Information Technology in Geo-Engineering: Proceedings of the 1st International Conference (ICITG) Shanghai [1 ed.] 9781607506171, 9781607506164

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INFORMATION TECHNOLOGY IN GEO-ENGINEERING

Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

Information Technology in Geo-Engineering Proceedings of the 1st International Conference (ICITG) Shanghai

Edited by

David G. Toll Durham University, UK

Hehua Zhu Tongji University, China

and

Xiaojun Li

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Tongji University, China

Amsterdam • Berlin • Tokyo • Washington, DC

Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

© 2010 The authors and IOS Press. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without prior written permission from the publisher. ISBN 978-1-60750-616-4 (print) ISBN 978-1-60750-617-1 (online) Library of Congress Control Number: 2010933984 Published by IOS Press under the imprint Millpress. Publisher IOS Press BV Nieuwe Hemweg 6B 1013 BG Amsterdam Netherlands fax: +31 20 687 0019 e-mail: [email protected]

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Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

Information Technology in Geo-Engineering D.G. Toll et al. (Eds.) IOS Press, 2010 © 2010 The authors and IOS Press. All rights reserved.

v

Preface

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Joint Technical Committee 2 (JTC2) * and Tongji University invited academics and practitioners in the field of information technology in geo-engineering from around the world to attend the 1st International Conference on Information Technology in Geoengineering, 16–17 September 2010. The conference was held in Shanghai, coinciding with the Shanghai World Expo 2010, which is a grand international gathering. Information technology has changed our lives and, at the same time, has become widely used in Geo-Engineering. As the science develops, the role that information technology plays becomes more and more important in every aspect of GeoEngineering, covering investigation, design, construction and maintenance. Moreover, innovative concepts, strategies and technologies have sprung up like mushrooms, and when properly applied in Geo-Engineering have facilitated design processes, improved construction efficiency and lowered maintenance costs. The conference aimed to provide a showcase for engineers, scientists, researchers and educators, to review recent developments and advancements of information technology in Geo-Engineering, and to offer a forum to discuss the future directions of this vital topic. This event was the first time where academics and practitioners worldwide in the field of information technology in geo-engineering came together, and it provided an insight into a new era of information technology in geo-engineering. We hope that this first conference, and this volume of proceedings, will form the foundation and the impetus for a long-running series of international conferences on a topic that is likely to gain even more importance in the future.

*

JTC2 is a Joint Technical Committee of the three international geo-engineering societies (International Association for Engineering Geology and the Environment (IAEG), International Society for Rock Mechanics (ISRM) and International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE)) on Representation of Geo-Engineering Data.

Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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Contents Preface

v

Keynote Lectures Innovation in Monitoring Technologies for Underground Structures Kenichi Soga, Krisada Chaiyasarn, Fabio Viola, Jize Yan, Ashwin Seshia and Roberto Cipolla

3

Integration of IT into Routine Geotechnical Design Chungsik Yoo

19

Integration of Surface and Subsurface Data for Civil Engineering Robert Hack

37

Study on Shield Tunnel Database on Construction Data Mitsutaka Sugimoto, Yasushi Arai, Yoshio Nishida, Koji Kayukawa, Wataru Sato and Minoru Kuriki

50

The European Project “Technology Innovation in Underground Construction” – Overview of IT Results Gernot Beer

58

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Artificial Intelligence and Data Mining An Intelligent Rock Mass Classification Method Based on Support Vector Machines and the Development of Website for Classification Wen-lin Niu and Tian-bin Li Application of Data Mining Techniques to the Safety Evaluation of Slopes Francisco F. Martins and Tiago F.S. Miranda Application of Data Mining Techniques to Estimate Elastic Young Modulus Over Time of Jet Grouting Laboratory Formulations Joaqui Tinoco, Antonio Gomes Correia and Paulo Cortez Research of Modeling System for Soil Classification in Geological Reconnaissance Based APNN-RBF Neural Network Tao Cheng and Keqin Yan Probabilistic Evaluation of the Parameters Governing the Stability of the Tailing Dams Gabriel Villavicencio, Claude Bacconnet, Pierre Breul, Daniel Boissier and Raoul Espinasse Research on Deformation Forecast of Deep Foundation Pit Based on Non-Equidistant Monitoring Data Jing Yan, Yawu Zeng and Rui Gao Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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Research on Reasoning Mechanism of Emergency Rescue Decision Support System of Geo-Hazards Under the Conditions of Extreme Snow and Ice Disasters Liangchao Zou, Shimei Wang and Haifeng Huang

126

Using Artificial Neural Networks for Evaluation of Collapse Potential of Some Iraqi Gypseous Soils Khalid R. Mahmood and Juneid Aziz

134

Discussion About Data Mining Application in Civil Engineering Deformation Measurement Analysis Y.J. Tang

144

Data Acquisitions and Monitoring 3D Reconstruction of Rock Cracks CT Image and Fractal Damage Study Fei Zhang, Haidong Zhou and Yunxia Zhao

157

Application of Geological Radar in Health Diagnosis of Nanjing Gulou Tunnel Yuan Wang, Songyu Liu, Yuehu Tan, Jianli Duan, Jing Zeng and Lei Gao

167

The Development and Application of Automatic Monitoring System for Dam Seepage Xiuguang Song, Hongbo Zhang, Yaoting Chen and Xin Zhuang Development of Real-Time Soil Deformation Monitoring System (RSDMS) M.A. Mohd Din, Z. Harun and L. Kang Wei

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Diagnosis of Ginza Line Subway Tunnel, the Oldest in Asia, by Acquiring Data on Deterioration Indices Tsutomu Yamamoto, Shunsuke Matsukawa and Haruo Hisawa

175 182

190

Exploratory Drilling with Recorded Parameters Using Wireless Technology Carla Alkassis, Eliane Nassif, Imad Elhajj, Shadi Najjar and Salah Sadek

199

Information Monitoring on Surrounding Rock of Tunnel and Its Application Yankai Wu and Xiaohua Xi

207

Mesoscopic Test Study of the Interface Between Geogrid Transverse Rib and Sand Jiaquan Wang, Jian Zhou, Xianyuan Tang and Liuyun Huang The Application of Modified Gaussian Model in Hyperspectral Image Analysis Caixia Yang, Yibo Han and Pu Han

216 222

The Role of DInSAR Techniques in the Analysis of Ground Deformations Related to Subsidence and Landslide Phenomena 230 Leonardo Cascini, Settimio Ferlisi, Gianfranco Fornaro and Dario Peduto A Study of Using Wireless Sensoring Network (WSN) to Improve Tunnel Disaster Prevention and Rescuing Scheme Dave Ta Teh Chang, Horng-Cheh Lee, Ming-Ru Lee and Liang-Tso Wang

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Application of Two Different Temperature Monitoring Systems in Liquid Nitrogen Ground Freezing Construction Xiangdong Hu, Wang Guo and Jun Zhang Detection of Tunnel Water Leakage Based on Image Processing ChuanPeng Hu, HeHua Zhu and XiaoJun Li

246 254

Data Standardization Standardization and Digitization of the ISRM Suggested Methods on Rock Mechanics Tests Ming Chi, Zuyu Chen and Yufei Zhao

265

The STREAM’s Testdefinition Facilitates Type of Test Independent Database Storage Paul E.L. Schaminée and Ardt A. Klapwijk

274

Geological Modeling and Integration with Numerical Model Building a Geological Model of the Copenhagen Area Using HoleBASE, MIKE Geomodel and KeyHOLE Sanne Louise Hanson and Ole Frits Nielsen

285

XML-Based Approach for Reporting and Exchanging Experimental Data Sets Using Metadata Model Fang Liu, Jean-Pierre Bardet and Nazila Mokarram

292

Study on the Integration of Digitalization and Numerical Analysis Based on the Digital Underground Space and Engineering HeHua Zhu and X.X. Li

301

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Information Systems and Application A Design and Construction Database for Cut-and-Cover Tunnel Maintenance Using 3D Models Takashi Aruga, Yasushi Arai, Hideya Kamachi and Keiji Oishi

311

A Knowledge Base System to Support Emergency Response of Geo-Hazards Under the Conditions of Extreme Snow and Ice Disasters Haifeng Huang and Shimei Wang

320

A New Information System for Underground Construction Projects Klaus Chmelina A System for Remote Monitoring Information Management and Risk Control in the Underground Engineering Xiaodong Long, Rong Wang, Bo Chen and Nan Wu An AJAX-Based Web Application for Disseminating Site Characterization Data Fang Liu, Yaping Zhou and Mingjing Jiang

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338 345

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Application of Asset Management Technique for Road Tunnel Maintenance Management Bo Li and Yujing Jiang Deep Foundation Pit Construction Monitoring Information System Based on GIS Yuan Wang, Songyu Liu, Jianyong Liu, Jing Zeng and Lei Gao

353 361

Development and Study of the Information Management System of Levee Project Based on WebGIS in China 368 Bin Zhang, Mingyu Bi, Jia Liu, Xizhong Shen and Haiting Dong Development of Tunnel 3D Information Inquiry System Based on ArcGIS Engine 377 Fang-hui Jiao, Yong-gang Jia and Tao Liu

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Application of 3D Information Technology in Geotechnical Engineering Practice Peng Zhang, Fang Zhang and Xuan Han

383

Research and Application of Artificial Ground Freezing Monitoring and Management System Xiangdong Hu, Chunlin Zhou and Zhiyong Zhou

393

Research on Computer-Aided Decision-Making Software of Advanced Geological Prediction in Tunnel Lubo Meng, Tianbin Li and Xing Fang

401

Research on Emergency Rescue Decision Support System of Geo-Hazards Under the Conditions of Extreme Snow and Ice Disasters Shimei Wang, HaiFeng Huang, Gang Wang and LiangChao Zou

409

Visual C++ Based Risk Assessment System for Ground Environment Damage Induced by Subway Tunneling Bo Liu, Li Huang, Yan Li and Bo Lu

416

The Research and Application on Visual Information System of Safety Monitoring During Tunnelling Based on GIS Zhi Lin, Yuan-Hai Li, Xing-Ping Li and Changjiang Yang

424

Study and Implementation of Urban Rail Transit Construction Engineering Security and Risk Management Information System Yi-qi Liao, Huai Jin, Pei-yin Lv and Jun-wei Li

433

Novel Computational Techniques and Numerical Methods A Parallel Factorization Algorithm for Stiffness Matrix Based on Threadpool Juntao Chen, Ming Xiao and Yuting Zhang

445

An Accelerated Meshfree Analysis of Three Dimensional Soil Slope Failure Under Finite Deformation Dongding Wang, Zhuoya Li, Ling Li and Youcai Wu

452

Meshless Natural Neighbor Method Based on Implicit Integration Algorithm for Elastoplastic Analysis Hehua Zhu, Wenjun Liu, Yongchang Cai and Yuanbin Miao

458

Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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The Mean Stress as the Governing Parameter in the Implicit GPM Stress Integration of Modified Cam-Clay Model Mirjana Vukicevic and Dragan Rakic

467

A Discrete Numerical Approach for Modelling Face Stability in Slurry Shield Tunnelling X.Y. Hu, Kieffer D. Scott and Z.X. Zhang

476

“Meshfree” Numerical Modeling of Slope Instabilities, Landslides and Mudflows 491 Thomas Zimmermann, Matthias Preisig and Andrzej Truty Numerical Analysis A Constitutive Model for Predicting Cumulative Deformation of Roadbed Filled with Silty Sands Induced by Repeated Traffic Loading Hongbo Zhang, Xiuguang Song and Honghong Wand

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Countermeasure Against Liquefaction Using Interlocked Soil Improvement Piles Jun Kawamura, Toshiyuki Kamata, Kazuhiro Araki, Takeshi Ishii and Kunio Saitoh

505 513

Influence Analysis on a Subway Shield Tunnel Crossing Below a High Speed Railway Gancheng Xu, Ping Hu and Chengxue Li

519

Modelling the Effects of Dewatering on Pile Settlement by Integrating Finite Element and Load-Transfer Analyses J.R. Omer

528

Slope Angle Influence on the Seismic Wave Amplification Effect in a Double-Sided Slope Shiguo Xiao, Zhijian Song and Jianjing Zhang

536

Numerical Simulation Analysis Applied to Excavation of Deep-Foundation Above the Highway Tunnel in Downtown Shu Xu and Zhi Li

545

Parametric Study and Design Charts Based on Movement of Reinforced Earth Retaining Wall Y.M. Mowafy, N.R. El-Sakhawy, R.R. El-Sakhawy and O.A. El-Gaaly

553

Quality Evaluation and Numerical Simulation of the Rock Mass of the Assembling Chamber of Shenxigou Hydropower Station 562 Lehua Wand, Xing Chen, Jianlin Li and Yuhong Qin Analysis of Safety Factors of Twin Tunnels with Small Spacing by Strength Reduction FEM Cheng Wang, Yongfu Wang and Qiang Xiao

575

Simulation Analysis on Inter-Space Rock Strengthening Project of Ultra-Small Spacing Tunnels with Large Section in Bad Rock Mass Jianwu Gong, Caichu Xia and Xuewen Lei

582

Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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Study on the Impact of Asymmetric Deep Pit Excavation on the Surrounding Environment Chunqiu Wu, Bin Yang, Detang Li and Bin Zhang

590

Study on the Relationship Between Particle Micro Parameters and Soil Mechanical Properties Chengbing Wang and Hehua Zhu

599

Application of an Implicit and Explicit Integration Rules Yunming Yang

607

Numerical Analysis of Ground Deformation During Horizontal Jet-Grouting Z.F. Wang, S.L. Shen, L.S. Chen and J.F. Yang

617

Cutoff Effect on Groundwater Seepage of Underground Structure in Aquifers Y.S. Xu, S.L. Shen and J.C. Chai

623

Numerical Analysis of Geosynthetic Reinforced Soil Above a Tunnel S.E. Ghoreishi Tayyebi, M.R. Babatabar and A. Tahmasebi Poor

632

Study on Failure Mechanism of Instability of High Rock Slope Chuanbo Zhou, Nan Jiang and Yingkang Yao

644

Study of Numerical Simulation on Supporting Parameters of Soft Rock Tunnel Xuedong Luo, Jianping Chen, Qiaosen Lǚ and Changqun Zuo

655

Finite Element Analysis of the Seismic Response of Large-Space Semi-Underground Structure in Soft Soil Liyu Liu, Zhiyi Chen and Yong Yuan

661

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Simulation, Visualization and Theoretical Study Computer Dynamic Simulation Analysis of the Setting Mode of Buffer Layer in Tunnel Hua Xu, Tianbin Li, Xing Fang and Zhiheng Ling

673

Research on Failure Modes in Fractured Rock Masses Under Triaxial Compression Using Distinct Element Method Lei Fan, Huiming Tang and Huoming Zhou

686

Study on Simulation Model of Ground Subsidence Based on Time-Space Evolution and Its Applications Xiaobo Liu, Huoran Sun and Yongjia Wang

694

The 3D Modeling and Visualization of Highway Slope Based on GIS and Its Application Yonghui Zhang, Guoliang Chen, Qian Sheng and Xiuguo Liu

701

Development and Application of Numerical Model for Rainfall-Induced Shallow Landslides Tl Tsai

713

Research on Swelling Characteristics of Limestone Bendong Qin and Yunjun Luo

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730

Subject Index

739

Author Index

743

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Pressure Meter Testing in Glacial Till Raddi M. Shwaik Al-Zubaidi

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Keynote Lectures

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Information Technology in Geo-Engineering D.G. Toll et al. (Eds.) IOS Press, 2010 © 2010 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-60750-617-1-3

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Innovation in Monitoring Technologies for Underground Structures Kenichi SOGAa,1, Krisada CHAIYASARNa, Fabio VIOLAa, Jize YANa, Ashwin SESHIAa and Roberto CIPOLLAa a University of Cambridge, Department of Engineering, Cambridge CB2 1PZ, UK

Abstract. One of the greatest challenges facing civil engineers in the 21st century is the stewardship of ageing civil engineering infrastructure. Nowhere is this more apparent than in underground structures in the major cities around the world. Much of them were constructed more than half a century ago and there is widespread evidence of deterioration. Advances in the development of computer vision and miniature micro-electro-mechanical sensors (MEMS) offer intriguing possibilities that can radically alter the paradigms underlying existing methods of condition assessment and monitoring of such infrastructure. This paper discusses potentials of these technologies for monitoring underground infrastructure. Keywords. Monitoring, Computer vision, micro electro mechanical systems

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1. Introduction Deterioration of ageing civil engineering infrastructure and the associated increase in the proportion of budgets spent on maintenance present significant challenges to our society. Dense spatial and temporal information, integrated with appropriate data analytical tools, is required to assess and reduce the likelihood of, or improve the efficient response to failures of key elements of critical infrastructure resulting from degradation, overload, or disasters due to natural and/or man-made causes. The maintenance, refurbishment and safe operation of ageing infrastructure under severe financial constraints forces civil engineers to strive for technological advances which will allow them to sense, monitor and better understand the behaviour of their engineering systems under both normal and extreme operating conditions. Nowhere is this more apparent than in large-scale critical systems such as the networks of tunnels and pipelines that lie beneath our cities. Much of this infrastructure was constructed more than half a century ago and there is widespread evidence of deterioration. Tunnels, particularly old ones, are vulnerable to adjacent ground disturbance, for instance piling and deep excavations. Excessive leakage and bursts of underground pipelines could cause enormous damage to neighboring infrastructure and disruption of critical services while contaminant intrusion could pose significant health hazards and public concern. The primary goal for infrastructure managers is the development of management strategies to maintain the safety and functionality of the infrastructure in the face of the deterioration of the component materials and ever increasing usage demands. 1

Corresponding Author.

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K. Soga et al. / Innovation in Monitoring Technologies for Underground Structures

Disruption due to closures for replacement, maintenance or strengthening impose a high economic burden and infrastructure managers are seeking tools to identify defects within infrastructure and, in particular, the cause of the defects, so that a suitable remedial strategy can be implemented. At the moment, network wide monitoring is prohibitively expensive and very limited in terms of obtaining the necessary data for quick assessment, especially in the case of emergencies due to natural or deliberately caused disasters. Advances in the development of innovative technologies such as fibre optics sensing, computer vision, micro-electro-mechanical sensors (MEMS) and wireless sensor network offer intriguing possibilities that can radically alter the paradigms underlying existing methods of condition assessment and monitoring. This paper discusses potentials of computer vision and MEMS for monitoring the conditions of underground structures.

2. Computer Vision

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2.1. Motivation For maintenance works of underground structures, visual inspection is a common practice for detecting and monitoring anomalies such as cracks, spalling and staining. Photographs are commonly used as a mean of recording anomalies, although over years, image collections become large and difficult to organize and browse. Improving the ways that an image database is accessed and visualized are expected to result in a substantial progress in the effectiveness of monitoring, in particular of remote monitoring, like shaft inspection, where inspectors cannot easily access the inspection site. One way to assist inspectors in organising a large collection of images and examining the tunnel surface is providing them with automatic tools that combine a large number of pictures into a single high-quality wide-angle composite view. This process is commonly referred to as image mosaicing. There are many image stitching software packages, such as Microsoft Image Composite Editor (ICE) [1] and Autopano [2], the vast majority of which rely on a number of strong assumptions on the camera motion or on the scene geometry to be strictly true. In fact, these packages are genuinely designed for generating panoramas, and they require images to be captured by a camera roughly rotating about its optical centre, or by capturing the plane at "infinity" (i.e. the scenes where all objects are distant and there is little or no parallax). The images from typical underground structure inspection do not meet such requirements and these packages fail to generate good mosaics, as shown in Figure 1. (Top). A new computer vision system has been recently developed at Cambridge University to perform mosaicing of images captured inside a tunnel from a standard digital camera. It can simultaneously cope with free camera motion and the more complex geometry of the scene. In the proposed system, starting from a set of pictures from a section of the tunnel linings, first the approximate 3D geometry of the scene and consequently warp each frame are recovered in order to generate a set of pictures that can be stitched together using any standard mosaicing technique. In fact, for images captured inside a tunnel, this warping procedure can be imagined as prompting pictures of a tunnel that has been flattened, or unrolled onto a plane. The system obtains the sparse 3D reconstruction of the tunnel using classical Structure from Motion (SfM) [3]

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from the input images only, without the need of user interaction. Further details are given in the next sections.

Figure 1. The results from the Bond Street 2 sequence, Top: the result by the homography-based mosaic using ICE [1], Bottom: our result after the corrected surface estimation. While, the parallel lines (tunnel ridges) curve along the horizontal axis of the image in the homography-based result, our result preserves all physical sense, e.g. line parallelism and straightness, which is important for tunnel inspection.

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2.2. Multiview Reconstruction A sparse 3D model 2, i.e. a 3D point cloud, can be recovered from digital photographs, together with the camera poses, i.e. translations and rotations with respect to a given reference frame. The reconstruction procedure is composed of two modules: point track generation; and multiple view geometry estimation. In the first module, interest points are extracted from each input image and matched across multiple frames in order to obtain a set of points that consistently appear in multiple images. The 2D coordinates of such points are collected and concatenated forming 2D trajectories over the multiple frames, i.e. tracks. Success at this stage relies on the robustness of the extraction and matching scheme of the interest points. An interest point is an image point whose neighborhood (i.e. an image patch centered at that point) displays distinctive features that are stable under perturbations arising from some degree of perspective transformations, illumination variations and noise such that the same interest points can be extracted with high degree of reproducibility. This invariance allows keypoints to be matched across multiple images. The Scale Invariant Transform Features (SIFT) is an example of stable image feature[4]. Interest points are detected in scale-space, and are assigned descriptors that 2

Other systems, such as the LIDAR laser scan system, can acquire a dense and accurate 3D point cloud. However, it should be noted that a dense 3D model of the tunnel is not our only objective here, since we are also interested in recovering the relative pose of the cameras in order to register the input images via a proxy geometry. Nonetheless, both technologies could be used jointly, as the sparse 3D model reconstructed by the proposed system could be used to register images onto a CAD or LIDAR model as shown in [5].

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summarize their appearance by the orientation histogram of the intensity gradients within a patch, thus achieving invariance to scale, orientation, affine distortion, and partial invariance to illumination changes. The second module is classically formulated as a large-scale optimization problem (see [6] for a detailed explanation of multiple view geometry). The output from the first module is used to initialize the optimizer, estimating the sparse point cloud and camera poses using overlapping triplets of images. The estimation is then refined by Bundle Adjustment (BA) [7]. The BA algorithm iteratively adjusts the positions of the 3D coordinates and the camera poses to minimize the sum of the distances between the reprojections of the reconstructed 3D points through the estimated cameras, and the interest points 2D coordinates. Figure 2. shows a sparse 3D reconstruction of the tunnel before (b) and after BA (c). The tunnel linings are clearly seen after the BA is performed. The convergence graph from the BA algorithm (a) quantitatively shows significant improvement in the global registration as the cost function converges to a local minimum.

Figure 2. The sparse multi-view reconstruction models and the surface estimation. (a) the convergence graph of the BA algorithm; (b) initialization of the estimated 3D point cloud and camera poses; (c) the reconstruction after BA, points in red indicating points lying on the cylindrical surface; (d) the estimated surfaces, the blue surface was estimated from points lying on the surface, the yellow surface was estimated from all reconstructed points.

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2.3. Learning Support Vector Machine

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When recovering the approximate geometry of the scene, the system exploits prior knowledge of the tunnel and shaft structure by estimating a proxy geometry, that is to say a representation of the real complex geometry within a class of simpler and compact descriptions. For example, the geometry can be assumed to be roughly cylindrical and a simple proxy for it is recovered by robustly fitting a quadric surface to a sparse set of reconstructed 3D points that lie on the tunnel wall surface. Of course, the true tunnel geometry is not purely cylindrical, but typically a mixture of a cylindrical surface and protuberant regions such as pipes, pans and tunnel ridges. However, this simplifying assumption will only cause minor artifacts to the warped frames and noise to be added onto the mosaicing process that a standard stitching algorithms, which we apply in the final stage, can cope with. Quadric surfaces are just an example of how the system works, but other choices are possible for different structures, such as a mixture of planar surfaces. The SVM classifier [8] is applied to discriminate the tunnel surface points from the non-surface points. The interest points detected by the SIFT algorithm on or near the protuberant regions are collected as the non-surface class and others as the surface class, see Fig. 4 (Right). The image patches of the two classes exhibit quite distinctive appearances, as shown in Fig. 3, hence they are expected to be separable as shown in Fig. 4 (Left). The 3D points classified as the surface points by the SVM classifier are marked as red in Figure 2.(c). The surfaces estimated with and without the SVM, respectively represented in blue and red, are shown in Figure 2.(d).

Figure 3. An example of two classes The interest points on the protuberant regions are Class 1 and the points on the surface are Class 2.

Figure 4. SVM classification results. ROC curves for the different kernel parameters and the penalty constants (Left); a test classification example: the points in black are estimated as the non-surface class, and the points in red as the surface class (Right).

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2.4. Image Warping and Final Mosaicing Cylinders are surface of zero Gaussian curvature so it is possible to define a local isometry for flattening the curved surface onto a plane. Moreover, given the constraints on the image collection process, cameras are located inside the cylinder and each ray intersects the surface in only a single visible point, defining for each image a one-toone mapping between image samples and points on the surface. These facts allow us to define a warping that produces the flattened versions of the input images (see Figs 5 and 6). The obtained output frames can then be registered via homographies, or planar projective transformations. The warped images can finally be mosaiced with standard stitching algorithms using the planar projective registration model. ICE [1] and Autopano [2] are used to obtain the final mosaics in our experiments.

Figure 5. Examples of the input images from the Bond Street Sequence

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Figure 6. Warped images from the input images from the Bond Street sequence

The system was tested with three data sets captured in two London Underground sites: Bond Street 1 sequence (Figure 8.), Bond Street 2 sequence (Figure 1.), and Aldwych sequence (Figure 7.). For the SVM training, the data set contains 1369 training points and 635 test points from 19 labeled images from the Aldwych sequence. The top figure in Figure 7.(a) shows the result of mosaicing before the BA is applied. The misalignment in the overlapping regions is clearly seen due to the errors in the camera registration. The bottom figure in Figure 7.(a) illustrates the result after the BA is run but without applying the SVM classifier. The skewness in the surface estimation induced by the non-surface points causes noticeable distortion in the mosaic image shown as the curvature in the tunnel ridges. In this result, where the amount of protruding 3D structure is significant, parallelism of the tunnel linings is somewhat preserved, but not the line straightness. Figure 7.(b) shows the result after the cylindrical surface is corrected by the SVM classification, allowing a better warping of the input images to be applied. Noticeably, both line parallelism and straightness are preserved. The angle between the vertical and horizontal lines is 90o, indicating the tunnel in the correct physical sense. This is important for tunnel inspection.

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Figure 1. shows qualitative comparison between the mosaic images from the homography-based method and the proposed method. Perspective distortion is clearly removed in our result. Artifacts generated by the new system still can be seen in Figure 8. The quality of the final composite image depends critically on the amount of protruding structure. In fact, the registration model can explain the perspective distortion that cylindrical surfaces undergoes in the image formation process but does not capture the distortion caused by 3D structure protruding off the proxy surface; the same way as homography-based mosaics exhibits considerable distortion when nonplanar scenes are captured.

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Figure 7. Mosaic image resutls (a) The top image is the result with BA but without SVM, the bottom image is the result before BA. (b) The final result with BA, SVM and warping applied.

Figure 8. The results from the Bond Street 1 sequence, multiple rings which covers around 8 metres.

2.5. Remarks A system for aiding visual inspection of tunnels is presented. The system is capable of generating composite images of large sections of tunnel surface, which offer a flattened view of the tunnel linings. The mosaic images are obtained by registering the input frames on an cylindrical surface approximating the real tunnel geometry. The proxy geometry is recovered from a sparse set of 3D points reconstructed from the input images only with human supervision. The accuracy of the proxy estimation is boosted by the use of a SVM classifier that is capable of separating 3D points belonging to the tunnel surface from those belonging to the protruding regions, thus improving the quality of the mosaic. With this system it is possible to identify regions of change in

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images of the same scene taken at different times since the images have spatial coordinates assigned. This is much needed for inspection since engineers will be able to assess the deterioration rate of a structure and then devise regime for repair. In the future, further validation will be conducted on more datasets. Furthermore, the prototype of the system including the software and the apparatus for acquiring images is currently being developed so that it can be practically adopted in the underground structure inspection procedure.

3. Micro Electro Mechanical Systems

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3.1. Motivation MEMS or Micro Electro Mechanical Systems are small integrated devices or systems that combine electrical and mechanical components varied in size from micrometers to millimeters, which can merge the function of computation and communication with sensing and actuation to produce a system of miniature dimensions [9]. MEMS extend the fabrication techniques for semiconductor industry to include mechanical elements and the inherently small size of MEMS enables high level integration of micromachined components or structures to realize multiple functions or capabilities on the same silicon chip for greater utility [10]. The majority of the MEMS applications in civil infrastructure monitoring act as sensors, which have emerged as a high sensitive monitoring candidate for structural control and assessment, health monitoring, damage repair and system preservation of civil infrastructure. MEMS sensors will offer major advantages in terms of smaller size, lower power consumption, more sensitive to input variations, cheaper cost due to mass production and less invasive than larger devices, and extend the performance and lifetimes over conventional systems [11]. A range of MEMS sensors is now available in civil applications, which can measure acceleration, inclination, temperature and pressure. In this paper, three types of well commercialized MEMS sensor are illustrated as successful examples for civil infrastructure monitoring applications. Recently research developed MEMS strain sensors are further described as a new MEMS sensing area in civil applications. 3.2. Commercial MEMS sensor examples MEMS accelerometer An accelerometer is a sensor that measures acceleration forces. The acceleration forces can cause a deflection of an inertial mass suspended by springs from its initial position, which is converted to an electrical signal as the sensor output. The accelerometer can be used to sense orientation, acceleration and vibration, which are very essential in civil infrastructure monitoring to detect and diagnose the deviation from normal conditions [12]. The use of conventional piezoelectric accelerometers in civil monitoring is well known and accepted, but at high cost especially if simultaneous multiple collection points are required. The application of MEMS technology to accelerometers is a relatively new development. MEMS accelerometers based on microfabrication technologies have demonstrated to be an attractive and cheaper alternative to conventional accelerometers because of lower power consumption and potential integration of sensing and build-in signal conditional units within one device. The

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MEMS accelerometer cost 10% or less compared to the conventional accelerometer together with the signal condition unit [13]. The miniaturization road map of the Analog Device accelerometer is shown in Figure 9. [14]

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Figure 9. Technique road map of Analog Device Accelerometers.

MEMS inclinometer MEMS sensors can measure both static and dynamic accelerations and therefore they can be utilized to measure inclinations that are typically static accelerations. The inclinometer includes uniaxial or biaxial accelerometers which measure the gravity. The commercial MEMS inclinometer commonly incorporates an onboard microprocessor to automatically compensate the temperature effect of the tilt data. MEMS inclinometer is essentially low power device and particularly suitable for industrial applications including geotechnical and structural monitoring, surveying equipment, satellite stabilization systems and automotive wheel alignment. More recently, Analog device announced the industry's most precise MEMS inclinometer, which can provide a fully compensated direct angle output with less than 0.1° linear inclination error, making it at least twice as accurate as competitive tilt sensors, which is a high accuracy, digital inclinometer and accommodates both single-axis (±180°) and dual-axis (±90°) operation as schematically shown in Fig. 10 [14]. The system dimension of the inclinometer including the evaluation board is within 3cm x 3cm.

Figure 10. (a) Function block diagram of the Analog Devices ADIS16209 Inclinometer (b) the dimension of the inclinometer with the evaluation board.

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MEMS inertial measurement unit Accurate location and time information are very important in large scale civil infrastructure monitoring networks. The location and time reference for each sensor node are usually provided by the Global Positioning System (GPS). However, sensor nodes in underground are usually lack of communication with the satellites and lack of precise location information. The incorporation of MEMS for multi-sensor systems, enable the hybridisation between the GPS and a combination of accelerometer and gyroscope to offer less accurate location and time reference which is usually very essential in harsh environment [15]. Fig. 11 shows Analog Devices ADIS16350 Inertial Measurement Unit [16]. Table 1. summarizes a comparison between conventional and MEMS based inertial sensing unit, which combines the angular rate and linear acceleration measurement function [17].

