specific cases of a composite skin-to-stringer assembly used in modern aircraft construction and a railroad track with a common section. The effects of different damage configurations are analyzed in both cases studying the reproduced scattered spectra related to specific incident waves. The results can be useful to select the best incident mode-frequency range in order to best identify specific defects in these structures. Keywords: Guided waves
Scattering
Global-Local approach
Damage detection
Semi-analytical finite element method
Composite structures
Aircrafts
Rails

1 Introduction When a waveguide (plate, rod, pipe, etc.) is excited ultrasonically, many reflections and wave-type conversions take place any time the excitation encounters the waveguide’s bounding surfaces. The superposition of these constructive and destructive beams after © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 838–847, 2021. https://doi.org/10.1007/978-3-030-64594-6_81

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a certain distance creates the so-called “wave packets” or “guided wave modes” [16]. Scattering of ultrasonic guided waves from defects or variations of geometry, if properly detected and understood, can be related to the discontinuity itself. This is of great importance in Non-Destructive Testing (NDT) and Structural Health Monitoring (SHM) applications. The analysis of the scattered spectra obtained from fixed incoming waves suggests the selection of the best mode-frequency combinations to be used in either a reflection mode (pulse-echo) or a transmission mode (through-transmission) test, to detect/determine the presence of a specific defect. Unfortunately, given the multimode and dispersive character of a guided wave, closed form solutions are available for simple geometries only. Analytical solutions of guided waves propagation through multi-layered structures exist, for example, using well known global matrix or transfer matrix methods [16]. Poddar and Giurgiutiu [12, 13] and Haider et al. [9] proposed, instead, an improved analytical approach to predict Lamb waves scattering from a geometrical discontinuity (i.e. a thickness change in isotropic plates or the presence of a horizontal crack). On the opposite side, only numerical methods can be used. Guo and Cawley [8] and Ricci et al. [15], for example, applied the Finite Element (FE) method on the propagation of guided waves in composites with delaminations. The main problem of FE methods is the computational cost, especially when the entire structure needs to be discretized. This is because the maximum dimension of the elements must satisfy specific relations with respect to the wavelength of the propagating wave. However, if the dimension of the elements is maintained small enough with respect to the wavelength, FE gives reliable results, even for complex structures. The best compromise between analytical and numerical solution is represented by hybrid methods. These methods consist in coupling both analytical and numerical methods, reserving the numerical method to a small portion of the structure (local region) where the analytical solution is not achievable. The rest of the structure (global region) exploits an analytical formulation to reduce computational cost. In most of the applications, the local region is discretized using the FE method. Goetschel et al. [7] worked on axisymmetric inclusions in homogeneous isotropic media. Rattanawangcharoen et al. [14] faced with axially-symmetric scattering problems. Chang and Mal [3, 4] concentrated on defects in lap-shear joints of isotropic plates and isotropic plates with notches and rivet-hole cracks. The problem of reflections of a fixed cracked edge was addressed by Karunasena et al. [11], while FE local-global techniques were applied to scattering in layered composite laminates with delaminations [20]. In some works, the FE was substituted by the BEM method, to study the scattering from a semi-infinite plate or plates with inclusions, different cracks and materials [5, 6]. Regarding the global solution, computationally more efficient is the application of the well-known semi-analytical FE (SAFE) mehtod, where the theoretical solution is applied in the propagation direction only, and FEs are used to discretize the cross-section of the waveguide [1, 10]. Examples of the coupled SAFEFE solution are represented by Srivastava and Lanza di Scalea [19] or Spada et al. [18] for composite panels and built-up skin-to-stringer composite assemblies, respectively. In this work some of the results obtained with the global-local numerical tool recently developed by authors [17, 18] are shown. In particular, it is shown how the proposed hybrid global-local method can be profitably applied for 2-D applications as

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well as for 3-D applications. Since ultrasonic guided wave techniques are nowadays largely diffused to inspect composites and rails, attention is payed to the specific cases of a built-up skin-to-stringer composite assembly (representative of part of modern commercial aircrafts), with a defect in the stringer or delamination in the skin, and a rail (136 lb AREMA cross-section rail), with a defect of different size in the head.

2 Theoretical Formulation Let us consider the general scattering case shown in Fig. 1. An incident time harmonic guided wave coming from a sound region (global region) is scattered into reflected and transmitted waves after interacting with a (local) region containing geometrical discontinuities (e.g. build-ups) and/or structural defects (e.g. cracks, delaminations, etc.). The equilibrium of each part (global or local) is guaranteed by the Principle of Virtual Work (PVW).

Fig. 1. Scheme of the scattering of an incident wave in a prismatic waveguide structure into reflected and transmitted waves from a local region with geometrical and/or material discontinuity.

Nodal displacements ql B recorded at the left boundary are obtained as a combination of the nodal displacements produced by the incident wave and the reflected waves: ql B ¼ qincident þ qreflected

ð1Þ

Nodal displacements qr B at the right boundary, instead, are those of the transmitted waves: qr B ¼ qtransmitted :

ð2Þ

In the subject case of multimode guided waves, the incident, reflected and transmitted waves can be thought of as the superposition of a finite number NM of global cross-sectional mode shapes, each amplified by a participation coefficient having the physical meaning of the amplitude of the single wave mode. In this work, the incident wave is generated by imposing the cross-sectional displacements for a unique selected

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mode. The amplitude of the incident wave is chosen equal to 1. The evaluation of the mode shapes is performed by solving an eigenproblem. If the symbol U is used to represent the generic mode shape, and the dependency on time is omitted by neglecting the factor ei x t for simplicity, Eqs. (1) and (2) are explicitly rewritten as: ql B ¼ Uinþ ei ½nin ðds xl B Þ þ þ

qlrB ¼

NM X j¼1

NM X j¼1

ðjÞ i ðnj A e j U 

Ajþ UðjÞ ei ðnj þ

þ



xl B Þ

xrB Þ

ð3Þ

ð4Þ

where x is the propagation direction, while the superscripts “+” and “−” represent a wave travelling towards the right and the left direction respectively. Therefore: Uinþ and ðjÞ and n ninþ are mode shape and wavenumber of the incident wave; A j ; U j are amplitude, mode shape and wavenumber of the j-th transmitted (+) or reflected (−) modes. ds ; xl B and xr B measure the distances of the source, left boundary and right boundary, respectively, from the origin of the reference system. Looking at the mechanical counterpart, the consistent nodal force vectors at the two global left and right boundaries are equal to: Fgl B ¼ Finþ ei ½nin ðds xl B Þ þ þ

Fgr B ¼

NM X j¼1

NM X j¼1

ðjÞ i ðnj A e j F 

Ajþ FðjÞ ei ðnj þ

þ

xr B Þ



xl B Þ

ð5Þ

ð6Þ



Analytical expressions for Finþ and FðjÞ vectors are provided in [18]. The amplitudes A j are the unique unknowns to be determined. Their evaluation is performed by applying the least squares method to the solution of the system: S U‘ ¼ F‘ ;

ð7Þ

which is valid for the local region. In Eq. (7) S ¼ K‘  x2 M‘ , with K‘ and M‘ the stiffness and mass matrices of the local region respectively and x the circular freh  iT T ‘ quency; U‘ ¼ U‘I qTlB qTrB , with UI the displacements of the inner nodes of h iT  g T T the local region; F‘ ¼ 0T . FlB ðFgrB Þ Once the amplitudes are determined, the energy carried by the j-th mode is: E ð jÞ ¼ 

 2 Aj  2

h i T  ð jÞ ; Re i x Fð jÞ U

ð8Þ

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 refers to the complex conjugate modeshape vector of the j-th mode. The where U evaluation of the energies allows to verify the conservation of energy between the incident mode and the scattered (reflected and transmitted) modes: Ein ¼

NM   X ð jÞ ð jÞ ERefl þ ETransm ;

ð9Þ

j¼1

ð jÞ

ð jÞ

with Ein ; ERefl ; ETransm the energy fluxes of the incident wave and of the j-th reflected and transmitted waves, respectively.

3 Numerical Applications 3.1

Scattering from Defects in Aircraft Composite Skin-to-Stringer Assembly

The geometry of the analyzed panel is shown in Fig. 2. It represents a scaled version of the skin-to-stringer assembly for commercial aircrafts [2]. In the model, the skin and the hat-shaped stringer are both composed of 8-layers of ½0= þ 45=45=0S carbonepoxy unidirectional laminae with a total thickness of 1.6 mm. An additional 0° lamina is placed at the interface between skin and stringer. The density of each lamina is 1530 kg/m3 and the elastic properties in the principal direction of material symmetry are given in Table 1, where 1, 2 and 3 identify an orthogonal system, with 1 parallel to the fiber direction. The local region was discretized using 12376 quadrilateral isoparametric linear elements, the global region using 16 monodimensional isoparametric linear elements.

Fig. 2. GL model of the composite skin-to-stringer assembly, with indication of the defects’ location and quantification considered in this study.

Table 1. Elastic properties for the CFRP lamina Property C11 C12 C13 C22 C23 C33 C44 C55 C66 GPa 135 5.70 5.70 14.20 8.51 14.20 2.87 4.55 4.55

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Fig. 3. Phase velocity (left) and group velocity (right) dispersion curves for the [0/+45/−45/0]S composite.

Fig. 4. Wave spectra in pristine (black lines) and defect 1 (red lines) case: (a) m1 incident, m1 transmitted; (b) m1 incident, m1 reflected; (c)) m2 incident, m2 transmitted; (d) m2 incident, m2 reflected. (Color figure online)

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Fig. 5. Wave spectra in pristine (black lines) and defect 2 (red lines) case: (a) m1 incident, m1 transmitted; (b) m1 incident, m1 reflected; (c)) m2 incident, m2 transmitted; (d) m2 incident, m2 reflected. (Color figure online)

Dispersion curves for the [0/+45/−45/0]S composite are given in Fig. 3 up to 500 kHz. Among the 4 visible modes, m1 (corresponding to the zero-order flexural A0 Lamb mode), and m2 (corresponding to the zero-order axial S0 Lamb mode) are used in the application as incoming modes in two different defected configurations, namely a complete damage in the stringer cap (damage 1) and a delamination in the skin (damage 2). In the case of defect 1, for an m1 incident wave, a reduction in the transmitted m1 wave energy is visible between 300 and 400 kHz with respect to the pristine condition, while an increase in the reflected m1 mode energy is clear between 100 and 300 kHz. When m2 mode is incoming, instead, the transmitted wave energy is strongly reduced in the 50–150 kHz and 200–450 kHz ranges and increased in almost the same range (200–400 kHz) for the reflected waves. Evidently, m2 in the 200–400 kHz range interacts more with the stringer cap, showing strong changes both in the damaged transmission and reflection spectra (Fig. 4). In the case of defect 2, for an m1 incident wave, some difference with respect to the pristine condition is visible in the 200–250 kHz and 380–480 kHz ranges in transmission and 120–150 kHz and 200–250 kHz in reflection. Almost the same spectra are obtained for an m2 incoming mode. This evidences how defect 2 does not significantly affect transmission and reflection properties of the assembly when probing with m1 and m2 modes (Fig. 5).

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Scattering from Defects in Rail Head

The geometry and FE discretization of the analyzed rail is shown in Fig. 6. It refers to an AREMA 136 lb section where a transverse damage of the head has been considered in the cases of a 15%, 50%, 85%, and 100% damaged head. The total local region length is equal to 20 cm, 10 cm at each side of the damage. According to the propagation velocity of the transverse waves derived by the dispersion curves reported in [17], the local region has been discretized in 22884 wedge linear elements, arranged on 81 columns. Damage is reproduced by eliminating the elements of the head in the 41st column for each damaged case (elements in red in Fig. 6).

Fig. 6. 136lb AREMA section rail with dimensions and FE discretization (left). 2nd to 5th subfigures: defected analyzed cases (15%, 50%, 85%, 100% damaged head). Removed FEs are highlighted. (Color figure online)

Fig. 7. Scattering energy spectra for a m5 incident mode in the cases of: (a) 15%, (b) 50%, (c) 85%, and (d) 100% damaged head.

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Results are reported in Fig. 7 for m5 incoming mode, which mainly excites the head and therefore one of the most suitable modes for the detection of damage in the head of the rail. Results are reported in terms of normalized energy of the reflected and transmitted waves in the 10–180 kHz frequency range. The defects create significant mode conversions: the larger the defect size (e.g. 15% to 50%), the stronger the mode conversion (30% to 75% of the total energy), and the lower the frequency at which mode conversion occurs (the transmitted mode 5 propagating energy reaches its collapse at 20 kHz instead of 50 kHz). This behavior is expected since larger wavelengths become sensitive to larger defects. For the 85% damage case, most of the wave is reflected (dashed lines). At 50 kHz, mode 5 couples with mode 29 in particular, with up to a 60% reflection when the entire rail head is damaged (i.e. 100%). Finally, the plots show that energy conservation starts breaking down for frequencies above 160 kHz, and in a manner that becomes more severe with increasing defect size. This is due to the transfer of energy from modes that are included in the analysis into modes that are not.

4 Conclusions In the present paper some of the results obtained with a GL approach recently developed by the authors are shown. The model utilizes the Semi-Analytical Finite Element method to handle the “global” region away from the scatterer and a full FE discretization of the “local” region around the scatterer. Two numerical applications have been shown, namely a 2-D application on a defected built-up skin-to-stringer composite assembly and a 3-D application on a rail with an increasing defect in the head. Scattering spectra are presented in both reflection and transmission for selected modes. The obtained results show how guided waves are sensitive to the specific defects. This can help selecting the appropriate mode-frequency combinations for high sensitivity to defects and/or assisting to estimate the size of a defect that is being detected by either a reflection or a transmission guided-wave test. Acknowledgments. A. Spada stay at the University of California San Diego was financially supported by the Fulbright Program under project E0584038 “Analytical-Numerical models for the simulation of ultrasonic guided wave propagation in composite structures”. M. Capriotti conducted this work as a PhD student at the University of California San Diego. F. Lanza di Scalea acknowledges funding from the Federal Railroad Administration under contracts #693JJ619C000008 and #693JJ618C000002 (Dr. Robert Wilson, FRA Program Manager) and from the Federal Aviation Administration Center of Excellence for Advanced Materials under contract #12-C-AM-UCSD-014 (Dr. Lynn Pham, FAA Contract Manager).

References 1. Bartoli, I., Marzani, A., di Scalea, F.L., Viola, E.: Modeling wave propagation in damped waveguides of arbitrary cross-section. J. Sound Vib. 295(3–5), 685–707 (2006) 2. Capriotti, M., Kim, H.E., Lanza di Scalea, F., Kim, H.: Non-Destructive inspection of impact damage in composite aircraft panels by ultrasonic guided waves and statistical processing. Materials 10(6), 616 (2017)

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3. Chang, Z., Mal, A.K.: A global-local method for wave propagation across a lap joint. ASME Appl. Mech. Div. Publ. AMD 204, 1–12 (1995) 4. Chang, Z., Mal, A.: Scattering of Lamb waves from a rivet hole with edge cracks. Mech. Mater. 31(3), 197–204 (1999) 5. Galán, J.M., Abascal, R.: Numerical simulation of Lamb wave scattering in semi-infinite plates. Int. J. Numer. Methods Eng. 53(5), 1145–1173 (2002) 6. Galán, J.M., Abascal, R.: Boundary element solution for the bidimensional scattering of guided waves in laminated plates. Comput. Struct. 83(10–11), 740–757 (2005) 7. Goetschel, D.B., Dong, S.B., Muki, R.: A global local finite element analysis of axisymmetric scattering of elastic waves. J. Appl. Mech. 49(4), 816–820 (1982) 8. Guo, N., Cawley, P.: The interaction of Lamb waves with delaminations in composite laminates. J. Acoust. Soc. Am. 94(4), 2240–2246 (1993) 9. Haider, M.F., Bhuiyan, M.Y., Poddar, B., Lin, B., Giurgiutiu, V.: Analytical and experimental investigation of the interaction of Lamb waves in a stiffened aluminum plate with a horizontal crack at the root of the stiffener. J. Sound Vib. 431, 212–225 (2018) 10. Hayashi, T., Song, W.J., Rose, J.L.: Guided wave dispersion curves for a bar with an arbitrary cross-section, a rod and rail example. Ultrasonics 41(3), 175–183 (2003) 11. Karunasena, W.M., Liew, K.M., Kitipornchai, S.: Hybrid analysis of Lamb wave reflection by a crack at the fixed edge of a composite plate. Comput. Methods Appl. Mech. Eng. 125 (1–4), 221–233 (1995) 12. Poddar, B., Giurgiutiu, V.: Complex modes expansion with vector projection using power flow to simulate Lamb waves scattering from horizontal cracks and disbonds. J. Acoust. Soc. Am. 140(3), 2123–2133 (2016) 13. Poddar, B., Giurgiutiu, V.: Scattering of Lamb waves from a discontinuity: an improved analytical approach. Wave Motion 65, 79–91 (2016) 14. Rattanawangcharoen, N., Zhuang, W., Shah, A.H., Datta, S.K.: Axisymmetric guided waves in jointed laminated cylinders. ASCE J. Eng. Mech. 123(10), 1020–1026 (1997) 15. Ricci, F., Monaco, E., Maio, L., Boffa, N., Mal, A.K.: Guided waves in a stiffened composite laminate with a delamination. SHM Int. J. 15(3), 351–358 (2016) 16. Rose, J.L.: Ultrasonic Guided Waves in Solid Media. Cambridge University Press, Cambridge (2014) 17. Spada, A., Capriotti, M., Cui, R., Lanza di Scalea, F.: Improved global-local model to predict guided-wave scattering patterns from discontinuities in complex parts. In Proceedings of SPIE - The International Society for Optical Engineering, SPIE 2019, vol. 10972, article number 109720 M, (2019) 18. Spada, A., Capriotti, M., Lanza di Scalea, F.: Global-local model for guided wave scattering problems with application to defect characterization in built-up composite structures. Int. J. Solids Struct. 182-183, 267–280 (2020) 19. Srivastava, A., Lanza di Scalea, F.: Quantitative structural health monitoring by ultrasonic guided waves. ASCE J. Eng. Mech. 136(8), 937–944 (2010) 20. Tian, J., Gabbert, U., Berger, H., Su, X.: Lamb wave interaction with delaminations in CFRP laminates. Comput. Mater. Continua 1(4), 327–336 (2004)

Smart Multifunctional Materials and Systems for SHM of Large Structures

Recent Advances and Open Issues on the Use of Smart Bricks for Seismic Monitoring of Masonry Buildings: Experimental Tests and Numerical Simulations Andrea Meoni(&), Antonella D’Alessandro, and Filippo Ubertini Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, PG, Italy [email protected]

Abstract. Masonry buildings are particularly prone to structural pathologies and brittle failures, typically caused by excessive stresses/strains, differential foundation settlements, aging of materials, and natural hazards, such as seismic events. Monitoring the health state of this type of structures during their service life plays a fundamental role in the identification of incipient damages or critical conditions and the optimization of maintenance interventions. In light of that, the Authors recently developed a novel class of sensors, called smart bricks, for structural health monitoring of masonry constructions. These novel sensors consist of fired bricks made by doping fresh clay with conductive stainless steel micro fibers that enhance the piezoresistive capability of the composite. Smart bricks are equipped with copper plate electrodes and can be deployed within masonry constructions, as normal bricks, for monitoring changes in strain, modifications in load paths, and development of damages. This paper deals with an investigation on the effectiveness of smart bricks for the estimation of strain under increasing compression loads, in particular when sensors are deployed within a typical structural setting. With this aim, smart bricks’ strain measurements are compared with those of traditional strain gauges applied onto each tested sample. Furthermore, numerical simulations are carried out for reconstructing strain field maps of a masonry wall subjected to eccentric axial compression tests, by exploiting strain measurements outputted by smart bricks embedded within the load-bearing structure. Overall, results have confirmed the effectiveness of the novel sensors in strain measurements under increasing compression states. Keywords: Smart bricks
Masonry structures
Structural health monitoring Smart sensors
Strain sensitivity




1 Introduction Masonry structures of the European historical and building heritage are a cultural value that must be maintained over time. Nowadays, their preventive conservation is a quite challenging task, since masonry constructions are particularly prone to structural pathologies and fragile collapses mechanisms, owing to excessive stresses/strains, © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 851–860, 2021. https://doi.org/10.1007/978-3-030-64594-6_82

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differential foundation settlements, aging of materials, and natural hazards, such as seismic events [1–3]. In light of that, the employment of tailored Structural Health Monitoring (SHM) systems results in noteworthy benefits for the conservation of masonry constructions during their service life, allowing the real-time identification of changes in the structural behavior of the monitored buildings, thus playing a role of utmost importance in the detection of developing damages or critical conditions, and in planning of retrofitting and maintenance interventions [4–6]. Exploiting their background knowledge in the development of construction materials with self-sensing capabilities for strain monitoring [7, 8], the Authors recently proposed a novel class of sensors, called “smart bricks”, for SHM of masonry structures [9, 10]. The new sensing technology consists of fired bricks made by adding electrically conductive stainless steel micro fibers to fresh clay, so as to boost the piezoresistive capability of the base material. Therefore, smart bricks, equipped with external copper plate electrodes, can be internally deployed within masonry constructions as components of the load-bearing structure while monitoring changes in strain, internal redistribution of load paths, and development of damages, through the assessment of variations in their electrical outputs due to changes in their compressive strain state. The paper investigates the effectiveness of smart bricks in strain estimation when subjected to increasing compression loads. In light of that, tested smart bricks were instrumented with a couple of traditional strain gauges to be used as benchmark. The organization of the rest of the paper is hereinafter reported: Sect. 2 describes the production process of the investigated smart bricks, along with the methodology adopted to conduct electrical measurements. The post-processing of smart bricks’ electrical outputs, for strain measurement estimation, is also introduced. Section 3 illustrates the methodologies followed to perform experimental and numerical tests, as well as the achieved results. Section 4 concludes the paper with comments and remarks.

2 The Smart Brick Technology 2.1

Production Process

Smart bricks investigated in this work were produced according to the manufacturing process reported in Fig. 1. Fresh clay was mechanically mixed with electrically conductive stainless steel micro fibers, model R.STAT/S, by considering a filler’s content of 0.50% with respect to the weight of fresh clay (Fig. 1a,1b). The obtained composite material was poured within prismatic wooden molds, of 7.0  5.0  5.0 cm3, properly wet and sprinkled with sand, to form brick samples (Fig. 1c). The latter were first dried in oven at a temperature of 90 °C for six hours, then fired up to a temperature of 900 °C for another six hours. Smart bricks were finally equipped with two external copper plate electrodes, placed onto their opposite horizontal surfaces with a dry contact, to perform electrical measurements. Each sample was covered with an insulating layer avoiding current flow propagation when inserted within masonry constructions (Fig. 1d).

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Fig. 1. Illustration of the production process of smart bricks.

2.2

Electrical and Strain Measurements

Smart bricks’ electrical measurements were performed by adopting a biphasic DC measurement approach [11]. A voltage square wave input of ±10 V (20 V peak-topeak), characterized by a duty cycle of 50% and by a frequency of 1 Hz, was applied to each sample by using a function generator, model RIGOL DG1022. The output, a current square wave signal, was measured by means of a digital multimeter, model NIPXI 4071, mounted within a DAQ, model NI-PXIe 1073, by adopting a sampling frequency of 10 Hz and a current measurement range of 1 µA. Smart bricks’ total electrical resistance, R, was calculated according to the Ohm’s Law, by considering current intensity measurements taken at the 80% of the positive constant part of the acquired square wave current signal: RðtÞjt¼^t ¼ V=I ðtÞjt¼^t

ð1Þ

where V is the applied constant voltage in the positive part of the input square wave, equal to +10 V, while I is the value of the current output taken at the time ^t. Smart bricks’ strain measurements were retrieved according to a recently proposed theoretical model, called series resistors model, defined by the Authors to describe the strain-sensing behavior of the novel sensors when strained in compression. Such an electromechanical model takes into account both contributions to the sensing due to the enhanced piezoresistivity of the smart bricks and the contact resistance at the electrodes, as follows [12]: DR R  Ri;0 ¼ ¼ a0 eb  ke þ c0 ffi a0 eb  ke Ri;0 Ri;0

ð2Þ

where Ri;0 is the unstrained smart bricks’ internal electrical resistance, a0 represents the relative sensing at the contact resistance, e is the applied uniaxial strain considered positive in compression, b is the exponential term of the equation (b ¼ 3), and k is the gauge factor. Equation’s parameters were determined by applying a properly defined calibration procedure on each smart brick [12]. Once terms of the series resistors model

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were determined, strain measurements provided by a smart brick were estimated by post-processing its electrical outputs with Eq. (2).

3 Experimental Investigation 3.1

Axial Compression Tests on Single Smart Bricks

Single smart bricks were tested under increasing axial compression loads to prove their effectiveness in measuring compressive strain by comparing their outputs with those of traditional strain gauges. Therefore, each investigated novel sensor was instrumented with a couple of resistive strain gauges, model Kyowa KFG-20-120-C1-11L1M2R, characterized by a gauge factor of 2.11 and directly attached onto the middle vertical surfaces of the bricks, along the loading direction. A data acquisition card, model NI PXIe-4330, hosted within a chassis NI PXI-1073, was used to acquire measurements from strain gauges, while three Linear Variable Differential Transformers (LVDTs), placed at 120° in plan, were employed to measure displacements for specializing Eq. (2) according to the calibration procedure [12]. The adopted testing setup is reported in Fig. 2, along with the load history applied on each sample. Figure 3a shows strain histories provided by a tested smart brick, whose strainsensing behavior has been characterized by setting its series resistors model with the parameters collected in Table 1, and by its corresponding couple of strain gauges, for which the average strain has been considered. Results demonstrate a fairly good match between the compared trends, pointing out a clear strain-sensing capability exhibited by the novel sensor, which detected each increment in the applied load by outputting an increase in its compressive strain state. It should be noted that smart brick’s measurements are characterized by a non-linear trend that is attributable to the capability of the novel sensors to take into account settlements of their macro porous structure under compression loads, a mechanical behavior typically exhibited also by the conventional bricks, when strained in compression, since possessing a similar internal structure. Such settlements were induced by the first application of the compression load on the sample, resulting in non-constant increments in its compressive strain under constant increases in the applied load. Further increases in the compressive strain condition of the tested smart brick produced internal settlements of negligible values, hence the novel sensor outputted constant increments in strain at high compressive states. Figure 3b compares sensors’ measurements versus an ideal trend, which exemplifies an equal strain-sensing behavior between the considered sensing technologies, thus remarking differences in the nature of their strain outputs. In particular, since the outputs provided by strain gauges are strictly related to the outer portions of the brick where they are punctually attached, rather than to its entire volume, strain gauges’ measurements were reasonably less influenced by the internal settlements of the macro porous structure of the brick, as also confirmed by the linear trend characterizing strain gauges’ outputs in Fig. 3a.

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Table 1. Series resistors model’s parameters adopted for computing strain measurements from the smart brick tested to axial compression loads: Ri;0 is the unstrained smart brick’s internal electrical resistance, a0 represents the relative sensing at the contact resistance, b is the exponential term of the equation, and k is the gauge factor. Ri;0 [MX] a0 [−] 5.80

3.2

b [−] k [−]

6.86E−12 3.00

363.00

Small-Scale Masonry Wall Under Eccentric Axial Compression Load

A small-scale masonry wall specimen, equipped with seven smart bricks, was subjected to eccentric axial compression tests to investigate the effectiveness of the novel sensors in measuring strain under compression loads when embedded within a structural setting. The specimen, a wall of 37.0  5.0  39.0 cm3 composed by bricks arranged in seven rows and five columns with mortar layers of thickness of about 0.5 cm (Fig. 4a), was tested by applying on its right side the load history reported in Fig. 4b, considering a distance of about 11 cm from its centerline. Smart bricks were fully integrated within the thickness of the tested structural element as exemplified in Fig. 5a, which also illustrates the cracking pattern visually detected after completing the test. It should be noted that cracks named c1, c2, and c3 were formed at the load step of 15 kN, while cracks c4 and c5 were developed after the application of the load steps of 25 and 50 kN, respectively. Formed cracks got larger while conducting the tests as the applied load increased. A couple of traditional strain gauges was attached onto each embedded sensor by allowing the comparison between strain measurements. These last were estimated from smart bricks’ electrical outputs by using Eq. (2) set with the equation’s parameters collected in Table 2 and obtained by considering the average value of each coefficient of the equation determined by testing a broader set of smart bricks made with a content of microfibers of 0.50%, while the average strain was considered for each couple of strain gauges, whose outputs were gathered through a DAQ, model IMC Cronos-PL 16, set with a nominal gauge resistance of 120 X. Figure 5b reports changes in strain versus applied load for smart brick 3 and its corresponding couple of strain gauges, for which the average strain has been considered. The application of the load on the wall specimen produced marked changes in the strain state of the considered smart brick until the load step of 15 kN, after which the increasing trend indicating increments in compressive strain provided by the novel sensor was interrupted in correspondence to the formation of cracks c1, c2, and c3, which induced a first load paths redistribution internally to the tested structural element. On the other hand, strain gauges recorded increasing changes in strain up to the load step of 50 kN, after which, conceivably, the opening of crack c1 was of such severity to induce an internal load redistribution that deviated compressive stresses from the outer surfaces of the smart brick 3 where the couple of strain gauges was attached. The traditional sensors recorded a decrease in compressive strain even if the load applied on the specimen was increased up to 60 kN.