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Figure 11. (a) Function block diagram of the Analog Devices ADIS16350 Inertial Measurement Unit (b) dimension of Inertial Measurement Unit system. Table 1. Comparison between conventional and MEMS based inertial sensing units Mass Size Power Cost

Conventional 1587.5 g 15x8x5 cm 35 W $20,000

MEMS based [10] 5g 2x2x1 cm 150mW $669

3.3. MEMS strain sensors Strain sensors are highly critical for civil infrastructure applications. The conventional metal film strain gauge and vibrating wire strain sensors are not very well suited for wireless sensing civil applications, in which numbers of strain sensors are required to be deployed within large-scale ageing infrastructures. Thus, high resolution, lower power and small size MEMS strain sensors are in great demand to replace the conventional strain gauge by use of silicon MEMS technology [18]. In this paper, the research work at Cambridge related to novel MEMS strain sensor suited for wireless civil infrastructure monitoring using resonant silicon based strain sensor is discussed.

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Crackmeter prototype A prototype MEMS crackmeter is realized with a thin steel bar fixed across the crack on the tunnel wall, onto which a multi-directional MEMS strain sensor is soldered (Fig. 12) [19]. A movement of the wall crack (contraction or expansion) can be detected by the sensors through the strain generated on the steel bar and transferred to the silicon chip. Since the resonant MEMS sensors are operated through a self-sustained electrostatic actuation using an electronic oscillation loop, no static bias current is needed for their operation and the dynamic power requirements can be comparatively small. The mechanical design of the crackmeter is illustrated in Fig. 13 [19]. It is composed by a 200 mm thin steel strip fixed by point welding to anchors suited for mounting the device across a crack on a concrete wall. The steel strip deformation induced by the structure movements can be detected by the silicon MEMS strain sensor based on the frequency shift, which is soldered on the strip with effective strain coupling from steel to silicon by the resonator anchors. The geometry has been conceived in order to pre-stress the support steel strip to measure possible crack contractions or extensions in the range, with no significant tensile force applied on the crack. The steel strip was equipped by a nut which allows for changing the unstrained length of the strain sensor.

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Figure 12. Concept of MEMS-based crackmeter operating in wireless mode.

Figure 13. Concept of MEMS-based crackmeter operating in wireless mode.

Fabrication technique The fabrication process of the strain sensor has been previous reported in [20]. The process is starting from silicon on insulator substrates. The silicon device layer is heavily doped and annealed in order to obtain a low sheet resistance. On the substrate,

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a low temperature oxide layer is then deposed by Chemical Vapour Deposition (CVD) and patterned by Reactive Ion Etching (RIE). A CVD polycrystalline silicon layer is then deposited and completely oxidized to shrink the gaps patterned on the initial oxide later. The deep RIE is then utilized to patent the reduced gap oxide mask on the silicon device layer with reduced line widths. The process is ended by an isotropic wet etching in buffered oxide etch. Double-Ended Tuning Fork MEMS Strain sensor Using the fabrication technology described above, Double-Ended Tuning Fork (DETF) parallel-plate resonators with reduced coupling gaps (< 1μm) have been fabricated [21]. The devices have been bonded to a thin steel bar by epoxy glue, packaged in vacuum and tested by applying strain to the bar, showing good tolerances to packaging parasitics, measurement reversibility, and strain sensitivLW\RI+]—İ The results are reported in Fig. 14. As may be observed, the strain-resonance response is fairly linear and reversible. The results shown in Fig. 14 are the first example of strain sensing on steel performed with MEMS resonators in vacuum, and demonstrate that the proposed technology is promising for strain sensing on structural materials.

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Figure 14. Optical micrograph of MEMS DETF strain sensor and its strain-frequency response.

H-shape MEMS Strain sensor H-framework flexural mode resonator is another type of MEMS strain sensor adopted in the crackmeter, which utilizes a central beam to mechanically couple twin clampedclamped beam resonators into vibration in the second harmonic mode as illustrated in Fig. 15a [19]. The resonator is grounded and electrostatically driven into vibration by two interconnected electrodes and capacitive sensing of the induced displacement is through a pair of similar interconnected electrodes as shown in Fig. 15a. Through this electrode arrangement, vibrations in the preferential mode are excited and modes that result in an out-of-phase capacitance variation between the driving / sensing electrodes and the resonator itself are suppressed. Initial open-loop measurements on the H-shape MEMS-based crackmeter prototype are shown in Fig. 15b. In these measurements, the open-loop device transmission is plotted as a function of electrostatically induced applied axial strain. The results show that a 0.079 micro-strain results in a frequency shift of 16 Hz. The measurements contrast with a minimum detectable frequency shift of 3.1 mHz previously demonstrated for a closed-loop double-ended tuning fork sensor fabricated at Cambridge [22] allowing for strain resolution potential below 100 pİ for a 1 s averaging time.

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Figure 15. (a) Optical and FEA graphs of MEMS H-shape resonator (b) Open-loop measurements demonstrating functionality of the MEMS strain sensor through electrostatically induced micro strain.

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MEMS disk resonator strain sensor Flexural mode MEMS resonators usually required high vacuum packaging to preserve the quality factor to enhance the sensing sensitivity and resolution. Recently, bulk mode silicon MEMS resonators have been presented as a means to reduce the air damping and have relative high-Q performance in the air [23], which opens a new door for MEMS resonant strain sensor functional in ambient environment and largely reduces the complexity and cost of the vacuum package technique. The resonant strain sensor used in the first crackmeter is a round-disk resonator with capacitive sensing, which was tested at atmospheric pressure under applied strain. The initial results indicated some sensitivity of the resonator to strain as shown in Fig. 16.

Figure 16. Optical of MEMS round disk resonator and open-loop measurement on sensor under applied strain

3.4. Remarks The work related to the development of a novel crack metering technology for low power, wireless structural monitoring based on MEMS strain sensors has been presented. Three types of MEMS-based strain sensors are described. Vacuum packaging of MEMS strain sensors soldered on a steel bar has been implemented in the crackmeter prototype. MEMS DETF resonator and H-shape resonator have been used as vacuum packaged strain sensors. MEMS disk resonator opens a new door for MEMS resonant strain sensor functional in ambient environment and might largely reduces the complexity and cost of the vacuum package for strain sensors. Future monitoring systems will undoubtedly comprise Wireless Sensor Networks (WSN) and will be designed around the capabilities of autonomous nodes. Each node

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in the network will integrate specific sensing capabilities with communication, data processing and power supply. The integrated MEMS sensors system offers a solution to large scale monitor and control physical and chemical parameters in many civil infrastructure applications such as refractive index variations of the surrounding environment, life-cycle assessment in building and construction. In many cases, the integrated sensors must be completely embedded in the structure, with no physical connection to the outside world. Due to the remote situation, the conventional method to power the system is to use a battery, which is limited to its life cycle. The replacement would be difficult since the sensors may be embedded and hard to reach. Especially, in large scale civil infrastructure monitoring applications, the sensor node number can reach thousands such as water supply system. Energy harvesting device have been considered as the most attractive candidates for small electric power sources of portable integrated sensors and wireless sensor networks. The powering of wireless devices by capturing and storing energy from external sources present in the environment offers an opportunity to replace or augment batteries [24]. The long term strategy of the research will offer a MEMS solution [25] to integrate the sensors with energy harvesters as well as communication systems, which need to be highly optimized towards the design constraints and ambient sources including ultra-low energy consumption budget studies, object-based smart power management solution and smart utilization of environmental energy sources. A system level MEMS-CMOS integration between MEMS sensor, MEMS power harvesting, RF MEMS as well as smart sensing circuits, power management circuits and signal processing circuits can be the long term ambition. The self-powered integrated MEMS sensor and wireless communication systems provide a real incentive for investigating alternative types of power sources to traditional batteries and offer a novel platform for the large scale monitoring applications for civil infrastructure, public health, and smart society engagement.

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4. Conclusion and Future Work One of the greatest challenges facing civil engineers in the 21st century is the stewardship (maintenance, upgrading and safe operation) of ageing infrastructure. Little is known of the long-term performance of such infrastructure. In recent years, sensor and communications research has been undergoing a quiet revolution, promising to have significant impacts on new generation of monitoring technologies for civil engineering infrastructure. The development of ‘smart’ infrastructure is essential to the viability of rehabilitation, repair and reuse. The immediate monitoring applications can be summarised as follows: (i) monitoring of safety critical elements where a failure of a key element is immediately notified to managers e.g. collisions by lorries or trains with supporting columns of a bridge or overhead gantries which may result in blockage of the carriageway or even in total collapse. Where possible parameters that give advanced warning of impending failure need to be identified and targeted for measurement. At present little monitoring of this type occurs primarily due to the cost and limited capacity to process and interpret data. (ii) monitoring of performance and condition. This might include the detection of excessive deformations as an indicator of deterioration/corrosion/degradation of key

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elements and the detection of ingress of deleterious materials such as corrosion promoters (eg de-icing salts) or water. For water distribution networks monitoring can assist in identifying leakage, patterns of deterioration in the pipe infrastructure and water quality, and in enabling real-time flow and pressure management. There are a number of benefits to innovative technologies that were identified in this paper for monitoring and assessment of ageing civil engineering infrastructure. The most obvious one is that these technologies will be able to reduce costs associated with end-of-life structures. Another important benefit is the increased safety levels they can provide to cope with natural disasters such as climate change, flood warnings and earthquakes. Designers and consultants will benefit from a deeper understanding of the actual performance of different types of infrastructure. These technologies will allow its performance to be monitored during its working life, which will lead to better operational performance and design for future infrastructure. Thus, the need for high quality measurements is of great importance to practitioners and is also of considerable interest within research and academic circles because of the resulting improved understanding of infrastructure performance.

Acknowledgements The research is funded by the UK Engineering and Physical Sciences Research Council (EP/E003338/1 Micro-Measurement and Monitoring System for Ageing Underground Infrastructures (Underground M3))

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References [1] Microsoft Image Composite Editor,http://research.microsoft.com/~enus/~um/redmond/~groups/ivm/ICE/. [2] Kolor Autopano, http://www.autopano.net, 2008 [3] N. Snavely, S. Seitz and R. Szeliski, Photo Tourism: Exploring Photo Collectioins in 3D, Proc. of SIGGRAPH (2005), 835-846. [4] D.G. Lowe, Distinctive Image Features from Scales-Invariant Keypoints, IJCV 60 (2004), 2:91-110. [5] I. Stamos, L. Liu, Ch. Chen, G. Wolberg, G. Yu and S. Zokai, Integrating Automated Range Registration with Multiview Geometry for the Photorealistic Modelling of Large-Scale Scenes, IJCV 78 (2008), 237-270. [6] A. Zisserman and R. Hartley, Multiple View Geometry in Computer Vision, Cambridge University Press, Cambridge, 1999. [7] M. Lourakis and A. Argyros, The design and implementation of a generic sparse bundle adjustment software package based on Levenberg-Marquardt, Technical Report (2004), http://www.ics.forth.gr/~lourakis/sba/ [8] C.J.C Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery 2 (1998), 121-167. [9] V.K Varadan. and Varadam V.V. Microsensors, microelectromechanical systems (MEMS) and electronics for smart structures and systems, Smart Material Structures 9 (2000), 957–972. [10] S. A. Vittorio, MicroElectroMechanical Systems (MEMS) (2001). [11] N. O. Attoh-Okine and S. Mensah, MEMS application in Pavement Condition Monitoring-Challenges, ISARC (2002), 387–391. [12] P. C. Chang, A. Flatau and S. C. Liu, Review paper: health monitoring of civil infrastructure Source: Structural health monitoring 2 (2003), no. 3, 257-267. [13] A. Albarbar, A. Badri, Jyoti K. Sinha and A. Starr, Performance evaluation of MEMS accelerometers, Measurement 42 (2009), no. 5, 790-795. [14] High Accuracy, Dual-Axis Digital Inclinometer and Accelerometer ADIS16209, Analog Devices, www.analog.com

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[15] A. El-Fatatry, Inertial Measurement Units – IMU, NATO Research and Technology Organisation (RTO) AVT Lecture Series on “MEMS Aerospace Applications”(2002), RTO-EN-AVT-105 [16] High Precision Tri-Axis Inertial Sensor ADIS16350/ADIS16355, Analog Devices, www.analog.com [17] MAG3 - 6 DOF Analog IMU with Triaxial Magnetometer, MEMSSense Company [18] W. H. Ko, Trends and frontiers of MEMS, Sensors and Actuators A 136 (2007) 62–67. [19] M. Ferri, F. Mancarella, J. Yan, J. E.-Y. Lee and A. A. Seshia, J. Zalesky, K. Soga and A. Roncaglia, A. , Design and Prototyping of a MEMS-based Crackmeter for Structural Monitoring, Proceedings of the 15th International conference on solid-state sensors, actuators and Microsystems (2009), 315-318 [20] M. Ferri, F. Mancarella, A. Roncaglia, J. Ransley, J. Yan and A. A. Seshia, Fabrication of DETF sensors in SOI technology with submicron air gaps using a maskless line narrowing technique’, Proceedings of IEEE Sensors (2008), 1131-1134 [21] M. Ferri, F. Mancarella, L. Belsito, A. Roncaglia, J. Yan, A. Seshia, K. Soga, J. Zalesky, Strain sensing on steel surfaces using vacuum packaged MEMS resonators, Eurosensor (2010), (Accepted) [22] J. E-Y. Lee, B. Bahreyni, and A. A. Seshia, An Axial Strain Modulated Double-Ended Tuning Fork Electrometer, Sensors and Actuators, Part A: Physical, 148 (2008), no. 2, 395-400. [23] A. T-H. Lin, J. E-Y. Lee, J. Yan, and A. A. Seshia, Enhanced Transduction Methods for Electrostatically Driven MEMS Resonators, Proceedings of the 15th International conference on solid-state sensors, actuators and microsystems (2009), 561-564 [24] G. Ye, J. Yan, Z. Wong, K. Soga, A. A. Seshia Optimisation of a Piezoelectric System for Energy Harvesting from Traffic Vibrations. In: Proceedings of the IEEE International Ultrasonics Symposium, (IEEE IUS2009) (2009), Sept. 20-23, 2009, Roma, Italy. [25] Z. Wong, J. Yan, K. Soga, A. A. Seshia, A multi-degree-of-freedom electrostatic MEMS power harvester. In: the 9th international workshop on micro and nanotechnology for power generation and energy conversion applications (Power MEMS 2009) (2009), Dec 1-4, 2009, Washington DC, USA, pp. 300-303.

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Integration of IT into Routine Geotechnical Design a

Chungsik YOOa Dept. Civil and Envir. Engrg., Sungkyunkwan University, Suwon, Korea

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Abstract.The use of information technology (IT) in a variety of engineering fields is increasing in order to expedite routine engineering design and analysis procedures. In geotechnical engineering, various types of information technology, such as geographical information system (GIS), artificial intelligence (AI), and numerical simulation, are now being actively used to predict, visualize, and analyze physical parameters. In this paper, the recent development in integration of IT into geotechnical engineering fields are presented with emphasis on the use of GIS in tunnelling risk management. A combined technique that couples the artificial neural network (ANN) and the GIS is then presented. The proposed approach involves the development of ANN(s) using a calibrated finite element model(s) for use as a prediction tool and implementation of the developed ANN(s) into a GIS platform for visualization and analysis of spatial distribution of predicted results. A novel feature of the proposed approach is an ability to expedite a routine geotechnical design process that otherwise require significant time and effort in performing numerical analyses for different design scenarios. Two illustrative examples in which the developed approach was implemented are given; one for an urban tunnelling design project and the other for a soft ground improvement design project. It is shown that the proposed approach can be an efficient and robust decision making tool for routine geotechnical design works. This paper describes the concept and details of the proposed approach and its implementation to an urban tunnel and a soft ground improvement design projects. Keywords. Information technology, geographical information system, numerical analysis, artificial intelligence, tunneling, soft ground improvement

Introduction The fast-moving world of information technology confronts the civil engineer with constant change. In recent years, the use of information technology (IT) in engineering fields has become increasingly popular for use in prediction, visualization, and analysis of physical parameters. Information technology relevant to civil engineering and/or geotechnical fields may include; • • • • • • •

Numerical methods Computational mechanics Hardware architecture, computer system architecture, and network concepts Software development in engineering Computer-aided decision systems GIS-GPS, remote sensing Data structures and database design

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• • • • • •

Optimization and control Computation Intelligence, soft computing Data mining and knowledge discovery Computer graphics, visualization, virtual reality and image reasoning Risk assessment, failure analysis, and reliability. etc.

Among the above, the use of geographical information system (GIS), artificial intelligence (AI), and virtual reality (VR), among others, in combination with numerical simulation has been increasing in the fields of geotechnical engineering. For example, the AI technique has gained its popularity as a prediction tool as experiences from a number of case histories have shown that the AI technique can be used to identify and determine certain dependencies between variables in various engineering fields. GIS has also been recognized as an efficient tool in a variety of geotechnical fields that require to handle a vast amount of data. A number of case histories have demonstrated successful integration of GIS capabilities in slope stability assessment, risk management in tunnelling, and soft ground improvement design for use as a decision making tool. Virtual reality in combination with other analytical tools is now being used as part of a decision making tool in tunnel design/construction practice. In this paper, a comprehensive review on integration of IT capabilities in geotechnical engineering works is given with emphasis on the ANN and GIS combined technique together with numerical simulation. Two illustrative examples are given, in which the concept and details of the proposed approach and its practical use in typical geotechnical design works are illustrated; one for an urban tunnelling design project and the other for a soft ground improvement design project.

1. Integration of IT in Geotechnical Engineering - Examples

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1.1. Soft computing - artificial intelligence The AI technique has been recognized as an efficient prediction tool in various fields. Among the various artificial intelligence techniques, the artificial neural network (ANN) is increasingly becoming a standard AI technique for solving civil engineering related problems. Artificial neural networks are simplified mathematical models inspired by the biological structure and functioning of the brain. The major advantage of ANN is an ability to learn, recall, and generalize from training data by assigning or adjusting the connection weights (Wkj) (Figure 1).

Feed Forward

DATA

Wkj

W kj

Output Layer Hidden Layer Input Layer ERROR Back Propagation

Figure 1. Typical structure of ANN

Once trained with proper data, an ANN can successfully describe relationships between variables what might otherwise be difficult when using mathematical terms. A summary of geomechanical applications of ANNs can be found in the paper by

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Attoh-Okine (1998). Theoretical background of the ANN is beyond the scope of this paper and can be found elsewhere (Demuth and Beale 1997). A set of generalized ANNs for a given geotechnical design/analysis problem can be integrated and used as an expert system, provided with appropriate graphical user interface (GUI) for input and output. One of the examples is the knowledge-based underground excavation design system (IT-UnEx) that has been recently developed by the author (Yoo at el. 2010). A distinct feature of IT-UnEx is an ability to carry out computationally intensive underground excavation stability analysis using ANNs generalized by the results of finite element and finite difference analyses on various design scenarios encountered in Korean underground construction environments. ITUnEx (Figure 2) has an ability to optimize a given underground excavation design based on the ANNs and the optimization technique in terms of stability. The system is aimed at expediting a routine tunnel design works such as determination of support patterns and stability analysis of selected support patterns. A number of sub-modules for determination of support patterns and stability assessment were developed and implemented to the system. It has been demonstrated that the ANN based tunnel design concept is a robust tool for tunnel design optimization. The details of the system architecture and the ANNs development are given in Yoo et al. (2010).

(a) Q-Design

(b) Stability assessment Figure 2. Illustration of IT-UnEx

1.2. GIS-based decision making system The advent of GIS has opened a window of new technological possibilities for geospatial data storage and analysis. These possibilities have shown much potential in various fields of civil engineering where large amount of geographical data are needed, stored, and manipulated. The geographical data are stored in a GIS system as a geodatabase in which relationships between individual geographic features can be permanently stored. The geodatabase has proven to be a robust structure for cataloging and storing data. The GIS has been proven to be an effective tool for storage, retrieval, and display of data but its analytical capabilities are only occasionally tapped. The GIS, however, can be used as an excellent analysis tool when interfaced with modules that calculate desired quantities. A recent excellent example is the application of GIS in the landslide

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hazard assessment as demonstrated by Xie et al. (2003), in which a GIS grid-based 3D deterministic slope stability analysis model was developed. Xie et al. (2006) later developed a GIS-based slope stability analysis program, 3DSlopeGIS, based on an approach that integrates the GIS grid-based data with four proposed column-based limit equilibrium models and demonstrated that the developed approach has the potential convenience for multicase studies and slope design making (Figure 3). The GIS technology is also well suited in the scope of tunnelling risk assessment where large geographical information needs to be managed. For example, provided with a digital site map and proper mathematical functions for estimating the tunnelling-induced ground movements, the ground movements at specified locations can be computed, stored, retrieved, and displayed. The results can then be used for damage assessment of Figure 3. Concept of Slope stability analysis using GIS buildings/utilities within the area of Grid-Based data (After Xie et al. 2006) influence. Furthermore, by defining spatial distributions of other quantities of interest, once computed, as layers within a GIS platform, the spatial distributions of the impact of tunnelling can be easily visualized. Netzel and Kallberg (1999) may perhaps be the first one in implementing the GIS in the field of tunnelling risk management . They developed a GIS system for the storage, rapid interpretation and visualization of measurement data before, during and after construction activities (Figure 4) of the North/South Metroline in Amsterdam. The structure of the GIS system and the database use unique codes to identify each monitoring sensor, which is registered digitally in the GIS system. The GIS is the important intermediary for settlement risk management with the observational method for TBM-tunnelling (Netzel and Kallberg, 1999).

Figure 4. Examples of GIS system by (after Netzel and Kallberg 1999)

Another similar example of a GIS-based tunnelling risk management system is the one by developed by GEODATA (2010). The system is an integrated Web-based system, named GDMS, for Geodata Data Management System for tunnelling, capable of handling the information coming from difference sources (monitoring, site

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investigation, building surveys, machinery performance, and ground treatments) in the framework of a unified modular system. GDMS differs from the earlier system by Netzel and Kallberg (1999) in such a way that it can collect a broader information relevant to a given tunnelling. The innovative functions introduced with the Web-GIS monitoring application include; (1) collect, georeference, and organize all the geographic information related to the site conditions, (2) collect, georeference, and organize the data flow generated by the construction process (excavation progress, monitoring data, investigation data, etc.), (3) allow the analysis and query of stored data based on predefined itemized geotechnical properties, and (4) review and compare different types of data (monitoring data vs. building condition, baseline geological model vs. actual geological model, etc.). GDMS has been used in a number of urban tunnelling projects, i.e., Porto Metro Project (1999) and Torino metro line 1 (2000), among others (GEODATA 2010). Figure 5 illustrates GDMS

Figure 5. Examples of GDMS (after GEODATA 2010)

The geotechnical group at Sungkyunkwan University led by the author has put a considerable effort in incorporating IT into routine geotechnical design works. As a first attempt, a Web-based tunnelling-induced building/utility damage assessment system (TURISK) was developed and implemented to the Daegu Metro Subway Line 2 construction site in Korea (Yoo and Kim 2003). A novel feature of TURISK is an ability to perform tunnelling-induced building/utility damage assessment on-line (Figure 6). The developed system employs currently available first-order simplified approaches for prediction of ground movements and assessment of risk of damage to

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Figure 6. Examples of TURISK (after Yoo and Kim 2003)

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adjacent buildings/utilities. An on-line engineering computing concept was employed in TURISK so that any authorized user can have access to the system and perform an assessment through the World Wide Web. As a continuing effort put forward by the author, the concept of GIS-ANN coupled approach (Yoo et al. 2002, 2004, 2005; Yoo and Kim 2007, 2009, 2010) has been developed and implemented in the fields of tunnel and soft ground improvement design works (Figure 7). The developed GIS-ANN coupled approach differs from the previous GIS based systems in that generalized ANNs are embedded within a GIS framework and used as computing engines to make relevant predictions for different design scenarios. Such a coupled GIS-ANN approach allows added levels of complexity that could not be accounted for in a conventional approach. More importantly, ANNs interfaced to a GIS platform can be further updated when additional training data become available. This approach can be an alternative to the conventional routine design procedures that involve rather computationally intensive design process. Detailed of the GIS-ANN approach will be presented later in this paper.

Figure 7. Examples of GIS-ANN based system (Yoo et al. 2005)

1.3. Virtual Reality - Visualization Visualization of a vast amount of data is also an essential part of engineering decision making process. A good example of innovative approach is visualization of results of

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numerical simulation and filed measurements in a three-dimensional (3D) space in tunnelling works using the virtual reality technique as demonstrated by Beer (2003). A research group in Graz University of Technology led by Prof. Genot Beer has developed a software, namely Tunnelling Visualization Software (TVS) that can visualize fuzzy geological structures and shear bands or areas of plasticity based on results of numerical simulation and measurements (Figure 8). TVS allows a user to perform a virtual walk through a tunnel during which the user may observe different results of numerical simulations and geological features. Such a VR technique is expected to be more widely used in various geotechnical fields in the near future.

Figure 8. Illustration of TUV (after Beer 2003).

2. Integration of Numerical Simulation-ANN-GIS - a Concept

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2.1. Artificial neural network as a prediction engine - application to tunnel design practice Current tunnel design practice adopts, in a broad sense, a rather empirically oriented approach, especially for drill and blast tunnels. For example, the ground along a proposed alignment is first classified into several general types using the rock mass classification systems such as RMR (Bieniawski 1989) and Q-system (Grimstad and Barton 1993) with due consideration of geologic features. Standard support patterns for the general rock types are then selected as a preliminary design. The preliminary design is then checked for its adequacy in meeting the design requirements in terms of tunnel stability as well as impact on the surrounding environments by analyzing representative sections along the entire route. Numerical analyses, i.e., finite element and/or finite difference analyses, are usually adopted. Modifications to the preliminary design are made if the proposed design does not meet the design requirements. Such a “design and check” process is in fact time consuming due primarily to high computational burden as the number of sections to be analyzed increases or an alternative route or vertical alignment needs to be additionally examined. Such a computational burden can be considerably reduced by adopting generalized ANNs that are capable of providing relevant tunnel stability results compatible to those

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from a numerical analysis for a given design. This can be done by developing site specific ANNs generalized by the results of a calibrated numerical model. The ANNbased approach basically involves: (1) develop a database pertaining to tunneling performance for a particular site using calibrated numerical models, (2) generalize ANNs using the database, and (3) deploy the ANNs to the site for prediction. The main advantage of the proposed approach is that ANNs, properly generalized by the results of a series of numerical analyses appropriate to a given tunneling site, can make tunneling performance related prediction for the entire route with minimal effort, but with comparable degree of accuracy to numerical analyses. Another important advantage is that the developed ANNs can always be updated to obtain better results by presenting new training examples as new data become available. The basic concept of the approach is given in Figure 9 for tunnelling. Such a concept can be easily extended to a number of routine geotechnical design works.

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Figure 9. Concept of GIS-ANN based approach

2.2. GIS-ANN coupled approach When dealing with a design project which requires to cover a number of design sections such as tunnelling and soft ground improvement projects, the design process can be expedited by integrating site specific ANNs into a GIS platform to take advantage of the GIS’s ability of data management and visualization. The GIS platform is basically used as a shell to call upon external modules that perform necessary design calculations for a given project site. For example, for a tunnel design project, spatial information of important features such as tunnel, road, buildings, and utilities are presented to the GIS platform as geo-database. Also presented to the GIS are the information pertaining to the support patterns and the ground conditions including stratigraphy and geotechnical properties at pre-defined transverse sections perpendicular to the tunnel drive, spaced at a given interval, i.e., 5 m for example, along the entire tunnel route. This information is also saved as part of the geo-database and therefore can be easily modified when necessary. All the necessary calculations are done in the GIS environment with the help of the ANNs integrated into the GIS.

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For the ANN prediction of tunneling performance, the information pertaining to the input variables are automatically retrieved from the geo-database. The GIS-ANN approach can in fact be used as a decision support tool for routine tunnel design works (Yoo et al. 2002, 2004; Yoo and Kim 2007). Figure 9 illustrates the concept of the GISANN integrated approach. Details of the GIS-ANN integrated approach can be found elsewhere (Yoo et al. 2004).

3. Implementation of GIS-ANN Combined Approach in Urban Tunnelling Design Project The aforementioned ANN-based approach was applied to an urban high speed railway design project in Korea (Yoo and Kim 2007). A summary of the approach is given in this section. More detailed information can be found in Yoo and Kim (2007)

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3.1. Project definition The high-speed railway tunnel design project involved the design of a 12.2 m diameter tunnel over a length of 5.6 km under heavily populated urban environments. The cross section of the excavation area ranges between 100~120 m2 and the drill and blast method is adopted as the primary excavation method. Figure 10 shows the plan layout of the tunnel. As can be seen in Fig. 3, the tunnel runs across a heavily populated urban area within which a number of old and historic buildings are present. The ground at the site consists of a 5 to 30 m thick layer of miscellaneous fill material including sand, gravel, and silty clay. Underlying the fill layer is a 1 to 50 m thick alluvial deposit followed by a 2 to 20 m thick decomposed granitic soil layer underlain by a 2 to 20 m thick weathered granitic rock layer. Below the weathered granite rock layer is a soft to hard granitic rock layer having a deformation modulus ranging between 8∼12 GPa. The ground along the tunnel route was classified into four general types of Class I to IV based on the RMR classification. The upper two soil layers were designated as Class V and VI. Eight different support patterns PD-1~PD-5 and PDS-5, PDS-5-1, PDS-6, were used. A typical tunnel cross section is given in Figure 11.

Figure. 10. General layout of high-speed railway tunnel

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Figure 11. Schematic view of typical section analyzed

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3.2. ANN development A series of finite element analyses (FEAs) were carried out on a number of design sections, representative of the tunneling conditions at the site, aiming at forming data sets required for training ANNs. A total of 95 sections were carefully selected, covering the aforementioned support patterns as well as the ground conditions. Although the number of sections analyzed appears to be significant, a typical number of sections required for analysis during a tunnel design project with a similar size to the current project easily surpass this number when considering standard tunnel design practice in Korea. A commercial finite element package Abaqus (Abaqus 2006) was used for conducting stress as well as seepage analyses. The stress analyses were conducted to form data sets pertaining to the tunnel and ground deformation, support stresses, and the ground surface settlement, while the seepage analyses were conducted to establish relationships between tunneling and its impact on water inflow and drawdown. Two ANN models were constructed; one for the stress-displacement prediction (ANN-ST) and the other for the groundwater-related prediction (ANN-GW). For the development of the ANNs, a commercial software package MATLAB® (Demuth and Beale 1997) was used to simulate ANN operation. Note that the FEAs on the selected 95 sections basically yielded 95 data sets for the above items for prediction. Figures 12 and 13 show the performance of the ANNs for the training and validation sets. As seen in these figures, excellent correlations between the ANN predictions and the target values are apparent in all output variables, indicating that the predictive capability of the ANN models for the validation sets is consistent with that of the training set. The statistical parameters, although not shown here due to space limitation, also indicate that the developed ANNs successfully generalize the relationships between the input and output variables established by the FEA, and therefore warrant the application of the developed ANNs to the prediction of tunneling performance for the site. Details of the ANN development is given in Yoo and Kim (2007). 3.3. GIS-ANN based prediction The developed ANNs were deployed to evaluate the appropriateness of the initial design. In order to expedite the design check process the developed ANNs were Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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Figure 13. Comparison of computed versus predicted values for training: (a) max. surface settlement; (b) water inflow rate; and (c) max. drawdown

embedded into a geographical information system (GIS) platform to take advantage of the GIS’s ability of data management and visualization. Commercial GIS software, ArcGIS (ESRI 2004), was used as a platform for the proposed procedure. The trained ANNs are embedded in a GIS platform using Visual BasicTM (MS VB.NET) scripts to dynamically incorporate tunneling performance calculations in the GIS environment. 3.3.1 Tunnel stability evaluation The results of the ANN prediction on the tunneling performance prediction are shown in Figures 14 and 15 for the entire route in terms of the tunnel crown settlement and the maximum shotcrete lining compressive stress, respectively. As seen in Figure 14, ANN-ST estimated excessively large crown settlements in four sections toward the northern end, i.e., stations 4km+500 ~ 4km+700, 4km+900 ~ 5km+200, and 5km+600 ~ 5km+900 giving the maximum crown settlements in the range of 80-115 mm, well in excess of the allowable limit of 50 mm. The maximum shotcrete lining compressive stresses in these sections registered 13-22 MPa as shown in Figure 15, also well over the allowable limit of 8.4 MPa, thus suggesting that the tunnel stability cannot be assured with the selected support patterns in these regions. Note that the tunnel in these regions runs through predominantly weak soil layers of Class V or VI with cover depths less than 20 m.

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3.3.2 Tunneling impact assessment Figure 16(a) presents the results of ground surface settlement prediction in the form of assessment map provided by ArcGIS Spatial Analyst, an extension module for visualization. Note that the settlement maps are in essence the contour plots of the transverse surface troughs for pre-defined transverse sections spaced at an interval of 5 m obtained by ANN-ST together with the error function [Eq. (1)] approach proposed by Peck (1969) and O’Reilly and New (1982). V ⎛ y2 ⎞ S v = s exp⎜⎜ − 2 ⎟⎟ (1) 2πi ⎝ 2i ⎠ The required parameters, the volume of settlement trough per unit distance of tunnel advance, V s , or the maximum settlement, Sv ,max , as well as i, the standard deviation of

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the fitted error function for a given tunneling condition, are predicted by ANN-ST. The assessment maps for the water inflow rate and the drawdown level are presented in Figures 16(b) and 16(c), respectively. As noted, the regions 5km+370 ~ 6km+900 were identified as being at high risk of excessive water inflow over 4 l / min/ 100m . The drawdown levels in stations 4km+940 ~ 5km+240 also registered significant values, as great as 25 m, suggesting that additional ground settlements may occur as a result of the reduction in pore pressures associated with the drawdown of groundwater. In fact, these areas are characterized by relatively high groundwater table, approximately 5 m below the ground surface, with a thick layer of alluvium having a hydraulic conductivity on the order of 10-4 m/s.

Figure 14. ANN prediction of crown settlement

Figure 15. ANN prediction of shotcrete lining stress

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The ANN predictions identified the regions required to modify the preliminary support patterns to assure the tunnel stability. The support patterns in the four sections toward the northern end, i.e., stations 4km+500 ~ 4km+700, 4km+900 ~ 5km+200, and 5km+600 ~ 5km+900, were raised to PDS-5, PDS-5-1, and PDS-6. Another run of ANN predictions on the modified design yielded satisfactory results. The modified design was further confirmed for its adequacy by analyzing the corresponding sections, listed above, using Abaqus. The demonstrated design review process for the entire tunnel route was done in a relatively short period of time with the help of the GIS-ANN integrated approach.

Figure 16. Assessment maps for impact of tunneling

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4. Implementation of GIS-ANN Combined Approach in Soft Ground Improvement Design Project The developed GIS-ANN approach was also implemented to a soft ground improvement project in Gwang-Yang area, located southern part of Korea. The project site covers a reclaimed area covering 1,944,000m2. Figure 17 shows a location plan for the site.

Figure 17. Location of project site

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4.1. Project definition The ground at the project site basically involves a 6 m thick dredged soft soil deposit below which a 20 m thick original soft clay layer exists. The dredged material is silty clay in nature with standard penetration blow count (N) less than 1 while the lower original soft clay layer has N values ranging from 1~13. Below the soft clay layer is a solid decomposed granitic rock layer having N values greater than 50 for 10 cm of penetration. The geotechnical properties of the soil layers are available elsewhere (Yoo et al. 2005). 4.2. Site specific ANNs The same approach adopted in the tunnelling work was employed. Two ANNs were constructed; one for consolidation settlement (ANN-Con) and the other for preloading height (ANN-Pre). Input variables for the ANNs were selected with due consideration typical design input parameters such as thickness of dredged layer, thickness of clay layer, original ground level, and planned ground level. The training data sets required for the ANNs training were obtained based on the results of a parametric study performed using Abaqus on a number of cases representing design cases for the project site.

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The results of the FE analyses were used to create a database describing relationships between the input and output variables. After dividing the data into their subsets, the data were preprocessed before introduced to the ANNs so that all variables receive equal attention during training. This was done by scaling the output variables to values between 0.0 and 1.0 so as to commensurate with the limits of the transfer function (sigmoidal function) used in the output layer. The ANNs were developed using the training data. Figure 18 shows the performance of the ANNs for the training and validation sets. The predictive performance of the optimal ANNs is summarized in Table 1 in terms of three statistical parameters; the coefficient of determination (R2), the RMSE, and the mean absolute error (MAE). As seen in Figure 18, excellent correlations between the ANN predictions and the target values are apparent in all output variables. The statistical parameters presented herein warrant that the developed ANNs are successfully generalized for the relationships between the input and output variables established by the FEA, warranting deployment of the developed ANNs to the project site.

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Figure 18. Performance of developed ANNs

Table 1. Statistical performance of developed ANNs. Type R2

RMSE

MAE

Training Testing Validation Training Testing Validation Training Testing Validation

Preloading Ht. 0.95 0.93 0.91 0.38 (m) 0.55 (m) 0.47 (m) 0.54 (m) 0.67 (m) 0.65 (m)

Settlement 0.96 0.94 0.98 0.20 (m) 0.19 (m) 0.15 (m) 0.40 (m) 0.41 (m) 0.36 (m)

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4.3. Prediction of optimum preloading height As before, the developed ANNs were fully integrated into ArcGIS, taking advantage of the highly customizable VBA environment as explained earlier. Figures 19 and 20 show the results in a contour form. A novel feature of the GIS-ANN approach is that a designer can make necessary calculations for different design scenarios and geotechnical design parameters. In addition, this approach can also be used during construction to revise initial design when field measurement data become available, thus making the decision process easier.

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Figure 19. Visualization of predicted optimum preloading height

Figure 20. Visualization of predicted consolidation settlement

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5. Conclusions In this paper, a review on the recent development in integration of IT into routine geotechnical design/analysis process is given with emphasis on the GIS application. A technique that combines the artificial neural network (ANN), the numerical analysis, and the GIS is then introduced for use in routine geotechnical design. The proposed approach involves the development of ANNs using a calibrated finite element model(s) for use as a prediction tool and the use of GIS for visualization and analysis of spatial distribution of the predicted results. A novel feature of the proposed approach is an ability to expedite routine geotechnical design processes that otherwise require significant time and effort in performing a series of numerical analyses for different design scenarios. Two illustrative examples are given, in which the concept and details of the proposed approach and its practical use in typical geotechnical design are illustrated; one for an urban tunnelling design project and the other for a soft ground improvement design project. It is demonstrated that ANNs, when generalized using the results of FEA, can make tunneling performance and consolidated related predictions with comparable degrees of accuracy to that of FEA, but with a minimal effort. It is also demonstrated that the integrated GIS-ANN approach can be effectively used as a decision making tool in routine geotechnical design works

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References [1] Abaqus users manual-version 6.7, Hibbitt, Karlsson, and Sorensen, Pawtucket, Providence, R.I. 2006. [2] N.O. Atto-Okine, Artificial Intelligence and Mathematical Methods in Pavement and Geomechanical Systems, Proc. Int. Workshop on Artificial Intelligence and Mathematical Methods in Pavement and Geomechanical Systems, Florida, USA, Balkema, 1998. [3] G. Beer, Numerical simulation in tunnelling, Springer, New York, 2003. [4] Z.T. Bieniawski, Engineering Rock Mass Classifications, Wiley, New York. 1989. [5] Environmental Systems Research Institute (ESRI). Getting to know ArcGIS desktop, 2nd Ed., Redland, Calif. 2004. [6] H. Demuth, M. Beale, Neural Network Toolbox User’s Guide, The Mathworks Inc., Natic, USA, 1997. [7] GEODATA Geoengineering Consultants. Website, 2010. [8] E. Grimstad and N. Barton, Updating of the Q-system for NMT. Proc. International Symposium on Sprayed Concrete, Fagernes, Norway, (1993), 46-66. [9] H. Netzel and F.J. Kallberg, Numerical damage risk assessment studies on masonry structures due to TBM-tunnelling in Amsterdam, Proceedings Geotechnical Aspects on Underground Construction in Soft Ground, Tokyo, Japan, (1999), 235ѝ244. [10] M.P. O'Reilly, B.M. New. Settlements above tunnels in the United Kingdon-their magnitude and prediction. Proc. Tunnelling '82, Inst. Mining & Metallurgy, London. (1982), 173-188. [11] R.B. Peck, Deep excavations and tunnelling in soft ground. State of the Art Report, Proc. 7th Int. Conf. SMFE, Mexico City, State of the Art Volume. (1969), 225-290. [12] M. Xie, T. Esaki, G. Zhou, and Y. Mitani, Geographical Information Systems-Based Three-Dimensional Critical Slope Stability Analysis and Landslide Hazard Assessment, Journal of Geotechnical and Geoenvironmental Engineering, 129 (12), 2003, 1109-1118. [13] M. Xie, T. Esaki, and M. Cai, GIS-BAsed Implementation of Three-Dimensional Limit Equilibrium Approach of Slope Stability, Journal of Geotechnical and Geoenvironmental Engineering, 132 (5), (2006), 656-660. [14] C. Yoo, Y.W. Jeon and B.S. Choi, IT-based tunnelling risk management system (IT-TURISK) – Development and implementation. Tunnelling and Underground Space Technology, 21(2) (2006), 190202. [15] C. Yoo and J.H. Kim, A Web-based tunnelling-induced building/utility damage assessment system: TURISK. Tunnelling and Underground Space Technology, 18(5) (2003), 497-511.

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[16] C. Yoo and J.M. Kim, Tunneling performance prediction using an integrated GIS and neural network. Computers and Geotechnics 34(1),(2007), 19-30. [17] C. Yoo, and J.W. Kim, GIS-ANN based soft ground improvement design-concept and implementation. Proc. New Frontiers in Computational Geotechnics 2010, Zhang, Lin, Yahima (ed.), Pittsburgh, USA, (2010), 145-148. [18] C. Yoo, S.B. Kim, H.Y. Jung, and J.M. Kim, Tunnel Construction Risk Assessment for Seoul-Pusan High-Speed Railway Contract 14-3 Tunnel Design Project, Report to Hyundai Engineering and Construction Co., Ltd., (2004). [19] C. Yoo, S.B. Kim, and H.Y. Jung, GIS-ANN based soft ground improvement design for Gwang-Yang area, Report to ESCO Consultant, (2005). [20] C. Yoo, J.H. Kim, Y.J. Park,, J.H. Yoo, A GIS-based tunneling-induced building/utility damage assessment system-development. Proc. ITA World Tunneling Congress, (2003), Saveur (ed.), Amsterdam, The Netherlands; (2002). , 1079-1087. [21] C. Yoo, K. You, and I.J. Park, Development and Implementation of Knowledge-based Underground Excavation Design System, Int. J. Geo-Engineering, 1(2)(2010), 19-30.

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Integration of surface and subsurface data for civil engineering Robert HACKa Engineering Geology, University of Twente, International Institute for GeoInformation Science and Earth Observation (ITC),Enschede, The Netherlands a

Abstract. Digital geoinformation for the surface and subsurface is virtually never integrated in civil engineering projects. Reasons are that the surface information is gathered by different person’s and that the subsurface information is in different formats from the surface information. Likelihood and uncertainty of subsurface models is not quantifiable. To quantify the likelihood and correctness of the subsurface information the civil engineer would have to have full access to the original data, which is not available due to the none integration of the data. In the last 10 years considerable progress has been made in use of geographic information systems but the progress in the integration of data and the addressing of likelihood of subsurface data is limited. Keywords. Subsurface, surface, geotechnical, engineering, geology, digital data

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Introduction Many years geographic information systems are available for use in geotechnical engineering and engineering geology; at first two-dimensional and now also threedimensional systems. However still geoinformation systems to model the subsurface are used sparsely in civil engineering projects and then mainly in large projects only. On the other hand CAD/CAM systems are already for year’s standard practice in civil engineering. What are the reasons for this seemingly strange discrepancy? The author considers the lack of integrated systems for handling surface and subsurface data and the lack of a proper handling of uncertainties in the subsurface as major bottlenecks for a full integration of subsurface modeling in civil engineering.

1. Digital versus traditional handling of data for geotechnical engineering and engineering geology The first introduction of the use of digital data and the work with digital data to make geological and geotechnical models of the underground is quite some time ago. At the introduction the general feeling was that these tools would largely facilitate the work of an engineering geologist and improve the results of engineering geological and geotechnical modeling. However, digital interpretation and the use of digital modeling techniques did not yet make a breakthrough in engineering geology or geotechnical engineering. The use is fairly limited and, if used, often confined to only visualize the results of the modeling. The benefits of a good presentation and visualization of data

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and underground models should not be underestimated, but is only one of the aspects which were expected to be beneficial at the introduction of digital data and computers. The reasons may be many, but a careful inspection of the way of working with geological and geotechnical data in engineering geology reveals a major flaw in the approach towards data in engineering geology. In the old times the traditional handmade geological model, interpretations and interpolations camouflaged this flaw. Before the digital times nobody in engineering geology had much interest in the accuracy of data interpolation and interpretation. Data was interpreted to the best knowledge of the engineering geologist or geotechnical engineer taking into consideration the geological environment and the available data (often a limited quantity). It was clear that the interpretation could not be quantified and nobody asked for accuracy. Strange enough, accuracy of field or laboratory measurements has always been regarded as important. Also nobody had much interest in the accuracy of geological maps. The consequence for the average site investigation in engineering geology was that the likelihood of the geological and geotechnical models of the underground to be correct was largely unknown. In the digital times data interpolation and interpretation are regarded as of major importance, and, consequently, the accuracy of geological maps and geological models become major topics. One way to solve the accuracy problem of the geological and geotechnical model is to increase the quantity of data to such a level that the no expertise of a geologist is required (just interpolation). The consequence is an increase in work and thus costs. The higher costs could be justified if it would lead to better results. This seems, however, not to be the case. Site investigations seem not to become a lot better if made with interpolation only, even not with large quantities of data. If no large quantities of data are used, but a geological interpretation, a disastrous and unexpected site effect is often observed. Everybody, including the clients, in general civil engineers, seems to find it necessary to ask questions about the accuracy of the model. It seems that because a computer is used the accuracy of the model has to be known. In most cases (according to the author: in virtual all cases) the answer can only be: "it looks good, but for the accuracy: no idea". This confirms then the existing ideas about geo-fantasy (and geologists in general). Hence, digital modeling in engineering geology leads then to more work, more costs, or, we show that geological models cannot be justified mathematically, and have to admit that the model largely depends on expertise only. Is 3D modeling totally useless in engineering geology? It is the opinion of the author that 3D modeling techniques can only find a place in engineering geology if they are considerably better than the traditional hand interpretations. Then it would result in better site investigations and would lead to cost reduction for the total project. As shown above the weakest point in engineering geology and geotechnical engineering is the geological model. The geological model is made based on the expert opinion of mostly one single geologist (or engineering geologist or geotechnical engineer). There is no mathematical justification for the model and the accuracy of the model is unknown. An improvement would be if it would be possible to use during the making of the geological model the expertise of more experts; i.e. if more than one geologist could be involved in making a model. Obviously for the average site investigation this would be far too expensive. An alternative may be the use of expert systems and knowledge bases. The knowledge base may include tools that facilitate the interpretation, but especially, should include geological standard models that in a particular geological environment can be fitted to a given set of data. If such a database could be developed by a large team of experts the database would get the status of a

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reference standard. Apart from reducing the influence of a single geologist, it would also (at least partly) rebut the criticism of non-geologists on accuracy of the model (the geo-fantasy) because the geological model can be referenced to the standard model in the standard database.

2. Two- versus three-dimensional modeling In many projects a full tree-dimensional system is used for the engineering part of the project, for example, Bentley [1] and Autocad [2], while for the surface or subsurface geology and geotechnical details a two-dimensional or so-called 2.5-dimensional geographic information system is used. A Two-Dimensional Geographic Information System (2D-GIS) uses a two-dimensional database. Therefore, it cannot be used for the construction of an underground model, which by definition is three-dimensional. In some systems it is possible to model one of the properties as third (mostly the depth or “z”) coordinate. This sort of systems is often called two and a half dimensional system (2.5D). A 2.5D-GIS also has a two-dimensional database, with the exception that it allows pseudo-3D viewing of surfaces. The space between the surfaces, however, is not described in the database. Hence, this space is by definition considered homogeneous, an assumption seldom true. The use of the z-coordinate as property limits also the use of the GIS to relative simple geological situations as it is not possible to have the same boundary (e.g. multiple z-coordinate values) at the same x, y coordinate. 3D-GIS use a fully three-dimensional database, in which every point in space is described. Therefore, the use of 3D-GIS has an essential added value over 2- or 2.5D-GIS.

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3. Accuracy and uncertainty Accuracy of geological and geotechnical data can thus not be quantified but worse also uncertainty of models cannot be quantified. This implies that to be able to judge the likelihood that a model is “good” all data has to be available that is used to make the model. Hence this not only means the data as measured from boreholes, penetration tests or samples has to be available but also all data that has been used to make the model.

4. Example of a full three-dimensional subsurface model An Intelligent Decision Support System (IDSS) for soft soil shield tunneling is presented as example [3, 4]. The IDSS development involves a integration of 3D modeling, visualization, and artificial intelligence technology for decision-making in tunneling projects. Modeling and geotechnical characterization of soil volumes is critical for the choice of the type of Tunnel Boring Machine (TBM) and to forecast the TBM performance. The modeling of the subsurface was done interactively based on available geological knowledge of the geological environment and boreholes and Cone Penetration Test (CPT). The area is located in the Netherlands near Rotterdam. The subsurface consists of lenses and interbedding layers of sand, clay and peat in a deltaic

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environment. The 2nd Heinenoord Tunnel was built between 1996 and 1999. It was the first tunnel in the Netherlands to be made by a TBM. The location of the tunnel site is shown in figure 1. The data flow in the project is shown in figure 2.

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Figure 1. Map showing the location of the Heinenoord Tunnel along the Oude Maas river in the southwestern part of the Netherlands [4].

Various types of data sources were available: geological maps from the Dutch Geological Survey, borehole data, cone penetration tests (CPT), interpreted geotechnical profiles based on CPTs, and seismic data. To be able to make a correct three-dimensional model the various data can all be visualized simultaneously on screen and at its correct position in three-dimensions (figure 3). The vertical scale in seismic data depends on the velocity of the various layers through which the seismic ray pass, hence the interpretation of seismic data depends on the lithology. Regrettable most 3D-GIS systems for use in mining or geotechnical work are not suited for a varying vertical scale. From the interpreted data sources, three volumetric (litho-) stratigraphic models were built (small, medium, and large scale). The various scales were chosen to identify the influence of the amount of data available for the model (small scale: large amount of data; large scale: small amount of data). Figure 4 shows lithostratigraphic models at various levels of detail. Property distributions were made by statistical distributions based on CPT-logs (e.g. cone resistance, sleeve friction, friction ratio, water pressure) and sample values. Figure 5 shows horizontal (XY-plane) and vertical (Z-direction) variograms of the cone resistance which was recorded by CPTs. The variogram indicates the amount of correlation that exists between pairs of measured cone resistances in space. The correlation (vertical axis) diminishes with distance (horizontal axis); 0 means perfect correlation (Figure 5).

5. Finite element (FE) modeling The volumetric model was transferred into a finite element modeling package. Typical soil strength values were assigned to the various layers. Stresses on the tunnel,

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Figure 2. Flow chart showing research methodology [4].

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Figure 3. Interactive visualization and modeling process [4].

induced by the (fictitious) construction of a building on the surface above the tunnel, were then calculated. The vertical displacement caused by the loading is shown in Figure 6. The model results were then exported back into the 3D-GIS. These kinds of calculations help to identify potential areas of high risk due to surface loading.

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Figure 4. 3D models of litho-stratigraphy at various levels of detail. The incision in the middle of model is the Oude Maas river (after [4]).

6. Results of the example study The project was done already some 10 years ago and therefore not every conclusion from the project holds for the geotechnical modeling today. However some conclusions from that time still apply today: 1) Format for the data sources varies widely. Conversions programs may be available to convert one format into another. Problem with these conversion programs is that it is often very difficult to check whether the conversion is correct and complete. In the experience of the author often some of the original information is not converted and this may go unnoted without a careful check. 2) Uncertainty or likelihood of the correctness of the model cannot be established. 3) Transfer from data from the GIS to for example three—dimensional finite element programs is very complicated and cumbersome, and sometimes simply impossible or only possible with major simplifications. For the return; e.g. transfer of the results to the GIS program, applies the same.

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Figure 5. Top: 3D grid model with simulated cone resistances. High cone resistances are indicated in orange, red and white (gravel and sand), low ones in grey and blue (peat; clay and silt). Five CPT con resistance logs are shown in black. The cell dimensions are 25×25×1 meter and the model dimensions are 255×1250×55 meter.; bottom: horizontal and vertical variograms of cone resistance (after [4]).

Figure 6. Vertical displacement caused by applying a fictitious load on the surface above the tunnel (after [4]).

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4) Varying scales depending on the interpretation such as velocity scales in seimics or resistivity scales in resistivity surveys cannot be handled in GIS systems and can only be interpreted in specialized geophysical programs. This would be no problem if the geophysical programs would allow for a proper integration of the boreholes and other geological and geotechnical data. Regrettable this is mostly not the case.

7. Present day situation 7.1. Visualization

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Visualization of subsurface models has improved considerable. Even to the extent that the subsurface model can be projected onto the real world while walking or moving through an area. A pair of goggles allows for the projection of subsurface details onto the visual real world on surface (figure 7). Three-dimensional visualization in the office is still a bit cumbersome, but possible even with little costs. The gaming industry and adventure movies (for example, the movie “Avatar”) filmed and to be viewed in threedimensions have caused an enormous boost in the development of cheap threedimensional visualization tools for home or office use.

Figure 7. User with a tracked handheld client (right) sees the real world augmented with a semantic 3D model of underground infrastructure (left) (after [5])

7.2. Various formats Formats of digital data are still varying however some major steps have been made to improve this. Various projects in the world are on steam to set formats for digital data handling such as the Joint Technical Committee 2 (JCT2) of the Federation of Geotechnical Societies [6, 7], and many other groups in the world [8, 9, 10, 11]. Another important development is the use of various internet standards such as XML for the definition of the GeoScience Markup Language (GeoSciML) [12], and the

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meta-standard for geotechnical data, the Geotechnical Markup Language (GeotechXML) [13] with a subset for slopes (SlopeSML) [14].

8. Integration of surface and subsurface data Integration of surface and subsurface data is in the first steps of being integrated. The Geo Building Information Modelling (GeoBIM) is a subset of CityGML [15], which among others integrates surface and subsurface infrastructure data (figure 8) [16].

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Figure 8. Building pit excavation in an area previously covered with buildings. Small infrastructure, geology and boreholes are indicated (after: [16])

However, GeoBim is not a complete set of integrated formats to handle surface and subsurface data. The requirement of integration of subsurface and surface data for use in civil engineering is already formulated in the 90’s of the last century [17, 18]. More recent articles [19, 20] have emphasised the point, but progress has been limited. Integration of surface and subsurface data not only is easy when planning or designing surface or subsurface structures, but is necessary to make risk assessment more transparent. Risk assessment, and thus assessment of the likelihood of the correctness of the subsurface model, for civil engineering structures becomes more and more important and required, accelerated by a series of disasters with underground excavations [21, 22, 23]. Another project working on the integration of surface and subsurface data is by the Delft and Twente Universities in The Netherlands [24, 25, 26]. A complete model for (semantic) model for data handling has been made and various tools to convert data from various formats (Figure 9).

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Figure 9. UML diagram indicating the required thematic semantic information for the description of subsurface geological and geotechnical objects, to be collected by means of site investigation, field measurements and laboratory tests of geological objects (after [27]).

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Recently uncertainty quantification has partially been solved by Clarke [28] with the “generic confidence evaluation scheme”. For each data point, a confidence value (in the range from 1 to 10) is assigned. The boundary that is defined with this data can then be attributed a confidence value based on the interpolation of the confidence values of the data points. Figure 10 gives an example.

Figure 10. a) Grid displaying confidence data in the Thames Gateway region between Woolwich and Gravesend for the boundary of the Chalk Group. Color variation from blue to red indicates a change from low to high confidence in figure b. The red rectangle in a) gives the approximate location (free after [29]).

9. Conclusions

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In recent years large progress has been made on three-dimensional Geographic information programs. The handling and operation of the programs is far friendlier and the visualization is many times better and cheaper than 10 years ago. However, the comparison between a project done 10 years ago and the practice today shows that relatively little progress has been made in integration of surface and subsurface data. Exchange of data may still lead to unwanted effects and data may still be lost or not transferred completely. The problem of quantifying of uncertainty of subsurface models is even less solved. First attempts to qualify or quantify data uncertainty are on its way but are not yet applicable in every geological environment.

References [1] Bentley. http://www.bentley.com/nl-NL/ . [2] AutoCad/Autodesk, 2010. http://usa.autodesk.com/ . [3] S. Ozmutlu, H.R.G.K. Hack, C. de Rooij, R. van der. Putten, 3D Modelling aspects of soft ground for tunnelling with TBM. Proc. 3D Modelling of Natural Objects: A Challenge for the 2000’s. 4-5 June. Nancy, France (1998) 10. [4] J.G. Veldkamp, H.R.G.K. Hack, S. Ozmutlu, M.A.N. Hendriks & R. Kronieger & Van Deen J.K., Combination of 3D-GIS and FEM modelling of the 2nd Heinenoord Tunnel, the Netherlands Proc. Int. Symp. Engineering geological problems of urban areas, EngGeolCity-2001, (2001), Ekaterinburg, Russia (article) [5] G. Schall, & D. Schmalstieg, Interactive Urban Models generated from Context-Preserving Transcoding of Real-Wold Data. Proc. 5th Int. Conf. on GIScience (GISCIENCE 2008), abstracts volume, Park City, Utah, USA (2008), 23-26. [6] D.G. Toll, Geo-Engineering Data: Representation and Standardisation, Electronic Journal of Geotechnical Engineering, (2007), http://www.ejge.com/2007/Ppr0699/Ppr0699.htm. [7] JTC2 Joint Technical Committee number 2 of the Federation of Geotechnical Societies. http://www.dur.ac.uk/geo-engineering/jtc2, (2010). [8] AGS AGS data format. Association of Geotechnical and Geoenvironmental Specialists (AGS), UK, (2009), http://www.ags.org.uk/site/datatransfer/intro.cfm .

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[9] Y.S. Chang, & H.D. Park, Development of a web-based Geographic Information System for the management of borehole and geological data. Computers & Geosciences 30 (8)(2004), 887-897. [10] Y.Choi, S.Y. Yoon, & H.D. Park, Tunneling Analyst: A 3D GIS extension for rock mass classification and fault zone analysis in tunnelling. Computers & Geosciences. 35 (6) (2009)ˈ 1322-1333 [11] GEF Geotechnical Exchange Format. http://www.geffiles.nl/, (2007) [12] GeoSciML,(https://www.seegrid.csiro.au/twiki/bin/viewfile/CGIModel/Geologic Feature?rev=2;filename=GeoSciML_mapped_feature_10_2007.gif ) 2010 [13] Geotech-XML, 2010 [14] SlopeSML, SlopeSML (Slope Stability Mark-up Language) http://www.ins.itu.edu.tr/bulent/slopesml/2010. [15] CityGML,: CityGML – Exchange and Storage of Virtual 3D City Models, http://www.citygml.org/1522/, (accessed February, 5th 2010). [16] F. Zobl, & R. Marschallinger, Subsurface GeoBuilding Information Modelling GeoBIM, GEOinformatics, 11(8), December 2008, 40-43. [17] P.G. Fookes, Geology for Engineers: the Geological Model, Prediction and Performance. The First Glossop Lecture. Quarterly Journal of Engineering Geology and Hydrogeology. 30 (4)( 1997), 293-424 [18] H. R. G. K. Hack, Digital data for engineering geology: disaster or benefit? European Science Foundation Conference: Virtual environments for the Geosciences: Space–time modelling of bounded natural domains. Rolduc, The Netherlands. World Wide Web Address: http://www.xs4all.nl/~hack/WORKHack/esf/esf_1997/abstracts [19] R. Hack, B. Orlic, S. Ozmutlu, S. Zhu, & N. Rengers, Three and more dimensional modelling in geoengineering. Bulletin of Engineering Geology and the Environment 65(2)( 2006), 143-153. [20] W. Yanbing, W. Lixin, S. Wenzhong, & L. Xiaojuan, 3D Integral Modeling for City Surface & Subsurface. In: Innovations in 3D Geo Information Systems. (2006), 95-105. [21] W. S. Atkins, The risk to third parties from bored tunnelling in soft ground. Research report 453. Health and Safety Executive., Sudbury, UK.(2006),78. [22] G. A. Fenton, & D. V. Griffiths, Risk Assessment in Geotechnical Engineering. Publ. Wiley. ISBN-13: 978-0470178201 480 (2008). [23] M. V. Staveren, Uncertainty and ground conditions : a risk management approach. ISBN: 978-0-75066958-0, Elsevier, Boston, MA. (2006), 332. [24] W. Tegtmeier, Harmonization of geo-information related to the lifecycle of civil engineering objects – including uncertainty and quality of surveyed data and derived real world representations (in preparation). (2010) [25] W. Tegtmeier, R. Hack, S. Zlatanova, and Van Oosterom, P.J.M., The problem of uncertainty integration and geo-information harmonization. In: Coors, V., Rumor, M., Fendel, E. & Zlatanova, S. (eds.), Urban and regional data management (2008),171-184. Taylor&Francis. [26] W. Tegtmeier, Van Oosterom, P.J.M., S. Zlatanova, and H.R.G.K. Hack, Information management in civil engineering infrastructural development : with focus on geological and geotechnical information. In: Proceedings of the ISPRS workshop Vol. XXXVIII-3-4/C3 Comm. III/4, IV/8 and IV/5 : academic track of GeoWeb 2009 conference : Cityscapes, Vancouver Canada, (2009), 27-31. [27] W. Tegtmeier, H.R.G.K. Hack, S. Zlatanova, and Van Oosterom P.J.M. An integrated 3D model including (sub-) surface real world and design information – supporting information management in infrastructural development. (in press). [28] S.M. Clarke, Confidence in geological interpretation. A methodology for evaluating uncertainty in common two and three-dimensional representations of subsurface geology. British Geological Survey Internal Report, IR/04/164. (2004), 29. [29] K.R. Royse, H.K. Rutter, and D.C. Entwisle, Property attribution of 3D geological models in the Thames Gateway, London: new ways of visualising geoscientific information. Bull. of Engineering Geology and the Environment. Publ. Springer Berlin / Heidelberg. ISSN: 1435-9529. Vol 68, (1) / February, (2009), 1-16.

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Study on shield tunnel database on construction data Mitsutaka Sugimoto1 * , Yasushi Arai2, Yoshio Nishida3, Koji Kayukawa4, Wataru Sato5 and Minoru Kuriki6 1

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Dept. of Civil Eng., Nagaoka Univ. of Technology, Nagaoka, Niigata 940-2188, Japan 2 Structure Technology Div., Railway Technical Research Institute, Kokubunji, Tokyo 185-8540, Japan 3 Civil Eng. Technology Promotion Dept., Taisei Corp., Nishi-shinjuku, Shinjyuku-ku Tokyo 163-0606, Japan 4 Tokyo office, Geo-Research Institute, 1-8-4, Yushima, Bunkyo-ku, Tokyo 1130034, Japan 5 Nakanojyo office, Tokyo Electric Power Co., Ltd., Nakanojyo, Gunma 377-0423, Japan 6 River Basin & Urban Infrastructure Div., Geology & Geotechnology Dept., Nippon Koei Co., Ltd., Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan Abstract: Shield tunnelling method has been used since 1920 in Japan. With the innovation of closed type shield technology around 1970’s, the number of shield tunnel constructions in urban area increased rapidly. As a result, more than 4000 shield tunnels have been constructed in Japan and maintenance work for aged shield tunnels has increased recently. To carry out the maintenance work efficiently, not only the maintenance data after the completion of shield tunnel construction, but also the construction data are necessary. Therefore, JSCE established the technical committee on shield tunnel database in 2007. At first, to grasp present status, the TC carried out the questionnaire survey on the database concerned with shield tunnels to the owners and contractors of shield tunnels. This questionnaire survey showed that the maintenance data have been well updated by the owners, but the detail construction data have become lost with time. Therefore, the TC focused on the detail construction data and discussed the methodology to establish the shield tunnel database on construction data. This paper presents the overview of the shield tunnel database, based on the interim report by the TC. Keywords: shield tunnelling, data base, construction data

Introduction Shield tunnelling method has been used since 1920 in Japan. With the innovation of closed type shield technology around 1970’s, the number of urban tunnel cons* corresponding author, email: [email protected]

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Table 1. Number of shield tunnel construction start (STA 2010)

Numver of construction start

Use 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Total Railway 20 6 10 10 17 4 0 5 6 3 81 Road 4 3 5 4 0 2 0 2 3 5 28 Underground reservoir & river 6 9 13 14 8 10 6 14 6 10 96 Waterworks 7 10 8 11 8 9 7 16 6 8 90 Sewerage 90 80 93 84 59 63 54 40 25 22 610 Gas 6 3 2 2 28 2 3 12 4 3 65 multi-service 4 4 6 7 7 5 4 2 3 7 49 Electricity 2 1 1 1 1 1 1 1 1 5 15 Telecommunication 2 0 1 0 0 0 0 0 0 0 3 Others 4 3 8 7 3 3 6 8 3 4 49 Total 145 119 147 140 131 99 81 100 57 67 1086

400 300 200 100 0 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 Fiscal year

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Figure 1. Number of shield tunnel construction start vs. fiscal year (STA 2010)

tructions using shield tunnelling method has increased rapidly. As a result, more than 4000 shield tunnels have been constructed in Japan. The recent status of shield tunnel constructions in Japan is shown in Table 1. (STA 2010). Figure 1 shows the trend of the number of shield tunnel construction start. From these table and figure, the following are found: 1) recently, shield tunnels have been constructed mainly for water treatment, that is, sewerage, reservoir, underground river and water works. And the others are for railway, gas, multi-service, road, etc. in descend order of share; and 2) the number of shield tunnel construction start rapidly decreases from 340 to around 60 in a year. This is because the sewerage pervasion in Mega cities reaches about 98% in 2007 (JSWA 2010), and the master plan of the subway network in Tokyo has been completed in 2008. Since the number of shield tunnel constructions decreases and the 1st baby-boom generation become retired, it is important to hand over the know-how on shield tunnelling to younger generation in Japan. On the other hand, maintenance work for aged shield tunnels has increased recently. As an example, the construction length of sewerage tunnels in Tokyo is shown in Figure 2 (TC on tunnel maintenance 2005). From this figure, the following are found: 1) the total length of sewerage tunnels in 1961 was only 35 km, after that its construction length increases rapidly and has a peak in 1972, recently its construction length becomes constant around 20 km in a year, and its total

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M. Sugimoto et al. / Study on Shield Tunnel Database on Construction Data

Construction length/ year (km)

length becomes about 1500 km in 2002; and 2) after around 1980, most of the sewerage tunnels in Tokyo are constructed by shield tunnelling method, since shield tunnelling method has much advantage from the view point of the less impact to surface traffic, compared with cut and cover method. Furthermore, the sewage tunnels over 50 years operation in Japan was 5 % in 2006, but they will become 14 % in 2016, 42 % in 2026 (MLIT 2008). Therefore, maintenance work for them becomes more important to escape their heavy deterioration. To carry out the maintenance work efficiently, not only the maintenance data after the completion of shield tunnel construction, but also the detail data during construction are necessary.