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Measurements acquired from smart bricks and strain gauges were further postprocessed to map the strain field throughout the wall specimen by using the Ordinary Kriging method, a statistical tool for data spatial interpolation [13]. Figure 6 shows maps of changes in strain computed for the load steps corresponding to applied loads of 10 and 60 kN, respectively. Plotted maps are consistent with the performed eccentric axial compression test since they highlight the right part of the wall as the most strained in compression due to the direct application of the load. It is worth noting that maps retrieved by spatially interpolating smart bricks’ strain measurements denote that the novel sensors, being internally deployed in the thickness of the wall specimen, were capable of revealing a more uniform internal redistribution of the applied load than strain gauges, which were less sensitive to changes in strain since externally attached to the tested structural element. Table 2. Series resistors model’s parameters adopted for computing strain measurements from smart bricks embedded within the small-scale masonry wall specimen: Ri;0 is the unstrained smart bricks’ internal electrical resistance, a0 represents the relative sensing at the contact resistance, b is the exponential term of the equation, and k is the gauge factor. Ri;0 [MX] a0 [−] 7.92

b [−] k [−]

5.86E−12 3.00

397.00

Fig. 2. Testing setup and load history adopted to perform axial compression tests on single smart bricks instrumented with a couple of traditional strain gauges.

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Fig. 3. Results obtained by performing axial compression tests on a smart brick instrumented with a couple of traditional strain gauges, for which the average strain (AVG) was taken into account: (a) Outputted strain histories; (b) Direct comparison between smart brick’s and strain gauges’ measurements versus an ideal trend.

Fig. 4. Eccentric axial compression tests on a small-scale masonry wall specimen: (a) Picture of the tested structural element; (b) Applied load history.

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Fig. 5. Results from the eccentric axial compression tests on a small-scale masonry wall specimen: (a) Smart bricks and strain gauges deployment with annotated the cracking pattern detected at the end of the test; (b) Changes in strain versus applied load for smart brick n. 3 and its couple of strain gauges, for which the average strain (AVG) was taken into account.

Fig. 6. Strain field maps of the small-scale masonry wall specimen reconstructed by interpolating measurements provided by smart bricks and traditional strain gauges, for which the average strain (AVG) was taken into account, through the Ordinary Kriging interpolator (1–7 represent the position of the smart bricks).

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4 Conclusions The paper has presented experimental and numerical investigations on the effectiveness of smart bricks for the estimation of strain under compression loads. Therefore, the strain-sensing capability of the novel sensors, made with a stainless steel microfibers’ content of 0.50% with respect to the weight of fresh clay, was assessed, first, by applying an increasing axial compression load on single samples, and then, by employing smart bricks for monitoring the strain state of a small-scale masonry wall specimen subjected to eccentric axial compression loads. A couple of traditional strain gauges were attached onto the outer surfaces of each novel sensor, thus allowing the comparison between strain measurements. Results obtained by carrying out axial compression tests on single smart bricks have demonstrated a clear strain-sensing capability of the novel sensors to compressive strains, by also highlighting some differences in the nature of the measurements outputted by the compared sensing technologies, which were however consistent between them. Indeed, smart bricks have shown an enhanced strain-sensitivity also capable of reproducing the non-linear mechanical behavior typically exhibited by the conventional bricks when strained in compression, since their internal macro porous structure is similar and prone to settlements under low compressive state. Results collected by performing eccentric axial compression tests on a small-scale masonry wall specimen have demonstrated the usefulness of smart bricks in revealing the internal load paths redistributions that occurred on the wall specimen due to the increasing applied load and consequent cracks formations, in comparison with traditional strain gauges. These last, in particular, being externally and punctually attached, were less sensitive to the internal stress modifications. Moreover, numerical results achieved by using the Ordinary Kriging interpolator for the reconstruction of the strain field maps of the wall specimen have shown the effectiveness of the novel sensor for mapping concentrations/relaxations in the strain field of masonry structural elements. Overall, obtained results have demonstrated that smart bricks are effective in strain estimation under compression loads, thus confirming the novel sensors as a powerful new sensing technology tailored for SHM of masonry structures. Acknowledgements. This work was supported by the Italian Ministry of Education, University and Research (MIUR) through the founded project of Relevant National Interest (PRIN) “DETECT-AGING (Degradation Effects on sTructural safEty of Cultural heriTAGe constructions through simulation and health monitorING)” (Protocol no. 201747Y73L). The first author also wishes to acknowledge the German Academic Exchange Service (DAAD) for partially supporting his Ph.D. through the founded project of Research Grants – Short-Term Grants, 2019 (57442045).

References 1. Lagomarsino, S.: On the vulnerability assessment of monumental buildings. Bull. Earthq. Eng. 4, 445–463 (2006) 2. De Lorenzis, L., DeJong, M., Ochsendorf, J.: Failure of masonry arches under impulse base motion. Earthq. Eng. Struct. Dyn. 36(14), 2119–2136 (2007)

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3. Atamturktur, S., Bornn, L., Hemez, F.: Vibration characteristics of vaulted masonry monuments undergoing differential support settlement. Eng. Struct. 33(9), 2472–2484 (2011) 4. Masciotta, M.G., Roque, J.C., Ramos, L.F., Lourenço, P.B.: A multidisciplinary approach to assess the health state of heritage structures: the case study of the Church of Monastery of Jerónimos in Lisbon. Constr. Build. Mater. 116, 169–187 (2016) 5. Gentile, C., Saisi, A.: Ambient vibration testing of historic masonry towers for structural identification and damage assessment. Constr. Build. Mater. 21(6), 1311–1321 (2007) 6. Carpinteri, A., Lacidogna, G.: Damage monitoring of an historical masonry building by the acoustic emission technique. Mater. Struct. 39(2), 161–167 (2006) 7. D’Alessandro, A., et al.: Static and dynamic strain monitoring of reinforced concrete components through embedded carbon nanotube cement-based sensors. Shock Vibr. 2017 (2), 1–11 (2017) 8. Meoni, A., et al.: An experimental study on static and dynamic strain sensitivity of embeddable smart concrete sensors doped with carbon nanotubes for SHM of large structures. Sensors 18(3), 831 (2018) 9. D’Alessandro, A., Meoni, A., Ubertini, F.: Stainless steel microfibers for strain-sensing smart clay bricks. J. Sens. 2018, 1–8 (2018) 10. Meoni, A., D’Alessandro, A., Cavalagli, N., Gioffré, M., Ubertini, F.: Shaking table tests on a masonry building monitored using smart bricks: damage detection and localization. Earthq. Eng. Struct. Dyn. 48(8), 910–928 (2019) 11. Downey, A., D’Alessandro, A., Ubertini, F., Laflamme, S., Geiger, R.: Biphasic DC measurement approach for enhanced measurement stability and multi-channel sampling of self-sensing multi-functional structural materials doped with carbon-based additives. Smart Mater. Struct. 26(6), 065008 (2017) 12. Meoni, A., D’Alessandro, A., Ubertini, F.: Characterization of the strain-sensing behavior of smart bricks: A new theoretical model and its application for monitoring of masonry structural elements. Constr. Build. Mater. 250, 118907 (2020) 13. Cressie, N.: Spatial prediction and ordinary kriging. Math. Geol. 20(4), 405–421 (1988)

Graphite-Cement Composites as Low-Cost Strain Sensing Multifunctional Materials H. Borke Birgin1(B) , Antonella D’Alessandro1 , Simon Laflamme2 , and Filippo Ubertini1 1

University of Perugia, via Duranti, 93, 06125 Perugia, Italy [email protected] 2 Iowa State University, Ames, IA 50011, USA

Abstract. Graphite, an allotropic form of carbon with high electrical conductivity, in the range of 2 × 105 to 3 × 105 S·m−1 , is a more affordable alternative to carbon nanotube nanoinclusions in the fabrication of conductive multifunctional cement-based materials, such as smart concretes, used in strain monitoring. The enhancement of piezo-resistivity is one possible functionality of graphite inclusions that has not yet been explored in depth in the literature. In order to bridge this gap, the authors investigate the piezo-resistive strain-sensing response of graphitecement composite materials. The composite samples were prepared with different amounts of graphite inclusions and experimentally subjected to electro-mechanical tests. The study discusses the improvements in conductivity, strain sensitivity, and signal linearity achieved with graphite inclusion. Because of the easier dispersion and lower cost of graphite particles, the investigated composites can be scaled up to large concrete elements, useful to create smart road pavements enabling intelligent weigh-in-motion sensing as intended in this research. Results demonstrate that multifunctional self-sensing composite pavements doped with graphite are capable of strain sensing with high linearity and sensitivity. In particular, it was found that a 20% graphite-to-cement ratio exhibited the best properties in terms of gauge factor, drift, reproducibility, and linearity. Keywords: Graphite · Cement composite materials · Weigh-in-motion · Smart materials · Multifunctional structural materials · Strain sensing · Sensors · Traffic loads

1

Introduction

Concrete is a popular structural material due to its simple fabrication process, relatively low cost, high load bearing capability, and well-defined design through established codes. The inclusion of carbon to structural cement-matrix materials as the potential to yield multifunctional capabilities. One possible functionality c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 861–869, 2021. https://doi.org/10.1007/978-3-030-64594-6_83

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is strain sensing which derives from enhanced piezoresistivity [1]. The piezoresistivity is improved around the electrical percolation threshold [2,3] when the resistivity of material decreases significantly. At this threshold, composite materials exhibit enhanced strain-sensitivity [4]. Some widely used fillers for cement matrices are carbon nanotubes [5–8], carbon fibers [8–10], graphene nano platelets (GNPs) [8,11] and carbon black [8,10,12]. Although carbon fillers with high aspect ratios are more popular, literature also discusses low aspect ratio carbon inclusions such as GNPs. While GNPs exhibit percolating behaviors, their particle shapes create imperfections inside the matrix and the gauge factor is known to decay with increasing loading [11]. Compared to high aspect ratio fillers, these particles are needed in higher concentration to reach percolation. Hence, the workability of such composite materials will decrease. A solution to increase workability is to add water, but this may negatively impact mechanical properties. In some studies, increasing the water ratio was shown to cause reductions in piezoresistivity due to agglomerations [13], while other studies found an opposite behaviour [14]. The use of dispersants and plasticisers may also yield a decrease of mechanical properties [15] and sensitivity by altering the conductivity of the material [16]. Cement-based materials doped with carbon nano- and micro-fillers have be shown as effective for small-scale samples, but they do not scale up well because of their high cost and difficult fabrication process [17]. In this study, graphite is proposed as an alternative filler for cement-based composites. Graphite possesses high electrical conductivity, in the range of 2 × 105 to 3 × 105 S·m−1 , is relatively less expensive compared to other variations of carbon based conductive fillers [18], and can be dispersed homogeneously in large volumes of cement matrix by mechanical mixing, making it suitable for in-situ applications [19]. The authors investigate the sensing capabilities of GNP-based smart cement composites for the weigh-in-motion (WIM) sensing applications, based on results from literature showing the general promise of smart cementitious sensors at sensing dynamic loads up 10 Hz [8,20,21]. The strain-sensitivity of GNP-doped cementitious matrices is investigated. The research begins with investigation of the percolation threshold of cement-graphite composite materials with doping levels of 0, 5, 10, 20, 30 and 40% graphite by the weight of cement (g/c). After the curing period, the samples are tested at different levels of compressive loads in order to observe the variation of the sensitivity and linearity with respect to the graphite content. The rest of the paper is organized as follows. Section 2 describes materials and experimental procedures. Section 3 discusses the percolation threshold. Section 4 presents the results of electromechanical tests. Section 5 concludes the paper.

2 2.1

Materials and Experimental Methodology Materials and Samples

The samples are fabricated using Portland Cement type 42.5R, graphite powder, and tap water. Different amounts of graphite and cement are mixed mechanically

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until homogenous mixture is achieved (see Fig. 1(a)). After, water is added to the mixture and mixed until a homogenous compound is attained (see Fig. 1(b)). Then, the compound is poured into oiled molds in order to prepare small cubic specimens (see Fig. 1(c)). graphite

cement tap water

molding

homogeneous mixture

dry mix

(a)

mixing

(b)

(c)

Fig. 1. The mixing and molding of the graphite-cement composite cubes; (a) dry mixing; (b) addition of water; and (c) molding.

The mix designs of the different modified cementitious materials are reported in Table 1. The mixes differ in the graphite content, from 0 to 40% with respect to the weight of cement. The water-to-cement ratio is selected initially as 0.45. It is raised to 0.50 for the mixtures with 10 and 20% g/c ratio, and, to 0.55 for the mixtures with 30 and 40% g/c ratio to increase the workability. A fragment of graphite is kept for carrying out inspections with an electron scanning microscope (SEM). The cubes are unmolded after two days and cured 30 days in laboratory conditions. Figure 2(a) shows a 5 × 5 × 5 cm3 cubic sample. The internal steel nets, used as electrodes for the electrical measurements, are placed at a distance of 2 cm. The experimental setup is shown in Fig. 2(b), where the voltage is applied to the red and black cables on left. The measurement of the voltage difference through the sample is taken by the red and black cables on the right. The sketch of the electric circuit is illustrated in Fig. 2(c), where the cubic sample with resistance Rc is connected in series with a shunt resistor. The electric current, I, is constant through the composite sample and the shunt resistor. The resistivity readings are taken 15 days after fabrication and after the curing period of 30 days when the electromechanical tests are conducted.

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2.2

Graphite-to-cement percentage 0% 5% 10% 20% 30% 40%

Cement (gr)

212 212 212

Graphite (gr)

0

212

212

212

21

42

64

85

Tap water (gr) 95.5 95.5 106

106

10

116.5 116.5

Devices and Experimental Tests

The biphasic measurement method is used [22] with a voltage square wave of 10 V amplitude 1 Hz frequency sourced by NI PXIe-4138, (V0 in Fig. 2(c)), with the purpose of eliminating the drift produced by the DC on the dielectric materials. The data acquisition system is supported by an NI PXIe-1092 that includes an analog input module NI PXIe-4302. The sampling rate of voltage reading is set 10 Hz. The electrical resistance of cubic sample is obtained by using Ohm’s law using the voltage readings through the 1 kΩ shunt resistor and through the sample, V1 and V2 respectively, from the channels are labeled as ch1 and ch2, respectively (see Fig. 2(c)). V0 : +/-10V biphasic squarewave

+-

ch1 V1

1kΩ

V2 ch2

I (a)

(b)

Rc (c)

Fig. 2. (a) A typical cubic sample; (b) sample placement and instrumentation in the compression machine; and (c) general circuit for measuring Rc .

The load is applied by an electric servo test machine from Controls, model Advantest 50-C7600, that has servo-hydraulic control unit, model 50-C 9842, with a maximum load of 15 kN. Strains are measured with three LVDT transducers placed at 120◦ in-plane. Their calibration is carried out using 20 mm-long strain gauges attached on benchmark specimens.

3

Percolation Investigation

The first step of the research is the investigation of the electrical properties of the cement-based materials with increasing amounts of graphite filler. The presence of the carbon-based fillers enhances the electrical properties of the matrix, up to

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the percolation threshold, when the conductive network of the inclusions governs the electric conduction mode. For increasing amounts of inclusions above the percolation threshold, no significant increase in electrical conductivity is expected. This feature is investigated by use of both analytical and experimental approaches. The analytical percolation of graphite fillers is investigated using the approach proposed in literature for doped materials [3]. The theoretical percolation threshold depends on the aspect ratio of the fillers. Figure 4(a) shows the variation of such values at different aspect ratios of the inclusions. In particular, for aspect ratios of 11, 10, 5, 3 and 2, graphite contents of 5%, 10%, 20%, 30% and 40% are expected, respectively.

d2

d1

d3 d3

40 μm

Fig. 3. The SEM micrograph of graphite particles.

Figure 3 shows an SEM micrograph of graphite powder. From the Fig. 3, the majority of the particles may be approximated as elliptical cylinders with axis dimensions (d1 , d2 ) larger than height (d3 ). The aspect ratio, AR, based on electroscope inspection is calculated as: AR =

d1 + d2 2d3

(1)

where d1 and d2 are the major and minor axes of the ellipse, respectively, and d3 is the height of the elliptical cylinder. The obtained aspect ratio for the particles is approximately 10, which falls into a g/c weight ratio range between 10% and 20% at the percolation threshold. The resistivity values measured during the curing period are presented in Fig. 4(b). The plot shows a clear decrease of resistivity above 20% graphite, exhibiting a percolation threshold located between 10 and 30% graphite, confirming the analytic results.

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6

15th day after curing 0.5 5

10 0.4

Resistivity [ .cm]

Volumetric fraction of filler at percolation

0.6

0.3 40% g/c 30% g/c

0.2

3

10

20% g/c 0.1

4

10

10% g/c 5% g/c

0 0 10

1

10

10

2

10

102 0%

3

5%

Aspect ratio of filler (a)

10%

20%

30%

40%

Samples with different percentage of graphite (b)

Fig. 4. (a) The analytic calculation of the percolation threshold versus aspect ratio of conductive fillers; and (b) experimental readings showing the variation in resistivity over curing.

4

Sensitivity

The second step of the research is the investigation of the sensitivity of the samples as a function of levels of fillers, characterizing their capability to identify variations in strain and stress. 4.1

Loads

A compressive preload of 1.5 kN is applied onto the sample, after which a triangular cyclic load is applied with peak values of 1 kN, 2 kN and 3 kN, corresponding to 0.4 MPa, 0.8 MPa and 1.2 MPa of incremental compressive stresses, respectively. The load is applied perpendicularly to the stainless steel nets (Fig. 2(b)). Figure 5 reports the load history adopted for the sensitivity tests. Load increment [kN]

3 2.5 2 1.5 1 0.5 0 0

10

20

30

40

50

60

Time [s]

Fig. 5. Load history for the electromechanical tests.

4.2

Electrical Outputs

Figure 6 plots the strain time histories and the normalized electrical resistance time histories recorded during electromechanical testing on cubic samples with increasing weight contents of graphite, from 0 to 40% g/c ratio.

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40 -0.015

0 20

40

40 -0.015

60

0 0

20

40

0.000

80 40

-0.015

60

0 0

20

Time [s] (b)

0.010

120

0.000

80 40

40

60

Time [s] (c)

0.010

Strain [ ] R/R 0 [-]

R/R 0 [-]

Time [s] (a)

120

]

80

150

0.000

100 50

0.025

Strain [ ] R/R 0 [-]

0

0.000

0.010

Strain [

80

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100 50

]

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0.010

Strain [

120

Strain [ ] R/R 0 [-]

strain

0.010

Strain [ ] R/R 0 [-]

R/R 0 [-]

resistance

867

-0.015 -0.015

0 0

20

40

0

60

0

20

Time [s] (d)

40

-0.050

60

0 0

Time [s] (e)

20

40

60

Time [s] (f)

Fig. 6. Typical change in resistivity and change in strain time histories for: (a) 0%; (b) 5%; (c) 10%; (d) 20%; (e) 30%; and (f) 40% g/c ratios.

From the figure, it can be observed that the compressive load provokes a decrease in resistance during loading and an increase in resistance during unloading. This is due to the behavior of the conductive particles that get closer during the mechanical loading process. One can also observe that there is a drift due to the polarization effect for the 0% g/c sample, indicated by a dashed black line. This drift vanishes with the inclusion of graphite. Also, the signal from the 30% g/c sample exhibits significant distortions, attributable to the reached percolation. The 40% sample shows a sensitive behavior despite beyond percolation. However, the material imperfections likely govern the piezoresistivity at this stage. A very low workability of the 40% sample was observed and as well as important deformations in the material, indicating that sample was not suitable. 140 120

Gauge Factor,

100 80 60 40 20 0 0%

5%

10%

20%

30%

40%

Samples with different percentage of graphite

Fig. 7. Gauge factor and 95% fit interval of the gauge factor under each g/c ratio.

Figure 7 shows the results of the electromechanical test. The gauge factors, GF or λ, and the 95% fit interval for all specimens are plotted. The values are calculated using discrete data points from the time history. Table 2 reports the gauge factor, 95% fit interval, and linearity of the signal for every sample. The calculation is made using all values on the strain and resistivity time series. The R2 values indicate the linearity of ΔR/Δ. The gauge factor increases with

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increasing content of graphite up to 20% g/c sample. The high sensitivity of the 40% g/c can be explained by he inhomogeneities in the material aforementioned. It can be concluded that the 20% g/c sample exhibit the best properties overall in terms of gauge factor, drift, reproducibility, and linearity. Table 2. Results of the electromechanical tests. g/c

0% 5% 10% 20% 30% 40%

Gauge factor

22

28

40

119

95

97

95% interval (±)

11

2

2

3

19

35

Linearity, R (%) 40

92

97

98

19

74

2

5

Conclusion

The paper presented a study on the sensing capabilities of cement-matrix materials doped with graphite for weigh-in-motion sensing applications. Firstly, the determination of percolation threshold has been analytically and experimentally analyzed. Secondly, the self-sensing characteristics of the samples with filler amounts of 0, 5, 10, 20, 30 and 40% graphite content have been investigated through electromechanical tests. Analytical results from the percolation theory suggested a percolation threshold between 10 and 30% graphite content. These results were confirmed by the experimental results, where the 20% of graphite content exhibited the best properties in terms of gauge factor, drift, reproducibility, and linearity. Acknowledgements. The authors acknowledge the European Union’s Horizon 2020 research and innovation programme under Grant Agreement N. 765057 - SAFERUP!

References 1. Laflamme, S., Ubertini, F.: Back-to-basics: self-sensing materials for nondestructive evaluation. Mater. Eval. 78, 526–536 (2019) 2. Dong, W., Li, W., Tao, Z., Wang, K.: Piezoresistive properties of cement-based sensors: review and perspective. Constr. Build. Mater. 203, 146–163 (2019) 3. Garc´ıa-Mac´ıas, E., Castro-Triguero, R., S´ aez, A., Ubertini, F.: 3d mixed micromechanics-fem modeling of piezoresistive carbon nanotube smart concrete. Comput. Methods Appl. Mech. Eng. 340, 396–423 (2018) 4. Ubertini, F., Laflamme, S., D’Alessandro, A.: Smart cement paste with carbon nanotubes. In: Innovative Developments of Advanced Multifunctional Nanocomposites in Civil and Structural Engineering, pp. 97–120. Elsevier (2016) 5. Han, B., Yu, X., Kwon, E., Ou, J.: Piezoresistive multi-walled carbon nanotubes filled cement-based composites. Sens. Lett. 8, 344–348 (2010) 6. Yu, X., Kwon, E.: A carbon nanotube/cement composite with piezoresistive properties. Smart Mater. Struct. 18, 055010 (2009)

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7. D’Alessandro, A., Ubertini, F., Laflamme, S., Rallini, M., Materazzi, A.L., Kenny, J.M.: Strain sensitivity of carbon nanotube cement-based composites for structural health monitoring. In: Proceedings of the SPIE, vol. 9803 (2016) 8. Pisello, A.L., D’Alessandro, A., Sambuco, S., Rallini, M., Ubertini, F., Asdrubali, F., Materazzi, A.L., Cotana, F.: Multipurpose experimental characterization of smart nanocomposite cement-based materials for thermal-energy efficiency and strain-sensing capability. Sol. Energy Mater. Sol. Cells 161, 77–88 (2017) 9. Shi, Z.-Q., Chung, D.: Carbon fiber-reinforced concrete for traffic monitoring and weighing in motion. Cem. Concr. Res. 29(3), 435–439 (1999) 10. Han, B., Ou, J.: Embedded piezoresistive cement-based stress/strain sensor. Sens. Actuators A 138(2), 294–298 (2007) 11. Tao, J., Wang, X., Wang, Z., Zeng, Q.: Graphene nanoplatelets as an effective additive to tune the microstructures and piezoresistive properties of cement-based composites. Constr. Build. Mater. 209, 665–678 (2019) 12. Laflamme, S., Eisenmann, D., Wang, K., Ubertini, F., Pinto, I., DeMoss, A.: Smart concrete for enhanced nondestructive evaluation. Mater. Eval. 76(10), 1395–1404 (2018) 13. Kim, H., Park, I., Lee, H.: Improved piezoresistive sensitivity and stability of CNT/cement mortar composites with low water-binder ratio. Compos. Struct. 116, 713–719 (2014) 14. Zhang, L., Ding, S., Han, B., Yu, X., Ni, Y.-Q.: Effect of water content on the piezoresistive property of smart cement-based materials with carbon nanotube/nanocarbon black composite filler. Compos. Part A Appl. Sci. Manuf. 119, 8–20 (2019) 15. Han, B., Zhang, K., Yu, X., Kwon, E., Ou, J.: Fabrication of piezoresistive CNT/CNF cementitious composites with superplasticizer as dispersant. J. Mater. Civ. Eng. 24(6), 658–665 (2011) 16. Gonz´ alez-Segura, K., Ca˜ nete-Rosales, P., Del Rio, R., Y´ an ˜ez, C., Ferreyra, N.F., Rivas, G.A., Bollo, S.: Effect of the dispersing agent on the electrochemical response of glassy carbon electrodes modified with dispersions of carbon nanotubes. Electroanalysis 24(12), 2317–2323 (2012) 17. D’Alessandro, A., Rallini, M., Ubertini, F., Materazzi, A.L., Kenny, J.M.: Investigations on scalable fabrication procedures for self-sensing carbon nanotube cementmatrix composites for SHM applications. Cem. Concr. Compos. 65, 200–213 (2016) 18. Sengupta, R., Bhattacharya, M., Bandyopadhyay, S., Bhowmick, A.K.: A review on the mechanical and electrical properties of graphite and modified graphite reinforced polymer composites. Prog. Polym. Sci. 36(5), 638–670 (2011) 19. Frattini, D., Accardo, G., Ferone, C., Cioffi, R.: Fabrication and characterization of graphite-cement composites for microbial fuel cells applications. Mater. Res. Bull. 88, 188–199 (2017) 20. Materazzi, A.L., Ubertini, F., D’Alessandro, A.: Carbon nanotube cement-based transducers for dynamic sensing of strain. Cem. Concr. Compos. 37, 2–11 (2013) 21. D’Alessandro, A., Ubertini, F., Garc´ıa-Mac´ıas, E., Castro-Triguero, R., Downey, A., Laflamme, S., Meoni, A., Materazzi, A.L.: Static and dynamic strain monitoring of reinforced concrete components through embedded carbon nanotube cement-based sensors. Shock Vibr. 2017, 3648403 (2017) 22. Downey, A., D’Alessandro, A., Ubertini, F., Laflamme, S., Geiger, R.: Biphasic dc measurement approach for enhanced measurement stability and multi-channel sampling of self-sensing multi-functional structural materials doped with carbonbased additives. Smart Mater. Struct. 26(6), 065008 (2017)

Combining Ultrasound and Surface Treatments for an Efficient Ice Protection Leandro Maio1(&), Filomena Piscitelli2, Salvatore Ameduri2, Antonio Concilio2, and Fabrizio Ricci1 1

Department of Industrial Engineering, University of Naples Federico II, Naples, Italy [email protected] 2 Italian Aerospace Research Center, Capua, Italy {f.piscitelli,s.ameduri}@cira.it

Abstract. Different strategies may be adopted to avoid ice formation, such as power-consuming active systems and passive coatings. Several categories of surface treatments with superhydrophobic/icephobic behavior have been developed in the last decade. The goal of the coating application is to repel water droplets, delay ice nucleation and significantly reduce ice adhesion. However, surface treatments alone are not sufficient to guarantee icing protection in a wide range of humidity and temperature conditions. They should be considered as a complementary solution to traditional protection active systems to reduce their power consumption and environmental impact. This study concerns the early stage of development about a hybrid system, characterized by a low energy consumption and based on both passive techniques, the superhydrophobic/ icephobic coating, and an active one, ultrasound, to remove ice build-ups from treated surfaces. Preliminary tests are conducted on a coated metal plate and the results coming from the investigation are presented. Keywords: Ultrasound Removal


Superhydrophobic coating
Ice
Prevention


1 Introduction Ice accretion events may have a dramatic impact on the flight, both in terms of performance and safety. Endless dramatic accidents attributable to the ice occurred since the beginning of the aviation, giving an idea of the scale of the problem. Its complexity is related to several aspects, such as: (i) the identification of the triggers, that is to say, the combination of unfortunate events determining ice accretions; (ii) the estimate of the impact of the ice on performance and safety; (iii) the implementation of the most effective mitigation actions, at aircraft and infrastructure level; (iv) the impact that these mitigation actions may have on the performance of the aircraft and on the original design. To make it worse, dramatic and sudden changes of the climate make more difficult weather forecasts and lead to atmospheric events even more frequently catastrophic [1, 2].

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 870–880, 2021. https://doi.org/10.1007/978-3-030-64594-6_84

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To date, many efforts have been spent to solve ice related problems [3, 4]. A critical aspect is represented by the impact that a certain ice protection system (IPS) may have on the aircraft layout [5]. This essentially for two reasons: (a) IPSs are extremely invasive and, thus, may upset the original design at different levels, depending on the class of the aircraft; (b) the costs related are significant, both in terms of maintenance and power consumption. In this scenario, new technologies are well doing for themselves, due to the possibility of mitigating the abovementioned problems. The idea would be to improve or substitute conventional IPSs (e.g. electro-thermal resistances, electro-mechanic burst actuators pneumatic systems, engine warm air thermal systems) with other architectures, operating as anti-icing (preventing ice accretions) and/or as deicing systems (removing ice accretions) [6]. In this work, the combination and the synergistic effect of two systems for ice control are investigated: one able to prevent or reduce ice accretions through a superhydrophobic coating [7–10] and another one causing ice detachment through shear actions generated by piezo-actuators bonded on the surface to protect [11–14]. The former strategy being a passive method, does not require any power supply, while the latter is characterized by a relatively modest power consumption (at least 1 order less than conventional systems). Over the reduced weight and power consumption, useful especially for small aircrafts, these systems give the possibility of a combined working modality, dramatically enhancing their relevant performance and, at the same time fitting redundancy logics compliant with the safety requirements [14–17].

2 Test Article and Surface Treatments The experimental prototype is constituted by an aluminum square plate with side 26 cm and thickness 1.2 mm. On the middle of one face, a piezoelectric disk, 1.5 thick and with a diameter of 70 mm, made of the hard material PIC 181, is placed. For the bonding process, the M-BOND AE-10 epoxy bi-component resin is used, characterized by a wide operational temperature range, specifically suited for the low temperatures. The vacuum bag process is implemented to guarantee both the complete fulfillment of the interface between the piezo and the plate and the minimization of the adhesive film, to maximize the strain transmission. Electrically insulating microspheres are dispersed in the glue, to avoid any kind of short circuit with the aluminum plate. In Fig. 1, the face of the plate integrated with the piezoceramic actuator is shown. The other face of the plate is split into two parts, clearly visible in Fig. 2. In practice, a part is left without any treatment, while the other one is suitably machined with a sandpaper to promote the adhesion of the superhydrophobic coating. In this way, during each test, detachment performance with and without the superhydrophobic coating can be investigated and compared. Roughness measurements are performed before and after the sanding by a SAMA SA6260 surface roughness meter, according to the ISO 4288 [18]. The results show that the roughness ranges from 0.15 ± 0.03 lm for the un-treated (UT) surface up to 2.7 ± 0.3 lm for the abraded sample (P50) (see Fig. 2). The sanded surface is then painted with a superhydrophobic coating [19, 20] which is able to create a hierarchical micro-nano structured roughness, and simultaneously to reduce the surface free energy.

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Fig. 1. Aluminum plate integrated with the piezo-actuator in the middle.

Fig. 2. Aluminum plate treated with the P50 (upper) and untreated (bottom).

The wettability of all surfaces is assessed by measuring the contact angles (CA) according to Young’s equation [21]: cLV cos # ¼ cSV  cSL

ð1Þ

where h is the contact angle, cLV and cSV are the liquid and solid surface free energy, respectively, whereas cSL is the solid/liquid interfacial free energy. The schematic illustration of the equilibrium among involved forces according to the Young equation is shown in Fig. 3.

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Fig. 3. Schematic illustration of an ink drop on a solid substrate with the surface free energy and the contact angle as described by Young’s equation [21].

The contact angle measurements are performed at room temperature in compliance with the ASTM D7490-13 [22] standard, using water and diiodomethane. Known the CA, the Surface Free Energy (SFE) is calculated as the sum of dispersive and polar components, according to the Owens-Wendt (OW) [23, 24]. The two components for the reference liquids are listed in Table 1. Tests are carried out by depositing 10 drops of 3 µl of each liquid at room temperature on the sample’s surface. Consequently, to overcome the stochastic nature of the measured CA measurements, a third liquid, the formamide is introduced to assess the SFE, and then the Work of Adhesion (WA) [20]. They are assessed using the following relationship: SFE ¼ cs ¼ cps þ cds

ð2Þ

WA ¼ cl ð1 þ cos #Þ

ð3Þ

or equally as: WA ¼ 2 cps cpl


1=2

þ 2 cds cdl


1=2

ð4Þ

Finally, the Surface Polarity (SP), which represents the percentage of the polar component by the solid SFE, is assessed as: SP ¼

cPS

cPs þ cD S

ð5Þ

Table 1. Surface components of the reference liquids [25]. Liquid Water Diiodomethane Formamide

Surface tension [mN/m] 72.8 50.8 57.0

Dispersive component [mN/m] 26.4 50.8 22.4

Polar component [mN/m] 46.4 0 34.6

The measured values of the SFE, WA and SP for the untreated (UT), sandpaper machined (P50) and the superhydrophobic coated (COAT) surfaces are shown in

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Fig. 4. The slight reduction of the SFE and the WA observable in the P50 sample, is ascribed to the morphological effect caused by the increased roughness [20]. Whereas, the combination of the hierarchical micro-nano roughness and a particular chemical composition of the coatings, having low surface free energy [20], is able to largely increase the water CA, from 68° to 158° (see x-axis shift in Fig. 4). As a consequence of this extraordinary reduced wettability, the SFE is reduced by 99%, the WA by 93%, and the SP by 100% (Fig. 4).

Fig. 4. Surface Free Energy (SFE, in orange), Work of Adhesion (WA, in blue) and Surface Polarity (SF, in red) of untreated (UT), abraded (P50) and coated samples (COAT), as a function of the water contact angle. 25°C

-27°C

(a)

(b)

(c)

(d)

Fig. 5. Water droplets on reference surface, (a) and (b), and coated surface, (c) and (d), at 25 °C and −27 °C, respectively.

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The low wettability observed at room temperature is also preserved at low temperature, i.e., −27 °C [26]. Figure 5 shows water droplets on untreated and coated samples at 25 °C and −27 °C. Moreover, the shape of the water droplets is also visible together with the CA that does not change with the temperature. The lower the surface contact area between the ice and the substrate, the easier the ice removal. As a consequence, any available weak force (e.g., the aerodynamic forces in flight) is able to shed away the formed ice. In the following block diagram an illustration of the experimental setup is provided (Fig. 6).

Fig. 6. Schematic of the experimental setup.

3 Ultrasound for Deicing The ultrasound propagation, traditionally adopted for damage monitoring in complex structures, can be likewise employed to reveal the presence of the ice [27] or to remove ice build-ups [12]. About this latter application, an active system founded on ultrasonic guided waves is herein adopted in combination with the surface treatment previously discussed to control the ice formation on the metal plate in Fig. 1. Basically, it is based on a piezoelectric disk working as actuator. Such system takes advantage of the disk radial mode able to generate at the interface ice-structure, through the guided waves, high transverse shear stresses, enough to overcome the ice shear strength and produce its detachment. To define a piezoelectric-based low-power ultrasonic deicing system, it is useful to identify the resonance radial mode at minimum impedance for a disk-shaped piezoelectric actuator [12]. This task can be fulfilled by a multifunction data acquisition (DAQ) and control instrument. The device selected for the purpose is programmed to perform both electro-mechanical impedance (EMI) measurements and identification of

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the proper work frequency for the piezoelectric disk. Its control takes place via customized software. Further details about the DAQ device are available in reference [28]. When the piezoelectric transducer is bonded to the plate, the identification of the right work-frequency, required for detaching the ice, is carried out scanning the impedance response for the minimum value in a frequency range enclosing the resonance condition associated to the disk radial mode. To facilitate this search, it is useful to exploit the material data concerning the chosen piezoceramic element. Among the available parameters for the material PIC 181 [29], the one useful to the aforesaid purpose is the frequency coefficient Np. This parameter describes the relationship between the geometrical dimensions of the transducer and the corresponding resonant frequency. The relationship between the constant Np and the diameter of the ceramic element, d, is expressed by: fr ¼

Np d

ð6Þ

The material of the selected piezoelectric disk is characterized by Np = 2270, resulting in a resonance frequency fr = 32428.57 Hz. The existence of fr can be verified by a simple electro-mechanical impedance measurement of the transducer before to bond it on the plate. Figure 7 shows that fr corresponds to the minimum of impedance (resistance).

Fig. 7. Electro-mechanical response of the piezoceramic transducer made of material PIC 181.

The frequency band used to search the disk radial resonance must include fr. In this regard, preliminary EMI measurements, first at room temperature and then at low one but without ice, are also necessary to detect the shift in frequency of the impedance

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curve, and so of the resistance minimum, due to the thermal effect. However, the high modal density of the structure at ultrasonic frequency, could cause a not straightforward identification of fr. This drawback can be avoided considering a wider band of frequencies to excite the actuator in the phase of ultrasound generation. However, this aspect depends on geometrical and mechanical features of the considered structure and it is not examined in this context.

4 Experimental Test The experimental campaign is carried out in compliance with a specific procedure, split into two main parts, namely “pre-tests”, already anticipated in the previous paragraph, and “tests” (see Fig. 8).

Fig. 8. Flowchart of the experimental procedure.

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The pre-test phase has the scope of setting the setup, to identify the optimal working condition and prepare a database about the key parameters useful for the ultrasonic de-icing. The system is excited at low power in three different configurations: (1) without ice, at room temperature; (2) without ice, at test temperature; (3) with ice accretions, at test temperature. The comparison between condition (1) and (2) highlights the impact of the drop in temperature on the overall dynamic response and on the electro-mechanic impedance of the system actuator-structure. Analogously, the comparison between condition (2) and (3) gives information about the electromechanic impedance of the system actuator-structure-ice. Then the analysis of the impedance curve in condition (3) is used to identify the resonance frequency of the piezoelectric actuator. The second phase has the scope of demonstrating the system ability to induce the ice detachment. The prototype in condition (3), that is to say, with the ice accretions at the test temperature, is excited. In this regard, a maximum voltage of 150 Vpp is provided with a max current absorption equal to 300 mA that leads to 45 W of power supply. A sine sweep over a narrow frequency band, including the optimal work frequency, is produced by the signal generator TGA 1240. The signal is amplified by a HVA 300 Falco system, according to the aforesaid power value, and sent to the piezoactuator. During the experiment period the plate is monitored through a high speed camera GoPro and the dynamic response and the impedance are acquired and stored by DAQ device. An oscilloscope is used for a real time monitoring of the excitation signal and of the dynamic response of the system. The experiment is carried out by depositing several water droplets on the untreated and coated zones of the aluminum plate, after its equilibrium at −27 °C. Once frozen the water, the aluminum plate is positioned vertically, according to Fig. 9a, and the actuator is powered up. It is observed that the hybrid system made of the passive coating and the active low-energy electromechanical system, is able to completely de-ice the treated zone of the plate (see Fig. 9b). The deicing action takes place in less than a second, resulting almost instantaneous on activation of the ultrasonic system.

Fig. 9. Test article with ice formations, before (a) and after (b) the system activation.

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5 Conclusions The synergic use of the ultrasonic guided waves and a surface treatment to remove the ice from a metal plate is investigated experimentally. A piezoceramic transducer bonded on the structure is excited properly to generate ultrasound able to induce, through shear stresses at the interface ice-structure, the ice detachment. The present study has highlighted that small ice accretions can be full removed from a metal plate by low power supply if the exposed surface is treated with superhydrophobic coating. In this regard, further studies are necessary to explore the effectiveness and reliability of the ultrasound–coating combination in removing wide and thick ice formations or to prevent the ice build-ups also by a smart control of the active system. Acknowledgements. The activities described in the present manuscript are performed in the framework of the SMOS (SMart On-Board Systems), a CIRA’s project funded on PRORA (PROgramma nazionale di Ricerche Aerospaziali).

References 1. CAPA - Centre for Aviation. Climate change: its impact on aviation (2019). https:// centreforaviation.com/analysis/reports/climate-change-its-impact-on-aviation-the-time-toplan-is-now-454475. Accessed 25 Feb 2020 2. The Conversation. Air Travel and Climate Change (2018). http://forhumanliberation. blogspot.com/2018/02/2825-air-travel-and-climate-change.html. Accessed 25 Feb 2020 3. HERE mobility blog. Top 8 Airport Technology Trends of 2019 (2019). https://blog. mobility.here.com/top-8-airport-technology-trends-of-2019. Accessed 25 Feb 2020 4. Ansys blog. 5 Trends in the Aerospace Industry (2019). https://www.ansys.com/blog/5trends-aerospace-industry-multiphysics-simulation. Accessed 25 Feb 2020 5. Buisinesswire Homepage. https://www.businesswire.com/news/home/20180302005252/en/ Aircraft-Ice-Protection-System-Market—Growing 6. Kreder, M.J., Alvarenga, J., Kim, P., Aizenberg, J.: Design of anti-icing surfaces: smooth, textured or slippery. Nat. Rev. Mater. 1, 15003–15039 (2016) 7. Lin, Y., Chen, H., Wang, G., Liu, A.: Recent progress in preparation and anti-icing applications of superhydrophobic coatings. Coatings 8, 208–241 (2018) 8. Asadollahi, S., Farzaneh, M., Stafford, L.: On the icephobic behavior of organosilicon-based surface structures developed through atmospheric pressure plasma deposition in nitrogen plasma. Coatings 9, 679–696 (2019) 9. Liu, J., Janjua, Z.A., Roe, M., Xu, F., Turnbull, B., Choi, K.S., Hou, X.: Superhydrophobic/icephobic coatings based on silica nanoparticles modified by self-assembled monolayers. Nanomaterials 6, 232–242 (2016) 10. Cao, L., Jones, A.K., Sikka, V.K., Wu, J., Gao, D.: Anti-icing superhydrophobic coatings. Langmuir 25, 12444–12448 (2009) 11. Palacios, J.L.: Design, fabrication, and testing of an ultrasonic de-icing system for helicopter rotor blades. Ph.D. thesis, The Pennsylvania State University, Engineering Science and Mechanics Department (2008) 12. Palacios, J., Smith, E., Rose, J., Royer, R.: Instantaneous de-icing of freezer ice via ultrasonic actuation. AIAA J. 49(6), 1158–1167 (2011)

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13. Zhu, Y.: Structural tailoring and actuation studies for low power ultrasonic de-icing of aluminum and composite plates. Ph.D. thesis, The Pennsylvania State University, Engineering Science and Mechanics Department (2010) 14. Maio, L., Ameduri, S., Concilio, A., Monaco, E., Memmolo, V., Ricci, F.: Development of a de-icing system for aerodynamic surfaces based on ultrasonic waves. In: Proceedings of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, Denver, Colorado, US, vol. 10600 (2018) 15. Strobl, T., Storm, S., Thompson, D.S., Hornung, M.: Feasibility study of a hybrid ice protection system. In: 6th AIAA Atmospheric and Space Environments Conference, Atlanta, GA (2015). https://doi.org/10.2514/6.2014-2060 16. Strobl, T., Storm, S., Kolb, M., Haag, J., Hornung, M.: Development of a hybrid ice protection system based on nanostructured hydrophobic surfaces. In: 29th Congress of the International Council of the Aeronautical Sciences (2014) 17. Strobl, T., Storm, S., Ameduri, S.: Synergic effects of passive and active ice protection systems. In: Morphing Wing Technologies Large Commercial Aircraft and Civil Helicopters, 1st edn, pp. 842–864. Elsevier, Amsterdam (2018) 18. ISO 4288—Geometrical Product Specifications (GPS)–Surface Texture: Profile Method– Rules and Procedures for the Assessment of Surface Texture. International Organization for Standardization, Geneva, Switzerland (1996) 19. Piscitelli, F., Tescione, F., Mazzola, L., Bruno, G., Lavorgna, M.: On a simplified method to produce hydrophobic coatings for aeronautical applications. Appl. Surf. Sci. 472, 71–81 (2019) 20. Piscitelli, F., Chiariello, A., Dabkowski, D., Corraro, G., Marra, F., Di Palma, L.: Superhydrophobic coatings as anti-icing systems for small aircraft. Aerospace 7(2), 1–17 (2020) 21. Young, T.: An essay on the cohesion of fluids. Philos. Trans. R. Soc. Lond. 95, 65–87 (1805) 22. D7490-13 Standard Test Method for Measurement of the Surface Tension of Solid Coatings, Substrates and Pigments using Contact Angle Measurements. American Society for Testing and Materials, Conshohocken, PA, USA, 7 (2013) 23. Owens, D.K., Wendt, R.C.: Estimation of the surface free energy of polymers. Appl. Surf. Sci. 13, 1741–1747 (1969) 24. Zenkiewicz, M.: Methods for the calculation of surface free energy of solids. J. Achiev. Mater. Manuf. Eng. 24, 137–145 (2007) 25. https://www.kruss.de/fileadmin/user_upload/website/literature/kruss-tn306-en.pdf 26. Piscitelli, F.: Superhydrophobic coating for aeronautical applications. In: 2020 IEEE International Workshop on Metrology for Aerospace, Virtual Conference, pp. 22–24 (2020) 27. Memmolo, V., Moll, J.: Investigation on guided waves propagation across ice layers. In: Proceedings of the SPIE, Health Monitoring of Structural and Biological Systems XIV, vol. 11381, p. 1138134 (2020) 28. Maio, L.: Electromechanical impedance measurement for de-icing applications based on piezoelectric actuators. In: 2019 IEEE 5th International Workshop on Metrology for AeroSpace, MetroAeroSpace (2019). 8869577 29. PI Ceramic, Piezoceramic Materials. www.piceramic.de

Human Performance Monitoring

An Aircraft Pilot Workload Sensing System Andrea Alaimo1 , Antonio Esposito1(B) , Alberto Milazzo2 , and Calogero Orlando1 1

2

Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy {andrea.alaimo,antonio.esposito,calogero.orlando}@unikore.it University of Palermo, Viale delle Scienze, Edificio 8, 90128 Palermo, Italy [email protected] Abstract. The workload evaluation is of great importance for human error avoidance training, particularly in the use of complex systems that requires different and concurrent activities. The excessive workload harms human performance even with adverse outcomes. In the aviation field, certain flight maneuvers, such as take-off and landing, are characterized by great attention and workload demand to the pilot. Thus, a system capable of measuring pilots’ workload levels during flight could be beneficial to increase pilots’ performance. This work aims to study the initial feasibility of a device called Cockpit Pilot Warning System that monitors the pilot workload level during flight. With this aim, an experimental campaign using a Level-D business aircraft flight simulator is conducted. Two sensors are used to acquire biological signals: a thermographic camera is used to obtain pilots’ Face Temperature Variation (FTV) while a Heart sensor is used to acquire their Heart Rate (HR). The nervous system modifies FTV and HR in response to stressing or high workload events and can thus be used to monitor pilots’ workload that affects their performance. The workload measurement with the thermographic camera is an indirect measurement, particularly indicated in aviation, since it is contactless. It does not interfere with the concentration and leaves pilots’ freedom of movement, thus not affecting their working functions. Keywords: Workload simulator

1

· Face temperature · Heart rate · Flight

Introduction

Infrared thermography is considered a non-destructive analysis technique, capable of acquiring thermal radiation emitted by a body. Through the use of optical sensors, such radiation can be collected and transformed into an electrical signal and sent to devices that reconstruct the image. This technique is used in the aerospace industry as a rapid and accurate non-destructive evaluation (NDE) method that can be used to inspect significant components, such as primary and c Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 883–892, 2021. https://doi.org/10.1007/978-3-030-64594-6_85

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secondary structures of aircraft, engine components, spacecraft parts or subsystems [4]. It is used to evaluate damage or defects in the material that may occur during the production of the component or its life cycle. It is possible to evaluate, i.e., fatigue cracks, impact damage not easily visible, delamination, debonding, matrix/fiber failure in composites materials [12]. The use of thermography affects many civil aspects, like the security sector. Infrared and thermographic cameras are increasingly present in numerous public areas, stations, or airports. Moreover, in medical and safety health areas, applications of particular importance involve the thermography [9]. Recent work in the automotive framework analyzes the correlation between facial temperature variation and mental workload. Kajiwara [7] uses an automotive simulator to evaluate mental load as driving speed increases. The evaluation, based on facial temperature changes, showed that the difference between nose and forehead temperature increased. Diaz-Piedra et al. [5] analyze participants that performed a 2-h simulated driving task and compare the brain activity and driving performance with the analysis of face temperature to evaluate fatigue level. Or and Duffy [13] experiment a significant correlation between the change in the nose skin temperature and the score of the subjective workload assessed by a Modified Couper Harped (MCH) scale test. In these studies, the results suggest that infrared thermal imaging can provide a valid, reliable, real-time, and non-invasive measure of fatigue variations as well as driving drowsiness. The temperature analysis of the nose region seems to provide more significant results; in particular, Genno et al. [6] and Veltman and Vos [15] show that the temperature in the nose decreases when the mental workload increases. It should be highlighted that the acquisition system and related software must be appropriately calibrated based on the application context. Kumar et al. [8] developed an algorithm for the facial temperature evaluation experienced on people with different skin tones, under various lighting conditions. Abdelrahman et al. [1] analyze the temporal latency of temperature changes. Stemberger et al. [14], through the use of an artificial neural network, demonstrate that facial thermography can reliably quantify participants’ workload in various cognitive tasks. The temperature of the facial skin allows automatic measurement without contact. It is valid for the objective evaluation of psychological changes such as stress and fatigue because the temperature varies with the autonomic nervous activity triggered by the onset of sensations. In these terms, it is useful to connect this technique with the analysis of heart rate. Recently Liu et al. [10] include the heart rate monitoring in a system concept based on different physiological measures. As in the present work, the instrument technology is supposed integrated into an aircraft to evaluate the pilot’s mental workload. Mansikka et al. [11] examine the fighter pilots’ heart rate and heart rate variability during instrumental approaches in a high-fidelity simulator. They are proving that the heart rate is sensitive to varying task demands. It is known that there are some maneuvers carried out by the pilot that requires a higher workload level [2,3].

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Referring, for example, to take-off or landing phases, the pilot, in addition to steering, pulling, and pushing the commands also checks the instruments to comply with the flight envelope while communicating for radio assistance. As part of this work, it is decided to take advantage of thermographic technology to assess the pilots’ facial area temperature and evaluate the presence of correlations with the workload levels and heart rate activities. The measure was acquired and recorded during different flight phases.

2

Flight Mission

This paragraph refers to the group of operations that characterize the simulator flight test. Certain maneuvers have been chosen in advance to identify the pilot’s ability. Before each session, the pilots were asked to compile a privacy consent form, with authorization to wear sensors for heart rate monitoring and facial image recording. Moreover, each pilot declared not to use heart drugs or any other substances able to influence the tasks performed. Each pilot, during a preflight briefing procedure, was informed about the maneuvers and tasks executed during the phases of the test. All the flight phases performed by the pilots are presented below. The mission includes several phases to determine the pilot’s skills and attitude on flying. Table 1 summarizes all the flight phases carried out. The exercise began with a ten-minute resting time for data acquisition as benchmark conditions. The briefing with the co-pilot for the aborted take-off action in case of failure was a successive step. Then, after the accomplishment of the take-off checklist, the pilot asks cleared for take-off. Once authorized, the data acquisition relating to this segment begins, followed by the climb segment, which is completed when the pilot has reached 10,000 [ft] and a level flight attitude. During the climbing, the aircraft have to maintain a constant and assigned vertical speed. Once the established altitude is reached, the cruise phase begins. In cruising phase the following maneuvers are carried out: i) right or left 360◦ turns, with a bank angle of 30◦ or 45◦ ; ii) maneuver including the stall of the aircraft, allowing the pilot to prove his/her skills, by losing the lowest altitude height; iii) unusual attitude, maneuver described by the return with leveling from a so-called nose-up or nose-down configuration; after these cruising tasks, a briefing for the approach and landing is performed, then the pilot starts the descent phase with radial direction to the airport. Once the final approach flight path is carried out, the landing gear lever is pulled down, and the pilot completes the flare reaching the touchdown. At this point, the aircraft’s speed is reduced until it stops, and the parking brake is pulled up. At the end of the flight, the pilot stays for 5 min on a resting time position. Figure 1 graphically shows a sketch of the flight mission profile.

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Flight phase

Acronims Action required for the pilot

Rest time

RT

Start of the exercise

Briefing

BR

Take-off briefing of operations to be performed

Take off

TO

The pilot begins the take-off procedure

Altitude 10.000 ft

LF

Reach altitude then maintaining a straight and level flight

Turn

TR

Right/left 360◦ turn with 30/45 bank angle

Stall

ST

Perform stall maneuver with the loss of lowest altitude

Upset recovery

UR

Use of appropriate techniques to recover the airplane

Approach briefing AP/BR

Landing briefing and autopilot activated

10 Nautical miles

10 NM

Passage to 10 nautical miles

Outer marker

OM

Passage over the outer marker

Touch down

TD

Touches the runway and begins the braking run

Parking brake set PBS

Set the parking brake lever

End rest time

Stop of the exercise

ERT

Fig. 1. Flight mission profile.

3

Sensors for Data Acquisition

To obtain a temperature map, a thermal imaging camera positioned in the cockpit is used. The position of the camera is set, avoiding to cover the cockpit instruments line of vision. The OPTRIS PI 640 camera was used. It offers sharp radiometric images and videos in realtime. Supplied with a software package named PIX Connect, it allowed the evaluation and analysis of data after the simulation tests. The PIX Connect software has made it possible to detect data using a circular 2 × 2 pixel array. Improving calculation accuracy is based on imposing the average temperature in the considered array. The positioning of the thermal imager in front of the pilot face, give the acquisition. Another instrument used to measure body temperature before the beginning of the exercise was the VISIOFOCUS laser thermometer. Lastly, the sensor for the analysis of heart rate and temperature in the chest area is the Movisense sensor model EcgMove 3. It is a scientific research tool for evaluating the ECG electrocardiogram, which can be used during physical activities of a different nature.

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Results

Some maneuvers have a more critical connotation if compared to the level of attention required in carrying it out. During a cruise phase, in order to keep straight and level flight, the pilot has a reduced number of activities to perform the maneuver if compared with take-off or a landing phase. Some maneuvers are characterized by a higher level of attention like checks of flight parameters, radio assistance communication, approach to the runway, or compliance with the required path. These tasks consequently increase the workload level. To validate what has just been said, in this work, the authors use heart rate monitoring to evaluate whether the heart rate level in some of the flight phases is higher than in others. At the same time, the temperature of the forehead and the temperature at the tip of the nose are analyzed through the recordings with the thermal imaging camera. In the next two subsections, initially, cardiac behavior and, subsequently, the behavior related to body and face temperature are analyzed. 4.1

Heart Rate

For all flight phases, it was decided to analyze the heart rate values acquired R sensor. The results obtained for the heart rate proved to be using the Movisens in line with the expected theoretical trends. An increase in heart rate occurs in agreement with high psychophysical stress. In this work exercise, furthermore, the trend is supported by the attention that the different phases require. Figure 2 shows how in specific flight phases, the pilots’ analysis gives local peaks in the recorded values. For the acronyms, refer to Table 1. Figure 2 remarks a drastic increase of HR in the initial stages with a peak value at the Take Off (TO) and a decrease for the leveled flight phase. The segment of the cruise with maneuvers exercises shows, for the different tasks performed, a median in the range between 110 and 115 heartbeats. In particular, the stall exercise shows a slight increase (column corresponding to ST) compared to the other tasks performed; the value is acquired when the stick shaker comes into action. During the approach and landing briefing, the aircraft control is entrusted to the first officer who works alongside the pilot with the autopilot system activated. The decrease in the heart rate, in this phase, is reliable due to the reduction of physical and mental effort. The successive flight phases are characteristic of the approach and landing segment. In Fig. 2 are given the values in correspondence with the passage at ten nautical miles on the descent path (column 10NM), the passage over the outer marker at about 5.5 miles (OM column), the touchdown (TD) and the subsequent parking brake set (PBS) at the end of the braking action. There is a gradual increase in the heart rate levels recorded to a maximum peak in correspondence with the touchdown. Furthermore, there is a contraction of the box plot whisker and degree of dispersion in acquired data; this proves that the event is characteristic for all the pilots. The result is independent of the gender and age of the pilots analyzed. When the pilot activates the parking brake, there is a reduction in the heart rate since the pilot has completed the landing tasks and, therefore, discharges

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Fig. 2. Heart Rate box plot on flight segment.

his physical and mental pressure level. The last column value corresponds to the end rest time; data acquired 5 min after the stop. The median value returns to the same level as the rest time at initial data recorded, demonstrating that the pilot reduced his cognitive workload load. 4.2

Body and Facial Temperature

In this subsection, initially, the body temperature results obtained, by the sensor positioned in the chest, are analyzed. Then results provided by monitoring with thermal imaging are presented. The objective is to correlate the increase in workload level to a decrease in temperature in the tip nose region. The chest sensor is not used to evaluate the pilot’s workload since there is no scientific soundness to support this thesis. In this research, the body temperature data are collected to determine the pilot’s acclimatization times into the cockpit; this made it possible to choose a specific time interval for subsequent analyzes carried out with the thermal imager. The sensor is turned on ten minutes before the start of the rest time, as the sensor user manual recommends overcoming transient measurement errors. However, the data show that, in this specific research case, the pilots need a prolonged acclimatization time to the environmental conditions present in the cockpit. Figure 3 shows the temperature trends, measured on the chest via the HR sensor, during the different flight phases. The results are expressed in terms of increment to the value acquired, for each pilot, at the rest time. The temperature rises drastically during the initial briefing and takeoff phase, continuing to grow gradually also throughout the cruise phase and maneuvering exercises. At this point, there is an increment of about 3◦ C. During the briefing for the approach, the temperature becomes steady, and the median remains constant during all the descent and landing phases. Based on the results obtained, the authors selected the time interval to use thermography analysis in these last flight phases, namely from 10 NM to PBS, when the body temperature shows an adaptation to the surrounding environment. The selected time interval is equal to 600 s to analyze most of the descent phase. The interval is taken from

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540 s before to the PBS (Parking Brake Set) and 60 s after this action. A 5 s sampling is imposed. Forehead and tip nose temperatures are measured. There are differences in the behavior of temperatures measured in the two regions. The temperature value of the forehead is also measured with the laser thermometer at the end of the flight mission. This measurement is made to reduce possible drift errors of the thermal images that are post-processed.