Total length (km)

Shield tunnel Cut & cover Tunnel Cumulative Length

Year Figure 2. Construction length of sewerage tunnels in Tokyo (TC on tunnel maintenance 2005)

12

Number

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10 8 6 4 2 0 0-0.5

0.5-1

1-2

2-5

5-10

Elapsed years

Figure 3. Number of cave-in in the road vs. elapsed years(Kinki regional development bureau, MLIT 2007)

Furthermore, the influence of shield tunnel construction on the surrounding ground and structures appears several years later after the completion of the shield tunnelling work sometimes. Figure 3 shows the elapsed time when cave-in on the road appears (Kinki regional development bureau, MLIT 2007). From this figure, it is understood that cave-in in the road occurs mainly within half year and 5 years later after the completion of shield tunnelling work. To investigate the

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cause of the influence, it is necessary to examine not only the maintenance data but also the detail construction data. Taking account of the above mentioned, JSCE established the technical committee on shield tunnel database in 2007. At first, to grasp their present status, the TC carried out the questionnaire survey on the database concerned with shield tunnels to the owners and the contractors of shield tunnels. From this questionnaire survey, it was found that the maintenance data are well updated by the owners, but the detail construction data become lost according to time passing. Therefore, the TC focused on the detail construction data and discussed the methodology to establish the shield tunnel database on construction data. Through the shield tunnel database, the following effects can be expected: 1) know-how on shield tunnelling can be passed to younger generation; 2) shield tunnels can be maintained effectively and economically; 3) new technology during the life cycle of shield tunnel can be developed efficiently and the developed technology can be validated using the stocked data; and 4) the durability of shield tunnel can be improved and the life cycle cost can be minimized. This paper presents the overview of the shield tunnel database on construction data, based on the interim report by the above TC (Sugimoto et al. 2008).

1 Shield tunnel database

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1.1 Questionnaire survey The TC carried out the questionnaire survey on the database concerned with shield tunnels to the owners and contractors of shield tunnels to grasp their present status. The results can be summarized as follows: 1) Present status of data: The owners have their own shield tunnel database on maintenance data after the completion of the construction, but detail construction data are not included in the database. Furthermore, the data format and database configuration depend on the owner. 2) As a user of database: The shield tunnel database is expected to provide information to a planed construction project with similar conditions and confirm the structure quality and the performance of the countermeasures. 3) As a provider of data: The comments are concerned with the management of the shield tunnel database mainly, that is, the manpower and technical issue to maintain it, the copyright of the data, its security, the leak of secret know-how, etc.. 4) Existing database: There are some databases on shield tunnels. Here three databases are introduced. Railway technical research institute (RTRI) has collected almost all of railway shield tunnel records from 1960. Shield tunnelling association of Japan (STA) has a shield tunnel database with more than 4000 shield tunnel records, which covers almost all of shield tunnels in Japan after 1983 (STA 2010). Ministry of land, infrastructure, transportation and tourism (MLIT) has a

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Table 2. User type for data open

User type Member Owner Contractor Researcher Permit basis

Definition Members who have an access permit to DB Members who order or maintain tunnels Members who design or construct tunnels Members who research tunnels at an academic sector or a research institute Members who get a permission from the owner

database on underground structures in Mega cities in Japan to use deep underground space. The database covers all of the underground structures for railway, road, communication line, electricity, gas, water works, sewerage, underground river, underground reservoir, well, building foundation, etc., under GL-20 m (MLIT 2010). But these databases do not include shield tunnel construction data. Therefore, the TC focused on the detail construction data. 1.2 Methodology

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1.2.1 Process to establish shield tunnel database Based on the results of the above questionnaire survey, the TC makes a plan to establish the shield tunnel database step by step as follows: Phase 1: As a first step, contractor makes a full data set in DVD, which follows “Manual to make up shield tunnel technical information” and it is shared by the owner and contractor. Through Phase 1, it is expected to unify the data format. Phase 2: As a second step, owner provides the tunnel identification data and the list of the data among a full data set to the administrator of the shield tunnel database. The administrator opens those data to users. Then user selects a shield tunnel, requests its owner to provide the full data set, and gets it from its owner with a certain condition. Phase 3: As a final goal of the shield tunnel database, owner provides a full data set to the administrator of the shield tunnel database. The administrator opens the tunnel identification data and the list of the data among the full data set. Then user selects a shield tunnel, requests the administrator to open the full data set, and get it from the administrator with a certain condition. The difference from Phase 2 is that the administrator deals with full data sets. From the viewpoints of the realization of the shield tunnel database, Phase 2 is considered to be feasible, since owners can control the own full data set by themselves to give permission to use or not. Furthermore, it is considered to be easy to step up from Phase 2 to Phase 3, when owners agree to share the full data sets among shield tunnel engineers. Therefore, the TC focused on Phase 2 at this time. 1.2.2 Measures against demerits To reduce the additional paper work when a user requests a full data set and an owner provides it, the TC proposes the user types as shown in Table 2, and re-

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Tunnel identification data List of data Documents

Design reports Documents on construction Completion reports Construction records

Site monitoring data

Excavation data

Other data Figure 4. Configuration of technical information Tunnel Launching and arriving shaft Lining

Shield Neighboring structures Grouting

Segment Secondary lining Invert Waterproof Corrosion prevention Fireproof Design method Construction loads

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Figure 5. Tunnel identification data

commends owners to set permission to each user type and each data when the full data set is made. Furthermore, the measures for the indicated demerits of the shield tunnel database through the questionnaire survey can be categorized as follows: Type 1: The demerits can be settled by the laws. Then the report by the TC describes the right for data in detail with help of a lawyer. Type 2: The demerits can be settled by the rules of the shield tunnel database, which are proposed by the TC. Type 3: The demerits can be settled by the cooperation among the persons concerned. Then the TC will publicize the merits of the shield tunnel database to owners and contractors through JSCE. 1.3 Contents of shield tunnel database 1.3.1 Policy For an example, an investigation on shield tunnels with a certain purpose collets data, but the collected data are not enough for another investigation sometimes. It means that the huge amount of manpower and time is consumed at each investigation. Furthermore, the required data are missing already sometimes. Therefore, the TC sets the configuration of the data set and the data format, which cover all of the data during construction as much as possible, and plans to make the full da-

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Shield Tunnel DB Site A 01 Tunnel ID data 02 List of data

Tunnel ID DB Site B 01 Tunnel ID data 02 List of data

Site C 01 Tunnel ID data 02 List of data

Site A 01 Tunnel ID data 02 List of data 03 Documents 04 Site monitoring data

Site B 01 Tunnel ID data 02 List of data 03 Documents 04 Site monitoring data

Site C 01 Tunnel ID data 02 List of data 03 Documents 04 Site monitoring data

Figure 6. Shield tunnel DB (Phase 2) Common rules Provider rules

DB managing rules

User rules

Figure 7. Configuration of rules for the shield tunnel DB system

ta set as it is, to save the manpower and time to make it. Moreover, the progress of technology enables the huge amount of data to store in one DVD.

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1.3.2 Contents of shield tunnel database Figure 4 shows the configuration of the technical information in the shield tunnel database. In Figure 4, “Tunnel identification data” are basic data of the shield tunnel as shown in Figure 5, which show the characteristics of the shield tunnel and will be used as an index in the shield tunnel database to select shield tunnels. The data format is specified in “Manual to make up shield tunnel technical information”. “List of data” shows the concrete data items included in the full data set, their file types, and the open level for each user type defined in Table 2. This data format is also specified in the Manual. “Documents” includes the drawings, reports, records, etc. which are used and/or obtained at construction stage. “Site monitoring data” is composed of the measured data during construction, that is, the excavation data concerned with shield behavior and operation and the other data with a special purpose, such as, the measured strain and stress in the segment, the ground movement around the shield, the behavior of the neighboring structures, etc.. Some of the data follow the unified format in the Manual. 1.3.3 Shield tunnel database at Phase 2 The image of the shield tunnel database at Phase 2 is shown in Figure 6. At Phase 2, the administrator of the shield tunnel database maintains “Tunnel identification data database” (Tunnel ID DB), which is composed of the tunnel identification data and the list of the data, and opens it to users.

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1.4 Management of shield tunnel database To manage the shield tunnel database, the TC proposes the rules for providers, users and administrators, as shown in Figure 7, so as to minimize the demerits and maximize the merits of the shield tunnel database, taking account of the results of the questionnaire survey. Those rules describe the right, obligation, responsibility, security, procedure, etc. for each in detail.

2 Conclusions To establish the shield tunnel database, the TC carried out the questionnaire survey and proposes the process to make up the shield tunnel database, its contents, and its management rules. The TC has a plan to provide a short course to shield tunnel engineers and expects to realize the shield tunnel database near future.

Acknowledgements

The authors greatly appreciate the members of the TC on shield tunnel database supported by the Japanese Society of Civil Engineering, who contribute to the report on shield tunnel database.

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References [1]JSWA (Japan sewage works association) Sewage pervasion (2010). http://www.jswa.jp/05_arekore/07_fukyu/index.html. Accessed 1 May 2010 (in Japanese) [2]Kinki regional development bureau, MLIT Approval conditions on land possession by sh-ield tunnel. Kinki regional development bureau, MLIT, Oosaka, Japan, (2007) (in Japanese) [3]MLIT (Ministry of land, infrastructure, transportation and tourism) White paper on MLIT in Japan. MLIT, Tokyo, Japan, (2008) (in Japanese) [4]MLIT Information system on deep underground use, (2010). http://www.mlit.go.jp/crd/crd_daisei_tk_000004.html. Accessed 1 May 2010 (in Japanese) [5]STA (Shield tunnelling association of Japan) (2010) List of shield tunnel construction records. http://www.shield-method.gr.jp/jiseki/20/20graph.pdf. Accessed 1 May 2010 (in Japanese) [6] M. Sugimoto, Y. Arai, K. Kayukawa and Y. Nishida Interim report of TC on shield tunnel data base. Proc. of tunnel engineering 18 (2008), 367-368 (in Japanese) TC on tunnel maintenance Tunnel maintenance in Japan. JSCE, Tokyo, Japan, (2005) (in Japanese)

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The European project “Technology Innovation in Underground Construction” Overview of IT results a

Gernot BEERa Institute for structural analysis, Graz University of Technology, Austria

Abstract. The European project “Technology Innovation in Underground Construction” is the biggest single research initiative ever undertaken on the topic. Some innovations that resulted from the project and that are associated with some topics of the conference are presented Keywords. Geotechnics, Geology, Database

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Introduction The European integrated project Technology Innovation in Underground Construction was the biggest project on this topic in the world. The total budget for the four-year project, which finished in November 2009 was 25 Million Euro and the consortium consisted of 41 partners from nearly all countries of the European Union. The aim of the project was quite ambitious: To reduce costs and risks in all aspects of underground construction, from the planning and design to the maintenance and repair. A part of the research dealt also with information technology aspects such as making all data of an underground facility easily accessibly, computer assisted planning and design as well as innovations in monitoring and the display of data. In this paper some of the innovations are discussed. Further details can be found in the book[1] describing the project results.

1. Underground construction information system The situation at the moment is that data of underground facilities are stored in different formats, at different locations, are sometimes incomplete and not easy to access. In many projects data are not easily accessible (if they can be found at all) or are not always available digitally. If they are available digitally, they are sometimes incompatible for further processing and much effort is required to get them into a suitable format. In addition as soon as the responsible company leaves the project the data may be lost or become unusable. For example, it is difficult to find data collected during tunnel excavation for use in the maintenance phase some years later where it would be useful to know what support has been installed and what geology was encountered during construction. Today, data management is still separate, non-linked and accessible via very different database applications or software.

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One of the aims of the integrated project TUNCONSTRUCT was therefore to try to solve this problem by creating a uniform system, where all the data from planning/design to the maintenance and repair are stored in a unified format and are easily accessible. This is the Underground Construction Information System (UCIS). Further information on this system can be found in a conference presentation[2]. 1.1. Architecture The UCIS relational database management system (UCIS RDBMS, see Figure. 1) is the MS SQL Server, the relational model database server produced by Microsoft. It provides all fundamental data management functionalities such as data store, data backup/restore, security management (user authorization, user rights and roles) and data access management (data exchange protocols and formats, interfaces). On top of this database, dedicated further software applications (referred to as domain specific UCIS applications) are used for data management, analysis, visualization, alarming, reporting and some additional functions, all specifically tailored for underground construction. Domain specific UCIS applications

UCIS simple GUI

Domain specific applications

Web based applications

O/R mapping

Visualisa tion

Data exchange (CSV) Data access (ODBC, OLE-DB…)

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Security (users, roles) Data store (tables, relations, views, indices) Maintenance (Backup/Restore, security management)

UCIS RDBMS

Figure 1. Overview of UCIS relational database management system and UCIS applications

1.2. Design and development The design and development of the relational data model of UCIS as well as of the UCIS-applications have been performed following an advanced rapid-prototyping process. This process allows to release new software versions as soon as a portion of the data model is finished. Furthermore, it permits early testing and bug-fixing.

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Taking into account two major use cases and technical aspects such as data storage and transfer limitations, UCIS has been divided into two modules, the UCIS particular module and the UCIS general module. The roles of the modules are: -

-

The UCIS particular module is designed to manage a very large amount of data of a single underground construction project. The data in this module has a high level of granularity and is mainly unreduced and and non-processed data. The UCIS particular module allows an expert (e.g. a geotechnical engineer) to get very detailed information for answering specific, projectoriented questions. The UCIS general module is designed to manage data of a high number of underground construction projects. The data in this module have a lower granularity and may contain preprocessed/corrected/averaged raw data. The UCIS general module allows a user to obtain and compare information from a number of projects for example to see their current construction progress status. In addition, this module can be used to optimize tunnel design.

1.3. Applications

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KRONOS - tunnel information system The most prominent domain specific UCIS application is the tunnel information system KRONOS of Geodata (see Figure 2). It is a front end for the UCIS particular module for managing data from design and construction phase. The application is a Windows client that is locally installed and allows – in a user friendly way - to perform all typical operations such as data input/import, output/export, editing, visualisation, etc. It provides a graphical user interface similar to GIS-software and also offers automatic services and functions for reporting, alarming and data collection. The application has remote access (e.g. via VPN-connection) to one or more database servers hosting UCIS particular module databases. A further UCIS front end, mainly designed for querying and reporting monitoring data and configuring an alarming service, is the web-based application KRONOS-WEB. It offers authorized users the possibility to connect to a UCIS particular module database via internet, to select monitoring objects and sensors and to view and download their actual monitoring data. The data is provided in form of reports that are produced online by a reporting service. The reports are provided in diverse file formats (e.g. html, pdf, xls, etc.). Users only need an internet browser to access KRONOSWEB. The system supports all sensor types and corresponding sensor data typically found in tunnel monitoring projects such as total stations, levels, inclinometers, extensometers, crack meters, load cells, pressure cells, tilt meters, sliding micrometers, strain gauges, etc. Furthermore, it allows the configuration of an alarming service that automatically and permanently checks all monitoring data inside the UCIS particular module database against alarm check rules. The rules can be defined and also stored in the database. The alarms are automatically sent via SMS and/or e-mail to alarm notification targets (= persons receiving alarms). The alarm check may execute standard criteria (e.g. checking data continuously with respect to thresholds and limits such as for an allowed maximum settlement) as

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well as the detection of missing readings for a damaged sensor. The system has been successfully installed and site-tested.

Figure 2. Kronos - tunnel information system: user interface showing performance data of an onlineconnected TBM

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1.4. UCIS general module For the UCIS general Module a WEB application has been produced that allows visualizing and administrating the database that integrates the relevant information of a number of different tunnels. For that purpose, a SQL SERVER 2008 database has been implemented. This database contains both tunnel descriptive information and monitoring information. Figure 3 shows a screenshot of the system.

2. Integrated optimization platform (IOPT) A main objective was the development of a decision support system for the best design of underground structures. Geologic data, expert knowledge, standards and numerical simulations are integrated into a single versatile software system by applying state-ofthe-art techniques of computational engineering. To this end, a set of sophisticated software modules has been developed and provided to the user by means of an integrated software system. Hereby, special emphasis has been put on the formalization and application of expert knowledge.

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2.1. Design method For a computer-oriented formalization of the different tasks in the design of underground constructions, a structured design method has been established. This tunnel design method reflects the current state-of-the-art and divides the overall process into a set of partial tasks, which can be performed independently of each other. It comprises the following tasks: 1. 2. 3. 4. 5. 6.

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7. 8. 9.

Search and selection of a feasible tunnel alignment. Ground characterization and division into ground types along the tunnel alignment. Assessment of the virgin stress state. Determination of the ground behavior, based on the ground type and the virgin stress level. Assignment of the boundary conditions and requirements. Determination of suitable excavation methods and the required support measures. Calculation of the system behavior. Evaluation of the system behavior for fulfillment of the requirements. Calculation of costs and comparison of the variants.

Figure 3. Screenshot of UCIS general module

Based on the analysis of the possible degree of automation, the computer-oriented concept for the design of underground construction has been refined with regard to the

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automation of the individual design steps. Here the automation of the individual steps is achieved through the integration of expert knowledge (rule base), simulation software for advanced numerical analyses (simulation) or models (time/ cost models) providing information about construction-related features/properties. The rule base for tunnel pre-design allows an automatic determination of the ground behavior and the identification of feasible excavation methods in combination with compatible support measures based on the ground conditions. Although the two steps, namely the selection of a tunnel alignment and the evaluation of the system behavior, have to be performed manually (by the engineer/designer), the rule base ensures that the “starting point” is close enough to the final design. Moreover, it also contains a time and cost model, which differentiates between material, equipment and personnel costs which is applicable internationally. Due to the relative simplicity of the entire system, time and costs can be calculated in near real-time, allowing interactive comparison of different technically feasible methods and combinations. The first set of rules covers an automatic determination of the ground behavior type. For every tunnel segment (of arbitrary length) the ground properties and other influencing factors are compiled. Simple calculations, based on closed-form solutions, are performed giving values such as depth of failure or day lighting safety factor. These are systematically checked against ground behavior types[3].

Figure 4. Time and cost estimation flowchart

The time and costs model for conventional tunneling is based on the flow chart depicted in Figure. 4 and requires the following parameters: x Uniaxial Compressive Strength (UCS) – indirectly indicates the “cutability” of rock and the associated estimated performance of the excavation equipment

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x x

Support Class – directly influences the amount of installed support meassures and the time required to install it Tunnel diameter – depicts the geometrical constraints posed by the construction logistics.

The cost estimate is split into personnel, material and equipment costs and overheads. Since the wage levels are dependent on the region, the personnel costs are excluded and have to be calculated by multiplying the user-defined wage factor with the time effort. This ensures that the applied cost estimate is internationally applicable. In order to allow for an easy software implementation, all costs are converted into “price tags” for slices of 1 meter tunnel length. The time and cost estimation scheme in case of TBM tunneling is basically similar, with the following essential points to be taken into account: x Capital cost of machine x lining + grouting of ring joints x personnel costs 2.2. Numerical modeling

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For the numerical modeling new software has been developed that offers a significant increase in user friendliness, efficiency and quality of the results over existing commercial software. Two types of programs were developed: EKATE for the simulation of TBM excavation mainly in soft ground and BEFE++ for the conventional excavation, mainly in rock. EKATE allows a more accurate simulation of all the processes in TBM excavation than existing software and also can simulate the flow of water and air in the ground. Figure 5 shows the modeling details.

Figure 5. Left: Schematic illustration of a TBM: (1) surrounding soil, (2) tail gap, (3) pressurized support medium, (4) cutting wheel, (5) shield skin, (6) hydraulic jacks and (7) segmented lining. Right: Modeling of interactions between soil and TBM in the simulation model ekate: (1) heading face support, (2) frictional contact between shield skin and soil and (3) grouting of the tail gap and components of the simulation model: TBM (blue), hydraulic jacks (yellow), lining (green) and grouting mortar (purple).

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BEFE++ allows the three-dimensional simulation of conventional tunnel construction by the New Austrian Tunneling Method. In contrast to all commercially available programs, that use domain methods, a boundary method is used. This means an increase of efficiency and user friendliness by an order of magnitude since only the (excavation) boundary has to discretised (see Figure 6 for a comparison). In addition the quality of the results is increased because the solutions satisfy the conditions of equilibrium and compatibility exactly.

Figure 6. Left: domain model and right: boundary model of a tunnel

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The result file will also be smaller since it includes only values at the boundary. Figure 7 shows an example of the simulation of a sequentially excavated tunnel with ground support

Figure 7: Results of analysis plotted as contours of displacements

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2.3. Implementation Based on an easy-to-use graphical user interface, the IOPT system offers access to specialized software applications for basic and advanced tasks such as the input and management of geologic data, numerical simulation or 3D-visualization. The main benefit and novelty of this approach is the seamless integration of specialized software applications and the coverage of the entire tunnel design process by a single software system. Figure 8 shows an overview of the services offered by the system.

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Figure 8. Services offered by IOPT

One main feature of the IOPT system is a high-level graphical user interface (GUI). This client-side application is the “user’s window” to IOPT. All possible interactions between users and the IOPT system are performed via this application. The GUI is split into two main windows: one window enables the user to input and manage geologic as well as design-related data; the other window allows an interactive tunnel design process. The design window is shown in Figure 9. In the upper area of the application window a longitudinal section of the underground structure is shown. The longitudinal section visualizes geological data on a choice of alignment in a 3-dimensional ground model. Below the longitudinal section, geologic and construction-related data (ground classification, ground behavior types, excavation methods, support measures, etc.) are displayed with reference to the tunnel chainage. Each data item is represented by a section (bar element) which can be interactively moved or edited by the user. In the central area, design results, such as suitable excavation methods and support measures, are displayed to the user. The results are computed on user’s request using the rule base for tunnel pre-design. The applicability of excavation methods is hereby indicated by the color of the cells. For recommended, possible or non-applicable excavation methods, a cell is coloured in green, yellow or orange, respectively. A console at the bottom area of the design window displays the output from the expert system and enables users to check the results.

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Figure 9: Part of the main screen of IOPT

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3. Augmented virtual reality The idea of introducing Virtual Reality (VR), Augmented Reality (AR) or Mixed Reality (MR) techniques and systems in tunneling is visionary. In the tunneling domain, up to now, VR-, AR- and MR-technology is rarely applied. Important economic aspects such as the potential benefit, impact, user acceptance, etc. are not investigated yet although various useful applications can be imagined. VR tutorial systems can be developed for training students and future tunnel engineers in tunnel design and construction (e.g. visualizing realistically what is designed). VR visualization systems can be used to improve the understanding and interpretation of complex geotechnical problems (e.g. the interpretation of tunnel deformation processes), AR systems can be used to visualize invisible information on site (e.g. displaying the geology behind the tunnel face, displaying the designed support), etc. Virtual reality (VR) is a technology, which allows a user to interact with a computersimulated environment, be it a real, or an imagined one. Most current virtual reality environments are primarily visual experiences, displayed either on a computer screen or through special stereoscopic displays. Augmented reality (AR) is a field of computer research, which deals with the combination of real world and computer generated data. At present, most AR research is concerned with the use of live video imagery, which is digitally processed and "augmented" by the addition of computer-generated graphics.

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3.1. Virtual reality system for underground construction The objective of the developed system is to improve the analysis and interpretation of complex underground construction data and processes by providing an integrated and simultaneous look on all relevant data in a virtual tunnel environment. In particular, the improvement is achieved because data is presented much more vivid and realistic than with common data presentation systems and techniques. The system, therefore, creates a stereoscopic 3-d visualization of the tunnel geometry, renders all relevant tunnel data into it and makes possible the group experience of a VR tunnel walk where tunnel data can be animated interactively in time and space. To allow for this, commercially available VR presentation equipment is used. All data needed to setup the VR presentation is retrieved from the UCIS particular module.

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Figure 10. 100 m of false color VR tunnel model generated from laser scanning data

In particular, this database contains the geometry data for creating the VR tunnel model, which can be rendered either from laser scanning data (see Figure 10). If laser-scanning data are available a geometrically correct VR tunnel model can be generated. Further data can be included into the model on demand and animated in time and space such as: x 3-d geological/geotechnical ground model showing underground features like litho-logical units, discontinuities, joints, groundwater levels, etc., x surface/height/terrain model showing the ground surface and above ground features like structures, vegetation, etc. incl. maps, x geological/geotechnical documentation data collected during the construction phase like tunnel face images and geological tunnel face sketches, etc. x construction progress data showing the advance of construction phases like top heading, bench and invert excavation with time, etc. x monitoring data showing monitoring points and sensors and corresponding monitoring results such as displacement vectors, etc.

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x x

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simulation data from design showing predicted displacements, stresses, etc. These can be displayed together with monitoring data to allow a comparison. tunnel related objects like boreholes, anchors, etc.

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Figure 11. Screen shot of VR System showing displacement vectors

Within Tunconstruct first studies have been carried out aiming to apply Augmented Reality (AR) techniques inside the tunnel. The particular objective of a corresponding AR system is to make data visible right where it is located. In such an implementation the engineer is equipped with special AR devices (e.g. a data helmet, (Figure. 12) or a see-through tablet PC (Figure. 13). The user is either optically tracked by an external tracking camera system or, alternatively, can track actively by use of a portable camera. Tracking is performed in real time so that data can be displayed correctly with regard to the momentary position and viewing direction of the user inside the tunnel. The AR system, in principle, can be used for the same tasks than the VR visualization system (e.g. for the interpretation of deformations, support design, training of engineers) but additionally can serve for other tasks. For example, the system can be used by tunnel workers for the visualization of the designed positions of anchors, boreholes, etc. The use of the AR system makes possible a future scenario called AR tunnel walk enabling a single user to walk through the real tunnel exploring tunnel data right on site. The development of an operational system reaches far beyond Tunconstruct. However, a feasibility study and a site test for one major component, the tracking camera system, have been done proving that a successful system development is possible. The application of AR technology in tunneling is estimated a bright future.

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Figure 12. AR tunnel walk of an engineer wearing a semi-transparent head-mounted display (data helmet) with reflecting markers on its top. The engineer can ‘see’ tunnel displacement vectors and a geological tunnel face sketch at correct position. The position and viewing direction of the engineer is captured in real time by use of digital cameras installed backwards in the tunnel and continuously tracking the reflecting markers.

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Figure 13. AR tunnel walk with tablet-PC in see-through configuration showing tunnel wall and overlaid data (e.g. color coded profile lines). The tablet PC consists of a camera actively tracking targets mounted on the tunnel wall.

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4. Summary and outlook In this paper some innovations of the European integrated project TUNCONSTRUCT were presented. Some of them such as UCIS and KRONOS are already in commercial use others such as the augmented reality system are in their first stages of development. More detailed information can be found in the TUNCONSTRUCT book[1] or on the website www.tunconstruct.org .

5. Acknowledgement The work reported here was partially supported by the European Commission. The contributions of TUNCONSTRUCT partners are gratefully acknowledged. In the development of the virtual reality system the competence center VRVIS was involved. References [1] G. Beer (ed.), Technology Innovation in Underground Construction, Taylor & Francis, London, 2010. [2] K. Chmelina, A new information system for underground construction projects, this conference [3] Austrian Society for Geomechanics, Guideline for the geomechanical design of underground structures with conventional excavation, 2008

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Artificial Intelligence and Data Mining

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An Intelligent Rock Mass Classification Method based on Support Vector Machines and the Development of Website for Classification

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a

Wen-lin NIUa,1 and Tian-bin LIa State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection of Chengdu University of Technology, Chengdu 610059, China

Abstract. A new data mining method of Support Vector Machines (SVM) is applied on the classification of rock mass in tunnels. SVM is a novel powerful leaning method that based on Statistical Learning Theory. SVM can solve smallsample learning problems better than neural network. Eight qualitative indexes, such as rock layer thickness, rock mass structure, inlay condition, weathering condition, groundwater characteristic, joint condition, hammer knocking sound and ground stress, are chose as the judge factors. Twenty two data samples from Niba mountain tunnel are used to train the SVM with different kernels, such as linear, quadratic and polynomial kernel. Use the mapping relationship between judge factors and rock mass classes which the SVM provided class-unknown data samples of rock mass can be discriminated. Ten samples of test data are used to test the accuracy of SVM with different kernels. The result of the classification shows that SVM with polynomial kernel have a high accuracy when classify the rock mass. A website that based on ASP.NET and database has been developed to store the geological data from the tunnel, and using these data the background program which based on SVM can calculate the class that the surrounding rocks of the tunnel belong to, and the result can be show on the web page for engineers to use. So this is an intelligent classification of rock mass method that can be applied to classify rock mass in tunnels and is also a kind of information technologies in Geo-Engineering. Keywords. rock mass classification, support vector machines, tunnel, information technology, Geo-Engineering

Introduction In tunnel engineering we should classify rock mass to choose support method. Rock Mass Rating(RMR) and Rock Mass Quality(Q) are widely used in the classification of rock mass in tunnels but these methods need to measure some quantitative or semiquantitative parameters, such as uniaxial compressive strength of rock material(ǻc), Rock Quality Designation(RQD), etc. to calculate the values. However, these methods require too much data measured on site, sometimes affecting construction. This leads us to think, can we use a fast and relatively accurate method for qualitative description 1

Corresponding Author: NIU Wen-lin, Chengdu University of Technology, 1 # east 3rd road Erxian bridge, Chengdu 610069,Sichuan, China; Email: [email protected]. Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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W.-l. Niu and T.-b. Li / Intelligent Rock Mass Classification Method

of the wall rock circumstances, and through them to determine the rock classification? At present, the main method is using artificial neural networks[1], which use quantitative and qualitative data as input parameters to intelligent judge the rock mass classification. But the neural network learning process in the data is easy to fall into local minimum, which affect the discriminate accuracy of the results. Developed in the 20th century, 90s, support vector machines based on statistical learning theory as the theoretical system, by seeking structural risk minimization to achieve the minimum of actual risk, pursuit optimal result under the conditions of the limited information. With the support vector machines theory of development and maturity, support vector machines is beginning to receive more extensive attention. Based on the above considerations, we developed a support vector machine based on qualitative input indicators to intelligence divide the rock mass classification.

1. The Basic Theory of Support Vector Machines for Rock Mass Classification Support Vector Machines (SVM) invented by Vapnik[3, 4] and his collaborators, is a machine learning methods based on Statistical Leaning Theory (SLT), which focuses on research of the statistical laws in small samples and of the nature of learning. Support vector machines can properly handle the issue of classification and regression, so they are widely used in text categorization, image recognition, biological sequence analysis, handwriting character recognition. Support vector machines are developed from optimal separating surface in linearly separable case, and the basic idea can be shown in Figure 1.