Fig. 3. Body temperature acquired by HR chest sensor during flight phases.

However, according to the literature [1,7,15], the forehead temperature does not show significant changes because of workload changes. The nose temperature is thus on to assessing. This region is more sensitive to changes in blood flow induced by the pilot’s emotional state. It has been decided through the aid of in house implemented script to interpolate the data employing a function based on a fifth-degree polynomial. The choice is based on the need to take into account the temperature slope variation, with good agreement, for all the pilots. Figure 4 shows the average of all tendency lines computed on the interval for all the pilots. For the sake of completeness, it should be noted that Fig. 4 shows the result obtained on a sample of sixteen pilots since two pilots showed temperature variations greater than 2.5 ◦ C and therefore are omitted. The entire flight phase shows a decrease in temperature with a minimum at 520 s, which on average, corresponds with the touchdown. When the airplane is on the runway, the temperature of the pilot’s tip nose begins to increase, already in the braking phase, (the 540 s represent the PBS for all the pilots), then the temperature continues to grow and return to the initial levels. The last 60 s correspond to a time interval in which the exercise is complete, and the simulation is stopped, the pilots start the end rest time. In this phase, there is an increase in temperature value related to a workload decreasing, following what is foreseen from the literature. On the contrary, during the whole descent phase, there is a decrease in tip nose temperature related to the increase in the mental effort required of the pilot. In particular, this occurs after 350 s, the time interval at which the pilots pass over the outer marker. During the phase between 400 and 500 s, the pilots

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are approaching and must maintain the correct descent path by flying in manual flight. The minimum temperature acquired coincides with the touchdown to demonstrate the higher level of workload according to the data examined for the heart rate. 0.1

T = Ti - T0

0 -0.1 -0.2 -0.3 -0.4 -0.5

0

100

200

300

400

500

600

Time [s]

Fig. 4. Mean temperature variation.

5

Conclusion

According to the ever-increasing importance that the study of human behavior and relationship with their services, with the work tools and procedures they must comply, the objective of this work has been to identify a correlation between temperature variations and pilots’ workload levels. The study focused on the experimental thermographic acquisition of the facial temperatures on a group of pilots and heart rate analysis over a defined flight mission profile. The heart rate analysis gives proper results of higher workload levels during some specific flight phases. In particular, the results show that take-off and touchdown are undoubtedly two of the most stressful phases for the pilots. After the initial analysis in terms of body temperature on all flight phases, the analysis of thermography is focused on the landing. The regions of interest are those of the forehead and nose tip, analyzed employing a thermal imaging camera placed in front of the pilots. The analysis of the measured temperature data has returned a similar trend among all the pilots. In the tip nose, the temperature variations follow a decreasing tendency during the approach and landing phase correlated to an increase of the workload level. Furthermore, in most of the subjects analyzed, during the subsequent resting phase, there is an increase in temperature, meaning a reduction in stress levels. The temperature in the forehead region is also measured, noting that no significant variation is detected. The proposed physiological monitoring system could be useful to detect an increase in workload level. The technology could be employed in other frameworks different from the cabin cockpit, i.e., the same technology should be useful

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to detect the workload level of Air Traffic Control Officer (ATCO) or of pilots that remotely control unmanned aircraft. The technology could be improved in several aspects to obtain a realtime monitoring system. In particular, some future development could be summarized as follows: – Measurement drift errors could be reduced with the use of a second thermal imaging camera, positioned laterally to the pilot, to obtain a more accurate measurement. – There are still aspects to be explored, especially about changes in heart rate and subjective assessments of the workers taken into account, focusing on the environmental temperature influence. – To obtain a realtime analysis of values and reduce the errors due to data sampling, the implementation of an automated algorithm is conceivable. Through a tracking system of the regions of interest, the system could record and supply evidence on the pilot’s workload levels, and by extension to monitoring the crew. Acknowledgments. The study was funded by the Italian Ministry of Education, Universities and Research (MIUR) under the RPASInAIR project (P.O.N. Grant ARS01 00820 CUP J66C18000460005).

References 1. Abdelrahman, Y., Velloso, E., Dingler, T., Schmidt, A., Vetere, F.: Cognitive heat: exploring the usage of thermal imaging to unobtrusively estimate cognitive load. Proc. ACM Interact. Mobile Wearable Ubiquit. Technol. 1(3), 1–20 (2017) 2. Alaimo, A., Esposito, A., Orlando, C.: Cockpit pilot warning system: a preliminary study. In: Proceedings of the IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI 2018), pp. 1–4. IEEE (2018) 3. Alaimo, A., Esposito, A., Orlando, C., Tesoriere, G.: A pilot mental workload case study in a full flight simulator. Aerotecnica Missili & Spazio 97(1), 27–33 (2018) 4. Ciampa, F., Mahmoodi, P., Pinto, F., Meo, M.: Recent advances in active infrared thermography for non-destructive testing of aerospace components. Sensors 18(2), 609 (2018) 5. Diaz-Piedra, C., Gomez-Milan, E., Di Stasi, L.L.: Nasal skin temperature reveals changes in arousal levels due to time on task: an experimental thermal infrared imaging study. Appl. Ergon. 81, 102870 (2019) 6. Genno, H., Ishikawa, K., Kanbara, O., Kikumoto, M., Fujiwara, Y., Suzuki, R., Osumi, M.: Using facial skin temperature to objectively evaluate sensations. Int. J. Ind. Ergon. 19(2), 161–171 (1997) 7. Kajiwara, S.: Evaluation of driver’s mental workload by facial temperature and electrodermal activity under simulated driving conditions. Int. J. Automot. Technol. 15(1), 65–70 (2014) 8. Kumar, M., Veeraraghavan, A., Sabharwal, A.: DistancePPG: robust non-contact vital signs monitoring using a camera. Biomed. Opt. Express 6(5), 1565–1588 (2015) 9. Lahiri, B., Bagavathiappan, S., Jayakumar, T., Philip, J.: Medical applications of infrared thermography: a review. Infrared Phys. Technol. 55(4), 221–235 (2012)

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10. Liu, J., Gardi, A., Ramasamy, S., Lim, Y., Sabatini, R.: Cognitive pilot-aircraft interface for single-pilot operations. Knowl.-Based Syst. 112, 37–53 (2016) 11. Mansikka, H., Virtanen, K., Harris, D., Simola, P.: Fighter pilots’ heart rate, heart rate variation and performance during an instrument flight rules proficiency test. Appl. Ergon. 56, 213–219 (2016) 12. Meola, C., Boccardi, S., Carlomagno, G.M.: Infrared Thermography in the Evaluation of Aerospace Composite Materials: Infrared Thermography to Composites. Woodhead Publishing, Sawston (2016) 13. Or, C.K., Duffy, V.G.: Development of a facial skin temperature-based methodology for non-intrusive mental workload measurement. Occup. Ergon. 7(2), 83–94 (2007) 14. Stemberger, J., Allison, R.S., Schnell, T.: Thermal imaging as a way to classify cognitive workload. In: Proceedings of the 2010 Canadian Conference on Computer and Robot Vision, pp. 231–238. IEEE (2010) 15. Veltman, H.J., Vos, W.W.: Facial temperature as a measure of mental workload. In: 2005 International Symposium on Aviation Psychology, p. 777 (2005)

Site-Specific Quality Assessment of Trabecular Bone Using Highly Nonlinear Solitary Waves Tae-Yeon Kim1(&), Sangyoung Yoon1, Andreas Schiffer2, In Gwun Jang3, and Sungmun Lee4 1

Civil Infrastructure and Environmental Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, UAE [email protected] 2 Department of Mechanical Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, UAE 3 The Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology, Daejeon 34051, Republic of Korea 4 Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi 127788, UAE

Abstract. We present a numerical study of highly nonlinear solitary wave interaction with adjacent bone microstructures towards the development of a novel diagnostic scheme for site-specific bone quality assessment. Highresolution finite-element models of the trabecular bone microstructures in the femoral head are generated using a topology optimization-based bone microstructure reconstruction scheme. Using the finite-element models, a hybrid finite-element/discrete-element method is developed to examine the characteristic features of the reflected highly nonlinear solitary waves in a granular chain with adjacent damaged bone microstructure models for the prediction of partial fracture due to the development of osteoporosis. Keywords: Topology optimization
Osteoporosis
Non-destructive evaluation
Finite elements
Granular crystal
Bone reconstruction

1 Introduction Osteoporosis can be characterized by the reduction of bone mineral density (BMD) and micro-architectural deterioration of bone quality [1]. Dual-energy X-ray absorptiometry (DEXA) measures reductions of average BMD in fracture-prone sites for diagnosis of osteoporosis. However, DEXA can only detect reductions of bone strength in 70–75% of patients due to its low-resolution and two-dimensional projection imaging technique [2], resulting in possible misdiagnosis of osteoporosis. On the other hand, bone microstructure-based assessment can predict up to 94% of bone strength [3, 4]. However, due to excessive radiation exposure, current imaging techniques are clinically unable to evaluate the actual loss in bone stiffness and strength which ultimately leads to osteoporosis-induced fracture in lumbar spine and femur without timely medical intervention. To address such limitations of current imaging techniques and © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 893–901, 2021. https://doi.org/10.1007/978-3-030-64594-6_86

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minimize the risk of misdiagnosis and/or overdiagnosis of osteoporosis, the goal of this study is to develop a novel solitary-wave based diagnostic scheme for non-invasive and site-specific evaluation of human bone in vivo. The present work focuses on studying the interaction between the highly nonlinear solitary waves (HNSWs) and damaged bone microstructures. The HNSWs can be generated in a granular chain by impacting a striker particle at low velocity (typically < 1.0 m/s), recording their propagation using a pressure sensor embedded in one of the chain particles [5, 6]. The unique features of the HNSWs are tunable wave speed, compact wave support, and high energy density [7–12]. For nondestructive testing, the last particle in the granular chain is in contact with an inspection medium, resulting in the formation of the reflected HNSWs whose characteristic features are sensitive to the mechanical properties of the medium [6, 11–13]. This sensitivity has been applied to various non-destructive evaluations. Examples include measurement of the mechanical properties of materials and structures including composites [6, 14–19], detection of non-visible defects, such as cracks, delaminations, and voids [20–23], and assessment of functional properties of a structural system [24, 25], such as the inspection of adhesive joints. Relatively few studies have been performed for biomedical applications. Yang et al. [26] examined the interaction of HNSWs with artificial and cadaveric bone samples, and demonstrated the sensitivity of the reflected HNSWs to detect differences in bone quality. They also used the same approach to evaluate orthopaedic implant stability in total hip arthroplasty [27], and observed significant differences in the formation of the reflected HNSWs with varying level of stem fixation quality. Recently, we numerically investigated the interaction between the HNSWs with the microstructure of trabecular bone in the femoral head [28], reporting the sensitivity of the reflected HNSWs on varying bone densities based on the effective compressive modulus of bone microstructures. Motivated by this work, this study focuses on numerical investigation of the interaction of the HNSWs with fractured bone microstructures of trabecular bone, towards the goal to predict the partial fracture of bone due to minor fall or sudden impact with the development of osteoporosis. In doing so, we first generate high- and low-density bone microstructures by reconstructing trabecular bone in the femoral head using a topology optimization-based computational framework. Then, two artificial (i.e., horizontal/vertical) strut damaged bone microstructures are created from the low-density bone samples. Using the generated bone samples, we set up a hybrid discrete-element/finite-element (DE/FE) model, consisting of a chain of elastic steel beads (modeled by DE) in contact with the FE model of the bone samples to be analyzed. Dynamic simulations are performed to examine the effects of the damaged bones on the characteristic features of the reflected HNSWs.

2 Modeling of HNSW Interaction with Bone Microstructures For accurate modeling of the HNSW interaction with bone microstructures, highresolution FE models of trabecular bone microstructures are generated using a topology optimization-based bone microstructure reconstruction scheme. Detailed descriptions of the scheme can be found in Kim and Jang [29] and Kim et al. [30]. In particular, for this study we generate two-dimensional femoral head microstructures of size

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10.2 mm  10.2 mm; two different bone densities are considered: low-density bone with bone volume fraction of BV/TV = 0.28 (i.e., 28% of the total volume (TV) is occupied by bone volume (BV)) and high-density bone with bone volume fraction of BV/TV = 0.54 as illustrated in Figs. 2a and 4b, respectively.

Fig. 1. Schematic of (a) a one-dimensional granular chain of particles interacting with a bone microstructure and (b) a hybrid DE/FE model.

A typical experimental setup consists of a one-dimensional granular chain of particles (steel spheres) directly contacting an adjacent inspection medium as shown in Fig. 1a. By dropping a striker particle from a given height, an incident HNSW is generated in the chain whose interaction with the inspection medium generally gives rise to the formation of multiple reflected HNSWs. The characteristics of the incident and reflected HNSWs are measured using a piezoelectric sensor embedded in one of the chain particles.

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For numerical modeling of the HNSW interaction with the bone microstructure, a hybrid DE/FE model is developed using ABAQUS/Standard. As shown in Fig. 1b, we model a granular chain of 21 steel spheres, with radius R = 7.5 mm and mass m = 0.0139 g, contacting a cylindrical bone microstructure. The radius and height of the bone sample are chosen sufficiently large (0.9R < rs < 1.8R and 1.2R < hs < 2R) to avoid boundary effects during the dynamic interaction. To limit the computational cost, the microstructure of all bone samples is constructed as two-dimensional axisymmetric models in all simulations. Furthermore, we assume that the contact deformations between adjacent steel particles are linear elastic and that dissipative effects (i.e., frictional losses) in the chain are negligible. We also assume that the contact radius between the chain particles is small compared to the particle radius R, and that the characteristic time of a propagating nonlinear wave is much smaller than the oscillation period of a chain particle [10]. Under these assumptions, the intergranular forces can be modeled using the Hertz law [31]:
Fi ¼

3=2

Adi 0

for di  0 ; for di \0

ð1Þ

where Fi being the contact force and di = ui − ui+1 (i = 1, …, N − 1) the relative displacement between two contacting particles during propagation of a wave. To quantify the stiffness of the Hertzian contact, the parameter A is given by pffiffiffiffiffiffi Es 2R ; A¼  3 1  m2s

ð2Þ

with Es and ms being Young’s modulus and Poisson’s ratio of the chain particles, respectively. A hybrid DE/FE model is schematically described in Fig. 1b. A chain of particles 1–20 is modelled as a linear array of discrete point masses (m = 0.0139 g) connected through non-linear springs according to Eq. (1). The interaction between the chain and the bone sample is modelled using the FE method through four-node axisymmetric finite elements with reduced integration (CAX4R in ABAQUS). Notice that the FE mesh in the vicinity of the contact between the chain and the bone sample is sufficiently refined to resolve the contact stress gradients. Moreover, the FE mesh for the bone sample is limited to 0.05 mm by the bone remodelling scheme. The nonconforming contact at the chain-sample interface is modelled using the “hard contact” interaction in ABAQUS, ensuring that contact pressure is applied as soon as an overlap between two contacting surfaces is detected. Both the bone material and the contacting sphere are treated as linear elastic and isotropic solids. We take Young’s modulus and Poisson’s ratio of E = 210 GPa and m = 0.27 for the chain particle. The bone’s modulus, Poisson’s ratio, and density are chosen as Eb = 8.5 GPa, mb = 0.35, and qb = 3000 kg/m3, respectively. The bottom surface of the bone sample is fixed in all directions, mimicking a fully clamped boundary. To enforce one-dimensional wave motion, the radial displacements of all discrete particles and the nodes along the center line of the last chain particle are

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constrained. The sensitivity of the gravitational pre-compression in a granular chain is included by imposing a concentrated force equal to the particle’s weight, i.e., w = mg, on each chain particle. An appropriate body force is applied over the entire FE domain. Once static equilibrium is achieved, the propagation of the incident solitary wave is initiated by imparting an initial velocity v0 = 0.3 m/s to the striker particle (particle 1 in Fig. 1a). To calculate the dynamic response of the system, we employ a HilberHughes-Taylor time integration scheme [32]. In experiments, the force transient in the chain is conventionally measured by embedding a piezo-electric disc element in the center of the chain particle. This sensor particle not only measures the shape and amplitude of the incident solitary wave but also the delay in the formation of reflected solitary waves. According to Daraio et al. [5], a massless and infinitely stiff sensor placed in bead 11, for example, would experience a force Fsens ðtÞ ¼

F11T ðtÞ þ F11B ðtÞ ; 2

ð3Þ

where F11T(t) and F11B(t) denote the contact forces applied on top and bottom of the sensor particle 11, respectively. In our calculations, F11T(t) and F11B(t) are determined by extracting from the numerical solutions, i.e., the dynamic force histories in the nonlinear springs on either side of particle 11, respectively, according to Eq. (3).

3 Results and Discussion We examine the effect of bone damages on the reflection of the HNSWs, taking into account partial bone fracture caused by a minor fall or sudden impact with the development of osteoporosis. In doing so, we generate two artificial damaged bone microstructures (i.e., horizontal strut damaged (DhLD) and vertical strut damaged (DvLD) bones) from low-density bone (LD) with BV/TV = 0.28 as illustrated in Fig. 2.

Fig. 2. Bone microstructures with artificial horizontal and vertical strut damages.

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Using the hybrid DE/FE model, we study the interaction of the HNSWs with both undamaged and damaged microstructures with three different sizes of S1 (10.2 mm 10.2 mm), S2 (10.2 mm  9.55 mm), and S3 (10.2  8.40 mm) as shown in Fig. 2. Notice that slightly smaller bone models of S2 and S3 are obtained by cropping S1 in the vertical direction to examine site-specific changes in the HNSW interaction within a single bone microstructure. In Fig. 3, we display the time histories of the effective force for S1, S2, and S3 of the undamaged bone (Fig. 2a). The first impulse is the incident HNSW generated in the granular crystal by the striker particle impacting with v0 = 0.3 m/s at time t = 0. The subsequent impulses are two reflected HNSWs, called the primary reflected solitary wave (PRW) and the second reflected solitary wave (SRW), from the interface with bone microstructures. The PRW delay represents the delay time between the arrival of the incident HNSW and the PRW, and the SRW delay represents the delay time between the arrival of the incident HNSW and the SRW. The first peak, representing the incident HNSW, is identical in all three cases, as expected. Slightly larger PRW and SRW delays for S2 and S3 than S1 are observed, indicating site-specific variation of the HNSW interaction within a single bone microstructure.

Fig. 3. Sensor force vs. time histories for the low-density (LD) bone.

In Fig. 4a, the predicted engineering stress vs. strain responses of the bone microstructure models are displayed under uniaxial compression. The linear elastic behaviors for all bone models are obtained within a strain range up to 0.02. The stressstrain responses of the low-density bone microstructures are compared with that of the non-damaged high-density (HD) bone microstructure with BV/TV = 0.54 shown in Fig. 4b. The results clearly show the reduction of the stiffness with decreasing bone density and the presence of the vertical and horizontal strut damages.

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Fig. 4. (a) Stress-strain responses under uniaxial compression and (b) high-density (HD) bone microstructure with BV/TV = 0.54.

Fig. 5. Average delay (a) and amplitude ratio (b) of the reflected HNSWs for the bone microstructures considered in this work.

In Fig. 5, we display average PRW and SRW delays and average amplitude ratios of the PRW and the SRW for both undamaged high-density and low-density bone and vertical and horizontal strut damaged bone microstructures. Each average is calculated from three different sizes of microstructures S1, S2, and S3. As shown in Fig. 5a, average delay times for both PRW and SRW are increased with decreasing bone density and the presence of horizontal and vertical strut damages. On the other hand, average amplitude ratios are slightly decreased for the PRW and increased for the SRW with the presence of damages. These results indicate that there is a possibility of the application of the HNSWs to identify the difference in the bone microstructure due to partial fracture of bone for the prediction of osteoporosis.

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4 Conclusion We numerically investigated the interaction between the HNSWs in a granular chain and damaged bone microstructures of trabecular bone in the femoral head. Damaged bone microstructures were artificially generated from low-density bone microstructures obtained by a topology optimization-based bone microstructure reconstruction scheme. By examining average delays and amplitude ratios of the reflected HNSWs for both damaged and undamaged bone microstructures, we found that the reflected HNSWs are sensitive to the presence of partial damages in bone microstructures. This result indicates a feasibility of using HNSWs for non-destructive prediction of the partial fracture of trabecular bone due to osteoporosis. Future work will focus on studying the relation between the mechanical properties and the characteristic features of the reflected HNSWs on damaged bone microstructures of trabecular bone in the femoral head. Acknowledgements. The authors gratefully appreciate the financial support from the Abu Dhabi Department of Education and Knowledge (ADEK) through the Award of Research Excellence (AARE) 2017 (No. AARE17-069).

References 1. Chang, G., Honig, S., Brown, R., Deniz, C.M., Egol, K.A., Babb, J.S., Regatte, R.R., Rajapakse, C.S.: Finite element analysis applied to 3-T MR imaging of proximal femur microarchitecture: lower bone strength in patients with fragility fractures compared with control objects. Radiology 272, 464–474 (2014) 2. Krug, R., Burghardt, A.J., Majumdar, S., Link, T.M.: High-resolution imaging techniques for the assessment of osteoporosis. Radiol. Clin. 48, 601–621 (2010) 3. Goldstein, S.A., Goulet, R., McCubbrey, D.: Measurement and significance of threedimensional architecture to the mechanical integrity of trabecular bone. Calcif. Tissue Int. 53, S127–S133 (1993) 4. Goulet, R., Goldstein, S.A., Ciarelli, M.J., Kuhn, J.L., Brown, M., Feldkamp, L.: The relationship between the structural and orthogonal compressive properties of trabecular bone. J. Biomech. 27, 375–389 (1994) 5. Daraio, C., Nesterenko, V.F., Herbold, E.B., Jin, S.: Strongly nonlinear waves in a chain of Teflon beads. Phys. Rev. E 72, 016603 (2005) 6. Yang, J., Silvestro, C., Khatri, D., De Nardo, L., Daraio, C.: Interaction of highly nonlinear solitary waves with linear elastic media. Phys. Rev. E 83, 046606 (2011) 7. Coste, C., Falcon, E., Fauve, S.: Solitary waves in a chain of beads under Hertz contact. Phys. Rev. E 56, 6104–6117 (1997) 8. Daraio, C., Nesterenko, V.F., Herbold, E.B., Jin, S.: Tunability of solitary wave properties in one-dimensional strongly nonlinear phononic crystals. Phys. Rev. E 73, 026610 (2006) 9. Job, S., Melo, F., Sokolow, A., Sen, S.: Solitary wave trains in granular chains: experiments, theory and simulations. Granular Matter 10, 13–20 (2007) 10. Nesterenko, V.F.: Dynamics of Heterogenous Materials. Springer, New York (2001) 11. Rosas, A., Lindenberg, K.: Pulse propagation in granular chains. Phys. Rep. 735, 1–37 (2018) 12. Kim, E., Yang, J.: Review: wave propagation in granular metamaterials. Funct. Compos. Struct. 1, 012002 (2019)

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13. Job, S., Melo, F., Sokolow, A., Sen, S.: How Hertzian solitary waves interact with boundaries in a 1D granular medium. Phys. Rev. Lett. 94, 178002 (2005) 14. Cai, L., Rizzo, P., Al-Nazer, L.: On the coupling mechanism between nonlinear solitary waves and slender beams. Int. J. Solids Struct. 50, 4173–4183 (2013) 15. Ni, X., Rizzo, P., Yang, J., Khatri, D., Daraio, C.: Monitoring the hydration of cement by means of highly nonlinear solitary waves. NDT E Int. 52, 76–85 (2012) 16. Schiffer, A., Kim, T.-Y.: Modelling of the interaction between nonlinear solitary waves and composite beams. Int. J. Mech. Sci. 151, 181–191 (2019) 17. Schiffer, A., Alia, R.A., Cantwell, W.J., Lee, D., Kim, E., Kim, T.-Y.: Elastic interaction between nonlinear solitary waves in granular chains and composite beams: Experiments and modelling. Int. J. Mech. Sci. 170, 105350 (2020) 18. Schiffer, A., Lee, D., Kim, E., Kim, T.-Y.: Interaction of highly nonlinear solitary waves with rigid polyurethane foams. Int. J. Solids Struct. 152–153, 39–50 (2018) 19. Yang, J., Khatri, D., Anzel, P., Daraio, C.: Interaction of highly nonlinear solitary waves with thin plates. Int. J. Solids Struct. 49, 1463–1471 (2012) 20. Kim, E., Restuccia, F., Yang, J., Daraio, C.: Solitary wave-based delamination detection in composite plates using a combined granular crystal sensor and actuator. Smart Mater. Struct. 24, 125004 (2015) 21. Schiffer, A., Alkhaja, A.I., Yang, J., Esfahani, E.N., Kim, T.-Y.: Interaction of highly nonlinear solitary waves with elastic solids containing a spherical void. Int. J. Solids Struct. 118–119, 204–212 (2017) 22. Singhal, T., Kim, E., Kim, T.-Y., Yang, J.: Weak bond detection in composites using highly nonlinear solitary waves. Smart Mater. Struct. 26(5), 055011 (2017) 23. Yang, J., Restuccia, F., Daraio, C.: Highly nonlinear granular crystal sensor and actuator for delamination detection in composite structures. In: Chang, F.K. (ed.) International Workshop on Structural Health Monitoring, Stanford, CA, pp. 1424–1433 (2011) 24. Nasrollahi, A., Lucht, R., Rizzo, P.: Solitary waves to assess the internal pressure and the rubber degradation of tennis balls. Exp. Mech. 59, 65–77 (2019) 25. Ni, X., Rizzo, P.: Highly nonlinear solitary waves for the inspection of adhesive joints. Exp. Mech. 52, 1493–1501 (2012) 26. Yang, J., Sangiorgio, S.N., Borkowski, S.L., Silvestro, C., De Nardo, L., Daraio, C., Ebramzadeh, E.: Site-specific quantification of bone quality using highly nonlinear solitary waves. J. Biomech. Eng. 134, 101001 (2012) 27. Yang, J., Silvestro, C., Sangiorgio, S.N., Borkowski, S.L., Ebramzadeh, E., De Nardo, L., Daraio, C.: Nondestructive evaluation of orthopaedic implant stability in THA using highly nonlinear solitary waves. Smart Mater. Struct. 21, 012002 (2012) 28. Yoon, S., Schiffer, A., Kim, J.J., Jang, I.G., Lee, S., Kim, T.-Y.: Numerical predictions of the interaction between highly nonlinear solitary waves and the microstructure of trabecular bone in the femoral head. J. Mech. Behav. Biomed. Mater. 109, 103805 (2020) 29. Kim, J.J., Jang, I.G.: Image resolution enhancement for healthy weight-bearing bones based on topology optimization. J. Biomech. 49, 3035–3040 (2016) 30. Kim, J.J., Nam, J., Jang, I.G.: Computational study of estimating 3D trabecular bone microstructure for the volume of interest from CT scan data. Int. J. Numer. Methods Biomed. Eng. 34(4), e2950 (2018) 31. Johnson, K.L.: Contact Mechanics. Cambridge University Press, Cambridge (1985) 32. Hilber, H.M., Hughes, T.J.R., Taylor, R.L.: Improved numerical dissipation for time integration algorithms in structural dynamics. Earthq. Eng. Struct. Dyn. 5, 283–292 (1977)

Structural Health Monitoring of Cultural Heritage Structures

Vibration-Based Novelty Detection of Masonry Towers Using Pattern Recognition Gabriele Marrongelli(&), Carmelo Gentile, and Antonella Saisi Department of Architecture, Built Environment and Construction Engineering (DABC), Politecnico di Milano, Milan, Italy {gabriele.marrongelli,carmelo.gentile, antonella.saisi}@polimi.it

Abstract. During the last decades, the increased availability of continuously monitored structures has attracted the attention of the Structural Health Monitoring (SHM) community towards the development of automated techniques capable of continuously providing useful information to timely assess the health state of a structure. Over the years, especially the SHM procedures based on Operational Modal Analysis (OMA) have proved to be effective tools for the continuous assessment of large infrastructures and ancient constructions. Within this context, the paper presents the development and validation of a vibration-based novelty detection strategy based on the application of pattern recognition models to the identified natural frequencies, with the latter being used as damage-sensitive features. The methodology presented herein is based on the forming of a decision boundary through the use of a Support Vector Machine (SVM) model: hence, SVM is exploited to separate data into two classes, associated to two different structural conditions (i.e., undamaged and damaged), without any prior assumptions on the propriety of the data. The robustness of the developed approach is exemplified using the natural frequencies automatically identified during the continuous monitoring of a historic masonry tower. Due to the occurrence of a far-field earthquake, the tower underwent structural damage demonstrated by a slight permanent variation in the natural frequencies. The obtained results highlight the capability of the proposed approach to automatically reveal slight damages in structures without any user interaction and without performing any removal of environmental and operational effects. Keywords: Damage detection
Masonry towers
Operational Modal Analysis
Structural Health Monitoring
Support Vector Machine

1 Introduction Support Vector Machines (SVMs) are popular techniques for forming decision boundaries that separate data into different classes. Interesting papers and books present in literature describe these techniques and their application in different research fields. Comprehensive reading of the main concept related to SVM can be obtained from [1, 2], whereas for a deeper analysis the excellent book [3] is suggested. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 905–914, 2021. https://doi.org/10.1007/978-3-030-64594-6_87

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Originally, the SVM models were developed because they are ideally suited for binary pattern recognition and used to perform the classification of data linearly separable. Hence, the input data can be separated and classified constructing and maximizing the separating margin between two classes [3]. Typically, SVM procedures are not sensitive to specific sampling or density of the target class because they describe the target using a boundary margin, or a domain, based on training data [2, 4]. Since the introduction of the original idea, several improvements have been implemented to make this approach more robust and efficient, such as: (a) the Robust Support Vector Machines (RSVMs) algorithm, developed to address the overfitting problem caused by the noise in the training data [4] or (b) the Support Vector Data Description (SVDD) method [2], which defines the novelty boundary adopting hyperspheres with minimum volume to cover all (or almost all) the “normal” class. Moreover, some extension of the SVDD approach have recently been proposed [5] to improve the margin boundary using small spheres and large margin or using some slack variables and set of hyperspheres with different centers and radii [6]. Over the years, different algorithms based on SVM have been implemented and improved aimed at solving most disparate classification and novelty detection problems in several research fields [7, 8]. This trend has also interested the Civil Engineering field, addressing special attention to applications regarding damage assessment. Hence, during recent years, several methods of novelty detection were proposed for SHM purposes. In general, most of these methods consist of evaluating some indexes or indicators that allow for detection of any possible anomalies and damages on the structure, possible locations and even the extension of the damaged regions [9], associating a probability of “true detection” (probability of occurred damage in the structure when it is affectively present in the mechanical system). In SHM approaches for civil engineering structures the first step consists of detecting the occurrence of anomalies in the “normal” structural behavior, and subsequently localize such anomalies in the structure. For this purpose, several studies have been performed using statistical tests and pattern recognition approaches based on a comparison of data extracted in healthy and damaged conditions [10, 11]. These approaches are efficient and useful when the structural response can be obtained with a high level of confidence. Moreover, these methods proved to be effective when relatively small sets of data are used for the training and testing phase [12]. Many other techniques were developed to detect several damage scenarios using modal parameter estimations, as proposed in [13] where an unsupervised learning classification algorithm was developed and used to detect several damage states through the natural frequencies extracted by vibration responses of a cantilever beam.