R

R

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R  

 

+

R

o

¦ Ø

 +   

R R R

R R

2/||¦ ||Ø

Figure 1. The classify line of SVM

Figure 1 is the diagram of SVM in two-dimensional case. Point "+" and "o" represent the two types of data samples. The solid line is the classification line of these two sets of samples; the dotted lines, which parallel to the solid line, pass through the data and are nearest to the classify line. The distance between the dotted lines is called the classification margin. The data which passed through by the dotted lines are support vectors. The optimal classify line that we need to find is the line that separate the two classes correctly and make the classification margin maximum. In three dimensional

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W.-l. Niu and T.-b. Li / Intelligent Rock Mass Classification Method

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spaces the optimal classify line is the optimal classify plane, and in high dimensional spaces the optimal classify line is the optimal hyperplane. The hyperplane can be show as follows:

ω ⋅x+b = 0

(1)

So we can get the decision functions:

⎛ l ⎞ f ( x) = sgn(ω ⋅ x + b) = sgn ⎜ ∑ α i yi K ( x ⋅ xi ) + b ⎟ ⎝ i =1 ⎠

(2)

In Eq.(2), K(xxxi) is the kernel function. By choosing appropriate kernel function we can convert nonlinear data into linear data to classify. sgn(x) is the sigh function, when ω ⋅ x + b ≥ 0 , f(x)=1; When ω ⋅ x + b ≤ 0 , f(x)=-1. Thus, the hyperplane can be divided into two parts. The distance between the dotted lines in fig.1 is 2/ýȁý. To make this margin maximum, we need to solve the optimization problem of variable ω and b. Thus, for the training sets of known (xi, yi), where i=1,2,̖, l, xiЩRn, yЩ{+1, -1}, solve the optimization problem of variable ω and b:

1 ω 2

min ω, b

2

s.t. yi ((ω ⋅ xi ) + b) ≥ 1, i = 1,L , l

(3)

(4)

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or solving ǩ l 1 l l y y α α K x ⋅ x − αj ( ) ∑∑ i j i j i j ∑ 2 i =1 j =1 j =1

min α

s.t.

l

∑ yα i =1

i

i

=0

α i ≥ 0, i = 1,L , l

(5)

(6)

(7)

Get the optimal solution ǩi*, there: l

ω * = ∑ yiα i* xi i =1

Accordingly calculate b*

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(8)

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W.-l. Niu and T.-b. Li / Intelligent Rock Mass Classification Method l

b* = y j − ∑ yiα i* K ( xi ⋅ x j )

(9)

i =1

Bundles Eqs. (8) and (9) into (2) can get the optimal classification surface. The above algorithm can solve the problem of two-class discrimination. For the problem of multi-class discrimination as rock mass classification, we use the method of “One Versus Rest, OVR”[7, 8] to convert multi-class into two-class to solve. The method of OVR is to construct k (a total of k classes) two-class SVMs. The No. j SVM separates class j from others. The No. j SVM set the j class as positive and the other classes as negative in training. So through k SVMs we can get k outputs from one input signal: f j ( x) = sgn( g j ( x)) , j=1,̖, k l

g j ( x) = ∑ yiα i j K ( x, xi ) + b j

(10) (11)

i =1

If there is only one +1, then the class of input signal is the corresponding class; if there are more than one +1, or no +1, then compare the outputs of g(x), the biggest one is the class of input. 2. The Realization of Intelligent Classification of Rock Mass

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2.1. The Choice of the Classify Indicators Li Tianbin[1] use Rock Quality Designation (RQD), uniaxial compressive strength of rock material (ǻ c), volumetric joint count (Jv) and other quantitative or qualitative indicators as the indicators of intelligent classify. But in practice, it is difficult to get the quantitative indicators such asǻc or Jv. Some even need to be measured by special instrument. Therefore the efficiency of classification is low, and we can’t adjust the support system in time. Therefore, in order to increase the efficiency of rock mass classification, we use the thickness of rock layer, rock mass structure, chimeric condition, joint development condition, weathering condition, groundwater condition, hammer percussion sounds and stress situation as the indicators of classification. Every indicator has some levels as show in table 1. The thickness of rock layer, rock mass structure, chimeric condition and joint development condition represent the integrity of rock mass; weathering condition and hammer percussion sounds represent the rigidity of rock; groundwater condition and stress situation represent other affecting factors. We can easily get these qualitative indicators just by observing the tunnel face and tunnel wall. As we don’t need to measure the quantitative data, so it has little interference to the construction.

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W.-l. Niu and T.-b. Li / Intelligent Rock Mass Classification Method

Table 1. The Qualitative Indicators of Rock Mass and Judge Factors Thickness of Rock Layer Very thick (㧪1m) Thick (0.5~1m) Moderate (0.1~0.5m) Thin (㧨0.1m) ̅

Rock Mass Structure

Ground Joint Hammer Stress Chimeric Weathering Water Development Percussion Situation Condition Condition Condition Condition Sounds

Intact

Very tight Fresh

Block

Tight

Cracked

Mosaic Fragmentary Loose

Dry

Not developed

Very crisp

Normal

Slightly weathered

Damp / wet

Slightly developed

Crisp

High

Friable

Moderately weathered

Seeping / dripping

Moderately developed

Not crisp

Very high

Very friable

Highly weathered

Rain-like

Highly developed

Deep sound

Decomposed Linear Stream gushing

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2.2. The Choice of Kernel Function and the Training and Classification of the Model Linear kernel, quadratic kernel, polynomial kernel, Gaussian radial basis function kernel, multilayer perceptron kernel are most commonly used in SVMs. We use these kernels to train the data samples (table 2) that we get from Yalu highway tunnel in Niba Mountains of Sichuan province. Then we use these SVMs to classify another 10 samples (table 3). We find that Gaussian radial basis function kernel and multilayer perceptron kernel can’t classify these samples correctly. The classification results of SVM with other kernels have been show in Table 4. As shown in table 4, using the SVM classifier with linear kernel to classify the data sets in table 3 we can get an accuracy of 60%, but there are 4 groups of data sets that can’t be classified. The SVM classifier with hard margin linear kernel has an accuracy of 70%, but classified the No.4 and No.5 data sets to wrong groups, and can’t classify the No.2 data sets. The SVM classifier with quadratic kernel has an accuracy of 80%, but cannot classify the No.1 and No.2 data sets of classΥ. The SVM classifier with polynomial kernel can classify all the 10 data sets accurately. 3. The Basic Design of the Classification Website The classification website based on ASP.NET, and combined with SQL database and MATLAB Application Program Interface (API). We use the SQL database to store the wall rock data and classes and MATLAB API to convert the SVM algorithm that build in MATLAB development environment to .NET methods. ASP.NET website is composed by lots of ASP.NET web pages. When visitors enter the address of an ASP.NET website in browser, the browser will send a request to browse the webpage. When receive the request, the Internet Information Server (IIS) will transfer it to ASP.NET website applications. Then the ASP.NET website

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W.-l. Niu and T.-b. Li / Intelligent Rock Mass Classification Method

Table 2. The Training Data Samples of SVM

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Hammer Sample Thickness Rock Mass Chimeric Weathering Ground Joint of Rock Structure Water Development Percussion Stress ID Condition Condition Layer Condition Condition Sounds

Sample Class

1

Very thick

Block

Tight

Moderately Seeping / Not weathered dripping developed

Crisp

2

Very thick

Intact

Tight

Moderately Damp / weathered wet

Not developed

Very crisp High

3

Thick

Block

Very tight

Moderately Damp / weathered wet

Slightly developed

Very crisp Normal Τ

4

Thick

Block

Tight

Slightly weathered

Seeping / Not dripping developed

Very crisp Normal Τ

5

Thick

Block

Tight

Fresh

Seeping / Slightly dripping developed

Crisp

High

6

Thick

Cracked

Very tight

Slightly weathered

Dry

Not developed

Crisp

Normal Υ

7

Thick

Cracked

Very tight

Moderately Seeping / Slightly weathered dripping developed

Very crisp Normal Υ

8

Thick

Mosaic

Tight

Slightly weathered

Dry

Slightly developed

Very crisp Normal Υ

9

Moderate Block

Very tight

Slightly weathered

Dry

Slightly developed

Crisp

10

Moderate Block

Very tight

Moderately Slightly weathered Rain-like developed

Very crisp High

11

Moderate Cracked

Very tight

Slightly weathered

Damp / wet

Slightly developed

Not crisp

Normal Φ

12

Moderate Block

Tight

Slightly weathered

Stream

Slightly developed

Not crisp

Normal Φ

13

Moderate Cracked

Tight

Slightly weathered

Slightly Rain-like developed

Deep sound

Normal Φ

14

Moderate Mosaic

Tight

Moderately weathered Linear

Slightly developed

Very crisp Normal Φ

15

Moderate Cracked

Very tight

Slightly weathered

gushing

Slightly developed

Crisp

Normal Φ

16

Moderate Fragmentary Tight

Highly weathered

Moderately Rain-like developed

Deep sound

Normal Φ

17

Thin

Slightly weathered

Damp / wet

Highly developed

Not crisp

Normal Φ

18

Moderate Fragmentary Tight

Highly weathered

Damp / wet

Moderately developed

Not crisp

High

19

Thin

Highly weathered

Seeping / Highly dripping developed

Deep sound

Normal Χ

20

Moderate Fragmentary Tight

Highly weathered

Linear

Slightly developed

Not crisp

Normal Χ

21

Thin

Fragmentary Friable

Slightly weathered

Linear

Moderately developed

Deep sound

Normal Χ

22

̅

Loose

Deep sound

Normal Χ

Fragmentary Friable

Fragmentary Friable

Very friable

Highly Decomposed Rain-like developed

Normal Τ Τ

Υ

Normal Υ

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Φ

Χ

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W.-l. Niu and T.-b. Li / Intelligent Rock Mass Classification Method

Table 3. Rock Mass Samples and Their Classifications in Engineering Ground Joint Hammer Thickness ConstrucRock Mass Chimeric Weathering Sample Water Development Percussion Stress of Rock tion Class Structure Condition Condition ID Condition Condition Sounds Layer 1

Thick

Block

Tight

Slightly weathered

Slightly developed

2

Thick

Cracked

Very tight

Moderately Seeping / Not weathered dripping developed

Crisp

Normal Υ

3

Thick

Intact

Very tight

Slightly weathered

Damp / wet

Crisp

Normal Τ

4

Moderate Fragmentary Friable

Slightly weathered

Seeping / Slightly dripping developed

5

Moderate Mosaic

Very tight

Moderately Damp / weathered wet

6

Moderate Mosaic

Tight

Highly weathered

Seeping / Moderately Not crisp Normal Φ dripping developed

7

Thin

Fragmentary Tight

Slightly weathered

Seeping / Moderately Crisp dripping developed

Normal Φ

8

Thin

Fragmentary Friable

Highly weathered

Seeping / Highly dripping developed

Deep sound

High

9

Thin

Loose

Very friable

Highly weathered

Linear

Highly developed

Deep sound

Normal Χ

10

Moderate Fragmentary Friable

Dry

Decomposed Stream

Not developed

Not developed

Very crisp High

Υ

Not crisp Normal Φ Crisp

High

Φ

Χ

Moderately Not crisp Normal Χ developed

Table 4. The Classification Result of SVMs with Different Kernels Linear Kernel

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Τ

Υ

Φ

Χ

Linear Kernel with Hard Margin

Quadratic Kernel

Polynomial Kernel

Τ

Τ

Τ

Ĝ

1

Υ

Φ

Χ

Υ

Φ

Χ

Ĝ

Φ

Ĝ Ĝ

Ĝ

Ĝ

Ĝ

4

Ĝ

Ĝ

Ĝ

5

Ĝ

Ĝ

Ĝ

Ĝ

Ĝ

Ĝ

Ĝ

Ĝ

Ĝ

6

Χ

Ĝ

2 3

Υ

Ĝ

7 8

Ĝ

Ĝ

Ĝ

Ĝ

9

Ĝ

Ĝ

Ĝ

Ĝ

10

Ĝ

Ĝ

Ĝ

Ĝ

applications will access the SQL database or call the SVM algorithm to achieve the functions of wall rock information storage and classification. Finally, the results of implementation will response to the visitors’ browsers.

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W.-l. Niu and T.-b. Li / Intelligent Rock Mass Classification Method

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4. Conclusions •

•

• •

We use the SVMs to classify rock mass. Using the data sets from Niba mountain tunnel we have verified this method can satisfy the requirements of the engineering project. So the SVMs have provided a new method for the intelligent rock mass classification. Using the qualitative indicators which we can get easily and quickly, we can classify the rock mass quickly. But there is some subjectivity to obtain the qualitative indicators. Different people maybe describe the same wall rock differently, and lead to a different classification. The kernels of SVMs have a great influence to the results of classification. Comparing several kernels, we have found that the polynomial kernel can satisfy the needs of rock mass classification. We have designed a website that can store information of wall rock and automatically classify rock mass.

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References [1] Tian-bin Li, Rui Wang. The application of ART1 neural network to the classification of surrounding rocks in tunnels. JOURNAL OF CHENGDU UNIVERSITY OF TECHNOLOGY (Science & Technology Edition), 33(5), (2006), 455-459. (in Chinese) [2] Peng Bai, Xibin Zhang, est. Support Vector Machines and its Application in Mixed Gas Infrared Spectrum Analysis. Xi’an: XIDIAN UNIVERSITY PRESS, 2008. (in Chinese) [3] V.N.Vapnik , The Nature of Statistical Learning Theory, N Y: Springer Verlag, 1995. [4] V.N.Vapnik , Statistical Learning Theory. John Wiley & Sons, Inc., 1998 [5] N. Cristianini, Shawe-Taylor J. An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2000 [6] Naiyang Deng, Yingjie Tian A new Data mining Method̅̅Support Vector Mechines. Beijing: Science Press, 2004. (in Chinese) [7] Gou Bo, Huang Xianwu SVM Multi-Class Classification. Journal of Data Acquisition & Processing, 21(3) (2006), 334-339. (in Chinese) [8] Rifkin R, Clautau A. In defense of one-vs-all classification. Journal of Machine Learning Research, (5), (2004), 101-141. [9] Feng Xiating, Diao Xinhong. INTELLIGENT ROCK MECHANICS (1) - INTRODUCTION. Chinese Journal of Rock Mechanics and Engineering, 18 (2),( 1999), 222 - 226.(in Chinese) [10] Feng Xiating, Yang Chengxiang. INTELLIGENT ROCK MECHANICS (2) - INTELLIGENT RECOGNITION OF INPUT PARAMETERS AND CONSTITUTIVE MODELS. Chinese Journal of Rock Mechanics and Engineering, 18 (3) (1999), 350 - 353.(in Chinese) [11] Xiating Feng. INTELLIGENT ROCK MECHANICS (3) - INTELLIGENT ROCK ENGINEERING. Chinese Journal of Rock Mechanics and Engineering, 18 (4) (1999), 475 - 478. (in Chinese)

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[12] Shufen Zheng, Minxiang Zhao. ASP.NET 3.5 The Best Practice ̅ ̅ Using Visual C#. Beijing: Publishing House of electronic industry, 2009. (in Chinese) [13] Suli Wang, Ji Gao e, Dexin Sun. MATLAB Hybrid Programming and Applications. Beijing: Tsinghua University Press, 2008. (in Chinese)

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Information Technology in Geo-Engineering D.G. Toll et al. (Eds.) IOS Press, 2010 © 2010 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-60750-617-1-84

Application of Data Mining Techniques to the Safety Evaluation of Slopes Francisco F. MARTINSa,1 and Tiago F. S. MIRANDA b Department of Civil Engineering, University of Minho, Guimarães, Portugal, [email protected] b Department of Civil Engineering, University of Minho, Guimarães, Portugal, [email protected]

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a

Abstract. In the present paper, Data Mining techniques has been applied to evaluate the stability of slopes. For this propose, the R (www.r-project.org) software was used together with a user defined application developed at the University of Minho called RMiner. The factor of safety (FS) and probability of failure (PF) were computed for 365 homogeneous slopes using the software SLOPE/W with varying geometric parameters (e.g. height, height of the water surface and slope angle) and geotechnical parameters (weight density, cohesion intercept and angle of shearing resistance). Heights between 10 m and 15 m and slope angles between 40º and 70º were considered. This data allowed building a database to be analyzed using the Data Mining techniques. In this process, several algorithms were used for the prediction of FS and PF, such as multiple regression, regression trees, artificial neural networks, support vector machines and k-nearest neighbours. To evaluate the performance of each technique REC curves (Regression Error Characteristic) and several error measures were used. This application allowed developing reliable models to predict important safety parameters for slopes without a classical limit equilibrium calculation carried out. They also allow performing quick parametric studies for the early stages of slope design. To predict FS, the support vector machines showed the best overall performance. In the case of PF, the artificial neural network proved to be more reliable to predict this parameter. By this study, it was also possible to conclude that cohesion intercept was the parameter with more influence on the assessed safety parameters. Keywords. Data Mining, slope stability, factor of safety, probability of failure

Introduction The slope stability analysis is generally performed using software based on limit equilibrium methods, ordinarily known as the slices methods (Fellenius, Bishop, Janbu, etc.) However, software based on Finite Elements Method has also been applied [1] [2]. This paper presents the prediction of the factor of safety and the probability of failure using Data Mining (DM) techniques. Some works have been developed by the first author in this domain [3] [4] [5]. A brief description of the used DM techniques, the global metrics performances and the application of the DM process are presented on the following chapters. 1 Corresponding Author˖Francisco F. Martins, Department of Civil Engineering, University of Minho, Guimarães, Portugal,; [email protected]

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1. Data Mining Techniques

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Data Mining can be defined as the application of methods and techniques in large databases to find trends and patterns with the aim of discover knowledge [6]. The DM techniques used in this paper are the regression trees (RT), the multiple regression (MR), the artificial neural networks (ANN), the support vector machines (SVM) and the k-nearest neighbors. The decision trees [7] are based on a tree structure where at each tree node a test based on attributes is established (Fig. 1a). Each branch descending from a node takes one of the possible values for this attribute. These trees are denoted regression trees when they perform the prediction for the value of a continuous variable. The technique of the k-nearest neighbors bases the prediction for a certain observation on the weight of the characteristics of the k nearest observations [8] (Fig. 1b). The multiple regression is similar to the simple regression. However, it describes the relation among several independent variables and a dependent variable. The artificial neural networks are based on the architecture of human brain. These networks have processing units (nodes) interlinked according to a given configuration such as the Multilayer Perceptron [9] which was used in this paper (Fig. 2a). The basic idea of the support vector machines [10] is to transform the input x∈ ℜΙ into a high m-dimensional feature space, using a non linear mapping. The SVM search the best linear separating hyperplane, related to a set of support vector points, in the feature space (Fig. 2b). The transformation performed in this work is based on the Radial Basis Kernel [11] (Fig. 2b).

Figure 1. a) Regression tree;

b) k-nearest neighbors

Figure 2. a) Example of a multilayer perceptron; b) Example of a SVM transformation [11]

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2. Regression Error Characteristic (REC), Global Metrics and Sensitivity Analysis The performance of different DM models can be compared through the Regression Error Characteristic (REC) curves [12] and the global metrics based on the error. The REC curves provide a measurement of the techniques expected performance plotting the error tolerance on the x-axis versus the percentage of points predicted within the tolerance on the y-axis. The curve with greater area under itself corresponds to the best predictive technique. In this work the following global metrics were used: Mean Absolute Deviation (MAD), Relative Absolute Error (RAE), Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE) and Pearson’s Correlation Coefficient (COR). These metrics are evaluated using the following formulae [11]:

MAD =

RAE =

1 × ∑ N y − yˆ i =1 i i N DAM ∑ iN= 1 y − y i i N

RMSE =

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RRMSE =

×100%

(

) ∑ iN= 1 y i − y i N

)2

RMSE N i =1

(1)

∑ (y i − y i ) 2

(2)

(3)

× 100%

(4)

N

where N denotes the number of considered cases, yi the desired value, yˆ i the predicted value, y i the average of the desired values and yˆ i the average of the predicted values. To quantify the importance of the input variables in the model it is performed a sensitivity analysis. This analysis is applied after the training phase to analyze the model responses when the inputs are changed. The weight of certain input variable is measured varying its value through its full range while the other input variables remain with there mean values [13]. If the considered input variable is very important its variance produced in the model output will be high. Therefore, the input variable that has higher variance in the model output is the most important variable.

3. Application of the Data Mining Process and Obtained Results To perform the DM process it was used the R tool [14]. The R tool is an open source with a set of software that allows manipulating data, performing calculus, drawing graphics and performing statistical analyses.

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Taking advantage of the open access, Cortez [11] developed the RMiner application that facilitates the use of the DM techniques. In the present work the RMiner application was used together with other available software packages. To apply the DM process a database based on the results obtained using the SLOPE/W software was built. In this process several techniques were tested to predict the factor of safety and the probability of failure. With the SLOPE/W software and selecting the Bishop modified method, 365 homogeneous slopes were studied. Therefore, the database has 365 lines, having each one the geometric parameters (height [h], height of groundwater level [hw] and slope angle [α]), the geotechnical parameters (weight density [γ], cohesion intercept [c] and angle of shearing resistance [φ]), the factor of safety [FS] and the probability of failure [PF]. The dataset attributes and range values are presented in Table 1. The complete database is presented in the Appendix. Table 1. The dataset attributes and range values Attribute

h (m)

hw (m)

α (º)

φ (º)

c (kPa)

γ (kN/m3)

Range

[10…15]

[0…15]

[40…70]

[28…37]

[5…50]

[16…21]

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To assess the predictive performance of the DM techniques a 5-fold crossvalidation scheme [15] using 10 runs was used. The average values of the global metrics and the confidence intervals obtained using a t-student statistical analysis with a confidence level of 95% were computed. In relation to the factor of safety, the REC curves are presented in Figure 3 and the global metrics are presented in Table 2.

Figure 3. REC curves related to the FS

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Table 2. Global metrics for the FS MR

ANN

SVM

RT

k-NN

MAD

0.109±0.001

0.067±0.003

0.055±0.000

0.213±0.006

0.148±0.001

RAE(%)

27.264±0.193

16.778±0.731

13.773±0.092

52.987±1.371

37.022±0.370

RMSE

0.153±0.001

0.116±0.003

0.105±0.000

0.270±0.006

0.205±0.002

RRMSE(%)

30.739±0.192

23.287±0.570

21.123±0.099

54.410±1.187

41.163±0.482

COR

0.952±0.001

0.973±0.001

0.978±0.000

0.841±0.007

0.928±0.004

Both the REC curves and the global metrics point out the best performance of the SVM followed by the ANN. In fact, the SVM have lower errors and higher correlation coefficient and area under the REC curve. The relative importance of the input variables for the SVM is presented in Figure 4. 50

Relative Importance (%)

45 40 35 30 25 20 15 10 5 0 h

hw

α

Φ

c

γ

It can be seen that the cohesion intercept is the most important parameter to assess the FS. The height, the angle of shearing resistance and the weight density have little importance. These results were confirmed with all the other DM techniques. 3 FS_est = 0.996 FS_cpt R2 = 0.970

2.5

FS_estimated

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Figure 4. Relative importance of the input variables for de SVM model concerning the FS

2 1.5 1 0.5 0 0

0.5

1

1.5

2

2.5

3

FS_computed

Figure 5. Comparison between the computed and the estimated the FS from the SVM model

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F.F. Martins and T.F.S. Miranda / Application of DM Techniques to Safety Evaluation of Slopes

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Concerning the PF parameter, the REC curves are presented in Figure 6 and the global metrics are presented in Table 3.

Figure 6. REC curves related to the PF

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Table 3. Global metrics for the PF MR

ANN

SVM

RT

k-NN

MAD

14.082±0.052

7.165±0.218

8.061±0.257

13.521±0.305

11.335±0.272

RAE(%)

50.195±0.184

25.541±0.776

28.733±0.915

48.196±1.086

40.404±0.969

RMSE

17.586±0.053

12.204±0.686

12.359±0.355

20.178±0.484

17.305±0.326

RRMSE(%)

52.546±0.157

36.462±2.051

36.928±1.060

60.290±1.446

51.704±0.974

COR

0.851±0.001

0.932±0.008

0.930±0.004

0.803±0.010

0.872±0.007

Both the REC curves and the global metrics point out the best performance of the ANN followed by the SVM. In fact, the ANN has lower errors and higher correlation coefficient and area under the REC curve. The relative importance of the input variables for the ANN is presented in Figure 7. It can be seen that the cohesion intercept is the most important parameter for the evaluation of PF. The height, the angle of shearing resistance and the weight density have little importance. These results were confirmed with all the other DM techniques, except the regression tree where the height of groundwater level has the highest importance. Figure 8 presents a plot of computed versus estimated PF values obtained with the technique with the best performance (ANN) and using all the available data. It can be seen that there is a reasonable agreement between the estimated and the computed PF

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values. There are some exceptions in the extremes of the values of the probability of failure where some values are below zero and above 100%.

Relative Importance (%)

60 50 40 30 20 10 0

h

hw

α

Φ

c

γ

Figure 7. Relative importance of the input variables for de ANN model concerning the PF 120

PF_est = 0.969 PF_cpt R2 = 0.945

100

PF_estimated

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80 60 40 20 0 -20

0

20

40

60

80

100

120

-20

PF_computed

Figure 8. Comparison between the computed and estimated PF for the ANN model

4. Conclusions In the slope stability analysis Data Mining techniques can be used instead of the traditional methods. However, it is necessary to build a database with a large amount of data. Concerning the used database the SVM and ANN techniques are those that give

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the best prediction of FS and PF. Nevertheless, the SVM method is slightly better in the FS prediction whereas the contrary happened in the prediction of PF. The cohesion intercept is the most important parameter in the evaluation both of FS and PF. In future studies it is of interest to generalize this methodology to higher slopes and heterogeneous slopes.

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References [1] J. M. Duncan, , State of the art: limit equilibrium and finite-element analysis of slopes. J. Geotechnical Engineering, vol. 122, n.º 7 (1996), 577-596. [2] F. F. Martins, , Valente, B. D. S. S., Vieira, C. F. S. and Martins, J. B., Slope Stability of embankments, GeoEng2000 – International Conference on Geotechnical and Geological Engineering, Melbourne, Australia, 2000, 19-24. [3] F. F. Martins, , Araújo, A. S. S. and Marques, R. F. P., Stability analysis and geometry optimization of slopes using artificial intelligence techniques (in Portuguese), XI Congresso Nacional de Geotecnia, Coimbra, Vol. 3 (2008), 131-138. [4] F. F. Martins, and T. A. M. V Almeida,., Slope stability analysis using intelligent tools (in Portuguese), 3.as Jornadas Luso-Espanholas de Geotecnia sobre Geotecnia nas Infra-estruturas Ferroviárias, Madrid, 25-26 de Junho de 2009, edição em CD-Rom. [5] F. F. Martins, M. A. R. Gabriel, and L. P. C. Moreira, Slope stability evaluation using data mining techniques (in Portuguese), XII Congresso Nacional de Geotecnia, Guimarães, CD Rom edition. [6] M. Santos, C. Azevedo, Data Mining – Descoberta de Conhecimento em Bases de Dados, Universidade do Minho, 2005. [7] J. Quinlan, Induction of Decision Trees. Machine Learning, 1: 81-106. Kluwer Academic Publishers, 1986. [8] K. Hechenbichler, and K. Schliep, Weighted k-Nearest-Neighbor Techniques and Ordinal Classification. Discussion Paper 399, SFB 386, Ludwig-Maximilians University Munich, 2004. URL. http://epub.ub.uni-muenchen.de/archive/00001769/01/paper_399.pdf. [9] S. Haykin, Neural Networks - A Compreensive Foundation. Prentice-Hall, New Jersey, 2nd edition, 1999. [10] C. Cortes, and V. Vapnik, , Support Vector Networks. Machine Learning, 20(3), 273-297. Kluwer Academic Publishers, 1995. [11] P. Cortez, , Data Mining with Neural Networks and Support Vector Machines using the R/rminer Tool, In P. Perner (Ed.), Advances in Data Mining, Proceedings of 10th Industrial Conference on Data Mining, Berlin, Germany, Lecture Notes in Computer Science, Springer, July, 2010. [12] J. Bi, and K. Bennett, Regression Error Characteristic curves. Proceedings of 20th Int. Conf. on Machine Learning (ICML), Washington DC, USA, 2003. [13] R. Kewley, M. Embrechts, C. e Breneman, Data Strip Mining for the Virtual Design of Pharmaceuticals with Neural Networks. IEEE Transactions on Neural Networks. Vol. 11(3), pp. 668-679. [14] R Development Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2010. http://www.R-project.org, ISBN 3-900051-00-3. [15] B. Efron, R. e Tibshirani, , An Introduction to the Bootstrap. Chapman & Hall, USA, 1993.

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Application of Data Mining Techniques to Estimate ElasticYoung Modulus Over Time of Jet Grouting Laboratory Formulations a

Tinoco, JOAQUIa, 1, Gomes Correia, ANTONIOa and Cortez, PAULOb Department of Civil Engineering/ C-TAC, University of Minho, Guimarães, Portugal b Department of Information System / Algoritmi Centre, University of Minho, Guimarães, Portugal

Abstract. Jet Grouting (JG) technology is currently applied in many geotechnical works for improving mechanics properties of soil, mainly soft-soils. In many geotechnical structures advance design incorporates the serviceability design criteria. For this purpose, deformability properties of the improved soils are needed. In this paper, three data mining models, i.e. Artificial Neural Network (ANN), Support Vector Machine (SVM) and Functional Network (FN), were used to predict the Elastic Young Modulus (E 0 ) of JG laboratory formulations of cases studies using JG technology for soils improvement. Furthermore, the results obtained were compared with the Eurocode 2 predictive formula, as well as with the CEB-FIP Model Code 1990 approach. The proposed predictive approaches of E 0 can give a valuable contribution in terms of improving the construction control process of JG columns and reducing the costs of laboratory formulations.

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Keywords. Ground improvement, Jet Grouting, Young Modulus, Data Mining, Artificial Neural Networks, Support Vector Machines, Functional Networks

Introduction Currently, there are several techniques for soil improvement. Jet Grouting (JG) technology is characterized by a great versatility in a variety of application soil types and treatment geometries and it has been applied to several Geotechnical Engineering tasks [1][2]. This paper will focus the JG initial stage, where a set of laboratory formulations, which are function of the soil type to be treated and the design properties, are used to set the soil-cement mixture that will be used in the construction works. In particular, this study allows the definition of the grout water/cement ratio, the amount of cement for cubical meter of treated soil and the cement type, needed to satisfy the design and economical requirements. However, at the design stage of JG, there are still uncertainties because there are no reliable methods that allow the prediction of the diameters and the mechanical properties of the soil-cement elements [3][4]. Thus, given the high potential of JG technology, there is need to develop more rigorous and accurate models of design. This will allow reducing field tests, optimizing all the constructive process and obtaining a higher technical and economical efficiency. 1 Corresponding Author: Tinoco, JOAQUI, Department of Civil Engineering/ C-TAC, University of Minho, Guimarães, Portugal; email: [email protected].

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To achieve this goal and satisfy the current project requirements, we start by develop some models to predict the uniaxial compressive strength (q u ) of JG laboratory formulations by application of Data Mining (DM) technique [5]. However, to deal with the serviceability state of the structure, deformability properties of the improved ground are also necessary. In this context predictive models for deformability moduli of JG laboratory formulations are required. In this paper a first step was done adapting predictive models of the Young Modulus (E) for concrete, such as the Eurocode 2 analytical model (EC2) [6] and the CEB-FIP Model Code 1990 approach (MC90) [7]. Moreover, Data Mining (DM) techniques, namely Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Functional Networks (FN) were also applied. These techniques allow to develop a novel predictive model of Elastic Young Modulus (E 0 ) over time of JG laboratory formulations.

1. Materials and Methods

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1.1. Jet Grouting Laboratory Formulations Data The models were trained and verified using data from huge JG laboratory formulations, prepared at University of Minho to analyze the influence of several parameters in q u and E 0 of JG materials, and obtained in unconfined compression tests with on sample strain instrumentation [8]. In a non linear stress-strain relationship different moduli can be defined. For this work the initial modulus at very small strains was adopted (Elastic Young Modulus) since, is a laboratory parameter that can be compared with geophysical field test results. The dataset includes 188 results, derived from 9 JG laboratory formulations and 8 input parameters, which are referred as the more relevant parameters in mechanical properties of soil-cement mixtures [9]. The parameters used are the same considered in q u prediction [5]: Water/Cement ratio – W/C; Age of the mixture – t; The relation between the mixture porosity and the volumetric content of cement – n/(C iv )d; Cement content of the mixture – %C; Percentage of sand – %Sand; Percentage of silt – %Silt; Percentage of clay – %Clay and Percentage of organic matter – %OM. The basic statistics of the numerical parameters used are presented in Table 1. The soils used in the preparation of JG laboratory formulations come from five field works in Portugal and Spain. The geotechnical soil properties were evaluated using laboratory tests and the respective soil classifications are presented in Table 2. While all of the soils were classified as fine soil they have different percentages of sand, silt, clay and organic matter. All formulations were prepared with cement CEM I 42.5R. and CEM II 42.5R. The results of unconfined compression tests with on sample strain measurements [8] show a log-normal distributions shape for the histogram of uniaxial compressive strength [5], which was also verified for the elastic Young modulus.