2 Development of the Novelty Detection Strategy The SVMs are generally based on a geometric approach, consisting of the construction of an optimal separating surface - a hyper-plane - which divides the data population in two groups with different statistical characteristics. The hyper-plane is equidistant from the two classes defining a margin zone between them and the outputs consist of the target binary vectors (labels) corresponding to each class. In case of linear separable

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data (see Fig. 1a) the SVM algorithm searches for the optimal solution by maximizing the distance between the hyper-plane and the extreme values of each class (Fig. 1b). More sophisticated algorithms use stack variables allowing the creation of a soft margin in which some data-points can fall on either side (Fig. 1c).

Fig. 1. Margin between two classes of data support vector; (a) general solution, (b) maximization of the margin, (c) misclassified points that fallen outside decision boundary.

Although, SVM algorithms are normally used for two-class classification problems, extension to multi-classes classification can be done but this aspect will not be treated in this work, because the proposed approach is developed to follow a binary condition: absence or presence of damage, and no other states are allowed. As previously pointed out, SVM algorithms are aimed at separating two different classes using a discrimination function which is automatically computed during the classification process of the training data. Within the context of SHM and damage detection of civil engineering structures [14], two classes of data are assumed over time, corresponding to undamaged and damaged condition. Hence, in order to simplify the discussion about the novelty detection algorithm proposed herein an important clarification has to be done: for each run of the algorithm, if the classification fails it means that data are not separable, then they belong to the same class. Otherwise they are separable. Therefore, if the classification does not provide a clear separation of the data, the structural damage is not identified. On the contrary, the successful detection of two classes implies a change in the normal behavior or an instantaneous damage occurred in the monitored system. The novelty approach proposed herein is developed to automatically detect structural changes in the “normal behavior” of the structures using the evolution of the modal parameters provided by the continuous monitoring process. In fact, the modal features, such as: natural frequencies estimates or mode shapes variations (using MAC, MPC and MPD indices) can be used as input values. As explained in [15], this methodology was implemented to provide a best classification of the input data reducing the contribution of possible misclassification. Its implementation was inspired by the so-called true damage approach [16] that consists of the construction of a SVM model thorough the definition of a separating margin of a specific representative system condition and, consequently, on the use of this margin to find any other possible scenario in which the data distribution has same (or similar) statistical characteristics.

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Hence, the algorithm is forced to recognize two pre-imposed classes by assigning binary labels to the input data, 50% of the labels as true and the other 50% false. This means that in case of correct classification all data-points belong to undamaged scenario should be associated to the true label, meanwhile all data-points belong to damage scenario should be associated to the false labels. Unlike what is generally done, in the damage detection algorithm herein proposed the SVM model is not developed to estimate a prediction of the experimental data; on the contrary, the model is defined and used to recognize a specific structural condition given by the occurrence of the two different scenarios. This condition is identified only when a clear change (or discontinuity) occurs in the trend of the data. As a matter of fact, when the damage occurs the trend of the modal parameters might permanently change, allowing the identification of the anomaly through the separation of the data into two classes. A simple scheme of the developed strategy is reported in Fig. 2.

Fig. 2. Scheme of the developed strategy for damage detection approach.

The SVM tries to find any possible anomalies checking the statistical variations between two consecutive data-segments (Fig. 2) which are selected from the evolutions of the identified sensitive features. As shown in Fig. 2, both data-segments move together following the continuous identification process. The main objective of the novelty strategy is to use the SVs to discriminate two different states (i.e., non-damaged and damaged) associating a probability value to the correct classification obtained after each run of the procedure. The aim of this purpose is to prove the occurrence of a structural damage through the accuracy of the model reached in the classification problem. Furthermore, in order to better clarify, it is worth remarking that the SVM accuracy can reach the maximum value (i.e. 100%) only when the 50% of data-points is associated to undamaged condition and the other 50% is associated to a damaged state and it can be possible only if the damage is present in the input data and it is located in the middle of the two data-segments (Fig. 2). It is worth highlighting that the novelty analysis is performed directly on the estimates “corrupted” by environmental and operational factors, because such effects are not filtered out before the application of the damage detection algorithm. This means that the SHM of the structure can be performed contextually with the automated identification and the continuous monitoring of the modal features avoiding any further manual interaction during the process. The validation of this approach is performed using experimental data collected during the monitoring of the Gabbia Tower [17–19].

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3 Application and Validation of the Proposed Strategy 3.1

Description of the Tower

The Gabbia Tower [17–19] with its 54.0 m in height, is the tallest tower in Mantua. The tower was probably completed in 1227 and it was part of the defensive system of the Bonacolsi family (i.e., Lords governing Mantua during the 13th century). The structure is built in solid brick masonry and the load bearing walls are about 2.4 m thick until the upper levels, where the wall thickness decreases to about 0.7 m and where a two-level lodge is hosted. While the main part of the building, until the height of about 46.0 m, did not exhibit any evident structural damage, the upper part of the tower turned out to be in a poor state of preservation [17–19].

Fig. 3. (a) View of the Gabbia Tower in Mantua, Italy; (b) Instrumented cross-sections and layout of the sensors during the preliminary tests (Nov 2012) and the continuous monitoring.

Fig. 4. Five vibration modes of the tower identified during preliminary AVT.

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After the Italian seismic sequence of May 2012, the tower was subjected to an extensive on-site inspection and post-earthquake investigation [17–19], including ambient vibration tests to extract the principal modes of vibration of the tower. The dynamic characteristics of the tower, identified on 27/11/2012, are summarized in Fig. 4. 3.2

Continuous Dynamic Monitoring of Natural Frequencies

A simple monitoring system was installed on the top of the tower (Fig. 3b) with continuous dynamic monitoring purpose. The application of the automated OMA algorithm [15] to the data series collected during seven months of continuous monitoring (i.e., from 17/12/2012 to 15/07/2013) is herein presented. The variations of the modal frequencies associated to the identified structural modes of the tower are reported in Fig. 5. It should be noticed that the vertical line (violet color) in Fig. 5 highlights the occurrence of a far-field seismic event, on 21/06/2013: this earthquake involved significant effects on the dynamic characteristics of the tower [17–19], including an instantaneous frequency shift in the investigated natural frequencies.

Fig. 5. Time evolution of the identified natural frequencies of the tower.

Fig. 6. Zoom of the first and second natural frequency obtained during the monitoring period from 01/06/2013 to 15/07/2013.

This phenomenon can not be appreciated by the global scale used to represent the frequency evolutions reported in Fig. 5, so a clearer representation of the first two

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frequencies estimates extracted on June 2013 are reported in Fig. 6. The daily fluctuation of the natural frequencies induced by temperature variations together with the frequency shifts occurred during the seismic event are clearly identified [17–19]. 3.3

Application of the Damage Detection Algorithm

Due to the fact that the unsupervised approach seems to work better if the classification does not involve also the temperature values, as demonstrated in [20], only the frequency estimates obtained during the monitoring process were used to set the SVM model and to demonstrate the robust performance of the implemented strategy. After some initial tests the length of the data segments used as input of the SVM model was defined equal to 48 elements. This value indicates that the algorithm works with a population of modal estimates equal to 96 elements (in which 48 elements are flagged as undamaged and other 48 elements are flagged with damaged labels). Moreover, this value seems to produce the best results in the investigation for small frequency drops. It is worth noting that selecting this length for both data segments (undamaged and damaged), the occurrence of any damages is fully provided after 48 runs of the algorithm. This means that, when the damage is occurred in the structure, this one should be automatically identified exactly after only 48 h. Moreover, the results presented in previous research [17–19] show that the structural anomaly is more evident in the lower frequencies. Hence, only the evolution of the frequencies associated to the first and second bending modes have been used to test the developed damage detection algorithm [15]. Once the length of data-segments was defined, some tests have been carried out to set the boundary conditions of the margin characterizing the SVM model. Such parameters were defined as follows: k-folds = 10, r = 1 and C = 10. For an in-depth understanding of the definition of these parameters, the interested reader is referred to [3] as well as to the applications developed in [20]. The diagrams in Fig. 7 show the application of the proposed damage detection strategy to seven months of monitoring. As shown in Fig. 7, the probability value associated to the damage occurrence generally fluctuates around its average value of 50% in the beginning of the monitoring period, meaning that the SVM model can not distinguish between the undamaged and damage state and the data are mixed. On the contrary, at the end of this period a clear peak in terms of occurred probability that reach 100% is visible in both diagrams. This means that the algorithm performs an almost perfect classification allocating 50% of the data to the undamaged condition and other 50% to the damaged one. This condition implies a clear changing in the behavior on the input data and a possible damage in the investigated structure. The obtained results confirm the information pointed out by previous research [17– 19], performed by using different damage detection approaches. From the inspection of Fig. 7, some considerations can be pointed out: 1. The structural change occurred in the tower is successfully identified by the algorithm through a clear peak in the probability associated to the damage occurrence. 2. No further anomalies were detected in the investigated period of monitoring.

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3. No spurious peaks of the damage probability appear during the monitoring phase, demonstrating the robustness of the implemented methodology. 4. The absence of false positive in the diagrams in Fig. 7 also confirms the robustness of the newly developed algorithm and its reliability on SHM applications. 5. The effects of the environmental factors on the natural frequency estimates (i.e. temperature fluctuation) do not affect the analysis and the detection of the damage. 6. The continuous assessment was carried out using a length of each input segment equal to 48 elements. This parameter permits the automated identification of the occurred damage in the structure after only 48 h from its occurrence.

Fig. 7. Application of the novelty algorithm to continuous monitoring data: natural frequency estimates related to the first and second bending mode (from 17/12/2012 to 14/07/2013).

These results point to an important outcome related to the novelty detection strategy herein presented. In fact, it is possible to define a-priori the accuracy of the model according to the input parameters and the population of input data provided to the model in order to define the boundary decision surface. As described in [15], the pattern recognition model defines a soft margin obtained by penalizing some datapoints (i.e. frequency estimates) that could fall outside the decision boundary; there values are treated as outliers [15]. Following this strategy, it is possible to define a soft margin in which to find the optimal separation surface that could be recursively used to classify the input-data within continuous assessment purposes.

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4 Conclusions This work focuses on the development of an alternative OMA-based SHM approach using the application of pattern recognition models on the modal parameters obtained by analyzing collected structural responses in operational conditions. A brief review of the classification algorithms based on SVM models is given in the beginning of this paper highlighting the main improvements obtained in different fields of application over the years. Subsequently, a novel damage detection strategy based on the classical SVM technique is proposed. The novel approach is aimed at recognizing two different classes of data associated with two structural conditions (i.e., undamaged and damaged) constructing an optimal separation surface and maximizing a margin between the two classes. Therefore, experimental data collected by a permanent monitoring system installed on the Gabbia masonry tower were used to validate the proposed strategy. From the results obtained by its application permanent damage occurred during the earthquake of 21/06/2013 was clearly identified. The structural anomaly was automatically detected analyzing the evolution in time of the natural frequencies associated with the first two fundamental modes of the tower without performing any removal of the environmental and operational effects on the identified parameters. Concluding, the application described in this paper reveals the capability of the proposed strategy in the context of SHM purposes, making this approach very promising for the continuous assessment of structures with monitoring systems composed of a limited array of sensors. In this way, the information given might be used by artificial intelligence models to generate an alarm in the case of structural damage after a very short time delay. It is worth highlighting that the novelty analysis herein proposed is performed directly on the estimates “corrupted” by environmental and operational factors, because such effects are not filtered out before the application of the damage detection algorithm. Hence the long training period devoted to investigating these effects from the identified features is overcome. This means that the SHM purpose can be performed contextually with continuous monitoring avoiding any further manual interaction during the process, reducing the high costs of monitoring and improving its efficiency.

References 1. Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. Neural Inf. Process. Syst. 12, 582–588 (2000) 2. Tax, D., Duin, R.: Support vector domain description. Pattern Recogn. Lett. 20(11), 1191– 1199 (1999) 3. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006) 4. Hu, W., Liao, Y., Vemori, V.: Robust anomaly detection using support vector machines. In: Proceedings of the International Conference on Machine Learning, pp. 282–289 (2003)

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5. Wu, M., Ye, J.: A small sphere and large margin approach for novelty detection using training data with outliers. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2088–2092 (2009) 6. Le, T., Tran, D., Ma, W., Sharma, D.: Multiple distribution data description learning algorithm for novelty detection. Knowl. Discov. Data Min. 6635, 246–257 (2011) 7. Manevitz, L., Yousef, M.: One-class SVMs for document classification. J. Mach. Learn. Res. 2, 139–154 (2001) 8. Li, Y.: A surface representation approach for novelty detection. In: Proceedings of the International Conference on Information and Automation (ICIA), pp. 1464–1468 (2008) 9. Yan, Y.J., Cheng, L., Wu, Z.Y., Yam, L.H.: Development in vibration-based structural damage detection technique. Mech. Syst. Signal Process. 21, 2198–2211 (2007) 10. Zhang, Q.W.: Statistical damage identification for bridges using ambient vibration data. Comput. Struct. 85, 476–485 (2007) 11. Iwasaki, A., Todoroki, A., Shimamura, Y., Kobayashi, H.: An unsupervised statistical damage detection method for structural health monitoring. Smart Mater. Struct. 13, 80–85 (2004) 12. Chun, X., Qu, W., Tan, D.: An application of data fusion technology in structural health monitoring and damage identification. In: Proceedings of the Smart Sensors Technology and Measurement Systems, Belgium, pp. 451–461 (2005) 13. Trendafilova, I., Heylen, W.: Categorization and pattern recognition methods for damage localization from vibration measurements. Mech. Syst. Signal Process. 17(4), 825–836 (2003) 14. Sohn, H., Farrar, R., Hemez, M., Czarnecki, J., Shunk, D., Stinemates, W., Nadler, R.: A review of structural health monitoring literature, 1996–2001. Los Alamos National Laboratory, Los Alamos (2002) 15. Marrongelli, G.: Vibration-based structural health monitoring of civil engineering structures: automated operational analysis and damage detection. Ph.D. thesis, Politecnico di Milano, Italy (2020) 16. Santos, J.P., Cremona, C., Orcesi, D.A., Silveira, P.: Multivariate statistical analysis for early damage detection. Eng. Struct. 56, 273–285 (2013) 17. Saisi, A., Gentile, C., Guidobaldi, M.: Post-earthquake continuous dynamic monitoring of the Gabbia Tower in Mantua, Italy. Constr. Build. Mater. 81, 101–112 (2015) 18. Gentile, C., Guidobaldi, M., Saisi, A.: One-year dynamic monitoring of a historic tower: damage detection under changing environment. Meccanica 51, 2873–2889 (2016) 19. Guidobaldi M.: Vibration-based structural health monitoring for historic masonry towers. Ph.D. thesis, Politecnico di Milano, Italy (2016) 20. Marrongelli, G., Finotti, R., Gentile, C., Barbosa, F.: An artificial intelligence strategy to detect damage from response measurements: application on an ancient tower. In: MATEC Web of Conferences, Lisbon, Portugal, vol. 211, p. 21002 (2018)

Health Assessment and Modal Analysis of Historical Masonry Arch Bridge Abhinav Kolla(&) , Ravi Naga Sai Kurapati , Sree Satya Venkat Meka , Venkata Sai Madhu Dinesh Vitakula and Venkata Dilip Kumar Pasupuleti

,

Ecole Centrale College of Engineering, Mahindra University, Hyderabad, India {abhinav170113,ravinagasai170116, sreesatyavenkat170136, venkatasaimadhudinesh170138}@mechyd.ac.in, [email protected]

Abstract. Masonry arch bridges in India indicate the heritage value of the nation. Most of these bridges had been in service for hundreds of years and yet being serviceable even today for transportation purposes indicates the robustness of the design and construction methodology. But, some of these bridges are abandoned due to its deterioration and absence of knowledge to retrofit these structures. Lack of proper maintenance and retrofitting could eventually damage the structural integrity as these structures are old enough to deteriorate and are prone to repeated weathering and unforeseen natural calamities such as earthquakes, floods, etc. In this study, a very old masonry arch bridge ‘Puranapul’ bridge inaugurated in the year 1578 across the river Musi in Hyderabad is considered for investigation of its health through basic visual inspection and non-destructive testing. Furthermore, the same is numerically modeled using the available finite element analysis software ANSYS in three dimensions for assessing the basic mode shapes of the structure and its behavior in different loading conditions. Keywords: Masonry arch bridge
Heritage structure
Visual inspection Finite element model
Nondestructive testing
Health assessment




1 Introduction Masonry arch bridges are one of the robust and prevalent types of structures constructed for transportation practices until the early part of the twentieth century around the world among which, many of them hold a history of thousands of years of service [1]. These bridges are heritage structures which, symbolize the cultural heritage of many nations across the world. Considering the age of these structures, they must have undergone continuous deterioration due to prolonged exposure to natural or manmade loads [2]. So, it is important for us to safeguard these structures to preserve them for the next generations. A thorough understanding and precise knowledge of the structural behavior of these bridges is extremely mandatory to maintain its structural integrity and also, such studies assist in coming up with some cost-efficient retrofitting methods. © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 915–926, 2021. https://doi.org/10.1007/978-3-030-64594-6_88

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Such, kind of heritage value always brings in awareness leading many researchers to seek fascinating experimental and theoretical understanding of these structures [3]. Hyderabad is known to be founded by Muhammed-Quli Qutb Shah, the fifth sultan of the Golconda kingdom in the year 1591 and is one of the largest cities in India located in the south-central region of the country alongside the river Musi [4]. Hyderabad is known for its heritage and most of the structures are almost 400 years old and are builtin masonry with lime as binding material largely. In this study, we have considered ‘Puranapul bridge’ which was one of the first Masonry arch bridges constructed across the river Musi connecting the old Golconda-Karwan area and the new city of Hyderabad. This bridge was built in the year 1578 which was almost fourteen years before the foundation of Hyderabad city i.e. 1591 using sandstone as the primary material and it consists of 22 arches aligned equidistantly over an entire span length of 185 m with a width of 10.9 m and a depth of about 12.8 m above the bed of the river Musi [5] as shown in Fig. 1.

Fig. 1. Purana pul Masonry Arch Bridge (a) Side view (b) Path way used by street vendors (c) extreme arches (d) location of measurements taken for one complete arch (e) Railing wall on either side of passageway

Purana Pul arch bridge was restored two times due to heavy floods in the year 1820 and 1908 [6]. Figure 1(b) shows that the bridge is currently being used by the vegetable vendors but not used for heavy vehicular loads from the last ten years as it has not been assessed for structural stability and integrity. Figure 1(c) and (d) shows the locations which are accessible for a structural health assessment to be carried out. This study has also focused on the sizes of stones used for the construction and properties of binding material to understand its deterioration. But this research paper limits itself to the fundamental analysis of the structure based on the properties of the materials used i.e.

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sandstone obtained from the site inspection. And Fig. 2 shows the schematic representation of the complete bridge consisting of all the 22 arches with varying ground levels at the ends. It also shows the dimensions in detail i.e. each arch opening is 5.9 m and its height is 10 m from the bottom earth level whereas, the arch thickness is 0.7 m and the pier thickness is 2.95 m. All these dimensions were calculated during the visual inspection of the bridge.

Fig. 2. Front view and componential dimensions of the arch bridge

Fig. 3. Three dimensional model developed in Maya with complete detailing

To have a detailed glimpse of this bridge, a three dimensional model with complete features has been developed using Autodesk Maya and rendered using Arnold as seen in Fig. 3. There have been multiple trails for importing the same model into FEM software but had difficulties in multiple layers. So, in this paper structural health of a

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masonry arch bridge which is 442 years old is assessed with a keen visual inspection, and numerical analysis of the same is performed to access its current condition. The study has also attempted to know the current load-carrying capacity by its frequency. The visual inspection phase is mostly comprised of examining the materials used for construction, any structural damages to the structure, and taking accurate measurements of the entire bridge to build the numerical model, which can give a better understanding of the structure. For generating a three dimensional model for analysis, a finite element based software ANSYS is used because of its simplicity in the complex modeling, incorporation of material properties, application of loading and boundary conditions. It can be used to generate a precise numerical model and test under various loading conditions to investigate the structural integrity. The loading conditions considered for this study are gravity and live loads apart from modal analysis to find the fundamental frequency and other possible frequencies. In consequence of this numerical analysis, principal stresses, mode shapes, and total deformations for the applied loads on the masonry bridge are assessed.

2 Background A lot of research work is being carried out on Masonry arch bridges from many decades all around the world, but yet it is still a challenge to create a realistic model [7]. Toth et al. [8] have detailed a good review of the past numerical models developed for understanding the behavior of masonry arches. The author has also mentioned that most of the 2D or 3D models of the masonry structures developed using FEM are continuum-based but masonry is fundamentally a discrete system. Few researchers have developed two-dimensional models with plane strain assumption and binding materials as a spring to understand the failure mechanism [9–11]. Other groups of researchers have concentrated on the material properties for more suitable behavior of the masonry arch bridges [12–14]. Similarly, few other researchers have developed numerical models with consideration of contact analysis between the stones for more specific deformation and sliding behavior [15]. Even though a lot of studies have been carried based on dimensions (1D, 2D, 3D), material properties (young’s modulus, linear, non-linear), contact behavior (normal, shear), continuum or discrete and loading conditions still linear continuum models play a vital role in understanding the basic behavior of the masonry arch bridges, especially if the bridge does not have any structural damage. So, the current study is largely concentrated on continuum modeling and incorporation of a material model for understanding its behavior.

3 Numerical Modeling The bridge has 22 arch spans and 185 m long, 10.9 m broad, and 12.8 m above the bed of the river. The thickness of the spandrel walls and arch is 10.7 m as shown in Fig. 2. This bridge was constructed using sandstone as the primary material for arches, spandrel walls, and abutments. Special site investigation for the substructure has not been carried. It is observed that the structural stone joints are filled with lime mortar

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and during the site investigation no structural damage or seepage of water from the top surface of the deck into the bridge is observed, which indicates that the structure is still in a good condition. Analysis of the masonry arch bridge is done using commercially available finite element based software in a macro modeling approach due to its minimalistic computational effort. The three-dimensional finite element modeling approach is preferred as the 3D model is generated by creating a finite element similar to the material used in construction provided with the properties such as density, young’s modulus, and poisons ratio of the actual material mentioned in Table 1 used in the construction of the bridge for a better understanding of the structural behavior in ANSYS workspace. As the study is conducted on an ancient masonry arch bridge comprised of the same type of material across arches, spandrel wall, and abutments the material properties of the bridge are assumed from appropriate literature [7]. Table 1. Physical and mechanical parameters of the masonry arch bridge Parameter Compressive strength Tensile strength Youngs modulus Poisons ratio Unit weight

Units Value MPa 66.9 MPa 3.7 GPa 1.13 n/a 0.279 kN/m3 27.5

Three dimensional finite element model developed for the complete bridge is shown in Fig. 4 with stone masonry properties. The model is tested for three types of meshes coarse (1.2 m), medium (0.6 m) and fine (0.3 m) but the results obtained were in the similar line. Current model has 31,103 nodes and 5,376 elements in total. As the bridge is symmetric in nature, an individual arch numerical model is also developed and analysis has been carried for modal and gravity analysis.

Fig. 4. Finite element model of the bridge with meshing

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4 Numerical Analysis and Results Majorly numerical analysis is done for three cases of which one is gravity analysis to understand the scale of deformations for the self-load and live load along with the maximum permissible stresses and strains. Secondly, modal analysis is carried out to know the longitudinal and transverse mode shapes with their respective frequencies. Lastly dynamic analysis is done to understand the seismic response with the foundations of the bridge being fixed. As this bridge consists of 22 uniform arches located equidistantly along its entire span to perform the numerical analysis on such kind of huge structures there is a need for high computational power, hence due to lack of high computational power, the analysis is done in a macro modeling approach as stated in a relatable literature [16] with a descent meshing size to obtain satisfactory results. 4.1

Gravity Analysis

On the application of the earth’s standard gravitational force uniformly over the deck of the bridge along the negative y-direction, the following results were obtained, the total deformation of the bridge tends to be maximum at the centers of all the arches with a value of 0.14018 mm deformation in the negative y-direction and minimum at the two end surfaces and foundations of the bridge with zero deformation as shown in the Fig. 5.

Fig. 5. Total deformations of the bridge due to its self-weight

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Fig. 6. Equivalent elastic strain distribution over the bridge due to its self-weight

Figure 6 shows the equivalent strain distribution all over the bridge and maximum strain is observed to be 0.03716 and at the foundation level and reentrant corners, whereas the minimum strain is observed exactly on top of the piers projected to the passage way surface as seen in the figure. Whereas Fig. 7 shows the equivalent stress distribution over the bridge and behavior is very much similar to that of strain. The maximum stress is observed to be 4.17e5 Pa at the corners of the foundation and minimum stress is observed exactly on top of the piers projected to the passage way surface as seen in the enlarged figure.

Fig. 7. Equivalent stress distribution over the bridge due to its self-weight

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Fig. 8. Maximum principal elastic strain distribution over the bridge due to its self-weight

Fig. 9. Maximum principal stress distribution over the bridge due to its self-weight

Maximum principal elastic strain and maximum principal stress are shown in the Fig. 8 and Fig. 9, and the obtained results show similar behavior of the elastic strain and elastic stress. 4.2

Modal Analysis

Modal analysis is performed to understand the behavior and characteristics of the Masonry arch bridge, which are vital when a structure is subjected to dynamic loads [17].

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Fig. 10. Natural frequencies and mode shapes determined by finite element method of single span

Fig. 11. Natural frequencies and mode shapes determined by finite element method for complete bridge

Figure 10 shows first six mode shapes and respective frequencies obtained from modal analysis. Boundary conditions are similar to that of gravity analysis. The frequencies obtained show the range of frequencies starting from 8.45 Hz to 12.6 Hz obtained for single span and Fig. 11 shows the first six modes of complete bridge. Frequenices are found to be higher than usual due to its size and continuum modeling. Figure 11(a) shows the first fundamental frequency 513.29 Hz in longitudinal mode and Fig. 11(c) shows the first transverse mode of frequency 637,7 Hz. Total six modes

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have been presented with first three longitudinal and first three transversal modes. The range of frequency are observed to be 513.29 Hz to 721.47 Hz for the first ten mode shapes and they are equally divided in to longitudinal and transversal modes shapes due to the symmetry of the structure. Modal analysis has also been carried for the model with only bottom fixed and obtained frequencies range from 483.13 Hz to 656.92 Hz. When the same is done for single arch masonry structure the frequencies of the first ten mode shapes ranged from 318.06 Hz to 1933.3 Hz. 4.3

Dynamic Analysis

The bridge is analyzed for seismic behavior to know the maximum deformation. As the Purnapul stone masonry bridge is located in the earthquake Zone-II according to IS 1893:2002, which has the zonation factor of 0.10 i.e. maximum horizontal acceleration that can be experienced by the structure in this zone is ten percent of acceleration due to gravity. To simulate the dynamic loading conditions for understanding the seismic behavior, the bridge was subjected to lateral accelerations that, were recorded on 21st Jan 2001 in Bhuj, India which was, one of the major earthquakes with a magnitude of 7.7 Mw and PGA of 0.6 g [18] causing much damage to short structures than compared to the taller ones. The ground acceleration is applied to the model in both the directions to know the maximum possible deformation of the bridge. Figure 12 shows the maximum deformations when the dynamic analysis was carried out and the maximum deformations were found to be very minimal 1.4  10−6 m and 6.14  10−8 m in x and z directions respectively. There are two major reasons for the negligible deformations, the first one would be due to the continuum modeling and the second one is due to its lesser height. So the structure is quite adequate for lateral loads also, but more precise modeling and detailed material properties can predict more probable behavior.

Fig. 12. Maximum deformations of the bridge for the Bhuj Earthquake ground motion in both the directions.

5 Conclusions A 442-year-old Purna pul stone masonry bridge with 22 arches was first visually inspected and based on observations, structural analysis has been carried using the finite element method for static, dynamic loads and to investigate the response of the structure, as it was abandoned from past ten years. Currently, it is being used by street vendors which could probably affect the structural stability and functionality. To the

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author’s knowledge, structural load carrying capacity or any other related tests have not been performed to qualitatively assess the structural stability. So, an attempt is made to understand the minimal nature of the structure and its stability. And based on the numerical analysis carried for static and dynamic loads, the current configuration of the stone arch bridge is adequate to take its self-weight and live loads coming from the vehicular traffic. As expected maximum deformations are observed to be at the middle portion of the arch and principal stresses show that they are very much in the permissible limit. Stone piers are also found to be stronger based on numerical analysis. Numerical modeling and analysis are always considered to be a very effective tool in assessing the structural health of the current heritage structures for prospective conservation and preservation. The quality of the numerical analysis is always higher and nearer to the insitu behavior if non-destructive testing results are incorporated in the numerical model. In the future, the work will be extended by incorporating the material properties in the numerical model and comparing them with the vibrational studies.

References 1. Sarhosis, V., De Santis, S., de Felice, G.: A review of experimental investigations and assessment methods for masonry arch bridges. Struct. Infrastruct. Eng. 12(11), 1439–1464 (2016) 2. Mai, K.Q., Lee, S.M., Lee, K.: Assessment of historic stone arch bridge characterisation: experiments and numerical model. Proc. Inst. Civil Eng.-Struct. Build. 172(7), 480–489 (2019) 3. Sevim, B., Bayraktar, A., Altunişik, A.C., Atamtürktür, S., Birinci, F.: Assessment of nonlinear seismic performance of a restored historical arch bridge using ambient vibrations. Nonlinear Dyn. 63(4), 755–770 (2011) 4. MIT Libraries Homepage. http://dome.mit.edu/handle/1721.3/45288 5. Appendices: Conservation of Historical Building and Areas in Hyderabad City, 1st edn. Hyderabad Urban Development Authority, Hyderabad (1984) 6. MIT Libraries Homepage. http://dome.mit.edu/handle/1721.3/20097 7. Banerji, P., Chikermane, S.: Condition assessment of a heritage arch bridge using a novel model updation technique. J. Civil Struct. Health Monit. 2(1), 1–16 (2012) 8. Tóth, A.R., Orbán, Z., Bagi, K.: Discrete element analysis of a stone masonry arch. Mech. Res. Commun. 36(4), 469–480 (2009) 9. Ford, T.E., Augarde, C.E., Tuxford, S.S.: Modelling masonry arch bridges using commercial finite element software. In: the 9th International Conference on Civil and Structural Engineering Computing, Netherlands, pp. 161–203 (2003) 10. Cavicchi, A., Gambarotta, L.: Two-dimensional finite element upper bound limit analysis of masonry bridges. Comput. Struct. 84(31–32), 2316–2328 (2006) 11. Gilbert, M.: Limit analysis applied to masonry arch bridges: state-of-the-art and recent developments. In: 5th International Arch Bridges Conference, pp. 13–28 (2007) 12. Jiang, K., Esaki, T.: Quantitative evaluation of stability changes in historical stone bridges in Kagoshima, Japan, by weathering. Eng. Geol. 63(1–2), 83–91 (2002) 13. Audenaert, A., Fanning, P., Sobczak, L., Peremans, H.: 2-D analysis of arch bridges using an elasto-plastic material model. Eng. Struct. 30(3), 845–855 (2008) 14. Crisfield, M.A.: Numerical methods for the non-linear analysis of bridges. Comput. Struct. 30(3), 637–644 (1988)

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15. Kamiński, T.: Three-dimensional modelling of masonry arch bridges based on predetermined planes of weakness. In: 5th International Conference on Arch Bridges, Madeira, Portugal, pp. 341–348 (2007) 16. Caddemi, S., et al.: 3D discrete macro-modelling approach for masonry arch bridges. In: IABSE Symposium 2019 Guimarães, Towards a Resilient Built Environment - Risk and Asset Management, 27–29 March, Guimarães, Portugal (2019) 17. Bayraktar, A., Türker, T., Altunişik, A.C.: Experimental frequencies and damping ratios for historical masonry arch bridges. Constr. Build. Mater. 75, 234–241 (2015) 18. Iyengar, R.N., Kanth, S.R.: Strong ground motion estimation during the Kutch, India earthquake. Pure. appl. Geophys. 163(1), 153–173 (2006)

Novel Structural Health Monitoring Software Systems Exploiting Heterogeneous Sensing Solutions and Data Fusion for Enhanced Local/Global Damage Identification of Historic Structures Enrique García Macías(&)

and Filippo Ubertini

Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy {enrique.garciamacias,filippo.ubertini}@unipg.it

Abstract. This work reports the development of two novel software solutions, named MOVA and MOSS, for the autonomous management of integrated monitoring systems. MOVA and MOSS, Italian acronyms of “MOnitoraggio delle Vibrazioni Ambientali’’ and “MOnitoraggio dello Stato di Salute’’, respectively, offer online operational modal analysis (OMA), pattern recognition, feature extraction through data fusion, and automated novelty detection capabilities. The functionalities of the developed codes are illustrated through the application case study of the monumental Consoli Palace in Gubbio, Italy. The palace was uninterruptedly monitored since July 2017 until August 2019 with a mixed static/dynamic/environmental monitoring system, and the SHM system has been recently upgraded in July 2020 with a considerable increase of the number of sensors deployed in the palace. Keywords: Control chart
Data fusion
Damage identification
Historic buildings
Novelty analysis
Structural health monitoring
Unsupervised learning

1 Introduction The management of ageing infrastructure represents one of the greatest challenges of modern Structural Engineering. The huge socioeconomic impacts stemming from the retrofitting, replacement and failure of structurally deficient constructions require the increasingly frequent implementation of SHM. Essentially, SHM exploits long-term monitoring data to track structural performance anomalies with the aim of conducting damage identification (detection, localization and quantification) and, desirably, predicting its evolution [1]. In this context, OMA-based SHM is becoming particularly popular owing to its non-destructive nature and minimum intrusiveness. Specially wellsuited for such systems are historic structures, where structural assessment must be performed without affecting their architectural value. In this line, considerable research efforts have been devoted to the analysis of correlations between modal features and © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 927–936, 2021. https://doi.org/10.1007/978-3-030-64594-6_89

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environmental/operational conditions (e.g. temperature, humidity, wind, traffic). A noteworthy contribution is the one by Peeters and De Roeck [2], who reported variations up to 18% related to daily and seasonal fluctuations in the first four resonant frequencies of the benchmark case study of the Z24-Bridge. Such fluctuations, which are inherent to the response of structures to variable environmental conditions, often mask the initiation of damage. Thus, the effectiveness of a SHM system is largely determined by its capability to identify and filter environmental/operational effects. Currently, there are plenty of statistical pattern recognition techniques available for phasing out such effects (see e.g. [3]), making long-term vibration-based SHM a quite mature technology. However, while proficient for detecting global damage, these systems have proved ineffective to detect local defects with minimum influence on the overall stiffness (e.g. corrosion, chemical/physical salt attack). As a solution, next generation SHM must encompass heterogeneous sensing solutions (e.g. dynamic, static, chemical) and exploit data fusion for efficient local/global damage detection. Specifically, in the realm of historic structures, an increasing number of scientific works report about the strong potentials of SHM combining dynamic, static, and environmental data. To a large extent, this has been motivated by the frequent appearance of complex environmental effects upon the modal features, which may vary considerably depending on factors such as construction materials, structural typology, or climate. For instance, positive correlations between environmental temperature and resonant frequencies are often observed in masonry structures, which is usually ascribed to thermal-induced crack closure phenomena [4, 5]. Nonetheless, some other works may be found reporting the opposite correlation such as the one by Gentile et al. [6] on the SHM of the Milan Cathedral in Italy. Another timely research line in the SHM field is injecting engineering knowledge into the decision process, through a closed loop between data and structural models, such as surrogate models and digital twins. Despite the advanced state of research, the number of software solutions that allow transferring integrated SHM systems into routine industrial applications remains considerably scarce. As an attempt to provide novel technical solutions to enable the technological transfer of real-time integrated SHM systems, this work reports the development of two new software solutions, named MOVA and MOSS. MOVA focuses on Ambient Vibration Testing (AVT), while MOSS is dedicated to the online management of permanent integrated SHM systems. Their functionalities are illustrated through the application case study of the monumental Consoli Palace in Gubbio, Italy.

2 MOVA/MOSS: Two Software Platforms for Integrated SHM 2.1

Software Architecture

The general workflow of a continuous SHM system using MOVA/MOSS consists of a heterogeneous sensor network deployed on the structure of interest and a data acquisition (DAQ) system that permanently collects the monitoring data. Subsequently, computer files containing records of certain time duration are sent through the internet

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or another transmission system to a server or to the cloud, where MOSS automatically processes the data and performs real-time damage assessment. Originally developed as MATLAB toolboxes, MOVA and MOSS have been implemented in C++ with compact graphical user interfaces (GUIs).

Fig. 1. MOSS main GUI.

The main GUI of MOSS, shown in Fig. 1, is organized in four consecutive steps: • System Identification: This step incorporates the main capabilities of MOVA, allowing the user to define the geometry of the structure and to set up the signal processing and OMA procedures. • Process of initial data set: This module generates an initial data population of structural features to characterize the healthy condition of the structure. • Frequency tracking and elimination of environmental effects: This module is devoted to the tracking of modal features and multivariate statistical analyses for pattern recognition. • Continuous SHM and damage detection: This last step enables the online system identification and damage detection based upon the previously defined damagesensitive features. This is accomplished by applying six sequential procedures to every new data file, namely (i) signal pre-processing; (ii) automated OMA; (iii) frequency tracking; (iv) pattern recognition; (v) updating of quality control charts, and (vi) online anomaly detection.

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Data fusion is performed at the pattern recognition stage. To do so, the user can freely decide which monitoring data are used as predictors and/or estimators. That is to say, non-dynamic monitoring data can be used to filter out the environmental effects over the resonant frequencies or, alternatively, they can be used as damage-sensitive features. In addition, MOSS permits the definition of multiple damage-sensitive features, which are used for real-time automated damage detection.

Fig. 2. Aerial view of Consoli Palace (a). Elevation of North-East façade, and sections in the East-West (d) and North-South (e) directions.

3 Case Study: The Consoli Palace in Gubbio, Italy 3.1

Description of the Structure and SHM System

Erected between 1332 and 1349, the Consoli Palace is the most iconic monument of the city of Gubbio in Italy (Fig. 2). The masonry palace is 60 m high and has a rectangular plan of about 30  20 m, including a bell-tower rising up to a total height of 42.76 m. A long-term integrated SHM system was installed in the Consoli Palace since July 2017 until August 2019 (see Fig. 3(a)). This included 3 uni-axial piezoelectric accelerometers (model PCB 393B12, ±0.50 g, and 10 V/g sensitivity), labelled from S1 to S3, deployed on the roof level of the palace. Additionally, two linear variable

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transducers (LVDTs), labeled with L1 and L2, were installed across two major vertical cracks located on the second floor of the building. Finally, two K-type thermocouples were also installed close to each LVDT. The sensors were connected to a NI CompactDAQ-9132 data acquisition system with a NI 9234 acceleration acquisition module and a NI 9219 crack amplitude and temperature acquisition module. The monitoring data were stored in consecutive separate files containing 30 min-long recordings. Ambient accelerations were sampled at 100 Hz, while crack amplitudes and temperature values were sampled at 0.1 Hz. The data files were sent through the Internet to a remote server located in the Laboratory of Structural Health Monitoring and Earthquake Engineering, where MOSS was used for automated SHM of the palace. The SHM system was recently upgraded in July 2020, adding 9 accelerometers, 2 LVDTs, and 3 thermocouples as sketched in Fig. 3(b).

Fig. 3. Long-term SHM system installed on the Consoli Palace in July 2017 (a) and 2020 (b).

3.2

Operational Modal Analysis of the Consoli Palace

MOVA is a software tool dedicated to OMA of structures, including six different system identification techniques: • Frequency-domain OMA methods: a. Frequency Domain Decomposition (FDD). b. Enhanced Frequency Domain Decomposition (EFDD). c. Polyreference Least Squares Complex Frequency Domain method (p-LSCF).

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• Time-domain OMA methods: a. Covariance driven SSI method (SSI-COV). b. Data-driven SSI method (SSI-DATA) c. Eigensystem realization algorithm (ERA). The SSI-COV, SSI-DATA and ERA methods are automated following the procedure introduced in [7]. This comprises the following steps: (i) modal identification for different numbers of rows and columns of the Toeplitz/Hankel matrices; (ii) noise modes elimination; and (iii) agglomerate hierarchical clustering analysis. The code also allows to analyze multiple setup measurements and includes a specific module for comparison of different identification results.

Fig. 4. Screenshot of the geometry module in MOVA with the geometry of the Consoli Palace.

Fig. 5. Identified mode shapes of the Consoli Palace using COV-SSI in MOVA.

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Figure 4 shows a screenshot of the geometry GUI specialized to the Consoli Palace and the new SHM system. Three-dimensional models can be constructed using node, line, and plane elements, as well as a variety of kinematic conditions, namely rigidplane diaphragms, link conditions, and a formula editor to define general symbolic constraints between node sets. Once the geometry is defined, the user can access the signal pre-processing and system identification modules. Figure 5 and Table 1 show the identified mode shapes and the identification results of the Consoli Palace, respectively, using the COV-SSI of 10-min ambient vibration records acquired on May 2nd 2020 with the new SHM system. Six clear modes are identified in the frequency range from 0 to 12 Hz. These correspond to two global first bending modes in the xand y- directions, denoted as Fx1 and Fy1, respectively, one global torsional mode, T1, and three local modes related to the interaction between the palace and the bell-tower, denoted as L1, L2, and L3. Table 1. Vibration-based system identification results of the Consoli Palace through the AVT conducted on May 2nd 2020 using the COV-SSI method in MOSS. Mode no. 1 2 3 4 5 6

3.3

Label Fx1 Fy1 L1 L2 T1 L3

Natural frequencies [Hz] 2.34 2.96 3.50 3.76 4.22 7.14

Damping ratio [%] 1.38 3.13 1.17 2.91 1.02 0.81

Mode Phase Collinearity (MPC) [%] 99.00 91.70 82.60 99.10 99.60 32.40

Feature Extraction and Pattern Recognition

The frequency tracking module allows to extract the time series of resonant frequencies, damping ratios, mode shapes and associated quality factors. Afterwards, the user can access the multivariate statistical analysis module, which permits the definition of different statistical models accounting for distinct damage-sensitive features. These include: • Input-Output regression models: Multiple Linear Regression (MLR), AutoRegressive with eXogenous input model (ARX), and coupled MLR and Principal Component Analysis (MLR/PCA). • Input-only regression models: PCA, Factor Analysis (FA), and Autoassociative Neural Networks (AANN). As an example, Fig. 6 shows the comparison between experimental data and the predictions of three different statistical models using one year of monitoring data acquired by the first SHM of the Consoli Palace, namely MLR, PCA, and AANN. The three models use the resonant frequencies of modes Fx1, Fy1, L1, T1, and L3 as estimators. As predictors, temperature and temperature-crack amplitude values are used by the MLR and AANN models, respectively. Note that the user has complete freedom

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to select dynamic, static, and/or environmental data as estimators and/or predictors. This allows the creation of multiple damage-sensitive features that may be assessed in parallel and in real-time in the subsequent damage detection module. As an example, Fig. 7 shows preliminary time series of temperature and crack amplitude values acquired using the new SHM system installed in the Consoli Palace.

Fig. 6. Plots of time histories of the natural frequencies of the Consoli Palace and the estimates obtained by MLR (a, b), PCA (c, d), and AANN (e, f).

3.4

Damage Detection

The damage detection module is based upon the construction of an observation matrix Y containing the user-defined damage-sensitive features. The masking effects of environmental/operational factors on Y are eliminated by computing a residual error matrix, E, obtained by subtracting the predictions of a statistical regression model Ŷ from Y, i.e. E = Y − Ŷ. Note that Ŷ is obtained by constructing a statistical model from a baseline in-control population (often a one-year period), also termed training period. This contains a set of structural features that statistically represent the healthy condition of the structure. Since Ŷ is assumed to account for the variance in the analyzed features related to variations in the environmental/operational conditions, if certain damage develops, this only affects the data contained in Y. Therefore, E concentrates the damage-induced variance apt for being used for damage detection purposes. This is performed in MOSS by two-class unsupervised classification (damaged or undamaged) through quality control charts. These furnish a certain statistical distance in time accounting for disturbances contained in E. By defining an in-control region, the appearance of out-of-control processes, possibly associated to damage, is detected in the shape of data points violating the in-control region. Specifically, MOSS includes three different control charts, including the Hotelling, Multivariate Cumulative

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Sum (MCUSUM), and Multivariate Exponentially Weighted Moving Average (MEWMA) control charts.

Fig. 7. Time histories of environmental temperature and crack amplitudes acquired by MOSS using the new SHM system installed in the Consoli Palace.

Fig. 8. Screenshot of the damage identification module applied to the real-time SHM of the Consoli Palace.

Finally, Fig. 8 shows a screenshot of the online damage detection GUI in MOSS applied to the real-time SHM of the Consoli Palace. The GUI automatically accesses the monitoring data stored in a local directory or in the cloud, and performs signal

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processing, system identification and pattern recognition of all the damage-sensitive features defined by the user. The code updates in real time several interactive graphs reporting the time series of estimators, predictors, and statistical predictions, reports quality control charts, displays statistical information of the monitoring data, and offers the possibility of connecting a webcam. MOSS finally implements an automated damage detection approach based upon the pruned exact linear time (PELT) method proposed by Killick and co-authors [8]. This algorithm searches for a change-point or time instant at which a shift in the mean value of the time series of residuals in E is found. When an anomaly is detected, the system triggers an alarm in the shape of visual and/or sound signals, as well as by e-mail.

4 Conclusions The presented software tools represent the first software available in the literature addressing data fusion for damage detection using OMA and Data Science/Machine Learning. Beyond its scientific contribution, the ultimate goal of MOVA/MOSS is to provide practitioners with some of the most recent state-of-the-art techniques and research breakthroughs in the field of SHM of structures through an intuitive graphical user interface environment. Acknowledgements. This work was supported by the Italian Ministry of Education, University and Research (MIUR) through the funded Project of Relevant National Interest “DETECTAGING - Degradation effects on structural safety of cultural heritage constructions through simulation and health monitoring” (protocol no. 201747Y73L).

References 1. Chen, H.P.: Structural Health Monitoring of Large Civil Engineering Structures. John Wiley & Sons, Hoboken (2018) 2. Peeters, B., De Roeck, G.: One-year monitoring of the Z24-Bridge: environmental effects versus damage events. Earthq. Eng. Struct. Dyn. 30(2), 149–171 (2001) 3. Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. John Wiley & Sons, Hoboken (2012) 4. Kita, A., Cavalagli, N., Ubertini, F.: Temperature effects on static and dynamic behavior of Consoli Palace in Gubbio, Italy. Mech. Syst. Signal Process. 120, 180–202 (2019) 5. Elyamani, A., Caselles, O., Roca, P., Clapes, J.: Dynamic investigation of a large historical cathedral. Struct. Control Health Monit. 24(3), e1885 (2017) 6. Gentile, C., Ruccolo, A., Canali, F.: Long-term monitoring for the condition-based structural maintenance of the Milan Cathedral. Constr. Build. Mater. 228, 117101 (2019) 7. Ubertini, F., Gentile, C., Materazzi, A.L.: Automated modal identification in operational conditions and its application to bridges. Eng. Struct. 46, 264–278 (2013) 8. Killick, R., Fearnhead, P., Eckley, I.A.: Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107(500), 1590–1598 (2012)

One-Year Dynamic Monitoring the Main Spire of the Milan Cathedral Carmelo Gentile and Antonello Ruccolo(&) Department of Architecture, Built Environment and Construction Engineering (DABC), Politecnico di Milano, Milan, Italy {carmelo.gentile,antonello.ruccolo}@polimi.it

Abstract. One of the most remarkable structural elements characterizing the Milan Cathedral is its main spire, reaching the height of about 108 m and supporting the statue of the Virgin Mary. The Main Spire, built in Candoglia marble and completed in 1762, is about 40 m high and stands on the tiburio of the cathedral (i.e., the prismatic structure with octagonal base built around the main dome). The spire consists of a central column which is connected through a spiral staircase to 8 perimeter columns, with each column being stiffened by a flying buttress. The structural arrangement is completed by (i) metallic clamps and dowels, connecting the marble blocks, and (ii) metallic rods, connecting the perimeter columns to the central core. A large monitoring system has been recently designed and installed in the Milan Cathedral, aimed at enhancing the knowledge and assisting the conditionbased structural maintenance of the historic building. The new monitoring system includes temperature sensors and seismometers (electro-dynamic velocity sensors) at 3 levels of the Main Spire as well as a weather station at the top of the same spire. After a concise historic background on the Main Spire of the Milan Cathedral and the description of the sensing devices installed in this sub-structure, the paper focuses on the dynamic characteristics of the spire and their evolution during the first year of monitoring. Keywords: Automated modal identification
Cultural heritage structures Dynamic monitoring
Environmental effects
Natural frequencies




1 Introduction The Milan Cathedral (Fig. 1a) is a monumental cross-shaped church partly designed in Gothic style, whose structural construction took more than 4 centuries, since the beginning of apse erection in 1386 until the façade finalization in 1813 [1]. The Main Spire (Fig. 1b, c), erected between 1765 and 1769, is one of the most iconic features of the cathedral [1, 2]. The preservation and maintenance of the many the structural integrity of the cathedral are currently hindered by the dimensions and the complexity of the building, as well as by the difficulty to reach and inspect several structural elements. A Structural Health Monitoring (SHM) project [3] has thus been devised, with the three-fold objective of assisting the structural inspections, early detecting the onset of anomalous © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 937–946, 2021. https://doi.org/10.1007/978-3-030-64594-6_90

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behaviour, and improving the knowledge of the building through the collection of a large archive of experimental data. The monitoring system of the Milan Cathedral has been designed with specific attention to the Main Spire, which already underwent several restoration works [1, 2], despite its relatively recent construction. A quite spread monitoring system has been installed on the Spire, aimed at monitoring both the dynamic properties of the spire and its quasi-static deflections, along with the environmental conditions. The effective application of vibration-based investigations of slender historical structures [4] has motivated the installation of a dynamic monitoring system in the Main Spire: the modal parameters are extracted from the collected time series, providing useful information on the global structural performance. Recent studies have revealed that a statistical analysis of the collected natural frequencies can successfully identify the occurrence of structural anomalies (see e.g. [5, 6]). The same studies also address the environmental influence over the natural frequencies, which should be removed to enhance the novelty detection. The methodology adopted to exploit the dynamic signatures of the Main Spire for SHM purposes and the results of the first year of the dynamic monitoring are addressed in this study.

Fig. 1. View of the (a) Milan Cathedral and (b) its Main Spire; (c) metallic rods connecting flying arches of the Main Spire (courtesy of Veneranda Fabbrica del Duomo di Milano).

The paper is subdivided as follows. A concise description of the Main Spire and its dynamic monitoring system is given in Sect. 2. The dynamic properties of the spire are described in Sect. 3, whereas the evolution in time of natural frequencies and the peculiar influence of environmental conditions are discussed in Sect. 4.

2 The Main Spire and the Monitoring System 2.1

Description of the Main Spire

The Main Spire of the Milan Cathedral, Fig. 1b–c, is a 45 m long vertical cantilever, resting on the main dome at 65 m from the ground. The typical cross-section of the

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spire is a hollow cylindrical pier surrounded by eight slender columns. The connections among the perimeter columns are provided by ornamental elements, whereas the main pier and the perimeter columns are connected by spiral staircase, that allows reaching the Upper Belvedere, rising at 91.7 m from the ground. Eight inverse flying arches (Fig. 1c) provide lateral stiffening to the spire, conveying the lateral thrust to the vertical load-bearing walls of the Tiburio. The spire is finally crested with a pinnacle 14.8 m high, on top of which is lodged the statue of the Virgin Mary (Fig. 1). The Main Spire differs from the spires of other Gothic Cathedral due late construction of the spire (between 1769–1774), both from an architectonic and a structural viewpoint. In particular, the spire was originally designed as a combination of metallic elements, Candoglia marble blocks and masonry. The presence of metallic reinforcement plays a key role in stiffening the spire, given the presence of (a) minor clamps connecting the adjacent marble blocks, (b) metallic rods connecting the eight perimeter columns and the inner pier (Fig. 1c), and (c) flat-rolled profiles running across the overall height of the central column. 2.2

Description of the Monitoring System

The monitoring system installed in the Main Spire comprises SARA SS45 seismometers (electro-dynamic velocity transducers (Fig. 2c), a class of sensors that has been recently employed in dynamic monitoring of historical structures (e.g. [3, 8]), due to several benefits, including: a) high sensitivity (78 V/[m/s]) and the excellent performance of electro-dynamic transducers in the low frequency range (f  100 Hz), fully suitable for the application in vibration testing or monitoring of civil engineering and cultural heritage structures; b) the possibility of estimating the displacement time series by integrating the velocity records, so that data directly related to the stiffness (and especially useful for the slender Main Spire) are conceivably available; c) the reduced cost of the sensors, when compared to state-of-the-art high sensitivity accelerometers of comparable technical characteristics. The instrumental setup comprises 9 seismometers installed at 3 levels of the Main Spire, as exemplified in Fig. 2: (a) 1 tri-axial seismometer at the base of the spire (+65.9 m); (b) 3 horizontal uni-axial seismometers at the level of lower Belvedere (+75.0 m); (c) 3 horizontal uni-axial seismometers at the level of upper Belvedere (+91.7 m), The uni-axial sensors installed in the lower and upper Belvedere are mounted on two opposite perimeter columns of the spire (Fig. 2b–c). Each sensors triad is wired to one 24-bit digitizer, with the digitizers being connected to a switch for data transfer.

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Fig. 2. Experimental setup of the dynamic monitoring system installed in the Main Spire: (a) in elevation and (b) in plan; exemplification of sensor mounting.

Fig. 3. Identification of natural frequencies via SSI-Cov to signals collected on the Main Spire.

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3 Dynamic Charateristics of the Main Spire A preliminary dynamic characterization of the Main Spire has been achieved through an Ambient Vibration Test on June 6th, 2018, whose experimental setup has been confirmed for the continuous monitoring system, active since October 16th, 2018 [3]. The modal parameters of the Main Spire are identified using a fully automated procedure, based on the covariance-driven Stochastic Subspace Identification (SSI-Cov) algorithm [8] and developed in previous studies [9]. The identification of resonant frequencies of the Main Spire is exemplified in the stabilization diagram of Fig. 3, which highlights a high density of principal modes in the frequency range 1–7 Hz, including both global modes of the entire Cathedral [3] and local modes of the spire. The dynamic features associated to the local modes S1–S9 are reported in Table 1 in terms of resonant frequencies and damping ratios, whereas the associated mode shapes of selected modes are exemplified in Fig. 4. The sequence of the spire local modes usually appear in couples with similar mode shapes and close frequencies: a) the first couple of modes, S1 (Fig. 7a) and S2 (Fig. 7b), involve bending in two orthogonal directions. The corresponding natural frequencies are equal to 1.77 and 1.79 Hz, respectively; b) a second couple of modes, S3 (Fig. 7c) and S4 (Fig. 7d), are characterized by bending of the spire in two orthogonal planes, as well. Modes S3 and S4 exhibit natural frequencies of 2.45 and 2.61 Hz, respectively; c) another couple of modes, S5 (Fig. 7e) and S6 (Fig. 7f), involve higher order bending of the spire associated to horizontal deflection of the base of spire. The associated natural frequencies are 2.97 and 3.13 Hz; d) mode S7 (Fig. 7g), exhibiting higher order bending of the Spire, with the modal displacements of both Belvederes comparable in direction and amplitude. The corresponding natural frequency is 3.81 Hz; e) finally, a couple of torsion modes S8 and S9 is identified at 4.32 and 5.94 Hz: mode S8 is associated to in phase rotation of the Upper and Lower Belvedere, whereas S9 to a rotation of the two levels in opposition or phase. Table 1. Dynamic features of the Main Spire estimated via SSI-Cov (June 06th, 2018 18:00). Mode Id. f (Hz) f (%) S1 1.770 1.56 S2 1.790 1.15 S3 2.454 0.60 S4 2.610 0.41 S5 2.966 1.09 S6 3.128 1.28 S7 3.811 0.74 S8 4.318 1.16 S9 5.937 1.12

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Fig. 4. Selected vibration modes of the Main Spire.