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J. Tinoco et al. / Application of Data Mining Techniques to Estimate Elastic Young Modulus

Table 1. Synopsis of the numerical input parameters Soil

Fine Soil

Parameter

Minimum

Maximum

Mean

Standard Deviation

W/C

0.69

1.11

0.98

0.12

t (days)

3.00

56.00

20.22

17.07

n/(C IV )d

51.21

75.04

64.80

7.80

%C

24.19

64.86

45.10

11.48

% Sand

0.00

39.00

13.44

12.82

% Silt

33.00

57.00

50.57

7.48

% Clay

22.50

45.00

35.85

7.48

%OM

0.40

8.30

3.51

2.28

Table 2. Summary of the soil classification Soil

Classification

% Sand

% Silt

% Clay

% OM

Nº of samples prepared

A

Lean clay (CL)

39.0

33.0

27.0

8.3

28

B

Organic lean clay (OL)

6.0

57.0

37.0

1.8

18

C

Fat clay (CH)

7.0

53.0

40.0

3.2

93

D

Silty clay (CL-ML)

25.0

52.5

22.5

0.4

27

E

Lean clay (CL)

0.0

55.0

45.0

3.9

22

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1.2. Predictive Models Nowadays, there are several models to estimate the E of concrete over time. Within this, the EC2 [6] and the MC90 [7] approaches are the most popular. Thus, despite these models are defined for concrete material, they were adapted for the JG laboratory formulation, due to some similarity of the materials. According to EC2 analytical model, the evolution of the E over time is given by the following expression: b

§ s˜ª«1§¨ 28 ·¸ a º» · ¨ « © t ¹ »¸ ¼ E (t ) ¨ e ¬ ¸ ˜ E cm ¨ ¸ © ¹

(1)

where: t - age of the respective formulation in days; E(t) – young modulus at age t (GPa); E cm - 28 day young modulus for each studied formulation (GPa), s = 0.2 to cement CEM I 42.5R. and CEM II 42,5R and a and b the coefficients to be adjusted. According to MC90 approach, the E can be estimated by the following expression: b

§ s˜ª«1§¨ 28 ·¸ a º» · ¨ « © t ¹ »¸ ¼ E (t ) ¨ e ¬ ¸ ˜ D E ˜ Ec0 ¨ ¸ © ¹

˜ª «¬

f cm

º f cm 0 »¼

c

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(2)

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Where: t is the age of the mixture in days; E(t) is young modulus at age t (GPa); s = 0.2 to cement CEM I 42.5R and CEM II 42,5R; Į E is a coefficient which depend on the type of aggregate (for soil clay was adapted the value 0.99); f cm0 = 10 MPa; f cm is the 28 day strength for each studied formulation (MPa); E c0 was determined for each formulation based on 28 days Yong Modulus and a, b and c are the coefficients to be adjusted. Three different DM techniques (ANN, SVM and FN) were applied to estimate the E 0 of JG material over time, which is a regression task. ANN and SVM models are more difficult to interpret, yet it is still possible to extract knowledge in terms of input variable importance [10]. ANN mimics some basic aspects of brain functions [11], which processes information by means of interaction among several neurons. We adopted the most popular model, the multilayer perceptron that contains only feedforward connections, with one hidden layer with H processing units. A network with H = 0 is equivalent to the multiple regression model. By increasing H, more complex mappings can be performed, yet an excess value of H will over¿WWKHGDWDOHDGLQJWRDORVVRIJHQHUDOLW\ To overcome this difficult, a grid search {2, 4, …, 10} was used to choose best H value [12]. We adopted an internal 5-fold cross validation over the training data and then selected the network with the lowest validation error. Next, this ANN was retrained with all training data. The general model of the ANN is: o 1

yˆ Wo ,0 

§

j I 1

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I

¦ f ¨¨© ¦ X i 1

i

(3)

· ˜ W j ,i  W j ,0 ¸¸ ˜ Wo ,i ¹

where W j,i represent the weight of the connection from neuron j to unit i, f is a logistic function 1/(1+e(-x)), and I is the number of input neurons. The SVM was initially proposed for classification tasks (i.e., to model a discrete labeled output). After the introduction of the İ-insensitive loss function, it was possible to apply SVM to regression tasks [13]. SVM has theoretical advantages over ANN, such as the absence of local minima in the learning phase, i.e., the model always converges to the optimal solution. The main idea of the SVM is to transform the input data into a high-dimensional feature space by using a nonlinear mapping ࢥ. Then, the SVM finds the best hyperplane within the feature space. The transformation depends on the kernel function adopted. We selected the popular Gaussian kernel:

 y˜ x x ' , 2

k ( x, x ' )

e

y!0

(4)

Under this setup, performance of the regression is affected by three parameters: Ȗ – the parameter of the kernel, C – a penalty parameter, and İ – WKHZLGWKRIDİ-insensitive zone. To reduce the search space, the first two values will be set using the heuristics of [14]: C=3 (for a standardized output) and H Vˆ / N , where Vˆ 1.5 / N ˜ ¦N ( yi  yˆ i ) 2 i 1

and yˆ i is the value predicted by a 3-nearest neighbor algorithm. To optimize the Kernel SDUDPHWHUȖZHDGRSWHGDJULGVHDUFKRI^-15, 2-13, …, 23}, which works as explained for ANN. FN are a general framework useful for solving a wide range of problems (e.g. engineering applications) and it has been successfully used in solving both prediction and classification problems [15]. In these types of networks, the functions of the

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neurons can be multivariate, multi-argument and it is also possible to use different learnable functions, instead of fixed functions. Moreover, there is no need to associate weights to connections between nodes, since the learning is achieved by the neural functions. It should be noted that these functions are not arbitrary but subject to strong constraints to satisfy the compatibility conditions imposed by the existence of multiple links going from the last input layer to the same output units. When compared with ANNs, there are some advantages [16]. Unlike ANN, FN can reproduce certain physical characteristics that lead to the corresponding network in a natural way. However, such reproduction only takes place if we use an expression with a physical meaning inside the function database. Also, the estimation of the network parameters can be obtained by resolving a linear system of equations, which returns a fast and unique solution, i.e. the global minimum is always achieved. In this work we use the FN to solve the following generic expression: N

yˆ

(5)

E 0 ˜ – xi Di i 1

where, {x 1 , …, x i } are the input parameters, ^ȕ 0 Į 1 «Į i } are the coefficients to be adjusted. To learn the coefficients in (5) the following minimization problem was used: S

¦G

Minimize Q

2 S

S 1

S

N

§

¦ ¨¨ y S 1©

S

 E0 ˜

·

2

(6)

– x D ¸¸¹ i

i

i 1

The ANN and SVM models were training using RMiner library [17], which facilitates the application of DM techniques in the R tool. The formulation and resolution of the FN was implemented in the free version of the GAMS [18].

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1.3. Evaluation of Predictive Models To evaluate the predictive performance of the models, we selected the Mean Absolute Error (MAD), Root Mean Absolute Error (RMSE) and Coefficient of determination (R2):

MAD

¦

N

i 1

y  yˆ

N

N

;

RMSE

¦i 1  y  yˆ 2 N

;

R2

§ ¨ ¨ ¨ ¨ ©

¦i 1  y  y ˜ yˆ  yˆ N

2

¦  y  y ˜ ¦ yˆ  yˆ N

i 1

2

N

i 1

· ¸ ¸ ¸ ¸ ¹

2

(7)

where y denotes the desired value, yˆ the predicted value, y and yˆ represent the mean of these variables. Lower values of MAD and RMSE correspond to a higher predictive capacity, while the R2 should be close to the unit value. When compared to MAD, the RMSE metric is more sensitive to extreme errors. We adopted the Leave-One-Out scheme for measuring the predictive capability of each model, where sequentially one example is used to test the model and the remaining data is used for¿WWLQJ WKH PRGHO 8QGHU WKLV VFKHPH DOO GDWD LV XVHG IRU training and testing. Yet, this method requires around N (the number of data samples) times more computation, since N models are ¿WWHG7KHILQDOJHQHUDOL]DWLRQHVWLPDWHLV

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evaluated by computing the MAD, RMSE and R2 metrics for all N test samples. To understand better the behavior of the JG material, the influence of each parameter was also quantified by applying a sensitivity analysis procedure [10]. This procedure determines the most important variables by successively holding all but one input constant and varying the other over its range of values to observe its effect on the system. A high variance observed in the outputs denotes a high input relevance.

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2. Results of Different Predictive Models The coefficients in EC2 analytical expression were optimized to JG laboratory formulations (a=0.0011 and b=959.56) and the performance reached was very good, with an R2=0.96 (see table 3). However, some limitations were also identified. The main one is related with the need to carry out laboratory tests to obtain the young modulus of each formulation at 28 days. Consequently, predictive models should avoid data of 28 days tests to facilitate mixture design at as early stage of project level. When we adjusted the coefficients in MC90 expression, was possible to observe that the coefficient E co cannot be constant (R2=0.48). So, this coefficient was determined for each laboratory formulation (see table 4) and the remains coefficients took the following values: a=1/2, b=1/2 and c=1/3. However, even considering different values for E c0 coefficient the performance reached is worse than EC2 adapted model (see table 3 and table 4). Furthermore, like in EC2 analytical model, the MC90 approach need to carry out laboratory tests to quantify uniaxial compressive strength at 28 days and consequently has the same limitation previously explained. Thus, since the main deference between EC2 and MC90 approaches is related with E cm and f cm parameters respectively, we can conclude that the prediction of E 0 based on E cm has more accuracy than based on f cm . After we train the three DM models, i.e. ANN, SVM and FN, a high performance was reached, as demonstrated by metrics MAD, RMSE and R2 (see table 3). The best result was obtained by ANN model, with an R2 = 0.97. In Figure 1 (left side) we can see the excellent relation between the E 0 measured and the predicted values by SVM model. For the remains models the relation is similar. Table 3. Comparison of the performance between the four models: ANN, SVM, FN and EC2 models Metric

ANN

SVM

FN

EC2 adapted

MAD (GPa)

0.17

0.18

0.22

0.16

RMSE (GPa)

0.24

0.25

0.30

0.25

0.97

0.96

0.95

0.96

2

R

Table 4. Values of the metrics MAD, RMSE and R2 in MC90 adopted MC90 modified models Metric

MC90 adapded

LF1 0.33

MAD (GPa) 0.84 RMSE 1.11 0.45 (GPa) R2 0.48 0.64 E c0 (GPa) 3.54 4.06 LF – Laboratory Formulation

LF2 0.82 1.01

LF3 0.24 0.31

MC90 modified LF4 LF5 LF6 0.32 0.13 0.18 0.43 0.17 0.23

LF7 0.21 0.26

LF8 0.15 0.18

LF9 0.15 0.22

0.75 6.64

0.89 2.59

0.93 4.03

0.53 3.17

0.80 2.88

0.48 1.80

0.92 3.08

0.93 2.08

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To understand better how these DM models work, namely ANN and SVM models, a sensitivity analysis procedure was carried out. The importance of each parameter, illustrated in Figure 1 (right side) shows that FN model is unsuitable to predict E 0 over time, despite its reduced values of MAD and RMSE and its good performance in q u estimation [5]. According to this model the E 0 of JG laboratories formulations is only controlled by clay percentage of soil, and we know that is not truth. So, the FN models just should be used to estimate the uniaxial compressive strength of JG laboratory formulations. Analyzing the importance of each parameter in SVM and NN models, we can observe that the relation n/(C iv )d and the time of cure have a strong influence in the behavior of E 0 over time. The soil properties are also important in E 0 prediction. It should be noted that they are the same parameters identified as a key parameters in q u study. It is still possible to observe, that the ANN model presents an interesting performance in E 0 estimation, despite had shown unsuitable in q u prediction [5].

Figure 1. Predicted versus measured E 0 of JG laboratory formulations using the SVM model (left side) and comparison of the importance of each parameter in the three models: ANN, SVM and FN models (right side)

3. Conclusions In this work, a novel data-driven approach to predict the Elastic Young Modulus (E 0 ) of Jet Grouting (JG) laboratory formulations over time is presented. A comparison between the five models used in this study show that the Support Vector Machines (SVM) and the Artificial Neural Network (ANN) have the best performance. In these Data Mining (DM) models, the key parameters that control the E 0 of JG laboratory formulations over time were also identified. The relation n/(C iv )d and the time of cure are the more influent in E 0 estimations. Moreover, the properties of the soil also have an important contribute. It should be mentioned that these were the same parameters identified as the key parameters in uniaxial compressive strength (q u ) study [5]. It should be stressed that tested models are only valid for the condition found in the dataset used in this study (e.g. fine soil type).

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The models trained, specially the SVM and ANN models, can give a valuable contribution in terms of improving the constructive process of JG columns and reducing the costs of laboratory formulations. Considering the results obtained, we intend apply similar Data Mining Techniques, to predict others types of deformability moduli of JG laboratory formulations, as well as to estimate the final diameter of JG columns and its real mechanical properties (Uniaxial Compressive Strength and Young Modulus).

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References [1] João F. Falcão, Alexandre L. Pinto and Francisco D. Pinto, Cases histories of ground improvement solutions using jet-grouting, 2000. [2] A. Barrios Padura, J. Barrios Sevilla and J. García Navarro, Study of the soil consolidation using reinforced jet grouting by geophysical and geotechnical techniques: "La Normal" building complex (Granada)., Construction and Building Materials (2008), doi:10.1016/j.conbuildmat.2008.07.011. [3] P. Croce and A. Flora, Analysis of single-fluid jet grouting, Géotechnique ,Vol. 51(2001),905 – 906. [4] Fabian Kirsh, and wolfgang Sondermann, Ground Improvement and its Numerical Analysis, Proceedings of 15th Int. Conf. Soil. Mech. Found. Eng., Istanbul, 2001. [5] J. Tinoco, A. Gomes Correia, P. Cortez, A Data Mining Approach for Jet Grouting Uniaxial Compressive Strength Prediction, 2009 Word Congress on Nature & Biologically Inspired Computing. NaBIC 2009, Coimbatore, India, 2009. [6] Normalisation, Comité Européen the. Eurocode 2: Design of concret structures - Part 1-1: General rules and rules for buildings, Bruxelas: CEN, 2004. [7] Thomas Telford, CEB-FIP Model Code 1090- Design Code, Comité Euro-International du Béton, London, 1993. [8] A. Gomes Correia, T. Valente, J. Tinoco, J. Falcão, J. Barata, D. Cebola, S. Coelho, Evaluation of mechanical properties of jet-grouting columns using different test methods, 17th International Conference on Soil Mechanics and Geotechnical Engineering,Alexandria, Egypt(2009), 2179-2171. [9] Mitsuhiro Shibazaki, State of Pratice of Jet Grouting, Grouting and Ground Treatment, New Orleans(2003), 198-217. [10] Robert H. Kewley, Mark J. Embrechts and Curt Breneman, Data Strip Mining for the Virtual Design of Pharmaceuticals with Neural Networks, IEEE Transactions on Neural Networks(2000), 668–679. [11] S. Kenig, A. Ben-David, M. Orner and A. Sadeh, Control of properties in injection molding by neural networks, Engineering Applications of Artificial Intelligence(2001),819-823. [12] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, NY, USA, 2001. [13] A. Smola and B. Scholkopf, A tutorial on support vector regression, Statistics and Computing(2004), 199–222. [14] Vladimir Cherkassy and Yunqian. Ma, Practical Selection of SVM Parameters and Noise Estimation for SVM Regression, Neural Networks, 17(1)(2004) , 113–126. [15] E. Castillo, A. Cobo, J. M. Gutiérrez, and E. Pruneda, Functional Networks with Applications: a neuralbased paradigm, Springer, 1998. [16] Yong-Quan Zhou, Deng-Xu He and Zheng Nong, Application of Functional Network to Solving Classification Problems, World Academy of Science, Engineering and Technology(2005),390-393. [17] P. Cortez, Data Mining with Neural Networks and Support Vector Machines using the R/rminer Tool, In P. Perner (Ed.), Advances in Data Mining, Proceedings of 10th Industrial Conference on Data Mining, Berlin, Germany, Lecture Notes in Computer Science, Springer(July, 2010). [18] GAMS, Development Corporation, On-line Documentation, Welcome to the GAMS Home Page, http://www.gams.com/docs/document.htm (accessed May 17, 2010). [19] D. Rumelhart, G. Hinton, and R. Williams, Learning Internal Representations by Error Propagation, In D. Rulmelhart and J. McClelland, editors, Parallel Distributed Processing: Explorations in the Microstructures of Cognition, vol. 1, MIT Press, Cambridge MA(1986),318-362. [20] C. Cortes and V. Vapnik, Support Vector Networks, Machine Learning, 20(3)(1995), 273–297. [21] Enrique Castillo, José Manuel Gutiérrez, Ali S. Hadi, and Beatriz Lacruz, Some Applications of Functional Networks in Statistics and Engineering, Technometrics 43(2001) , 10-24. [22] Emad A. El-Sebakhy, Software reliability identification using functional networks: A comparative study, Expert systems with applications 35(2009),4013-4020.

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[23] Enrique Castillo, Ali S. Hadi, Beatriz Lacruz, and Rosa E. Pruneda, Semi-parametric nonlinear regression and transformation using functional network, Computational Statistics & Data Analysis (2008), 2129-2157. [24] Amparo Alonso Betanzos, Enrique Castillo, Fontenla Oscar Romero, and Noelia Sánchez Marono, Shear Strength Prediction using Dimensional Analysis and Functional Networks, European Symposium on Artificial Neural Networks, Bruges, Belgium(2004) , 251-256. [25] Mohammad Rezania, and Akbar A. Javadi, A new genetic programming model for predicting settlement of shallow foundations, Canadian Geotechnical (2007),1462-1473. [26] Sergio Lai and Mauro Serra, Concrete Strength Prediction by means of Neural Network, Constrruct Build Mater, 11(2(1997),93-98.

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101

Research of Modeling System for Soil Classification in Geological Reconnaissance Based APNN-RBF Neural Network Tao CHENGa,1 and Keqin YANb School of Civil Engineering㸪Huangshi Institute of Tecnology Huangsh 435003, China b State Key Laboratory for Disaster Reduction Tongji University Shanghai 200092, China a

Abstract. This paper introduced the method and principle of a traditional probability neural network (PNN) and an adaptive probability neural network (APNN). Based on inverse problem theory, the question of soil classify is investigated. A new method based on the APNN and RBF neural network is put forward. And an intellectualized analysis system of soil classification is established, consisting of parameter estimation and pat-tern recognition. In the system, the variability of soil physical parameters is thought to be small, whereas variability of mechanics parameters is large. A RBF neural network model is established to reflect mechanics pa-rameters according physics parameters. It can offer a good approach to soil classify by APNN. Examples presented in the paper indicate that this method is neat and effective.

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Keywords. PNN, pattern recognition, inverse problem, simulation system, soil classify, mechanics parameters

Introduction How to determine parameters of soil is the premise for the geological reconnaissance in civil engineering. The physical parameters of soil that can be obtained through examination in the lab have high precision and little variability. Whereas mechanics parameters for stratum bearing capacity such as c, ȭandȘ v, etc have lager variability. Some achievements about geotechnical parameter’s statistics relation have been acquired. The statistics relation between mechanical and physical parameter of clay has been studied through regression analysis [1]. The problem of silt has been investigated through regression analysis [2]. The scope of rock’s mechanics parameter has been studied through perturbation method [3]. Rock Mechanics parameters of rock have been acquired conversely with BP network [4]. Methods above can be concluded into two kinds. Statistics principle was used in the first method to establish unitary linear regression function relation between physics and mechanics parameters. The methods are useful in establishing function variable. 1 Corresponding Author: E-mail: [email protected]

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But simplify function relation into linear regression is somewhat unreasonable. In the other method, mechanics parameters are deduced conversely through spot survey displacement. But the precision of this method lies on constitutive relationship. Moreover constitutive relationship is based on accurate classify of geotechnical material in survey and accurate mechanics parameter.

1. Principle of Model Identification In geotechnical reconnaissance, confirming mechanics parameters belong to the problem of parameter estimation. Classifying soil belong to the problem of pattern recognition. The former is the basis of the latter [5]. This is an inverse problem in fact. Due to particularity of geotechnical material mentioned above, the question is high nonlinear. In surveying engineering, assume that models presented belong to aggregate. There are models marked as M 1 , M 2 , ..., M i , … M l (1 d i d l ) .Therefore,

M

[ M 1 , M 2 , ˜˜˜M i , ˜˜˜, M l ]

(1)

Whereˈ M i can be expressed by eigenvector p . That is,

pi [ p1 p2 ˜˜˜ p j ˜˜˜ pmi ]T  M i

(1 d i d l ,1 d j d mi )

(1)

Model recognition is to choose a best model M opt from aggregate M . In this problem, it can be expressed as following.

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M opt

M k if M k

min (Odc ) (1 d k d l ) i

k

(3)

Where, Odc is just the function of difference between vector p and model error. Due to high nonlinear of this inverse problem, the solution is always ill-posed.

2. APNN-RBF Network Structure Artificial neural network (ANN) is a complicated network connected by large quantity of biology NN cells. It is especially useful for complicated nonlinear problem, where function expression ambiguity, such as calculate parameters conversely, forecast result etc. Usually, there are BP network, RBF network and PNN network. Thereinˈ PNN network is excellent at pattern distinguishing mode under noising conditions [6]. Probability neural network (PNN) has two forms, which are traditional and DGDSWLYH'LIIHUHQFHEHWZHHQWKHWZRIRUPVLVWKDWZKHWKHUWKHSDUDPHWHUıLVFRQVWDQW Using LWHUDWLYH PHWKRG DGDSWLYH 311 RSWLPL]H GLIIHUHQW SDUDPHWHU ı WR GLIIHUHQW surveying space dimension [7].The structure of PNN network is shown in Figure 1 and RBF shown as Figure2.

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T. Cheng and K. Yan / Research of Modeling System for Soil Classification X1

X2

Z11

Xi

Z1n

Xn

Zs1

ș1

X1

X2

Xi

Xn

Zsn

șj

Y1

șs

Figure 1. Structure of PNN network.

Yj

Ym

Figure 2. Structure of RBF network.

Assuming Wqj X qj , X and X qj are normalized, the probability density function of estimate value in the Parzen window can be express as following. nq

fq ( X )

¦ g(z

qj

)

j 1

1 nq (2S ) p / 2 V p

nq

¦ exp[

( X  X qj )T ˜ ( X  X qj )

j 1

2V 2

]

(4)

Where 㸪 g ( z j ) exp[( z j  1) / V 2 ] is transfer function; X is checking vectors to classify;

is probability density of X ; nq is number of the class q in training

fq ( X )

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vector; p is dimension of training vectors; X qj is no. j training vector of class q ; V is smoothness parameter. PNN converge to one Bayes classifier, linear classifier or one close classifier respectively, if V const z 0 , V f or V 0 . PNN completes classification in the light of the distance which is confirmed by important parameter between X and X qj .But PNN has a biggest defect: V const . Whereas adaptive probability neural network (APNN) is designed to have different V varies as each survey dimension. And each V is optimized to be a relatively important measurement for its corresponding parameter. On this basis of it, a more common distance function can be adopted as the Euclid distance between X and X qi . p

D( X , X qi )

¦[ j 1

X j  X qi ( j )

Vj

]2

(5)

The density estimation of Gause kernel function is fq

1 nq

nq

¦ exp[ D( X , X

qi

)]

i 1

(6)

To optimize V , standard error function is as follow: eq

( X ) [1  bq ( X )]2  ¦ [b j ( X )]2 jzq

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(7)

104

T. Cheng and K. Yan / Research of Modeling System for Soil Classification

Where

bj ( X )

is Bayes degree of confidence.

3. Soil Classification Based on APNN-RBF Network Firstly㸪the mechanics and physics parameter that have largest relativity by regression analysis are chosen as independent and corresponding variable. Then take them as the input and output vectors to train network until perfect corresponding relation is gained to correct forecast. Take character parameter from the output vectors as basic quantum whose functions make up decision vectors. Then the vectors are inputted into new networks trained to optimize parameter so that best approaching effect can be gained.Therefore, calculate flow from basic parameter estimating to stratum model classification is presented as following. RBF Network

tu pt uo

Physics data

APNN Network

Classification of stratum

Mechanics data

t u pt u o

Start

Training

Training

Extracting Feature p, r

Optimization target value M opt

Figure 3. Calculate flow picture

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4. Simulation According to large quantity geological reconnaissance report of Huangshi, considering influence of different geological conditions on parameters of soil, the samples of close stratum distributing and geological conditions are adopted as training samples. However, to validate the trained networks, the samples of different stratum distributing and geological conditions are adopted as the check samples. Obtain training samples according to regression analysis and some basic laws. Firstly, the common mechanics parameters in numerical calculate are chosen as object variable, such as cohesion c , internal friction angle M , compress coefficient av while the common physics parameters are adopted as independent variable, such as water content w , density U , specific gravity G and plastic index I p , etc. Considering nonlinear function relation among these parameters, three groups of sample couples are obtained according the method above: ( w; Gs ; I p o c) 㸹 ( I p ; I L ; Gs o M ) 㸹

(e; U ; w o av ) .Constitute NN model and put the chosen sample versus into ANN network for training.

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Step1. Inversion of function relation, that is to estimate parameters av , M and c to compose decision vector. The sample number is 60, object error is 0.001. The network trainings for the parameters are finished by1350, 11560 and 21350 times iterations respectively. Then put 5 groups of different check samples to check its effect. Forecast mechanics parameters and compare them with actual value. Part result shows as following. Table 1. Network for Parameter c Input vector

Actual output

Expectation

Relative error

㸦14.6000;2.7100;11.1000㸧

18.8000

18.4000

2.2%

㸦22.0000;2.7300;15.6000㸧

18.8365

18.3000

2.9%

㸦26.5000;2.7400;21.3000㸧

21.9642

21.6000

1.7%

㸦19.6000;2.7100;10.2000㸧

24.4746

23.7000

3.3%

Input vector

Actual output

Expectation

Relative error

㸦14.8000;0.3600;2.7200㸧

52.4075

52.0000

0.8%

㸦21.3000;0.2300;2.7400㸧

43.7644

41.6000

5.2%

㸦11.6000;0.1000;2.7100㸧

39.0240

36.1000

8.7%

㸦15.4000;0.2900;2.7300㸧

54.2132

56.0000

3.2%

Input vector

Actual output

Expectation

Relative error

㸦0.6650;2.0000;22.0000㸧

0.1805

0.1900

5.0%

㸦0.7460;1.9800;26.2000㸧

0.2473

0.2300

7.5%

㸦0.6130;2.0500;21.1000㸧

0.1748

0.1600

9.3%

㸦0.6930;1.9900;24.5000㸧

0.1936

0.1800

8.9%

Table 2. Network for Parameter

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Table 3. Network for Parameter

M

av

Conclusions can be drawn from table 1to table 3, that according stat principle combine with networks, mechanics parameters are forecasted effectively. It means large quantity of space sample become optimized. Reasonable parameter mapping couple is gained. This can be used as basic information for next step. Furthermore, it avoids using exceptional physics or mechanics index and uncertainty brought by its lager disperse as traditional method done. Step2. Based on parameters estimated of the above network, decision vector can be formed. Then training network, classify different soil stratums. The target of soil classification in practical reconnaissance is adopted as referent system combining APNN-RBF network. Part forecast results of APNN-RBF, PNN and RBF are given as following.

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T. Cheng and K. Yan / Research of Modeling System for Soil Classification

Table 4. Network for soil classification Input vector

APNN— RBF

Actual output by PNN

Actual output by RBF

Target value of system

㸦22.9000;35.1000;0.1700㸧

2

2

2.0126

2

Silty soil

㸦23.7000;56.0000;0.1000㸧

2

2

1.9965

2

Silty soil

㸦12.2000;58.0000;0.1100㸧

1

1

1.0679

1

Sandy soil

㸦13.6000;57.0000;0.1100㸧

1

1

1.1235

1

Sandy soil

㸦21.6000;41.6000;0.1800㸧

3

3

3.0000

3

Cohesive soil

㸦28.3000;7.6000;0.2600㸧

1

2

1.2562

1

Sandy soil

㸦28.0000;21.3000;0.1100㸧

2

3

2.5506

2

Silty soil

㸦21.6000;41.6000;0.1800㸧

3

3

3.3267

3

Cohesive soil

㸦23.6000;41.6000;0.1760㸧

3

3

3.5345

3

Cohesive soil

Total precision of forecast

92.31%

71.46%

63.24%

Classification of stratum

It can be include from table 4, that by the system of APNN-RBF we can obtain the best objective forecast value. And also has better effect to soil classification. Thus it is fit for judging character of soil than traditional PNN and RBF.

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5. Conclusion Combining statistics principle and ANN, using regression analysis to choose parameters as few as possible is help for training network and approaching effect. ANN has better ability to distinguish models. The key to network is to choose parameters as few as possible that can better reflect interior character of soil. Functions of mechanics parameters that have trained after function approaching are adopted as objective decision vector. It has clear physics meaning and good forecast effect. RBF network is introduced into adaptive PNN during its process of optimizing parameters. This improved distinguish effect of PNN. It can be used to soil classification effectively. And also it is robust and has good fault-tolerance. That is to say, it will not seriously affect final result forecasting because of some false inputs. It is helpful to decrease influence to result due to disperse of measured parameters in geologic reconnaissance.

Acknowledgment The authors appreciate the support of the Research Foundation of Education Bureau of Hubei under Grant Q200830002 and HSIT Research Foundation of under Grant 07yjz03R.

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107

References

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[1] H.R. Cao, Application of Regression Analysis in Acquiring Physical Indexes. West-china Exploration Engineering. No.3(2004),45㸫47.. [2] Chen C H.Fuzzy logic and neural network handbook. New York:McGraw-Hill, Chapter 3(1996),1-16. [3] S.J. Liu, et al. Interval papameter perturbation back analysis on mechanical parameter of surrounding rocks. Chinese Jounal of Geotechnical Engineering.24(6)(2002),760㸫763. [4] Sprecht D F. Probabilistic neural networks for classification, Mapping and associative memory. IEEE ICNN San Dieg CA(2008),I525-532. [5] L. D. Yang,et al., Inversion theory and practice in geotechnical engineering. Beijing, Chinese Science Press, 2005. [6] J. Y. Zhai, W.M. Leng, The variability of physical and mechanical properties and their interrelation of clay soil. Railnay Corstruction Technology. No.1(2001),49㸫51. [7] J. C. Zhou,et al., Back analysis on rock mechanics parameters for highway tunnel by BP neural network method. Chinese Journal of Rock Mechanics and Engineering .23(6)(2004),941㸫945.

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Probabilistic Evaluation of the Parameters Governing the Stability of the Tailing Dams Gabriel VILLAVICENCIOa,1, Claude BACCONNET b, Pierre BREUL b, Daniel BOISSIER b and Raoul ESPINASSE a a Pontificia Universidad Catolica de Valparaiso - Chili b UniversitéBlaise Pascal - LaMI - Clermont-Ferrand – 24 avenue des landais 63174 AUBIERE cedex France

Abstract. Although the mining residues in a global way can be classified in a single class of granular material, a set of causes lead to a variability of the geotechnical characteristics of the material and of the internal structure of the dams constituted from these. Consequently, to adopt a hypothesis of homogeneity to estimate the mechanical stability of this kind of works (slope stability, liquefaction risk,…), can drive to a not realistic evaluation of the safety. To study the risk of stability of tailings dams, it is necessary to have a probabilistic approach. Indeed, the variability of the soils properties is the main cause of the uncertainty in the geotechnical design. It is thus necessary to know the distribution laws and the characteristics of average, deviation or distribution of the input parameters of soil mechanical models. The obtaining of these laws by laboratory test is boring, expensive and long. This article suggests demonstrating that it is possible to estimate these input parameters and them characteristics of distribution from the measure of the variability on the physical and state characteristics by means of in situ tests. By this way, authors show it is possible to obtain a very good estimation of the friction angle and carry out a probabilistic study of dam stability taking into account the mechanical parameters variability.