4 Evolution of Natural Frequencies SHM projects of historic structures [3, 5, 7, 10, 11] are usually assisted by the monitoring of environmental factors that might affect the mechanical properties or the structural response. The assessment of environmental effect over the dynamic features of the structure at study can be a challenging task, though provides useful information on the structural arrangements, especially in masonry structures with a metallic reinforcement [11]. The description of the environmental conditions is provided by 3 couples of temperature sensors placed at 3 levels of the spire, as well as by a weather station installed in the upper Belvedere. Nevertheless, the temperature data measured at the 3 levels of the spire and by the weather station are highly correlated, so that only the last is adopted for SHM purposes. Moreover, the amplitude of the collected velocities is strongly correlated to wind speed and also accounts for other sources of excitation (e.g. subway transits, maintenance activities on the Cathedral roof, far-field earthquakes, etc.). Hence, the root mean square of the velocities collected on the upper Belvedere is considered as an indirect measure of the level of dynamic excitation of the spire. The results of the first year of dynamic monitoring of the Main Spire are summarized and discussed in this section. The time histories of the natural frequencies associated to local modes of the spire (S1–S9) are illustrated in Fig. 5, whereas the statistical description of the natural frequencies is summarized in Table 2, in terms of mean value (fave), standard deviation (rf), maximum (fmax) and minimum (fmin) values, and identification rate, which is defined as the ratio between number of identification over number the 1-h collected datasets. Figure 5 highlights that the natural frequencies of the 9 local modes that were preliminarily identified (see Fig. 3) are identified with high occurrence during the first year and allows to draw the following conclusions:

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(a) the identification rate ranges from 59.5% for mode S5 to 99.5% for S7; (b) all identified frequencies evolve accordingly to regular patterns mainly driven by the temperature, tending to increase with decreased temperature. This trend is especially detectable for modes S1–S2 and S5–S9; (c) the negative dependence of natural frequencies on temperature is a distinctive behavior of the Main Spire and of the Milan Cathedral [3], with this trend being very different from what generally reported in the long-term studies on masonry slender structures, i.e. towers [6–8, 10].

Fig. 5. Evolution of natural frequencies from October 16th, 2018 to October 15th, 2019.

Table 2. Statistics of identified frequencies from October 16th, 2018 to October 15th, 2019. Mode S1 S2 S3 S4 S5 S6 S7 S8 S9

fave (Hz) 1.823 1.845 2.465 2.614 2.976 3.220 3.895 4.435 6.238

rf (Hz) 0.054 0.055 0.012 0.016 0.073 0.064 0.076 0.095 0.178

fmin (Hz) 1.675 1.705 2.395 2.534 2.766 2.978 3.646 4.213 5.705

fmax (Hz) 1.952 1.994 2.532 2.688 3.149 3.355 4.042 4.744 6.793

id rate (%) 94.5 95.9 98.5 81.0 56.1 75.3 99.4 99.3 91.3

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Nevertheless, the long-term and short-term influence between natural frequencies and outdoor temperature reveals peculiar behavior. As exemplified in Fig. 6a with reference to fS2, some natural frequencies exhibit both negative correlation with seasonal (long-term) outdoor temperature, and positive correlation with hourly measured air temperature (short-term): the frequencies of some modes increase with increased temperature on a daily base, as it is exemplified in Fig. 6a with reference to an interval of 10 days. Such a dual dependency is conceivably driven by the effects exerted by the different materials that constitute the Main Spire. In more details, the thermal expansion of the Candoglia marble induces the closure of micro-cracks with increased temperature, leading to a stiffening of the spire and to the daily increase of the natural frequencies.

Fig. 6. Zoom of daily variations of air temperature and (a) fS2, and (b) fS3.

Conversely, the increase of seasonal temperature results in a slackening of the metallic confinement of the spire, leading to a global loss of stiffness and therefore to an overall decrease in the natural frequencies. The superposition of these two temperature-driven opponent effects conceivably explains the distinctive behavior of the frequency fS2. It is worth mentioning that a similar complex dependence on temperature is observed for modes S1 and S5–S6 as well, whereas the frequency of modes S8–S9 continues to exhibit an overall negative dependence on temperature but the daily effects are less clear.

Fig. 7. Wind effects on frequencies fS3 and fS4.

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The natural frequencies of modes S3–S4 and S7 exhibit a simpler correlation with the air temperature, as exemplified in Fig. 6b for fS3: both seasonal and daily variations of frequency and temperature are in phase opposition. Hence, the evolution of mode S3 seems to be conceivably driven by the stiffening effect exerted by decreasing temperature on the metallic elements of the spire. In addition to the influence of temperature, it should be remarked that the excitation associated to wind gusts induce temporary drops of natural frequencies fS3 and fS4. The typical correlation between frequency drops of fS3 and fS4 and the r.m.s. velocity (representing an indirect measure of the excitation level) is shown in Fig. 7 during a period of 10 days characterized by limited variations of the air temperature.

5 Conclusions This study investigates the performance of a vibration-based Structural Health Monitoring (SHM) program to assist the preservation of an historic structure, the Main Spire of the Milan Cathedral. The structure at study consists of a slender octagonal structure in Candoglia marble, supported by the tiburio of the Cathedral. The monitoring system includes seismometers (electro-dynamic velocity sensors) and temperature sensors at 3 levels of the Main Spire as well as a weather station at the top of the spire. The SHM project of the Main Spire, comprises: (a) pre-processing and statistical analysis of the collected velocity signals; (b) continuous estimation of the modal parameters; (c) removal of the environmental influence over natural frequencies through PCA and (d) novelty analysis to detect slight changes in the statistical properties of the identified natural frequencies. The analysis of the dynamic signatures collected over the first year of monitoring (i.e. from October 16th, 2018 to October 15th, 2020), lead to the following conclusions: • The monitoring system and the application of automated operational modal analysis allows the identification and tracking of 9 local modes of vibration of the spire in the frequency range 1–7 Hz; • The evolution in time of the natural frequencies during the first year of continuous monitoring reveals a distinctive correlation between resonant frequencies and temperature. In more details, all frequencies increase with decreased seasonal temperature and some of them (fS1, fS2, fS5 and fS6) also exhibit a positive correlation with temperature on a daily basis, especially during the hot season; • A couple of resonant frequencies (i.e., fS3 and fS4) is also sensitive to the level of dynamic excitation associated with wind, with clear frequency drops occurring in correspondence to wind gusts.

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References 1. Veneranda Fabbrica del Duomo: Annali della Fabbrica del Duomo di Milano: Dall’origine fino al presente (in Italian), Veneranda Fabbrica del Duomo, Milan, Italy (1885) 2. Nava, A.: Relazione dei ristauri intrapresi alla Gran Guglia del Duomo di Milano (in Italian), Tipografia Valentini & C, Milan, Italy (1848) 3. Gentile, C., Ruccolo, A., Canali, F.: Long-term monitoring for the condition-based structural maintenance of the Milan Cathedral. Constr. Build. Mater. 228, 117101 (2019) 4. Diaferio, M., Foti, D., Potenza, F.: Prediction of the fundamental frequencies and modal shapes of historic masonry towers by empirical equations based on experimental data. Eng. Struct. 156, 433–442 (2018) 5. Gentile, C., Guidobaldi, M., Saisi, A.: One-year dynamic monitoring of a historic tower: damage detection under changing environment. Meccanica 51(11), 2873–2889 (2016) 6. Ubertini, F., Cavalagli, N., Kita, A., Comanducci, G.: Assessment of a monumental masonry bell-tower after 2016 Central Italy seismic sequence by long-term SHM. Bull. Earthq. Eng. 16(2), 775–801 (2017) 7. Azzara, R.M., De Roeck, G., Girardi, M., Padovani, C., Pellegrini, D., Reynders, E.: The influence of environmental parameters on the dynamic behaviour of the San Frediano bell tower in Lucca. Eng. Struct. 156, 175–187 (2018) 8. Peeters, B., De Roeck, G.: Reference-based stochastic subspace identification for outputonly modal analysis. Mech. Syst. Signal Process. 13(6), 855–878 (1999) 9. Cabboi, A., Magalhães, F., Gentile, C., Cunha, À.: Automated modal identification and tracking: application to an iron arch bridge. Struct. Control Health Monit. 24(1), e1854 (2017) 10. Zonno, G., Aguilar, R., Borosheck, R., Lourenço, P.B.: Analysis of the long and short-term effects of temperature and humidity on the structural properties of adobe buildings using continuous monitoring. Eng. Struct. 196, 1–21 (2019) 11. Saisi, A., Gentile, C., Ruccolo, A.: Pre-diagnostic prompt investigation and static monitoring of a historic bell-tower. Constr. Build. Mater. 122, 833–844 (2016)

A Transfer Learning Application to FEM and Monitoring Data for Supporting the Classification of Structural Condition States G. Coletta1(&), G. Miraglia1, P. Gardner2, R. Ceravolo1, C. Surace1, and K. Worden2 1

2

Polytechnic University of Turin, Corso Duca Degli Abruzzi 24, 10129 Turin, Italy [email protected] Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK

Abstract. One of the main problems concerning the field of Structural Health Monitoring (SHM) is the unavailability of data from different structural conditions. This is especially true for civil structures, where the collection of data from different damage states is often infeasible or economically inconvenient, particularly when dealing with architectural heritage structures. In the last few years, this issue has been addressed by using a Transfer Learning (TL) strategy, which allows one to transfer the knowledge obtained from systems where several conditions are known, to different (but related) systems, for which limited data are available. In particular, recent studies have demonstrated the effectiveness of Domain Adaptation techniques, a subcategory of transfer learning, for both homogeneous and heterogeneous populations. By transferring knowledge, these methods improve the classification of different structural conditions. This paper shows results from the application of a domain adaptation technique - Transfer Component Analysis (TCA) - between the monitoring data of a structure and those of its Finite Element Model (FEM). The FEM is a precious resource for this purpose as it allows one to simulate manifold system conditions and obtain the related data without affecting the real structure. The case study considered here is the Sanctuary of Vicoforte, a monumental building from the 17th century located in Italy, equipped with a permanent static and dynamic monitoring system. The research has shown promising results in distinguishing, via a Relevance Vector Machine (RVM) classification, different environmental conditions affecting the building. Keywords: Transfer learning
Architectural heritage TCA
RVM classification
Sanctuary of Vicoforte


Domain adaptation


1 Introduction In recent years, the use of Artificial Intelligence has spread widely in many engineering sectors that have used Machine Learning (ML) to extract information from large amounts of data and perform predictions [1]. Classical ML algorithms exploit statistical © Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 947–957, 2021. https://doi.org/10.1007/978-3-030-64594-6_91

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models that are calibrated (or trained) on previously collected data whose output (labels) may or may not be known, respectively supervised and unsupervised learning problems. Between these approaches is semi-supervised learning in which the availability of a limited number of labels leads to the implementation of methodologies that make use of both labelled and unlabelled data in the training phase. All these problems are based on the idea that these data are drawn from the same distributions. Transfer learning, a further strategy to compensate for insufficient labelled data, addresses the problem in which the domains, the distributions and the tasks do not match in the training and testing sets, manipulating them using various methodologies, including the so-called Domain Adaptation techniques [2, 3]. The concept behind TL is very simple and is unconsciously applied by people in many daily practices. For example, gaining experience (or better, training) to ride a motorcycle simulator in a video game can definitely help with driving a real motorbike. TL is a convenient way to deal with problems in which one wants to investigate a littleknown system, whilst jointly using a lot of available information from another system, which is somehow connected to the first. In SHM, the use of TL is motivated by the lack of labelled data relating to damaged conditions, or to particular operational conditions, which for some reason are not present in the training set. For many structures, especially in the civil sphere, it is not possible to concretely obtain data relating to structural damage, and this is particularly true if one is dealing with an asset protected by law. In other cases, although obtainable, the labelled data relating to damage conditions are expensive, as their labelling requires a lot of time, as well as advanced and specific professional knowledge in structural engineering [4]. A FEM is a valuable tool in this scenario because it allows simulation of a large number of structural conditions, obtaining data without the real structure being affected. However, although similar, the data obtained from the model cannot be considered to be extracted from the experimental data distribution, since inevitably the model assumes simplifications due to the fact that not all physical phenomena are considered. For these reasons, domain adaptation techniques could prove to be fundamental in moving data from the two systems closer together, therefore making the FEM data exploitable in implementing a better generalisation on the monitoring data. The effectiveness of the adaptation is assessed here by comparing the performance of a Relevance Vector Machine classifier [5] trained on the FEM data and tested to the unlabeled monitoring data, before and after the transformation. In the next section the basic theory of domain adaptation techniques is reported with particular attention to Transfer Component Analysis (TCA). In Sect. 3 the case study is described, outlining the construction of both domains and the comparison between a classifier trained in the original and transformed data space. Finally, in Sect. 4, conclusive reflections are set out.

2 Domain Adaptation Domain adaptation is a sub-discipline of TL that attempts to transfer shared knowledge across different but related scenarios represented by domains of data [2, 3]. The term domain includes two components: a feature space of inputs X and the marginal

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distribution of a sample set of inputs X ¼ fx1 ; . . .; xn gT 2 X ; Pð X Þ. Two domains are defined: a source and a target domain. The first, Ds , contains the labelled data, i.e. the information intended for transfer. The target domain, instead, contains data that comes from the system to be investigated. Each domain is associated with a task, defined by T ¼ fY; f ð
Þg, where Y is the label space and f ð
Þ the objective predictive function that can be used to predict the corresponding label (which can also be seen as a conditional distribution PðyjxÞ). In the more general case, the source domain data are written as DS ¼ 

T 
 xS;1 ; yS;1 ; . . .; xS;nS ; yS;nS and the target domain DT ¼ xT;1 ; yT;1 ; . . .;
 xT;nT ; yT;nT gT , where xS;i 2 X S is the input and yS;i 2 YS is the corresponding output, both related to the source domain which contains nS samples; xT;i 2 X T are the nT observations in the target domain, where the data can be partially labelled or unlabelled (yT;1 may or may not exist for all feature observations xT;1 2 X T Þ. In domain adaptation methods, it is assumed that the feature and label space are the same for source and target domains, i.e. X S ¼ X T and Y S ¼ Y T , while they differ in the marginal and (in some cases) in the conditional distributions: i.e. PðXS Þ 6¼ PðXT Þ and PðYS jXS Þ 6¼ PðYT jXT Þ. Because of these differences in distributions, a classification algorithm may fail to classify in moving from one domain (source) to another (target). In the light of this issue, numerous techniques have been developed to minimise the distance between the densities of the domains, taking advantage of a nonlinear mapping function /ð
Þ that aims to match the distributions, such that Pð/ðXS ÞÞ  Pð/ðXT ÞÞ and PðYS j/ðXS ÞÞ  PðYT j/ðXT ÞÞ. In this paper, a recently developed learning method - Transfer Component Analysis - has been applied to reduce the distance between data distributions from a real structure and its corresponding numerical model. 2.1

Transfer Component Analysis

Transfer Component Analysis is a domain adaptation technique introduced by Pan et al. in 2010 [3]. The algorithm tries to learn some transfer components across domains in a Reproducing Kernel Hilbert Space (RKHS) using the Maximum Mean Discrepancy (MMD) as an embedding criterion. As a result, the data distributions from different domains are brought closer together in the subspace spanned by these transfer components. In this new subspace, machine learning algorithms can be trained for classification or regression problems on data from the source domain and tested on the (unlabelled or partially–labelled) target domain. TCA assumes that PðXS Þ 6¼ PðXT Þ but PðYS jXS Þ ¼ PðYT jXT Þ. This approach avoids explicitly defining the nonlinear transformation /ð
Þ, instead exploiting the kernel trick,

 0 k xi ; xj ¼ /ðxi Þ / xj . The MMD distance between the empirical means of the kernel embedding from the two domains can be written as,  0 0 Dist XS ; XT ¼ tr ðKLÞ

ð1Þ

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0

in which XS and XT are the transformed inputs from the source and target domains, K is the Kernel matrix, containing the kernel matrices of source, target and cross domains (i.e. K ¼ kðX; X 0 Þ where X ¼ fXS ; XT gT ) and L is the MMD matrix, formed as, 8 1 2 if xi ; xj 2 XS > > < nS Lði; jÞ ¼ n12 if xi ; xj 2 XT T > > : 1 nS nT otherwise:

ð2Þ

By a decomposition of the kernel matrix, the empirical kernel map can be obtained using a ðnS þ nT Þ  m matrix of weights, W, which transforms and reduce the feature vector into a m-dimensional space,    ~ T K 12 K ¼ KWW T K ~ W ~ ¼ KK 12 W K

ð3Þ

The distance between the empirical means of the two domains can then be rewritten as,  0 0
 Dist XS ; XT ¼ tr W T KLKW

ð4Þ

In minimising the distance, a regularisation term is introduced to check the complexity of the weight matrix. The kernel learning problem becomes, ð5Þ

where l is a trade-off/regularization parameter, H is a centring matrix, I is an identity matrix and the constraint W T KHKW ¼ I avoids the trivial solution W ¼ 0. At this point, writing the Lagrangian of the previous equation and going through a short mathematical demonstration, it can be proved that the Eq. (5) can be solved efficiently by the following equivalent trace optimization problem,
1 maxW tr ðW T ðKLK þ lI ÞW W T KHKWÞ

ð6Þ

The solutions for W in Eq. (6) are the m leading eigenvectors of ðKLK þ lI Þ1 KHK, where m  nS þ nT  1, which are used to define the space of the transformed features through Z ¼ KW, where Z 2 RðnS þ nT Þm .

3 Case Study The concept of adaptation between the experimental data and numerical model of the same system can be put into practice for many types of structure, and therefore is extendable to numerous research fields. In this paper, the case study is represented by a

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monumental building of the 17th century - the Sanctuary of Vicoforte - located in the north west of Italy. This Italian church has the largest oval masonry dome in the world. In the field of civil structures, the Sanctuary of Vicoforte represents an ideal case study as it is one of the few historical buildings to be equipped with a permanent static and dynamic monitoring system. Furthermore, a calibrated numerical model of this construction is available, and will constitute the source domain in this application [6]. In this scenario the two systems are considered to belong to a homogeneous population since they are intended to be identical in topology, geometry and materials [7]. This application of TL to the Vicoforte Sanctuary data aims to improve the recognition of different environmental conditions, expressed by a temperature variation, within the distribution of dynamic parameters. Actually, the final practical purpose is not to indirectly detect a temperature variation from the analysis of the dynamics of a building, as one has the possibility to read it on a thermometer trivially. This research instead represents a test field for a future TL application in a damage detection perspective. The temperature, as well as other environmental parameters, have a strong influence on the dynamics of a system [8–10], so strong that in some cases it causes data alterations comparable to damage [11]. In fact, just for this comparability of effects, strategies for the removal of Environmental and Operational Variations (EOVs) from dynamic data are continuously being developed in the SHM community, in order to effectively identify the appearance of damage. In this application, instead, this similarity of effects is exploited to define two classes and test a transfer of information from the FEM to the real structure. This is done because large amounts of data are available regarding environmental changes, whereas it is not equally true for data relating to damage, therefore the effectiveness of the technique could not have been tested in a damage detection context. However, the authors plan to apply the proposed methodology for damage detection purposes on civil structures. In fact, a positive result would suggest that the procedure can provide a valuable tool for detecting damage, by appropriately changing the target domain. 3.1

Source Domain

The FEM of the Vicoforte Sanctuary is used to construct the Source Domain. For this building, a numerical multi-physics model is available. The FEM had already been calibrated before the development of this study for other research purposes [6]; however, the source domain showed some differences with the experimental data distribution, here selected as the target domain (Fig. 2). These differences are probably due to the construction of the domain, i.e. the fact that it does not consider other possible factors that influence the dynamics of the structure (including operational effects) and the non-coincidence between these experimental data and those used in the calibration. This represents an opportunity to demonstrate that TL can be used to deal with situations like this. In [7], in fact, the potential of this approach is demonstrated in the use of models that are not perfectly validated, and which are perhaps influenced by modelform errors, which are able to grasp, to a certain extent, the changes in the characteristics caused by damage. In order to build the Source Domain, the FEM eigenvalue problem was solved several times by varying its mechanical parameters. In particular, a Gaussian

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distribution has been associated with the elastic modulus of each macro-element. The mean was set equal to the calibrated value [6], with the variance selected based on literature values and the variability of the results of available experimental tests. Elastic moduli randomly extracted from these distributions have been assigned in every eigenvalue problem, resulting in a variation in the frequencies and the mode shapes extracted. 100 analyses were performed with as many combinations of elastic moduli: half of these analyses were associated with a temperature of 10 °C, as the initial model has been calibrated on the basis of data collected in a time range, whose average temperature corresponded to about 10 °C. For the second class, an excursion of 7 °C has been supposed and applied to the second half of the dataset. In order to obtain this new class, the simplified model described below was developed. The procedure involved only the first three frequencies of the Sanctuary, corresponding to the first two bending modes in the y and x directions (the minor and the major axes, respectively) and to the first torsional mode. These experimental frequencies are considered the most reliable as they show less variance in their estimated values and a higher identification rate [12]. Simplified Frequency-Temperature Model. Assuming at the microscale a symmetric portion of a homogeneous and isotropic linear elastic material with uniformly distributed porosity filled by liquid (e.g. water), and assuming a uniform distribution of temperature over the liquid and solid phase of the portion of matter, it is supposed to heat the material with some kind of source. In this case, if the solid is assumed free to expand, the volume changes, V, are evaluated using V ¼ V0 þ V0 as T, where V0 is the volume at zero Kelvin, the reference temperature, while as and T are the coefficient of thermal expansion of the solid and the absolute temperature, respectively. Concurrently, the liquid phase will undergo an expansion at constant pressure, and an increase of pressure at constant volume, supposing that in this case the stiffness of the solid phase to be much higher than that one of the liquid phase. If, in a first instance, the solid phase is assumed incompressible, the increase of pressure in the liquid, pl , from the reference state would be, pl ¼ p0 þ p0 al T

ð7Þ

where p0 is the pressure at zero Kelvin, while al is the coefficient of thermal expansion of the liquid. However, due to the initial hypothesis, the solid phase was assumed free to expand, thus a certain amount of pressure should be purified by pl to get the actual pressure value in the matter. This pressure, p*, is the pressure hypothetically accumulated due to the fictitious impossibility of expansion of the solid: p ¼ p0 as T. Thus, the actual pressure (uniformly distributed over the liquid and solid phase) in the portion of material is, p ¼ pl  p ¼ p0 þ p0 al T  p0 as T p ¼ p0 ½1 þ ðal ðT Þ  as ðT ÞÞT 

ð8Þ

Now, supposing the investigated portion of material to be far from the boundary of the system to which it belongs, at the macroscopic scale a zero deformation is supposed

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to occur inside the system; the deformations being concentrated to the boundaries. Under this assumption, the stress and strain relation (representative of the portion of material inside the system) becomes, p ¼ Ee0 E ¼ p0 ½1 þ ðal ðTe0Þas ðT ÞÞT  p0 ¼ EA e0 E ðT Þ ¼ EA ½1 þ ðal ðT Þ  as ðT ÞÞT 

ð9Þ

where E ðT Þ represents an equivalent Young’s modulus which considers the effect of the differential thermal expansion (indirect thermal effect) of the liquid and solid phase of a portion of material, and it applies to portions of the matter far from the boundaries of the system. More generally, temperature is also known to have a direct effect on the value of the Young modulus of materials; this is due to the thermal agitation of the particles that increases with temperature. This temperature-Young’s modulus relationship is commonly decreasing and nonlinear; however, for temperatures close to environmental values (e.g. −50/+50 °C) a good approximation can be achieved by supposing a linear variation. In this context, to account for the variation of the Young’s modulus because of thermal agitation, one can introduce a linear form of the EA parameter (i.e. Young’s modulus contribution that considers the variations due to the thermal agitation) so that EA ¼ ðE0  rT Þ, and, E ðT Þ ¼ ðE0  rT Þ½1 þ ðal ðT Þ  as ðT ÞÞT 

ð10Þ

where r is the tangent at small relative temperatures (i.e. around 273.15 [K]) of the temperature-Young’s modulus law that describes the thermal agitation effects, while E0 is a fictitious (fictitious because the law is linearised at small relative temperatures) zero Kelvin Young’s modulus. Generally, al ðT Þ as ðT Þ and thus al ðT Þ  as ðT Þ  al ðT Þ. For the present study, because the analysed structure is masonry, in the first approximation a negligible contribution is considered for the thermal agitation effect in the variation of the Young’s modulus with environmental temperatures values and, accordingly, r is assumed to be zero. Finally, because the liquid component in masonry (as in many other materials) is mainly constituted by water, the law becomes, E ðT Þ  E0 ½1 þ aH2O ðT ÞT 

ð11Þ

where aH2O ðT Þ is the coefficient of thermal expansion of water. With the hypothesis of uniform temperature over the entire analysed system, the solution of the eigen-problem performed on the structural matrices (mass and stiffness matrices) provides an estimate of the natural frequencies of the system at changing temperature, fk ðT Þ, for the kth mode, fk ðT Þ  f0;k

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 þ aH2O ðT ÞT

ð12Þ

where f0;k is a fictitious zero Kelvin frequency for mode k. Figure 1 reports the first two experimentally-identified natural frequencies of the Sanctuary of Vicoforte, compared

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with the assumed model (see Eq. (12)) and plotted as a function of the air temperature (a source of uncertainty exists, since the model assumes a dependence of natural frequency on the internal temperature of the body). In Fig. 2a, the resulting distribution of the frequencies at 3 °C and 10 °C is illustrated.

Fig. 1. Air temperature-natural frequency law for the first (a) and second (b) experimentally identified natural frequencies (red dot) compared with the model predictions (black line).

3.2

Target Domain

The first three experimental frequencies of the Sanctuary with their relative distributions constitute the target domain.

Fig. 2. Source (a) and Target (b) domain features.

In order to obtain a larger dataset, two available sets of experimental data were merged, belonging respectively to the period from December 2016 to March 2017 and to the entire year of 2018. Both the data sets have been obtained through an automatic dynamic identification procedure exploiting an algorithm from the Stochastic Subspace Identification (SSI) family [12]. All observations associated with an external

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temperature of 10 °C and 3 °C and in which the frequencies were correctly identified were selected. The data related to the 2 classes of temperatures, respectively with 32 and 69 observations each, are shown in Fig. 2b. 3.3

Classification

An RVM classifier [5] has been implemented on the Sanctuary data, before and after the use of the domain adaptation technique. A radial basis function was chosen for this problem. In addition to the source data, a small subset of target data has been selected and used for defining the kernel scale (r ¼ 0:3) that maximises the average classification accuracy, applying a five-fold cross validation procedure. The final classifier was trained on a set consisting of all the source data and a part of the first class of the experimental data (the samples that are not circled in the Fig. 3) and was tested on the remaining target data. In Fig. 3a the results of the classification are graphically illustrated while in Table 1 the classification accuracy is reported. Domain Adaptation. The domain adaptation technique was applied to the data of the Sanctuary of Vicoforte. A quadratic kernel was used for the transformation. Again, a five-fold cross-validation procedure was performed and the hyperparameters which returned the highest average accuracy were selected.

Fig. 3. RVM classification in the original (a) and transformed (b) feature space.

Table 1. Classification accuracy. RVM TCA-RVM Accuracy 62.8% 79.1%

Adaptation resulted in a number of transfer components m equal to 2, a regularisation parameter l ¼ 107 and a kernel scale equal to 15, while r ¼ 2:9 has been

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selected for classification, still with a Gaussian kernel. In Fig. 3b the outcome of the RVM classifier trained in the transformed feature space is shown, and its accuracy is presented in Table 1. The application of TCA led to an almost 20% improvement in the classification of the experimental data of the Sanctuary, subjected to two different temperatures.

4 Conclusion The lack of labelled data related to structural damage conditions represents a very significant problem in the field of SHM. Numerical models, due to inevitable simplifications and possible errors, do not always improve the interpretation of experimental measurements, unless an appropriate “bridge” between the two systems is conceived. With this in mind, the application of TCA led to a marked improvement in the classification of the experimental data of the Sanctuary of Vicoforte, subjected to two different environmental temperatures. The good results obtained in the recognition of different environmental conditions encourages the application of transfer learning for the purpose of damage detection, in which the FEM would be used to produce data related to virtual damages that would otherwise be difficult or impossible to obtain. Insights will be needed to recreate the optimal composition of this virtual data domain and to define to what extent the simplifications of a model can be crucial in improving SHM methods.

References 1. Farrar, C.R., Worden, K.: Structural Health Monitoring: A Machine Learning Perspective. John Wiley & Sons, Hoboken (2012) 2. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010) 3. Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22(2), 199–210 (2010) 4. Gao, Y., Mosalam, K.M.: Deep transfer learning for image-based structural damage recognition. Comput.-Aided Civil Infrastruct. Eng. 33(9), 748–768 (2018) 5. Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1(Jun), 211–244 (2001) 6. Ceravolo, R., De Lucia, G., Miraglia, G., Pecorelli, M.L.: Thermoelastic finite element model updating with application to monumental buildings. Comput. Civ. Infrastruct. Eng. 35 (6), 628–642 (2020) 7. Gardner, P., Liu, X., Worden, K.: On the application of domain adaptation in structural health monitoring. Mech. Syst. Signal Process. 138, 106550 (2020) 8. Ubertini, F., Comanducci, G., Cavalagli, N., Pisello, A.L., Materazzi, A.L., Cotana, F.: Environmental effects on natural frequencies of the San Pietro bell tower in Perugia, Italy, and their removal for structural performance assessment. Mech. Syst. Signal Process. 82, 307–322 (2017) 9. Cabboi, A., Gentile, C., Saisi, A.: From continuous vibration monitoring to FEM-based damage assessment: application on a stone-masonry tower. Constr. Build. Mater. 156, 252– 265 (2017)

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10. Ramos, L.F., Marques, L., Lourenço, P.B., De Roeck, G., Campos-Costa, A., Roque, J.: Monitoring historical masonry structures with operational modal analysis: two case studies. Mech. Syst. Signal Process. 24(5), 1291–1305 (2010) 11. Sohn, H.: Effects of environmental and operational variability on structural health monitoring. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 365(1851), 539–560 (2007) 12. Pecorelli, M.L., Ceravolo, R., Epicoco, R.: An automatic modal identification procedure for the permanent dynamic monitoring of the sanctuary of Vicoforte. Int. J. Archit. Herit. 14(4), 630–644 (2018)

Earthquake-Induced Damage Localization and Quantification in Historic Masonry Towers Using OMA and IDA Alban Kita(B) , Nicola Cavalagli , Ilaria Venanzi , Laura Ierimonti , and Filippo Ubertini Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy {alban.kita,nicola.cavalagli,ilaria.venanzi,laura.ierimonti, filippo.ubertini}@unipg.it

Abstract. In the context of relevant seismic events that recently hit Italy, like L’Aquila 2009, Emilia 2012 and the Central Italy seismic sequence 2016, there has been an increasing scientific interest on Cultural Heritage buildings’ assessment, with key concepts like the preventive conservation and condition-based maintenance. In this regards, low-cost and non-destructive vibration-based Structural Health Monitoring systems can provide very useful information on the global dynamic and structural behavior, enabling detection of small structural damages that occurred during earthquakes, even far-field ones of moderate intensity. This paper presents a methodology aimed at addressing the rapid post-earthquake damage localization and quantification tasks in historic masonry structures, based on Operational Modal Analysis (OMA) and non-linear Incremental Dynamic Analysis (IDA). While the OMA-based damage detection approach was already presented in previous work by the authors, this paper focuses on the IDA-based part of the methodology. Validation is presented through application to a medieval masonry structure: the bell tower of the Basilica of San Pietro located in Perugia, Italy. It is a monumental Cultural Heritage (CH) building permanently monitored since December 2014. The numerical FEM model together with experimental continuous vibration data and those recorded during the 2016 Central Italy seismic events are successfully exploited for earthquake damage localization and quantification. Keywords: Earthquake-induced damage identification · Incremental Dynamic Analysis · Operational Modal Analysis · Intensity measure · Damage Measure · Structural Health Monitoring · Structural assessment · Cultural heritage structures · Historic masonry tower · Preventive conservation

1

Introduction

Data-driven damage identification methods have received strong increasing interest among the scientific community. Successfully validated within vibrationc Springer Nature Switzerland AG 2021  P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 958–967, 2021. https://doi.org/10.1007/978-3-030-64594-6_92

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based long-term SHM for earthquake-induced damage detection, they represent precious tools for preventive conservation of historic masonry buildings. Typically, modal parameters act as damage-sensitive features for damage detection, even at stages when it is not yet detectable by standard visual inspections [1–7]. At the actual state of the art, earthquake-induced damage localization and quantification are still considered open issues, especially in historic masonry structures. Surrogate models have been approached recently as a rapid and computationally low-cost tool to reproduce the behavior of a numerical model [8]. On the other hand, model-based approaches and advanced numerical tools can be exploited to perform non-linear dynamic analyses. They can provide a more reliable assessment of the structural response of constructions when the non-linear behavior of the masonry material is properly defined, and a better understanding of the seismic behavior of these structures along with their potential failure mechanisms [9,10]. This paper presents a data-driven & model-based methodology for earthquake-induced damage detection, localization and quantification in longterm monitored historic masonry towers subjected to earthquakes. The proposed methodology relies on the use of a data-driven OMA-vibration-based tool and Incremental Dynamic Analysis (IDA). The IDA-based procedure has been previously developed and validated by the same authors in the case of a low-rise masonry building, an international benchmark reduced-scale laboratory masonry structure, called Brick House [10]. The methodology is now applied and validated in the case of an historic slender masonry structure, the San Pietro bell tower. It is a monumental medieval building located in Perugia, Italy, that has been continuously monitored by a permanent long-term vibration-based SHM system since 2014. The results demonstrate that the proposed methodology enables a rapid post-earthquake damage identification, allowing to immediately reveal the presence of damage and to subsequently localize and quantify it in different parts of the structure with an acceptable level of confidence. The paper is organized as follows. Section 2 describes the proposed methodology for rapid earthquake-induced damage localization and quantification in permanently monitored historic masonry towers. Section 3 presents the case study. Section 4 presents the results and validation of the IDA-based procedure. Finally, Sect. 5 summarizes the main conclusions of the work.

2

The Proposed Methodology

The methodology proposed in this paper consists of the extension of the datadriven method, successfully validated for earthquake-induced damage detection, through the introduction of the innovative IDA-based method in the case of long-term monitored historic masonry towers subjected to seismic events, aimed at the localization and quantification of earthquake-induced damages.

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The IDA-based procedure relies on multidimensional non-linear seismic IDA simulations carried out using a numerical model together with seismic data recorded via long-term vibration-based SHM systems. After IDA simulations, IDA curves sets are built regarding certain specific portions/parts of the structure, allowing multidimensional relations of a set of meaningful local damagedependent parameters, denominated as Damage Measures (DMs), against one or more selected earthquake ground motion intensity measures (IMs). When an earthquake occurs, the seismic/response intensity parameters are measured by the monitoring system(or directly computed from the measurements), and local damage conditions in each part are immediately estimated using the prior multidimensional IDA relations. Local damage conditions are finally averaged in terms of weighted mean ranges (maximum and minimum value) and weighted mean values, as proposed in [10]. Typically, only seismic input IMs have been used in the literature so far. Unlike the Brick House application where only they have been investigated [10], the seismic recordings from the vibration-based SHM system installed at the top of the bell tower allow the newly proposal and investigation of some relevant seismic response IMs.

3

The Case Study: San Pietro Bell Tower

The Basilica of San Pietro dates back to the 13th century and due to its historical and architectural value is considered to be one of the major symbols of the city of Perugia. With several structural and architectural interventions throughout the centuries, the bell tower was damaged by the 1997 UmbriaMarche earthquake, whose damages were concentrated mostly in the belfry and the cusp. Today, it is in a good state of preservation and no significant and visible damages were observed after the most recent major earthquakes of Central Italy: L’Aquila Earthquake, 2009, Emilia Earthquake, 2012, and the Central Italy seismic sequence, 2016 [6,11,12]. With a total height of 61.45 m, the tower is restrained up to the first 17 m by the surrounding buildings. It can be ideally subdivided into three main structural parts: (i) the shaft with a dodecagonal cross-section, (ii) the belfry with an hexagonal cross-section and (iii) the cusp which completes the tower on the top and has the shape of a pyramid with hexagonal base, as illustrated in Fig. 1. The bell tower has been continuously monitored since 2014 by a simple lowcost permanent vibration-based SHM system to monitor the structural integrity for preventive conservation and condition-based maintenance. Continuous data are processed for more than five years through an ad hoc developed MatLab code based on a fully automated SSI-based modal identification technique, whereby relevant seasonal and daily fluctuations associated with changes in ambient temperature have been already investigated by some of the authors [13]. The bell tower was hit by the three main shocks of the 2016 Central Italy seismic sequence. Despite the long-term vibration monitoring data clearly detected

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Fig. 1. Structural parts of the San Pietro bell tower (shaft, belfry and cusp) (a), mesh discretization of the FE numerical model (b) and detailed view of mesh on the belfry (c).

earthquake-induced damage, identifying small consistent decays in natural frequencies subsequently revealed as an anomaly in the structural behavior through a control chart, no clear relevant structural damages were observed in any part of the structure, most importantly in the belfry [6].

4

Damage Localization and Quantification by IDA

Seismic input selection represents a key issue in non-linear dynamic analysis of structures [10,14]. Seven natural ground motions have been selected for IDA on the San Pietro bell tower. The unscaled accelerograms are spectrum-compatible, in the sense that their response spectra result compatible with the response spectrum of the bell tower’s site. The detailed 3D numerical model of the structure has been used to carry out non-linear incremental dynamic analyses. It is built in the framework of the Finite Element Method (FEM) by using solid hexahedral and tetrahedral elements, in the ABAQUS 6.14 platform [15]. For the interested reader, further details of the FE model, its calibration and linear and non-linear mechanical parameters of the masonry material can be found in [6,12]. The widely used Concrete Damage Plasticity (CDP) constitutive model has been used for the latter, allowing to perform numerical non-linear dynamic analyses.

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The key aspect of local multidimensional IDA curve sets, so as to allow a local estimation of damage with confidence intervals, is applied considering the three main structural parts of the bell tower: the shaft, the belfry and the cusp. Local multidimensional IDA curve sets are constructed using a reasonable DM (e.g. tensile damage CDP parameter) and a suite of IMs. Regardless of the case study, nineteen uncorrelated IMs have been identified in [10], based on statistical correlation analysis. In the case of the San Pietro bell tower, some response intensity parameters have been also investigated, being computed from numerical response acceleration, velocity and displacement time histories obtained from the node which corresponds to the exact position of the real two uni-axial accelerometers installed on top of the tower. In this context, the response seismic records gained from the SHM monitoring system have been used. All these IMs have been used for the construction of the IDA curve sets of the San Pietro bell tower, being related to the local DMs (with reference to shaft, belfry and cusp parts). For the sake of brevity, only some of the less dispersed curve sets are presented in this paper. The IDA curve sets obtained from tensile damage, dt , versus three acceleration-related IMs (PGA, IA and Root Mean Square Response Acceleration (RMSRA)) are depicted in Fig. 2. The work reported in [10] demonstrated that the tensile damage as a cumulative parameter seems conceptually more adequate for damage characterization and localization. While the seismic loadings applied in the numerical model for IDA are bidirectional, with components in the two horizontal directions, the IDA curve sets are graphically represented as mean IMs (computed using their mean direction) versus DMs, the latter represented by numerically computed average values weighted over the volume of the numerical elements of every single part of the FE model (volume-averaged damage parameter). The IDA curve sets immediately and directly identify the belfry as the most vulnerable and/or damageable part of the structure (maximum tensile damage dt is equal to about 0.85), as it was logically expected due to the substantial structural differences in the cross-section and the presence of important openings. By applying the methodology developed in [10], once all the IDA curve sets are constructed and their dispersion investigated, higher levels of identification of earthquake-induced damage have been achieved for the three parts of the structural model. The Norcia Mw6.5 earthquake of October 30th 2016 has been used for this purpose. East-West and North-South components of the ground seismic records of the nearest station and the time histories of recorded seismic accelerations on top of the bell tower are plotted in Fig. 3. By processing all the IDA curve sets (considering input as well as response IMs), intensity measures of the Norcia earthquake have allowed to firstly localize and subsequently quantify earthquake-induced damages for the three parts of the structural model. Eleven seismic input IMs have been computed from the base seismic records depicted in Fig. 3a, while seven seismic response IMs have been calculated from the response seismic records on top of the tower plotted in Fig. 3b. Figure 4 depicts IDA-based estimated damages on the belfry using the IMs of the Norcia earthquake, in terms of minimum, maximum and mean values

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Fig. 2. The IDA curve sets (shaft, belfry and cusp) and corresponding mean curves: plots of tensile damage, dt , versus PGA (a), IA (b) and RMSRA (c).

Fig. 3. Norcia earthquake: ground motion record (a) and recorded seismic response on top of the bell tower (b).

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(obtained from the mean curves). Also, actual damages have been reported in the plots, which represent the numerically computed mean tensile damages, dt , as obtained from the FE non-linear dynamic analyses carried out considering the seismic inputs plotted in Fig. 3a. In general, good consistency can be observed, where actual damages, dt , fall within the corresponding IDA-based estimated average ranges. After applying the IDA-based procedure validated in [10], IDA-estimated damages obtained from each single IM (Fig. 4) have been averaged through dispersion-based weighting coefficients. The agreement between actual damage and weighted mean IDA-based estimation, as well as the desirable minimum IDA-based estimated range of damage, have been appropriately taken into account for earthquake-induced damage quantification. In the present case, the best consistency between mean damages is obtained using Arias Intensity, IA , while the estimated range of damage is very small using Characteristic Intensity, IC . This reasoning is the basis of the idea of using not all IMs, but carefully selecting only some of them, thus improving IDA-based damage estimation. Three scenarios of proper combinations of them have been used, as displayed in Fig. 5, allowing a better consistency of weighted mean values with actual damages and desirable reduced weighted average ranges of damage. Also, for a better understanding of the damage pattern obtained via IDA, contour plots of numerical actual damages obtained at the last step of the non-linear dynamic analysis are positioned on the side of each plot (contour range from 0 to 1.0). The concentration of tensile damage on the belfry region is clearly visible.

Fig. 4. Belfry: IDA-based tensile damage (minimum, maximum and mean values) estimated with Norcia earthquake by means of eleven seismic input and seven seismic response IMs.

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Fig. 5. Comparison between actual damage (dt ) and IDA-based estimated tensile damage, considering different combinations of seismic input and seismic response IMs of the Norcia earthquake.

Finally, for better visualization of the benefits of fusion of seismic input and response IMs, Fig. 6 illustrates weighted mean IDA-based tensile damages plotted against actual damages, dt , with focus on the belfry. The consistency of IDA-based damage estimation has been investigated in three cases: (i) using only seismic input IMs, (ii) using only seismic response IMs and (iii) using seismic input and response IMs together. If compared with separated applications of IMs (i, ii), the best consistency has been obtained in the 3rd case with both combined.

Fig. 6. Comparison between actual damage (dt ) and weighted mean IDA-based estimated tensile damage on the belfry using only seismic input IMs (a), only seismic response IMs (b) and seismic input and seismic response IMs combined (c).

5

Conclusions

The major objectives of this paper have concerned the development and the validation of the proposed OMA&IDA-based methodology aimed at the earthquakeinduced damage detection, localization and quantification in historic masonry towers. The proposed methodology has been applied to the monumental San

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Pietro bell tower, an historic masonry structure located in Perugia, Italy, that was hit by important earthquakes during the 2016 Central Italy seismic sequence. The main results are summarized below. – The proposal of IDA for earthquake-induced damage localization and quantification in historic masonry structures constitutes a new and original contribution, never explored so far in the literature. Previously validated in low-rise masonry building, the IDA-based methodology has been here extended also in the case of historic masonry towers. – The IDA requires an adequate non-linear numerical model and uses the recorded seismic response for damage identification. – Earthquake-induced damages have been identified for the shaft, belfry and cusp regions of the San Pietro bell tower, whereas the belfry has been immediately and clearly identified as the most damaged/damageable one. – Damage is quantified in terms of overall weighted average ranges and weighted mean values. – The introduction of some additional original seismic response IMs, in addition to only seismic input ones, has been proposed for IDA and found to be equally efficient. This has refined the IDA-based earthquake-induced damage quantification results in terms of higher consistency between numerical mean actual damages and weighted average IDA-based ones. In conclusion, OMA&IDA proposed in this paper have been successfully applied and validated in the San Pietro bell tower. It has demonstrated to be innovative for earthquake-induced damage localization and quantification in specific regions of historic masonry towers. In the context of long-term monitored buildings, it can be considered as a valuable tool for priority post-event decision making, condition-based maintenance and preventive conservation. Acknowledgement. This work was supported by the Italian Ministry of Education, University and Research (MIUR) through the funded Project of Relevant National Interest” DETECT-AGING - Degradation effects on structural safety of cultural heritage constructions through simulation and health monitoring” (protocol no. 201747Y73L).

References 1. Ramos, L.F., Marques, L., Louren¸co, P.B., DeRoeck, G., Campos-Costa, A., Roque, J.: Monitoring historical masonry structures with operational modal analysis: two case studies. Mech. Syst. Sig. Process. 24(5), 1291–1305 (2010) 2. Saisi, A., Gentile, C., Guidobaldi, M.: Post-earthquake continuous dynamic monitoring of the Gabbia Tower in Mantua. Italy. Cons. Build. Mater. 81, 101–112 (2015) 3. Masciotta, M.G., Roque, J., Ramos, L.F., Louren¸co, P.B.: A multidisciplinary approach to assess the health state of heritage structures: the case study of the Church of Monastery of Jer´ onimos in Lisbon. Cons. Build. Mater. 116, 169–187 (2016)

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Author Index

A Ahmad, Rais, 276 Alabd-Allah, Ahmad, 564 Alaggio, Rocco, 661 Alaimo, Andrea, 883 Alegre, André, 69 Aloisio, Angelo, 661 Altammar, Hussain, 749 Ameduri, Salvatore, 870 Ameyaw, Daniel Adofo, 467 Amini, Abbas, 129 Antonacci, Elena, 661 Aranguren, Gerardo, 830 Araniti, Giuseppe, 50 Avendaño-Valencia, Luis David, 109 B Bandara, S., 739 Barroso, Daniel Fontoura, 266 Battaglia, Domenico, 50 Bedon, Chiara, 306 Beeram, Sree Keerthe, 79 Beligni, Alessio, 636 Belloli, Marco, 172 Benedetti, Lorenzo, 172 Benedictus, Rinze, 616 Bergamo, Enrico, 306 Bernagozzi, Giacomo, 335 Bernasconi, Andrea, 605 Bijudas, C. R., 161 Birgin, H. Borke, 861 Blachowski, Bartlomiej, 286 Bolton, Gary, 683

Bouzaffour, K., 447 Brackmann, Lukas, 564 Braml, Thomas, 403 Brettschneider, Jonas, 788 Briand, William, 140 Broer, Agnes A. R., 616, 626 Bulling, Jannis, 759 Buonanno, Matteo, 172 Butt, Sahir, 564 C Calamita, Giuseppe, 23 Capriotti, Margherita, 838 Carboni, Michele, 605 Carrison, Parry, 749 Carso, Raffaele Franco, 23 Carvalho, Ezequiel, 69 Castillero, Joseba, 830 Cava, David Garcia, 109 Cavalagli, Nicola, 958 Cawley, Peter, 819 Çelebi, Mehmet, 12 Ceravolo, R., 947 Cha, Young-Jin, 533 Chamorro, Antonio Muñoz, 798 Chehami, Lynda, 349 Chiu, Wing Kong, 647 Cinquemani, Simone, 172 Cirella, Riccardo, 661 Civera, Marco, 481 Claude, Servant, 226 Coletta, G., 947 Colombo, Luca, 553

© Springer Nature Switzerland AG 2021 P. Rizzo and A. Milazzo (Eds.): EWSHM 2020, LNCE 127, pp. 969–972, 2021. https://doi.org/10.1007/978-3-030-64594-6

970 Concilio, Antonio, 870 Cross, Elizabeth J., 494 Cunha, Álvaro, 316 D D’Alessandro, Antonella, 851, 861 D’Alessandro, Antonino, 89 Dawood, Tariq, 683 De Marchi, Luca, 327, 769 Deng, Guojun, 367 Díaz-Maroto Fernández, Patricia, 798 Diez, Jesus, 236 Díez-Hernández, Jesús, 245 Diotallevi, Pier Paolo, 335 Ditommaso, Rocco, 23 Dobie, Gordon, 683 Doroshenko, Olga, 809 Durmazgezer, Erkan, 32 E Emmanuel, Cachot, 226 Epple, Niklas, 266 Eremin, Artem, 809 Esposito, Antonio, 883 Etxaniz, Josu, 830 F Fasan, Marco, 306 Fedele, Rosario, 50, 437, 594 Feistkorn, Sascha, 403 Fernández-Navamuel, Ana, 236, 245 Fiborek, Piotr, 707 Forero, Juan Carlos, 414 Fragiacomo, Massimo, 661 Fragonara, Luca Zanotti, 481 Franchi, Fabio, 60 Francis Rose, L. R., 647 François-Baptiste, Cartiaux, 226 Franosch, Georg, 759 Fritzen, Claus-Peter, 505, 543 G Gad, E., 739 Galanopoulos, Georgios, 616, 626 Galeota, Dante, 661 Gallée, F., 447 Gallipoli, Maria Rosaria, 23 García Alonso, Jaime, 798 Garcia Vazquez, Luis Miguel, 573 García-Sánchez, David, 236, 245 Gardner, P., 947 Gautam, Ankur, 298 Gentile, Carmelo, 905, 937 Ghorbani, Esmaeil, 533

Author Index Giambanco, Giuseppe, 424 Giglio, Marco, 553, 636 Gil-Garcia, José M., 830 Gillich, Gilbert-Rainer, 457 Gillich, Nicoleta, 457 Giraldo, Carlos Miguel, 389 Giurgiutiu, Victor, 515 Golpayegani, Fatemeh, 695 Golub, Mikhail V., 505, 543, 809 Gomes, Jorge, 316 Gomez, Ricardo, 414 Graziosi, Fabio, 60 Guerrero Vázquez, Santiago, 798 Guskov, Mikhail, 140, 190 H Hamat, Codruta, 457 Haridas, Aswin, 389 Heinlein, Sebastian, 819 Himoto, Satoshi, 674 Hou, Edison Z. Y., 181 I Ierimonti, Laura, 958 Iglesias Vallejo, Manuel, 798 Iglesias, Francisco, 236 Iñesta González, Daniel, 798 J Jang, In Gwun, 893 Janowski, Lukasz, 286 Jayesh, P., 161 Jiménez-Fernández, Carlos, 245 Jiménez-Fernandez, José Carlos, 236 Jones, Robin, 819 K Kadarla, Sushmita, 79 Kalapatapu, Prafulla, 79, 96 Kamel, Mina, 683 Kasai, Shigeru, 256, 674 Kersemans, Mathias, 378 Kexel, Christian, 769 Kim, Manuel, 119 Kim, Tae-Yeon, 893 Kinoshita, Shohei, 256, 674 Kırlangıç, Ahmet Serhan, 359 Kita, Alban, 958 Klewe, Tim, 213 Kolla, Abhinav, 915 Kowalczyk, Kamil, 636 Kraemer, Peter, 788 Kudela, Paweł, 707, 779, 788 Kumar, Punit, 298

Author Index Kurapati, Ravi Naga Sai, 915 Küttenbaum, Stefan, 403 L La Malfa Ribolla, Emma, 424 Lafdi, Khalid, 716 Lafdi, Khalil K., 716 Laflamme, Simon, 861 Landi, Luca, 335 Lanza di Scalea, Francesco, 838 Leahy, Wayne, 494 Lee, Sungmun, 893 Lemos, José, 316 Leon-Medina, Jersson X., 414 Lescop, B., 447 Li, Xixi, 190 Limongelli, Maria Pina, 12 Loutas, Theodoros, 616, 626 Lugovtsova, Yevgeniya, 759 Lupu, David, 457 M Maack, Stefan, 403 Macías, Enrique García, 927 MacLeod, Charles N., 683 Magalhães, Filipe, 316 Maio, Leandro, 870 Maji, Arup, 41 Malekjafarian, Abdollah, 695 Mariani, Stefano, 819 Marotta, Andrea, 60 Marrongelli, Gabriele, 905 Marzani, Alessandro, 327 Masurkar, Faeez, 149, 181 Matsinhe, Bruno, 69 Mechbal, Nazih, 140, 190 Meka, Sree Satya Venkat, 915 Mendes, Paulo, 69 Meoni, Andrea, 851 Meschke, Günther, 564 Miguel Giraldo, Carlos, 573 Milanoski, Dimitrios, 626 Milazzo, Alberto, 883 Milewczyk, Josh, 819 Miraglia, G., 947 Mohaniya, Antariksh, 3 Moll, Jochen, 769, 788 Moloney, Callum, 695 Monteiro, Eric, 190 Moulin, Emmanuel, 349 Mueller, Inka, 505, 543, 564 Mukhopadhyay, Suparno, 298 Murugesan, Sathish Subbaiah, 161

971 N Nedelcu, Dorian, 457 Nestorović, Tamara, 584 Ng, Alex, 129 Ng, Tang-tat, 41 Niederleithinger, Ernst, 266 Nieto, Juan, 683 Noé, Salvatore, 306 O Oboe, Daniele, 553 Oliveira, Sérgio, 69 Orlando, Calogero, 883 Osika, Mariusz, 200 Ostachowicz, Wiesław, 779 Ostrowski, Mariusz, 286 Ozcelik, Ozgur, 32 P Paixão, José, 316 Pardo, David, 245 Pasupuleti, Venkata Dilip Kumar, 79, 96, 915 Pellicano, Gianfranco, 594 Pereira, Sérgio, 316 Perrone, Angela, 23 Petladwala, Murtuza, 256, 674 Pierce, S. Gareth, 683 Pietrapertosa, Domenico, 23 Piñero, Iñaki, 236 Piscitelli, Filomena, 870 Pizzi, Sara, 50 Ponzo, Carlo Felice, 23 Prager, Jens, 759 Praticò, Filippo G., 50, 437, 594 Priebe, Sebastian, 564 Proença, Jorge, 69 Purohit, Sharadkumar P., 3 Q Quqa, Said, 335 Qureshi, Yumna, 716 R Radecki, Rafal, 200 Radzieński, Maciej, 779 Rahimi, Hamid, 119 Rajeev, P., 739 Rajic, Nik, 647 Rashidyan, Saman, 41 Rébillat, Marc, 140, 190 Ricardo, Jose, 414 Ricci, Fabrizio, 870

972 Rinaldi, Claudia, 60 Rioual, S., 447 Roberts, Callum, 109 Rosalie, Cédric, 647 Rostami, Javad, 149, 181 Röttger, Arne, 564 Ruccolo, Antonello, 937 Rueda, Bernardo, 414 S Saisi, Antonella, 905 Salowitz, Nathan P., 749 Sánchez Sánchez, Alejandro, 798 Sánchez, Diego Zamora, 236 Sattarifar, Afshin, 584 Sbarufatti, Claudio, 553, 636 Schiffer, Andreas, 893 Scudero, Salvatore, 89 Serlenga, Vincenzo, 23 Shao, Shuai, 367 Shpak, Alisa N., 505, 543 Sikdar, Shirsendu, 378 Smithard, Joel, 647 Söffker, Dirk, 467, 525 Soleimanpour, Reza, 129 Spada, Antonino, 424, 838 Speckmann, Holger, 389 Stabile, Tony Alfredo, 23 Staszewski, Wieslaw J., 200 Strangfeld, Christoph, 213 Surace, Cecilia, 481, 947 Suthar, Jahanvi M., 3 Swiercz, Andrzej, 286

T Taffe, Alexander, 403 Takaku, Hideaki, 256 Talbot, P., 447 Tarfaoui, Mostapha, 716 Tauzowski, Piotr, 286 Terzi, Marina, 349 Testoni, Nicola, 327, 769 Thierry, Vayssade, 226 Thomas, Renjith, 161 Tibaduiza, Diego A., 414

Author Index Tragni, Nicola, 23 Tse, Peter, 149, 181 U Ubertini, Filippo, 851, 861, 927, 958 V van der Velden, Stephen, 647 Van Paepegem, Wim, 378 Varona-Poncela, Tomás, 245 Vempati, Apuroop Sai, 276 Venanzi, Ilaria, 958 Véronique, Le Corvec, 226 Vignola, Luigi, 23 Vitakula, Venkata Sai Madhu Dinesh, 915 Vitale, Giovanni, 89 Vogt, Thomas, 819 von Vietinghoff, Jörg, 119 Vundekode, Narasimha Reddy, 96 W Wandowski, Tomasz, 779 Watson, Robert J., 683 Wei, Xiao, 525 Wickramarachchi, Chandula T., 494 Wilde, Maria, 809 Worden, Keith, 494, 947 Wu, Zhanjun, 727 Y Yang, Zhengyan, 727 Yelve, Nitesh, 149, 181 Yoon, Sangyoung, 893 Yu, Zhongru, 367 Yucel, Umut, 32 Z Zamora-Sánchez, Diego, 245 Zarouchas, Dimitrios, 616, 626 Zauli, Matteo, 327 Zhang, Dayi, 683 Zhou, Zhixiang, 367 Ziabari, Sayed Mohammad Soleimani, 129 Ziaja-Sujdak, Aleksandra, 200 Zonzini, Federica, 327, 769 Zuñiga Sagredo, Juan, 573 Zurita, Oscar, 414