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Keywords. Tailing dam, stability, probabilistic approach, variability

Introduction There are a very large number of tailings dams for which the coarse fraction of tailings form the body of the dams while the fine saturated fraction is poured into the reservoirs of the dams thus formed. Due to the construction methods and materials used, these dams comprise failure mechanisms such as loss of stability, liquefaction, and internal and external erosion leading to major risks for the populations and their environments, highlighted by the accidents that have occurred around the world [1, 2]. In order to manage these risks, it appears necessary to employ a probabilistic approach to predict their behaviour during construction and after closing. However, applying such an approach in practice at present is limited by the difficulty of managing the data (random variables and stochastic fields) to be introduced in the reliability calculations for the limit conditions involved. This is the reason why, in this article, we propose a method of acquiring these input data that can be applied by the body responsible for supervising these structures.

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G. Villavicencio et al. / Probabilistic Evaluation of Parameters Governing Stability of Tailing Dams 109

Our observation at the outset is that the mine tailings used in these dams are much more homogenous than the materials usually found in rockfill dams, due to production process of materials. However, heterogeneity is introduced by the mineralogy which varies from mineral deposit to mineral deposit, introducing dispersion in grain specific weight and by the aging process leading to variations in compaction within the body of the dam. Since it can be seen that this type of dam is relatively homogenous, it is possible to formulate a dual hypothesis: the laws of probabilities that can be linked to the physical characteristics of dams composed of mine tailings are invariant regarding type and variation coefficients and the relations linking the physical properties to mechanical calculation parameters are also invariant. This article presents an approach of estimating calculation parameters (friction angle I’ and relative density D r ), governing the stability of these dams. Initially, we present an analysis of the causes for the variability of mine tailings and dam characteristics. This is followed by the statistical characterization of the physical, state and resistance properties to penetration of mine tailings. Then models are proposed for all dams composed of the same mine tailings types, making it possible to link a probability law to the calculation parameters I’and D r .

1. Variability of Mine Tailings and Proposed Approach 1.1. The Variability of Mine Tailings

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Although mine tailings can be classified in one large family, several factors can lead to variability in the geotechnical characteristics of the material and the internal structure of the dam. Therefore employing the hypothesis of homogeneity to evaluate the mechanical stability of this type of structure can lead to unrealistic safety evaluation. Table 1 below provides an evaluation of the sources of variability and their effects on mine tailings. Table1. Classification, origin and effects of factors linked to the material and structural variability and geotechnical properties of mine tailings. Source

Effects on mine tailings

Variability Type

Scale

One or more mineral deposits

Homogeneity or heterogeneity of mine tailings

Material

Spatial

Type of mineral and mineralogical quality

Variability of constituent minerals

Material

Spatial

Mineralogical species Content of cut-point of mineral deposit

Grain size distribution and the addition of polymers and lime, mineralogical characteristics

Material

Spatial

Cycloning and construction method

Variation of physical and mechanical characteristics

Structural

-

Effect of aging

Cohesion by cementation

Material

Temporal

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110 G. Villavicencio et al. / Probabilistic Evaluation of Parameters Governing Stability of Tailing Dams

This paper focuses on taking into account spatial variability and most particularly the study of variability of physical, state and resistance properties. The aim is to determine the degree of homogeneity of this variability and obtain the laws of distribution and their characteristics for each of the parameters studied. 1.2. Proposed Methodology We made use of a global bibliographical synthesis of the data employed to design real structures [3], and of experimental analyses performed on three real copper tailing dams (Dams No. 1, No. 2 and No. 3) based in Chile, whose mine tailings all came from processing units relying on a similar fabrication process and which can be considered as being representative of copper mine tailings dams. From these syntheses, statistical studies were performed in order to find the theoretical distribution laws best adapted to the parameters. The main statistical characteristic calculated is the coefficient of variation (CV%), which is an excellent tool for controlling the natural variability of soil properties. The adaptation of distribution laws was obtained on the basis of statistical tests (Kolmogorov-Smirnov). Then the choice of relation for estimating the calculation parameters (effective friction angle I’and relative density Dr) as a function of cone resistance has been performed from analyses of sensitivity of bibliographic relations and the comparison with experimental data obtained on mine tailings from different mineral deposits. At last, using these relations and from in situ penetration tests carried out on representative dams, the theoretical distribution law of calculation parameters best adapted to their variability is computed and can be used for the stability study of the dam. In the following, the method will be presented for estimating effective friction angle I’, but it can be applied in the same way for estimating relative density Dr which is with I’, the two main parameters for tailing dams stability study.

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Table2. Physical properties of mine tailings. Range of bibliographic values and statistical analyses of experimental data from three representative dams.

Physical properties

Experimental data

Bibliographic data Range of values

Dam No. N

Mn

Dam No. 2

C.V(%)

Dam No. 3

N

Mn

C.V(%)

N

Mn

C.V(%)

Js

2.68 - 3.88

9

2.86

-

6

2.78

-

11

2.93

-

Dmax (mm)

0.60 - 2.00

3266

0.721

27.31

262

0.285

82.23

2958

1.831

42.43

D50 (mm)

0.05 - 0.25

3266

0.127

19.03

262

0.111

15.20

2958

0.251

8.67

12 -25

3266

28.0

28.70

262

33

26.30

2958

17

10 .00

0

3

0

0

3

3

3

6

6

6

F.C (%)

IP

Legend: J s : specific weight, D max : maximum diameter, D 50 : median diameter, F.C.: percentage of fines less than 80 (Pm), IP: plasticity index, N: Number of analyses, Mn : mean, C.V. (%) : coefficient of variation

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G. Villavicencio et al. / Probabilistic Evaluation of Parameters Governing Stability of Tailing Dams 111

2. Estimation of the Variability of Physical and Mechanical Properties 2.1. Characterization of the Variability of Physical and State Properties Table 2. summarizes the results of granulometric distribution and the specific weights of the tailings. Analysis of these results highlights that this type of mine tailings can be grouped, in terms of the granulometry and the plasticity of fines, as a single family of materials: fine, non plastic silty sands. This synthesis also highlights one of the particularities of mine tailings in comparison to natural soils which, is the considerable variation of specific weight (Js) that can range from 2.68 to 3.88, in association with the mineralogical origin of the minerals in the tailings. The influence of specific weight and granulometric distribution of mine tailings, inherent to the type of material and its method of fabrication, leads to variability of the state properties of the material, such as maximum (Proctor) density. Table 3. gives an evaluation of the ranges of values of the physical properties of mine tailings obtained from different analyses. Table 3. State properties of mine tailings. Range of bibliographical values and statistical analysis of experimental data from three dams. State parameter

Bibliographic data

Experimental data

Dam No.

Dam No. 3

Range of values

N

Mn

C.V (%)

N

Mn

C.V (%)

N

Mn

C.V (%)

15 – 22

392

18.16

6.21

262

20.80

8.02

495

2.93

-

w op (%)

-

392

15.22

9.40

262

14.4

10.30

495

1.831

42.43

J d (KN/ 3)

15.8 – 17.0

3266

17.50

6.56

275

20.05

8.18

2958

0.251

8.67

w nat (%)

7.0 – 13.0

3266

10.97

22.33

275

3.25

43.13

2958

17

10 .00

Jh (KN/m3)

-

3266

19.43

6.74

275

20.70

8.21

2958

19.49

3.51

J dmax (KN/m3)

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Dam No. 2

Legend: J dmax : Proctor dry density, w op : Proctor water content, J d : dry density in situ, w nat : water content in-situ, J h : density in-situ, N: Number of analyses, Mn: mean, C.V (%): coefficient of variation.

The measurements of density in-situ (J h ) of mine tailings varied from 16.9 (KN/m3) to 18.4 (KN/m3) [5]. This variability is related to that inherent to the material,

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112 G. Villavicencio et al. / Probabilistic Evaluation of Parameters Governing Stability of Tailing Dams

and to that contributed by the method used to build the dam in which the mine tailings are stored. Globally, the values of the coefficients of variation obtained experimentally for the granulometry and Proctor normal properties mirror the variability of the materials stored in each of the dams. The coefficients of variation selected are certainly higher for dams using mine tailings from several mineral deposits. 2.2. Characterization of the Variability of Mechanical Properties The study of mechanical properties focused on analysing resistance to shearing and penetration. The influence of the variability of physical and state properties on mine tailings results in a range of values for an effective friction angle (I’), from 28° to 40° (table 4). By considering that the shape of the grains is always angular due to the fabrication method, the factors giving rise to this variation are mainly the percentage of fine particles (80 Pm) and the compactness of the material. The influence of the percentage of fines undoubtedly has an effect, though it is difficult to point out as it is linked to other less well-controlled factors. In most cases, the cohesion of mine tailings is considered as null, as shown by the results of Peters [4]. Resistance to penetration (qd or N according to the tool used) is a parameter which includes a large number of phenomena and makes it possible to observe the spatial variability of mechanical parameters at a given moment. The results obtained from the statistical analysis of resistance to penetration values (qd, N DCPT , N DCPT60 ) obtained from penetration tests performed on three dams are given in table 4. Table 4. Mechanical properties of mine tailings. Ranges from the bibliography and statistical analyses of experimental data from the three dams. Resistance properties

Experimental data

Bibliographic data

Dam No.1

Dam No. 2

Dam No. 3

Range of values Copyright © 2010. IOS Press, Incorporated. All rights reserved.

N

Mn

C.V(%) N

Mn

C.V(%) N

Mn

C.V(%)

qd (Mpa)

-

10

4.8

50.63

5

2.87

45.89

10

19.5

52.75

N DCPT

-

38

37

62.48

11

19

58.76

-

-

-

N DCPT60

-

38

22

62.49

11

12

58.89

-

-

-

I’ (°)

30 – 40

5

33

-

4

31

35

-

-

-

5

Legend: N: Number of tests qd : cone resistance provided by the PANDA test, N DCPT : penetration resistance index provided by the DCPT test (Dynamic Cone Penetration Test), N DCPT60 : corrected penetration resistance index, I’ : effective friction angle, Mn: mean, C.V (%) : coefficient of variation.

Coefficients of variation in the region of 50% are significant but conform to those found in the literature on penetrometric tests [5, 6]. This dispersion may be due to several causes (tests quasi-punctual, depth of resistance to penetration variable, the construction method used, ageing effects).

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G. Villavicencio et al. / Probabilistic Evaluation of Parameters Governing Stability of Tailing Dams 113

3. Probability Laws Linked to Physical Properties, State and Cone Resistance

Fonction density

The distribution of physical and mechanical properties in the soil cannot be perfectly known because it is impossible to measure this parameter at every point. Using experimental distributions is impractical which is why in most cases efforts are made to model data distribution by a known probability distribution law. The data collected on the three dams show that the flattening coefficients (kurtosis) and asymmetry (skewness) of physical and state properties are close to the values of the normal law (flattening coefficient = 1, asymmetry = 0), which is the contrary for both the densities and penetrometric properties. After applying the KolmogorovSmirnof test at a threshold of 5% it was concluded, that in general the random variables linked to the main physical characteristics of mine tailings are normal or log normal. Figures 1 shows examples of physical and mechanical parameters (median diameter D 50 ,dry density Jd, dynamic cone resistance qd distributions obtained for the 3 dams. The analysis of these three dams made it possible to characterise the in-situ variability of the physical properties and penetration resistance of stored mine tailings. This study highlights that the normal law can be chosen to describe the physical properties and that the log-normal law can be chosen to describe the resistance properties. 24 TailingDams 1 TailingDams 2 TailingDams 3

20 16 12 8 4 0 0

0,1

0,2

0,3

0,4

0,8

0,4

Fontion density

Fonction density

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Averageparticle diameter

0,6 0,4 0,2 0

0,3 0,2 0,1 0

14

15

16

17

18

19

20

21

22

23

24

25

In place dry density

0

20

40

60

80

100

Dynamic cone resistance, qd (Mpa)

Figure 1. Distribution of the mean diameter of particles (D 50 ), the dry density and the cone resistance for the three dams.

4. Estimation of the Effective Friction Angle (I') The effective friction angle (I’) is the input parameter of models used to study the slope stability of mine tailings dams under static and dynamic conditions, the most difficult to determine and estimate. This parameter is greatly influenced by the origin and mineralogy of the particles, by the physical characteristics and state of arrangement

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114 G. Villavicencio et al. / Probabilistic Evaluation of Parameters Governing Stability of Tailing Dams

of the grains determined by the state of compacting and by the extent of stresses in-situ [7, 8]. We have demonstrated that the physical properties and state of mine tailings are quite homogenous. On the contrary, the methods used to implement them lead to the prevalence of stratified internal structures that can be heterogeneous. This can result in variations of resistance properties and especially of the effective friction angle as a function of depth. Thus it is important to estimate the values and variability of this parameter. To do this, we propose an estimation method based on measuring the dynamic penetrometric point resistance (qd) which can be measured relatively easily on this type of structure. The existence of a relation between the friction angle I' and static penetration resistance (qc) has been highlighted in previous works. Our study is based on the use of penetrometric resistances (qd) obtained by using a Panda, a light dynamic penetrometer [9] that is capable of performing a large number of tests in situ thanks to its small size and the short time required to set it up. From experimental study based on carrying out dynamic penetration tests in a calibration mould for different states of density to obtain the relation Jd/qd and on performing tests with a shear box apparatus on samples with the same degree of compaction as those of the calibration moulds to obtain the relation Jd/I’, we have obtained the relation I’/qd. The model used is given by the following equation:

I ' 14.79  5.54 ˜ lnqd N 1

with 10.0 ”TG N1 ”

(1)

I ') Friction angle (I

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As can be seen in figure 2, the results of the model are very close to the experimental results. In addition, the relation proposed by Díaz and Rodríguez-Roa [10] was used by replacing qc N1 by qd N1 . Figure 2 shows that the experimental points obtained by the dynamic penetrometer are very close to those calculated by Diaz's expression. 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28

Expression proposed Direct shear stress data Díaz, E. and Rodríguez-Roa, F. (2007) Prediction limit

0

25

50

75

100

125

150

175

200

225

250

275

300

Normalized dynamic cone resistance, qdN1

Figure 2. Experimental points, proposed and bibliographic relations for estimating the effective friction angle (I’) of mine tailings as a function of the normalised dynamic cone resistance qd N1 .

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G. Villavicencio et al. / Probabilistic Evaluation of Parameters Governing Stability of Tailing Dams 115

On the basis of equation 1, it is possible to estimate the profiles of the friction angle I’ as a function of depth from the penetrometric tests performed in situ. Figure 3 shows this approach for dam N°1. The measurements processed at the scale of the dam are called global level measurements while those processed at the scale of a layer of the dam are called local level measurements. At global level, a value of I’ is obtained by introducing the equation of a measured cone resistance. The distribution of all these values of I’ for each dam can be adjusted by a normal law as shown in Figure 3.

Figure 3.. a)Variation of the effective friction angle (I’) versus depth for the dam No. 1 b) Distribution of I’ obtained from on site cone resistance for the 3 dams

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5. Conclusions This article first presented a synthesis of knowledge on the variability of the physical properties and state of mine tailings dams. It showed that the granulometry of the constituent materials (crushing process, etc.) and their state (construction process), are homogeneous, whereas the heterogeneity of these materials stems from differences in the mineral deposits from which they are extracted, aging (cementation, microearthquakes) and variation as a function of depth. This relative homogeneity can be used to deduce the statistical characteristics by default of this family of materials and formulate a law capable of transforming physical characteristics into mechanical ones. It was then shown that, dynamic cone penetrometry can be used for this type of dam to obtain the effective friction angle (I’) as a function of depth on the basis of a single relation and thus the distribution law of I’ either globally for the dam or locally in each layer identified.

References [1] G.E. Blight, A.B. Fourie, A Review of Catastrophic Flow Failures of Deposits of Mine Waste and Municipal Refuse, University of the Witwatersrand, Johannesburg, South Africa, 2003. [2] M. Rico, A. Salgueiro, A. Díez-Herrero, H. Pereira, Reported Tailings Dam Failures. A review of the European incidents in the worldwide context, Journal of Hazardous Materials ,152 (2008), 846–852.

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116 G. Villavicencio et al. / Probabilistic Evaluation of Parameters Governing Stability of Tailing Dams

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[3] G.Villavicencio, Méthodologie pour Evaluer la Stabilité Mécanique des Barrages de Résidus Miniers, Thèse de doctorat de l’Université Blaise Pascal-Clermont Fd, Juin 2009. [4] G. Peters, Propiedades Geotécnicas de Arenas de Relave y sus Implicancias en el Diseño de Tranques, V Congres Chilien d’Ingénierie Géotechnique, Santiago, Chili, 2004. [5] F. Deplagne, C. Bacconnet, Analyse Structurale d’une Digue en Argile, Cahiers de Géostatistique. Compte Rendu des Journées de Géostatitisque. Fontainebleau. 25-26 Mai. France (1993), 181- 188. [6] A.L. Jones, S.L. Kramer, P.Arduino, Estimation of Uncertainty in Geotechnical Properties for Performance-Based Earthquake Engineering, PEER Report 2002/16. Pacific Earthquake ngineering Research Center. College of Engineering. University of California, Berkeley, 2002. [7] M. D. Bolton , The strength and dilatancy of sands, Géotechnique, London, 36 (1986), 65-78. [8] P.W. Mayne Stress-Strain-Strength-Flow Parameters from Enhanced In-Situ Tests, International Conference on In-Situ Measurement of Soil Properties, Bali, Indonesia, (2001), 27-48. [9] R. Gourves, S. Zhou, The in situ characterization of the mechanical properties of granular media with the help of penetrometer, 3rd International Conference on Micromécanique of Granular Media, (1997), 57- 60. [10] E. Díaz, F. Rodríguez-Roa , Ensayos in-situ en Arenas,. VI Congres Chilien d’Ingénierie Géotechnique. Université Catholique de Valparaíso. Société Chilienne de Géotechnique. Valparaíso. Chili. (2007).

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Information Technology in Geo-Engineering D.G. Toll et al. (Eds.) IOS Press, 2010 © 2010 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-60750-617-1-117

117

Research on Deformation Forecast of Deep Foundation Pit Based on Non-equidistant Monitoring Data a

Jing YANa,1, Yawu ZENGa and Rui GAO a School of Civil Engineering, Wuhan University, Wuhan 430072 China

Abstract. This paper introduces a solution of unequal interval deformation prediction by using non-equidistant Grey Model (1,1) (GM(1,1)) which is an effective tool to study uncertain system and can establish a mathematical model based on a spot of data. Firstly, the Grey System Theory is introduced briefly and the modeling process of non-equidistant GM(1,1) is shown. Secondly, the idea of real-time forward simulation is emphasized, which can improve prediction accuracy greatly. Thirdly, an algorithm for unequal interval deformation prediction is studied and realized by MATLAB, and a data fitting problem is verified as well for algorithm’s correctness. Fourthly, a comparative analysis between the predicted and actual data is conducted based on a practical engineering, the result shows that the unequal interval grey model is effective whose prediction result is close to the reality and the feedback monitoring information is very important for the accuracy of the prediction. Then, a rebuilding of grey background level based on Lagrange interpolation is carried out. A better accuracy of simulation is got. Finally, some useful suggestions on prediction accuracy enhancement and some problems need to be noticed are mentioned.

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Keywords. Deformation of deep foundation pit, Unequal interval prediction, Information updating, Grey background level, Grey system

Introduction With the rapid development of infrastructure in China, the construction scale of deep excavation engineering becomes larger and larger, and following the extent of injury of engineering accident also become heavier and heavier. Due to this condition, a rigorous monitor and control system to find unstable factors should be built and some repairs should be conducted during the construction process of deep foundation pit. However repairs are a kind of passive control measure after all, when the force and deformation of the structure has already reached the level of damage, the damage will be happened. In order to prevent the hazard in germination phase and minimize the economic loss, it is highly necessary to build a system or method which could predict the tendency of deformation with an acceptable accuracy based on historical monitoring data. Rock and soil mass is a kind of heterogeneous, anisotropic, porous natural geologic body, and its rheological property makes research more difficult. The complexity of inherent characters in rock and soil mass, and a variety of uncertain 1

Corresponding Author. Postgraduate of Geotechnical Engineering, School of Civil Engineering, Wuhan University, Wuhan, China; E-mail: [email protected]. Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

118

J. Yan et al. / Research on Deformation Forecast of Deep Foundation Pit

factors in construction make traditional numerical methods are hard to calculate the actual deformation of deep foundation pit. The value of deformation shows a certain degree of randomness, which contributes big troubles to design and construction. However, the Grey System could avoid the constitutive relation of rock and soil mass and provide engineers a new method to predict deformation. There are many successful examples using GM(1,1) to predict deformation based on equidistant monitoring data worldwide. However, an equidistant GM(1,1) can not be used to predict deformation when a continuous and equidistant monitoring for a point is difficult or monitoring records are incomplete. So, how to build a unequal interval deformation prediction method with non-equidistant GM(1,1) is the focus of this paper .

1. Non-Equidistant Grey Model GM(1,1)

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Grey Theroy was first raised by Professor Deng Ju-long at the international economics meeting in 1982. It is a new method to study uncertain problem which uses a small amount of data and poor information. It takes “partial information known and partial information unknown” and “small sample” uncertain system as its research object, then obtains the valuable information from the “parts” we known by using some mathematical means, at last realizes the correct description and effective monitoring of the system’s evolution law. It was first introduced to geotechnical engineering in 1980’s, and widely used in the space-time expansion analysis of rock and soil mechanics, displacement prediction of deep pit or slope, bearing capacity forecast of pile foundation, analysis of ground stress and so on. Unequal interval deformation prediction is a kind of forecast which makes good use of non-equidistant deformation monitoring data from a point to calculate its deformation in the future. Now assume the sequence of the original deformation is

x

(0)

'ki

(0)

(0)

(0)

{ x ( k1 ), x ( k 2 ),  , x ( k n )}

(1)

ki  ki 1 z constant, i

(2)

2, 3,  n

And the time interval 'ki between the two continuous deformation records isn’t a constant. So x

(0)

is a non-equidistant deformation sequence, and its first time

cumulative sequence (1-AGO) x

(1)

{x (1) ( k1 ), x (1) ( k 2 ),  , x (1) ( kn )} can be generated by

i (1)

x ( ki )

¦x

(0)

( k j )'k j , i

1, 2,  n ( 'k1

1)

j 1

Then construct the first order differential equation followed

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(3)

119

J. Yan et al. / Research on Deformation Forecast of Deep Foundation Pit (1)

dx (t ) dt

(1)

 ax (t )

u , t  [0, f )

(4)

Take an integral on both sides of the Eq.(4) in the interval [ ki , ki 1 ]

³

ki 1

(1)

dx (t )  a

ki

k i 1

³

ki

(1)

x (t )dt

u

³

k i 1

ki

dt

i

¦x

(1)

 x ( ki )

(0)

( k j )'k j

j 1

?

³

ki 1

ki

(1)

(1)

(1)

x ( k i 1 )  x ( k i )

dx (t )

(0)

x ( ki 1 ) 'ki 1 (1)

(1)

Here, assume the grey background level of x (t ) in the interval [ ki , ki 1 ] is Z ( ki 1 ) , so a

³

ki 1

ki

x (1) (t )dt

a³

ki 1

ki

Z (1) ( ki 1 )dt

x (0) ( ki 1 ) 'ki 1  aZ (1) ( ki 1 ) 'ki 1

aZ (1) ( ki 1 ) 'ki 1 , and then u 'ki 1

The difference form of Eq.(4) can be described as

x (0) ( ki 1 )  aZ (1) ( ki 1 )

u , i 1, 2, n  1

(5)

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Expand Eq.(5) and get the matrix equation below

ª x (0) (k2 ) º « » « M » «¬ x (0) ( kn ) »¼ When t

ª  Z (1) ( k2 ) 1º « » ªa º M M» u « » « u «¬  Z (1) ( kn ) 1»¼ ¬ ¼

(1)

k1 , x ( k1 )

(1) xˆ ( ki )

(6)

x (0) ( k1 ) , and the corresponding function x (1) (t ) is

u  a ( k k ) u (0) ( x ( k1 )  )e  ,i a a i

1

1, 2, 

(7)

At last carry out a subtraction with Eq. (7) and get the prediction model of deformation (0) xˆ ( ki 1 )

1 'ki 1

(1  e

a 'ki 1

u a(k (0) )( x ( k1 )  )e a

i 1

 k1 )

(8)

Here a, u are undetermined coefficients and can be estimated with least square method.

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J. Yan et al. / Research on Deformation Forecast of Deep Foundation Pit

(BT3 B 3 ) 1 BT3 Y3

( a , u )T

a

B3

ª  Z (1) ( k2 ) « (1) «  Z ( k3 ) « M « (1) ¬  Z (kn )

1º

» » , Y3 M» » 1¼ 1

(9)

ª x ( 0 ) (k2 ) º « (0) » « x ( k3 ) » « M » « (0) » ¬ x (kn ) ¼ (1)

(1)

According to the hypothesis, Z ( ki ) is the grey background level of x (t ) in the interval [ ki 1 , ki ] , and usually be reckoned by (1)

Z ( ki 1 )

1 2

(1)

(1)

[ x ( ki 1 )  x ( ki )]

(10)

2. Real-time Forward Simulation

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With the development of modern communication technology and computer science, the speed of resource sharing and information feedback becomes faster and faster, which offers a strong support for geotechnical engineering’s deformation prediction. The key points of foundation pit deformation forward simulation is feedback monitoring information timely, getting first-hand materials, updating data system real-time, and constantly adjusting forecast model in order to raise prediction accuracy. In particular, for deformation prediction based on non-equidistant sequence, its own data is discontinuous or incomplete and only by replenishing information continuously can the prediction model has a good effect. The simulation model discussed above described in Figure 1.

Historical Data

Establish Prediction System

Predict

Future Data

Analyse System Reliability Compare Information Feedback and Data Updating

Measured Data

Figure 1. Simulation model

A simple example will be used to prove that the model can simulate a nonequidistant sequence successfully. Take 10 consecutive points of the monotone

t as a sample (Table 1), and remove the point of 2, 5 and 9, increasing function y then use the remaining incomplete data sequence to forecast the function values of 4 consecutive points behind.

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J. Yan et al. / Research on Deformation Forecast of Deep Foundation Pit

Table 1. Historical data t

1

2

3

4

5

6

7

8

9

10

y

1

1.414

1.732

2

2.236

2.449

2.646

2.828

3

3.162

Method 1: Use the algorithm introduced in part 1 and make a conventional forecasting. Method 2: Consider data updating, and when predict a new data, add its corresponding measured data to historical sequence to re-establish the prediction model and forecast the next data. Suppose if historical sequence is incomplete seriously, and the old information could not be removed during the calculation process. The results produced by real-time simulation model can be seen in Table 2. and Figure 2. According to the results, the following conclusions could be drawn: (1) The non-equidistant Grey Model GM(1,1) is effective for simulating incomplete or non-continuous sequence. The average relative error of Method 1 is 15%, and generally it can be accepted in engineering. (2) Information updating is very useful for raising forecast accuracy. Compared with the Method 1, Method 2 reduces the error by 50%.

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Table 2 . Prediction results t

11

12

13

14

True value of y

3.317

3.464

3.606

3.742

Average relative error

Method 1

3.614

3.917

4.245

4.6

15.67%

Method 2

3.614

3.703

3.837

3.982

7.16%

Figure 2. Prediction curves

3. Engineering Example 3.1. Engineering Background National anorectal Medical Center is located in No.1 Jinling Road, Nanjing, and its total building area is 10,000m2. The area of pit is 3,000m2, and the circumference is

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122

J. Yan et al. / Research on Deformation Forecast of Deep Foundation Pit

270m. It is a deep foundation pit and excavation depth reachs 7.0m. During the excavation process, the deformation of the foundation pit was monitored. Some monitoring data of the horizontal displacement of top ring beam (Point 7)were gained (see Table 3) with a time series, the interval is 2 days, and the monitored data of time No. 2, 4, 8 and 9 were lost for some reason. Table 3. Actual horizontal displacement Time serial number Date

1

3

5

6

7

10

05.4.10

05.4.14

05.4.18

05.4.20

05.4.22

05.4.28

3.0

5.6

6.6

7.0

7.3

8.5

Measured deformation/mm

3.2. Prediction A table 3 data based prediction is conducted for Point 7’s future deformation (between Apr. 28 and May, 6) according to the Method 1 and 2 in part 2. Results as followed. Table 4. Prediction results Time serial number

11

12

13

14

05.4.30

05.5.2

05.5.4

05.5.6

8.9

9.2

9.5

9.7

Method 1 foreast/mm

9.471

10.037

10.639

11.277

10%

1.031

Method 2 forecast/mm

9.471

9.574

9.843

10.248

4.9%

0.459

Date

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Measured deformation/mm

Average

Average

relative error error/mm

Figure 3. Prediction curves

When the information is not feedback (Method 1), the accuracy has already reached 90%, and usually this error is allowed in engineering. When the information is feedback (Method 2), the average relative error is less than 5%. So, the non-equidistant Grey Model GM(1,1) has a good applicability for deep pit deformation.

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J. Yan et al. / Research on Deformation Forecast of Deep Foundation Pit

123

4. Further Discussion on Grey Background Level The final purpose to construct grey background level is simplifying the mathematical (1)

operation when x (t ) needs integral in the interval [ ki , ki 1 ] . Different construction methods produce different effects on accuracy. Under common circumstances, grey background level can be gotten by midpoint formula (1)

Z ( ki 1 )

1 2

(1)

(1)

[ x ( ki 1 )  x ( ki )]

L  'L

(11)

Actually, it is the length of median line. Whatever equidistant or non-equidistant GM (1,1) model, its fitting curve is always an exponential curve, so the value of (1)

x ( k i 1 / 2 ) is always smaller than length of median line (L is certainly smaller than L+ƸL in Figure 4.). When the shape of curve is similar to a straight line, the value of ƸL can be ignored and L is approximately equal to L+ƸL, and when the curve is a sharp increasing exponential curve, ƸL will be big and the error it brings can not be (1)

ignored. So it is easy to conclude that regarding x ( k i 1 / 2 ) as the grey background level is more appropriate than L+ƸL.

Z (1) ( ki 1 )

x (1) ( ki 1/ 2 )

(12)

x (1) (t )

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x (1) ( ki 1 )

ƸL (1)

x ( ki ) L

o

ki

ki 1/ 2

k i 1

t

Figure 4. The principle of constructing background grey level (1)

For getting the value of x ( k i 1 / 2 ) , the Lagrange interpolation is inducted. First, (1)

consider data pairs ( ki , x ( ki )) as a series of points in a function curve, then use the Lagrange interpolation polynomial to calculate the function value when abscissa is ki 1/ 2 . According to the analysis above, using Eq.(12) to reconstruct the grey background level and considering information updating at the same time, another prediction results is gotten (Table 5 and Figure 5).

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Table 5. Prediction results Time serial number

11

12

13

14

Measured deformation/mm

8.9

9.2

9.5

9.7

Midpoint formula/mm Way to construct grey background level Lagrange interpolation /mm

9.471

9.574

9.843 10.248

0.459

9.387

9.348

9.666

0.269

Average error/mm

9.976

Figure 5. Prediction curves

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In Table 5, the average error of Lagrange interpolation based grey background level with actual value is 0.269mm, and midpoint formula is 0.459mm, it shows that a good grey background level can improve precision of forecast model obviously. Compare Table 5 with Table 4, the reduced extent of average error of real-time forward simulation(1.031-0.459=0.572) is bigger than reconstructing grey background level(0.459-0.269=0.19). It means the real-time forward simulation has a good sensitivity for non-equidistant GM(1,1) prediction.

5. Conclusions and Suggestions This paper solves the problem of unequal interval deformation prediction of deep foundation pit, and discusses two ways to optimize the prediction model. One is realtime forward simulation by replenishing monitoring information constantly; another is ameliorating the grey background level. The achievements of this paper are listed as followed: x

For deformation prediction based on discontinuous or incomplete historical record, information feedback is vital and plays a major role in improving accuracy.

x

A good grey background level can bring a positive effect on prediction accuracy, but its influence is smaller than information feedback.

x

Information updating is not always adding data in historical sequence. When the information volume reaches a certain degree, the old data should be

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eliminated because the old information can’t reflect the recent dynamic change of foundation pit deformation. x

During the early stage of foundation pit excavation, monitoring data is poor and theoretically non-equidistant Grey Model GM(1,1) can forecast the deformation at any time in the future, but thinking about the accuracy of prediction, the predicting time should not be too long.

x

When new data reaches a certain amount, the equidistant Grey Model GM(1,1) can be used again.

References

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[1] HU Dong, Xiaoping ZHANG, Research on Predicting Deformation of Foundation Pit Based on Grey System Theory, Chinese Journal of Underground Space and Engineering, 5(1),(2009), 74-79.(in chinese) [2] Sifeng LIU, Yaoguo DANG, Zhigeng FANG, Grey theory and its application, Science Press, Beijing, 2003. (in chinese) [3] Helin FU, Peng Sitian, Han Rucai, The new mehods of Geotechnical Numerical Analysis, Central South University Press, Changsha, 2006. (in chinese) [4] Jingpen SHU g, Shenyong RUAN, Tutorial of MATLAB practice, Publishing house of electronics industry, Beijing, 2005. (in chinese) [5] Xuemeng WANG, Jizhong ZHANG, Rong WANG, Grey system analysis and practice program design, Huazhong University of Science and technology(HUST) Press, Wuhan, 1996. (in chinese) [6] Zuolei WANG, Guoliang CAI,The Problem in Modeling of Nonequidistance Squence and Its Improvement, College MATHEMATICS 19(2)(2003), 46-50. (in chinese) [7] Zhongxian WANG, Chundu WU, Xuerong SHI, A Grey Mold for Non-equidistant Sequence, Mathematics in practice and theory 33(10)(2003), 6-20. (in chinese) [8] Cuifeng LI, Wenzhan DAI, Determinator of the background level in the non-equidistant GM(1,1) model, J Tsinghua Univ(Sci&Tech) 47(S2)(2007), 1729-1732. (in chinese)

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Research on Reasoning Mechanism of Emergency Rescue Decision Support System of Geo-Hazards under the Conditions of Extreme Snow and Ice Disasters

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a

Liangchao ZOUa Shimei WANGa,1 and Haifeng HUANG a Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, China Three Gorges University, Yichang 443002, China

Abstract. A quick and effective emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters relies heavily on information technology tools, particularly decision support system; as the core content of decision support system, reasoning mechanism is significant because it directly determined the decision efficiency of the system and the accuracy of decision results. According to the characteristics of decision of geo-hazards emergency rescue, a rule-based (RBR) and case-based (CBR) hybrid reasoning mechanism is introduced into the emergency rescue decision support system of geo-hazards under the conditions of extreme snow and ice disasters (ERDSS-GHESID). The reasoning mechanism of ERDSS-GHESID is designed by the object-oriented programming method. It is showed that RBR and CBR hybrid reasoning mechanism can better simulate the inference procedure of human experts in the process of emergency rescue decision of geo-hazards under the conditions of extreme snow and ice disasters, showing a high practical value in the ERDSSGHESID. Keywords. Emergency rescue of geo-hazards, reasoning mechanism, rule-based reasoning; case-based reasoning

Introduction It has great abruptness and harmfulness of geo-hazards under the conditions of extreme snow and ice disasters, for which can lead to many damages, such as road block, power and communications interruption and so on, which cause further difficulties for emergency rescue and aggravate the losses of lives and properties. Just like the rare extreme snow and ice disaster occurred in South China early in 2008, it caused numerous landslides, collapses, debris flows and other geo-hazards, which resulted in enormous losses to the slope engineering, power transmission engineering, civil engineering as well as the safety of people’s life and property; furthermore, it seriously affected the healthy development of national economy and social stability [1-3]. 1

Corresponding Author. Email:[email protected]

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Therefore, it’s urgent to strengthen the research of key technologies of emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters based on the existing technology. As an important means of informationization, decision support system is an important technical support which can secure the fast and effective emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters; it is also one of the focuses of key technology research [4-6]. ERDSS-GHESID is a large and complex information system, which includes database, model base, method base, knowledge base and reasoning mechanism, and of them reasoning mechanism is the core content [1]. The reasoning mechanism is to use the knowledge of emergency rescue program of geo-hazards under the conditions of extreme snow and ice disasters in knowledge base, select reasonable models and methods based on the information in comprehensive database, conduct inference analysis according to some reasoning mechanism and obtain the programs and measures of emergency rescue. Therefore, a reasonable and effective reasoning mechanism is significant for the results and efficiency of ERDSS-GHESID. Rule Based Reasoning (RBR) and Case Based Reasoning (CBR) are two common reasoning mechanisms. For example, Zhang S, Hu QH and Wang XW studied the application of RBR in traction substation design[7]; Forbes DR, Smith SD and Horner RMW use the CBR in the system of risk management in construction project[8]and so on[9-14]. In this paper, characteristics of these two reasoning mechanisms will be analyzed as well as the decision-making process of emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters. Then, the RBR and CBR hybrid reasoning mechanism will be introduced into ERDSS-GHESID and also, the object-oriented programming method will be used to design the reasoning programs.

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1. RBR and CBR Mechanisms Under the extreme snow and ice conditions, decision of measures and programs should be more comprehensive in considering the effects of various factors compared to conventional geological disaster emergency rescue. How to consider these various factors? How to choose the best measures and programs of geology disaster emergency rescue from the knowledge base according to actual situation under the local conditions? They are issues worthy of research, and also are issues which should be solved in ERDSS-GHESID. In order to better solve the above issues, it is essential to choose a reasoning mechanism coinciding with the characteristics of emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters. At present, there are two commonly used reasoning mechanisms, RBR and CBR [7-13]. 1.1. RBR Mechanism RBR is a reasoning chain and the system which uses RBR mechanism is known as production system. Many successful decision support systems adopt the typical structure of production system, using the rules to describe knowledge. Usually, several basic components as follows are consisted in the production system [7-10]: x Fact base: stores narrative knowledge of facts, including the state and properties of problems.

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x

Rule base: stores some procedure knowledge of the relevant problems such as the state transition, nature changes and so on, which can be described as: IF condition and THEN conclusion. Condition can be logic combination of any clause; conclusion can be combination of many sub-conclusions or operations. x Controller: selects control strategy and matches rules with facts according to the controlling knowledge of problems; controls the process of reasoning and solves the conflicts. RBR mechanism is an important reasoning mechanism for its natural knowledge expression, simple form and easy for users to understand. However, it also has many disadvantages, for example, mutual relations between rules is not obvious; the overall image of knowledge is difficult to grasp; some knowledge is difficult to be accurately described by rules; the knowledge structure is inconsistent with that of real experts; low efficiency , lack of learning ability and so on.

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1.2. CBR Mechanism CBR mechanism is another mode of reasoning mechanism which is developed based on the artificial intelligence in recent decades. CBR is an analogical reasoning method, which obtains solutions of current new problems directly by accessing knowledge base of successful experience of past similar problems or case data. So it makes possible to solve complex problems in a short time. CBR mechanism includes the following process [11-13]: x Case search: by searching in the case database and calculating similarity, chooses some cases which are similar to the objective case to compose the case set. x Case match: chooses the best case or part of case from the searched case set; decides whether the solution of the chosen case accords with the object case by similarity vector, which can denote the similarity between the objective case and related cases. x Case Optimization: according to similarity vector, revises and optimize the solutions of chosen cases or part of cases to obtain the final solutions of the objective case. x Case study: evaluates solutions of the current problem and save them in the case base if the solutions are valuable to increase species and number of cases in the case base and makes the system with the ability of automatic learning. CBR mechanism doesn’t need matching rule and makes good use of previous successful experience, which is correspond better to the integrated thinking of experts. However, due to the complexity of problems in many areas, the results obtained by CBR mechanism will not agree with the realistic or relevant rules if copy indiscriminately the experience of others.

2. Analysis of Reasoning Characteristics of ERDSS-GHESID As mentioned before, the decision support system of emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters is affected by many factors, such as the complexity of structure, variety of expression in the knowledge base, the urgent time for decision and so on. Therefore, it is necessary for the reasoning

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mechanism to simulate expert thinking effectively and give the decision scheme as soon as possible. On the one hand, due to the obvious regional characteristics and complex mechanism of geo-hazards, it’s difficult to establish the geological model and acquire accurate mechanical parameters within a short time. Therefore, it is necessary to use the engineering analogy method to offer assistance for decision, which is based on the statistics data, observation results and experience of emergency rescue program of previous similar geological disasters. So that, the CBR mechanism should be taken to ensure a rapid and effective emergency rescue program. On the other hand, the emergency rescue of geo-hazards should be suitable for local conditions because it is affected by more complex factors under the conditions of extreme snow and ice disasters, which increases the uniqueness of geo-hazards emergency rescue. Therefore, the emergency rescue program should be verified by the rules of relevant regulations and preparedness under complex condition to add to emergency rescue measures under special conditions, which means, the RBR mechanism needs to be taken to ensure a correct and scientific emergency rescue program. Considering the characteristics of emergency rescue of geo-hazards under extreme conditions (such as complexity, time urgency, uniqueness and so on) and the advantages as well as disadvantages of RBR and CBR mechanisms, it’s suitable to combine the two reasoning mechanisms, which called RBR and CBR hybrid reasoning mechanism. It possesses advantages of the two reasoning mechanisms so that it can not only use the experience of past successful cases but also adjust emergency rescue by relevant rules according to specific characteristics of geo-hazards. Consequently, it can provide the decision support system capability to produce the right and best plan of emergency rescue rapidly. The structure of RBR and CBR hybrid reasoning mechanism is shown in Figure 1. RBR

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CBR

Case base

Case reasoning

Case study

Rule reasoning

Programs evaluation

Rule base

Decision of programs

Figure 1. Structure of RBR and CBR hybrid reasoning mechanism.

3. Design of RBR and CBR Hybrid Reasoning Mechanism 3.1. Process of Hybrid Reasoning Based on the above analysis, the main reasoning process of RBR and CBR hybrid reasoning mechanism in ERDSS-GHESID is shown in Figure2, including:

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L. Zou et al. / Research on Reasoning Mechanism of ERDSS-GHESID start Get attribute information of geo-hazards

Search in the case base

Whether exists similar case˛

Yes

Whether exists conflicts˛

No

No Search in the rule base

No

Verify by rules

Whether exists relate rules˛

Whether exists conflicts˛

Yes Whether exists conflicts˛

Yes

Solve the conflicts

No

Yes No

Solve the conflicts

Yes Solve the conflicts

No solution

Output the emergency rescue program

End

Figure 2. RBR and CBR hybrid reasoning in ERDSS-GHESID.

x

x

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x

x x

x

Step 1: Save the attribute information of geo-hazards (including properties of disaster spot, environmental attributes and socio-economic technical attributes, etc.) into a dynamic database. Step 2: Search whether any similar case set of emergency rescue exists in the case base. If yes, implement step 3; if not, implement step 4. Step 3: Judge whether there are conflicts between the searched similar cases. If yes, solve the conflicts by preliminary solution tactics, then implement step 5; if not, implement step 5 directly; Step 4: Search whether any corresponding matching rules exist in the rule base. If yes, implement step 6; if not, show no solution and end the reasoning. Step 5: Verify the inferred similar cases by related rules and judge whether they conflict with the existing rules. If yes, solve the conflicts by preliminary solution tactics and output emergency rescue program and end reasoning process; if not, output the emergency rescue program directly and end the reasoning process. Step 6: Judge whether the matched rules conflict with each other. If yes, solve the conflicts by preliminary solution tactics, output emergency rescue program and end reasoning; if not, output the emergency rescue program directly and end the reasoning.

3.2. Strategies of Conflict Resolution In the reasoning process of RBR and CBR hybrid reasoning mechanism, conflicts will appear if the searched cases don’t match the existing rules [7-13] and then it is needed to be solved by some conflict resolution strategies.

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The most similar cases and rules are selected to solve the conflicts in reasoning process of ERDSS-GHESID, including: x Determine the normalized weight vector of each attribute of geoi n

hazards W ( w1 , w2 ,..., wi ,..., wn ), 0 d wi d 1, ¦ wi

1 , in which, wi denotes the

i 1

relative importance of attribute i of geo-hazards before the reasoning. Then, according to the attributes of geo-hazards and the searched case set, establish a normalized similarity vector between the object problem and the searched similar case, i.e. P(r1 , r2 ,..., ri ,..., rn ), 0 d ri d 1 , in which, ri is the similarity of i n

attribute i of geo-hazards. Finally, calculate the value of

¦wr

i i

in each

i 1

x

x x

similar case and select the case with maximum value as the reasoning result. Preset priority hierarchy for each knowledge in the knowledge Base. Follow the principles that fact knowledge is prior to rule knowledge and the finer the knowledge granularity is, the higher the priority level will be. If the similarity and priority level are equal in the reasoning process, artificial selection will be prompted by the system. Rules are supposed to be prior to the searched cases when the two are in contradiction and the contradictions will be amended by rules

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3.3. Designing of Hybrid Reasoning Mechanism Program Reasoning mechanism is actually a computer program, which can be analyzed and designed by software designed methods .The object-oriented programming is applied widely in the design of large-scale projects for its advantages of flexibility and maintainability. The RBR and CBR hybrid reasoning mechanism is designed by the high-level programming language of C#, which is based on the object-oriented programming in ERDSS-GHESID. The pseudo codes of RBR and CBR in hybrid reasoning mechanism in ERDSSGHESID are as follows: public static void Reasoning() //Reasoning method { Fact=Fact_Read(); //Read the attribute data of geo-hazards ID = Case_Search(Fact); //Search in the case base and return to the ID of searched cases if (ID!=null ) { CaseResult=High_Similarity(); //Calculate the similarity of searched cases, return to the ID of highest bool RuleValidation=Rule_Validation(CaseResult); //Verify if conflicts with rules if (RuleValidation==true ) { Result=Rule_Amendments(); //Modify the result by rules Output(Result); //Output the reasoning result } else

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{ Output(Result); //Output the reasoning result } } else { RulesResult=Rules_Mark(Fact); //Match the rule base if (RulesResult!=null ) { Sort_byPriority(); //Sort the matched rules by priority hierarchy RulesResult = Conclusion_Result(); //Summarize the reasoning result Output(Result); //Output the reasoning result } else { Output(NoResult);//Display no solution } } }

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4. A Simple Example of Reasoning One county suffered continuous disasters of extreme snow and ice. With the temperature increasing, the accumulated snow and ice melt rapidly, causing obvious deformation and even local collapse, which posed a serious threat to the provincial highway in the front of the slope and an 110kV high-voltage pole on the side of provincial highway. Therefore, emergency rescue must be performed to ensure the traffic flow and the safety of the high-voltage lines. In order to develop emergency rescue program rapidly and effectively, the RBR and CBR hybrid reasoning mechanism is adopted to infer in the ERDSS-GHESID. Firstly, input basic information of the slope such as properties, scale, object of harmfulness, geological characteristics, terrain features and so on. Then the system begins to infer automatically according to the reasoning process shown in Figure.2 above. A similar case is searched out based on the CBR. It is another small and successful landslide case on the side of this provincial highway. The geological characteristics, terrain features and the inducing factors are quite similar to the present problem. Using experience of this case for reference, the emergency rescue program and control measures of the object problem are as follows: x Excavate drainage ditches on the boundary of landslide and excrete the snow melted water out of the boundary of the landslide immediately. x Cover the landslide and tensile cracks with plastic film to prevent snow and rainfall from infiltrating into the landslide. x Set up monitoring sections to monitor displacement of the landslide along the sliding direction. x Set up warning fences and send professional personnel to guide near the landslide.

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x

Take measures of cutting slope and reducing load and put mortar laid stone works on the inner side of the provincial highway. According to the result of rule verification by RBR, the 110kV high voltage pole is in danger. So additional emergency rescue measures need to be taken as follows: x Inform the power department to remove the dangerous high voltage pole to a safe location immediately. x Before the relocation completed, close the 110kV high voltage line if the landslide slides down in a large-scale to prevent secondary disasters caused by the collapse of the high voltage pole.

5. Conclusion The reasoning mechanism which is the core content of ERDSS-GHESID is discussed in this paper. Reasoning mechanism of RBR and CBR are introduced separately and their advantages and disadvantages are analyzed too. According to the characteristics of reasoning process of emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters, the RBR and CBR hybrid reasoning mechanism is introduced into ERDSS-GHESID. The RBR and CBR hybrid reasoning mechanism can better simulate the thought process of experts and produce the best and right emergency rescue plan rapidly. Then, the RBR and CBR hybrid reasoning mechanism is designed by the high-level programming language of C#, which based on the object-oriented programming in ERDSS-GHESID. Finally, the reasoning process of RBR and CBR hybrid reasoning mechanism are demonstrated through a simple example.

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References [1] Y.P. Ying, A Review and Vision of Geological Hazards in China. Scientific and technological management of land and resources 18 (2001), 26–30. [2] J.A. Wu, Recalled of Emergency Response to Natural Disasters in 2008 of China. Disaster Reduction in China 1 (2009), 12–14. [3] Y. Liu, Geologic Hazard Prevention in China. The Chinese jouranal of geological hazard and control 10 (1999), 79–80. [4] C. Tang, Approaches on Emergency Response System of Abrupt Geological Hazard Associated with Urban Areas, The Chinese Journal of Geological Hazard and Control 16 (2005), 104–110. [5] Y.C. Zhang L. Zhang, Q.H. Gao, Guiding Ideology in and Countermeasures for the Reduction of Geological Hazards, Management geological science and technology 17 (2000).34–36. [6] X.D. Wang, On Emergency Management for Natural Disasters Taken by Chinese Government. Soft Science 18 (2004), 47–52. [7] Forbes DR, Smith SD, Horner RMW, The selection of risk management techniques using case-based reasoning, Civil Engineering And Environmental Systems 27 (2010),107–121. [8] S. Zhang, QH. Hu, XW. Wang, Application of Rule-Based Reasoning for Traction Substation Design, Proceedings of International Workshop on Information Technology and Security(2008),29– 32,Shanghai, China [9] R. Barletta, An Introduction to Case Based Reasoning. AI Expert 8 (1991), 43–49. [10] L. Wang, P.H. Yang, Case-based Reasoning and Rule-based Reasoning for Agriculture Expert System, Journal of Taiyuan University of Technology 37 (2006), 267–269. [11] C.F. Wei, Y.Z. Li, J. Wang, et al, Design on inference engine of the spacecraft thermal fault diagnosis expert system, Journal of Beijing University of Aeronautics and Astronautics 54 (2006), 60–62. [12] L. Zhou, X.F. Feng, Q. Xie, et al, Design of Quality Fault Processing System Based on CBR, Journal of Jilin University (Information Science Edition) 24 (2006), 192–196. [13] J.H. Zhang, Z.Y. Liu, Case-based Reasoning and Rule-based Reasoning for Emergency Preparedness Information System, Journal of Tongji University 30 (2002), 890–894.

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Using Artificial Neural Networks For Evaluation of Collapse Potential of Some Iraqi Gypseous Soils

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a

Khalid R. MAHMOODa,1 and Juneid AZIZb Assist. Prof. Dr. Civil Engineering Dept., College of Eng., University of Anbar -Iraq b M.Sc., Civil Engineering Dept., College of Eng., University of Anbar-Iraq

Abstract. In this research, Artificial Neural Networks (ANNs) is used in an attempt to predict collapse potential of gypseous soils. Two models are built; one for collapse potential obtained by single oedemeter test and the other is for collapse potential obtained by double oedemeter test. A database of laboratory measurements for collapse potential is used. Six parameters, which are 1.Gypsum content, 2.Initial void ratio, 3.Total unit weight, 4.Initial water content, 5.Dry unit weight, 6.Soaking pressure. are considered to have the most significant impact on the magnitude of collapse potential and used as an input to the models. The output model is the corresponding collapse potential. Multi-layer perceptron trainings using back propagation algorithm are used in this work. A number of issues in relation to ANN construction such as the effect of ANN geometry and internal parameters on the performance of ANN models are investigated. Information on the relative importance of the factors affecting the collapse potential are presented and practical equations for prediction of collapse potential of single oedemeter test and double oedemeter test in gypseous soils are developed. It is found that ANNs have the ability to predict the collapse potential of single oedemeter test and double oedemeter test in gypseous soil samples with a good degree of accuracy. The ANN models developed to study the impact of the internal network parameters on model performance indicate that ANN performance is sensitive to the number of hidden layer nodes, momentum terms, learning rate, and transfer functions. The sensitivity analysis indicated that the initial void ratio and gypsum content have the most significant affect on the prediction of collapse potential. Keywords. Artificial Neural Networks, collapse potential, gypseous soils

Introduction Gypseous soil is a term used to denote soil with gypsum content. They are found in many regions in the world, mainly in arid and semiarid regions. Gypseous soils cover about 30% of the surface area of Iraq with gypsum content differs from one area to another, (Nashat, 1993). Gypseous soils are usually stiff when dry, but great losses in strength and sudden increase in compressibility occur upon wetting. Several investigators studied the collapsibility behavior of gypseous soils and agreed to consider a term named "Collapse Potential" proposed by Jennings and Knight, 1957, as a guide in the design of the foundations on gypseous soils. This term can be 1

Corresponding Author. Civil Engineering Dept., College of Eng., University of Anbar±Iraq, [email protected]

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measured through testing an oedometer sample after a simple alteration of the procedure of the test. Over the last few years, the use of (ANNs) has increased in many areas of engineering. In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. Scope of this paper to explore the use of Artificial Neural Network (ANN) models for predicting the "Collapse Potential (CP)" for Single Collapse Test and Double Oedometer Test under different conditions, provide a mathematical equation for prediction of "Collapse Potential (CP)" for two tests based on ANN technique and carry a sensitivity analysis to identify which of the input variables have the most significant impact on "Collapse Potential (CP)" for two models predictions. Single Collapse Test This method is generally used for assessing collapse potential. In this method, each test requires only one sample. An undisturbed soil sample in an oedometer with in-situ moisture content is consolidated with stress increments. When the applied vertical stress becomes equal or slightly higher than the overburden pressure, the sample is inundated. The strain observed after inundation is called h\GURFROODSVHVWUDLQİZ$IWHU hydroconsolidation, additional stress increments are applied to allow the soil consolidates. Double Collapse Test Two parallel oedometer tests on identical soil samples are used in this method. The first test is performed on a sample at its in-situ moisture content; the second on a soaked sample. Test results are plotted together. The vertical distance between the results UHSUHVHQWVWKHSRWHQWLDOK\GURFROODSVHVWUDLQİZDVDIXQFWLRQRIQRUPDOVWUHVV.

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1. Brief overview of artificial Neural Networks An artificial neural network is an attempt to simulate the manner in which the brain interprets information as determined by the current knowledge. Artificial neural networks behave in much the same manner as biological neural networks. Many authors have described the structure and operation of ANNs (Zurada 1992). ANNs consist of a number of artificial neurons variously known as processing elements (PEs), nodes or units. For multilayer perceptrons (MLPs), which is the most commonly used ANNs in geotechnical engineering, processing elements in are usually arranged in layers: an input layer, an output layer and one or more intermediate layers called hidden layers. Each processing element in a specific layer is fully or partially connected to many other processing elements via weighted connections. From many other processing elements, an individual processing element receives its weighted inputs, which are summed and a bias unit or threshold is added or subtracted. The bias unit is used to scale the input to a useful range to improve the convergence properties of the neural network. The result of this combined summation is passed through a transfer function (e.g. logistic sigmoid or hyperbolic tangent) to produce the output of the processing element. For node j, this process is summarized in Equations.

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K.R. Mahmood and J. Aziz / Using ANNs for Evaluation of CP of Some Iraqi Gypseous Soils

IJ

¦ wij xi  T j

yj = f(Ij)

Summation

(1)

Transfer

(2)

where: Ij= the activation level of node j, Wij= the connection weight between nodes i and j, xi= the iQSXWIURPQRGHLL ««Q, șM= the bias or threshold for node j, yj= the output of node j; and f(.)= the transfer (activation) function

2. Development of ANNs models Over the years, several investigators studied the collapsibility behavior of gypseous soils and achieved many empirical relationships of this process depending on many factors affect on it and agreed to consider a term "Collapse potential (CP)" proposed by Jennings and Knight,1957, as guide in the design of the foundations on gypseous soils. The data used to calibrate and validate the neural network models are obtained from the literature, and include laboratory measurements of collapse potential as well as corresponding information regarding the soil properties, apparatus used and testing conditions. The data cover a range of soil types. The database comprises a total of (345) case record, and can be found in the literature. The steps for developing ANN models as outlined include the determination of model inputs and outputs, preprocessing and division of the available data, scaling of data, the determination of appropriate network architecture and optimization of the connection weights. A PCbased commercial software system called Neuframe Version 4.0 (Neusciences 2000) is used, in which optimal network architecture is determined by trial-and-error.

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2.1. Models Inputs and Outputs It is generally accepted that six parameters have the most significant impact on the collapse potential in gypseous soils, and are thus used as the ANN model inputs. These include the following:x Gypsum content (GC) %. x Initial void ratio (eo) x ,QLWLDOWRWDOXQLWZHLJKWȖW x Initial water content (wo) x ,QLWLDOGU\XQLWZHLJKWȖG  x Soaking pressure (Pso) KPa The output of the model is Collapse potential of Single Oedometer Test and double Oedometer Test. 2.2. Pre-processing and Data Division Data processing is very important in using neural nets successfully. It determines what information is presented to create the model during the training phase. It can be in the form of data scaling, normalization and transformation. Transforming the input data into some known forms (e.g. log., exponential, etc.) may be helpful to improve ANN performance.

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The next step in the development of ANN models is dividing the available data into their subsets training, testing, and validation. The training set is used to adjust the connection weights of the neural network. The testing set is used to check the performance of the network at various stages of learning, and training is stopped once the error in the testing set increases. The validation set is used to evaluate the performance of the model once training has been successfully accomplished. (Shahin, 2003). In total, 80% of the data are used for training and 20% are used for validation. The training data are further divided into 70% for the training set and 30% for the testing set. These subsets are also divided in such a way that they are statistically consistent and thus represent the same statistical population. In order to achieve this, several random combinations of the training, testing and validation sets are tried until three statistically consistent data sets are nearly obtained. To examine how representative the training, testing and validation sets are with respect to each other t-test and F-test are carried out. The t-test examines the null hypothesis of no difference in the means of two data sets and the F-test examines the null hypothesis of no difference in the variances of the two sets. For a given level of significance, test statistics can be calculated to test the null hypotheses for the t-test and F-test respectively. Traditionally, a level of significance equal to 0.05 is selected. Table 1. The range of data used in two models of inputs variables Single oedemeter test

Double oedemeter test

Input variables Max

Min

Max

Min

Gypsum content

81

6

81

5

Initial void ratio

0.75

0.29

0.75

0.29

Total unit weight

19.3

13.8

20.8

13.8

25

0

20.46

0

Dry unit weight

18.3

12.2

17.3

13

Soaking pressure

800

50

800

25

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Initial water content

2.3. Scaling of data The input and output variables are pre-processed by scaling them to eliminate their dimension and to ensure that all variables receive equal attention during training. Scaling has to be commensurate with the limits of the transfer functions used in the hidden and output layers. The simple linear mapping of the variables, extremes to the neural network¶s practical extremes is adopted for scaling, as it is the most commonly used method, (Shahin, 2003). As part of this method, for each variable x with minimum and maximum values of xmin and xmax, respectively, the scaled value xn is calculated as follows:

xn

x  x min x max  x min

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(3)

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2.4. Model architecture, optimization and stopping criteria One of the most important and difficult tasks in the development of ANN models is determining the model architecture (i.e. the number and connectivity of the hidden layer nodes). A network with one hidden layer can approximate any continuo function, provided that sufficient connection weights are used, (Shahin, 2003).Consequently, one hidden layer is used in this research. The general strategy adopted for finding the optimal network architecture and internal parameters that control the training process is as follows: a number of trials is carried out using the default parameters of the software used with one hidden layer and ««KLGGHQOD\HUQRGHV,WVKRXOGEHQRWHGWKDWLVWKHXSSHUOLPLWIRUWKH number of hidden layer nodes needed to map any continuous function for a network with 6 inputs, (Caudill, 1988) and consequently, is used in this work. The network that performs best with respect to the testing set is retrained with different combinations of momentum terms, learning rates and transfer functions in an attempt to improve model performance, since the back-propagation algorithm uses a first-order gradient descent technique o adjust the connection weights, it may get trapped in a local minimum if the initial starting point in weight space is unfavorable. Consequently, the model that has the optimum momentum term, learning rate and transfer function is retrained a number of times with different initial weights until no further improvement occurs. 2.5. Single oedemeter test, The optimal model has three hidden layer nodes with learning rate equal 0.2, momentum term equal 0.8, transfer function in layers is tanh, transfer function in output layer is sigmoid and the error difference in RMSE being 0.85% and 1.02% for training and testing respectively.

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2.6. Double oedemeter test, The optimal model has seven hidden layer nodes with learning rate equal 0.6, momentum term equal 0.15, transfer function in layers is tanh, transfer function in output layer is sigmoid and the error difference in RMSE being 0.946% and 1.289% for training and testing respectively.

Figure 1. Effect of hidden layers nodes of testing set in single Oedemeter test

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139

Figure 2. Effect of hidden layers nodes of testing set in double Oedemeter test

3. ANN Models Equation The small number of connection weights obtained for the optimal ANN models enables the network to be translated into relatively simple formula. 3.1. Single oedemeter test mode Using the connection weights and the threshold levels, the predicted collapse potential of single oedemeter test can be expressed as follows:

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CP =

8.44 + 0.16 ( -1.255 + 3.156tanhx + 2.656tanhx - 3.802tanhx ) 1 2 3 1+ e

(4)

where: x1= 5.172+10-3[31.92GC-7172eo-ȖWȦR±ȖG-2.53Pso]

(5)

x2=3.23+10-4[-344GC+30780eoȖWȦR-ȖG-0.093Pso]

(6)

x3=6.487+10-3[11.4GC±345eo±ȖWȦR±ȖG-1.01Pso]

(7)

3.2. Double oedemeter test model Using the connection weights and the threshold levels, the predicted collapse potential of single oedemeter test can be expressed as follows:

CP =

11 1+ e

(1.14+0.44tanhx1+1.92tanhx2 +2.1tanhx3 +2.13tanhx4 -1.67tanhx5 +0.98tanhx6 1.75tanhx 7 )

where: Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

(8)

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K.R. Mahmood and J. Aziz / Using ANNs for Evaluation of CP of Some Iraqi Gypseous Soils

x1= -0.190+10-3[0.76GC-258.6eo-ȖW -Ȧo+ ȖG-0.444Pso]

(9)

x2= 0.495+10-3[-23.4GC+2876eo+ 43.1ȖW +97.8Ȧo+166.3ȖG-2.11Pso]

(10)

x3= 4.278+10-3[15.2GC-1621.3eo-150.7ȖW-Ȧo-91.67ȖG-1.32Pso]

(11)

x4= 2.312 + 10-3[5.37GC-4475.7eo+19.9ȖW+75.1Ȧo-143.9ȖG-0.28Pso]

(12)

x5=1.262+10-3[1.67GC+5398eo-91.5ȖW-28.9Ȧo-219.4ȖG+8.65Pso]

(13)

x6=1.659+10- 3[7.08GC-2889.3eo+21.4ȖW+36.9Ȧo-30.9ȖG-0.98Pso]

(14)

x7=-4.85+10-3[28GC+416.3eo+123.7ȖW-20.7Ȧo+207.5ȖG-2.052Pso]

(15)

where: CP= Predicted collapse potential, GC= Gypsum content, eo= Initial void ratio, ȖW  7RWDO XQLW ZHLJKW N1P , ȦR  7RWDO XQLW ZHLJKW N1P), ȖG  'U\ XQLW weight (kN/m3), Pso= Soaking pressure (kN/m2)

4. Sensitivity Analysis of the ANN Model Inputs In an attempt to identify which of the input variables has the most significant impact on collapse potential of single and double oedemeter test predictions, a sensitivity analysis is carried out on the ANN models. A simple and innovative technique proposed by Garson (1991) is used to interpret the relative importance of the input variables by examining the connection weights of the trained network.

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4.1. Single oedemeter test The results indicate that the gypsum content and initial void ratio had the most significant effect on the predicted the collapse potential of single oedemeter test with a relative importance of 27.1 and 26.7% respectively, followed by dry unit weight, initial water content, soaking pressure and total unit weight with a relative importance of 13.9, 12.9, 10.1 and 9.11% respectively. The results are also presented in Figure 3. 4.2. Double oedemeter test The results indicate that the initial void ratio has the most significant effect on the predicted collapse potential followed by initial water content with a relative importance 24.6 and 19.1%. The results also indicate that soaking pressure, gypsum content and dry unit weight has moderate impact on the collapse potential with a relative importance equals to 17.4, 15.5 and 14.4 %, respectively, while total unit weight has the smallest impact on the collapse potential with relative importance of 9.1%.The results are also presented in Figure 4.

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141

Figure 3. Sensitivity Analysis of single Oedemeter test

Figure 4. Sensitivity Analysis of double Oedemeter test

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5. Robust of the models The model is deemed to be able to generalize and is considered to be robust. The coefficient of correlation, r, the root mean squared error, RMSE, and the mean absolute error, MAE, are the main criteria that are often used to evaluate the prediction performance of ANN models (Shahin, 2008).ANNs have the ability to predict the collapse potential of single oedemeter test and double oedmeter test in gypseous soils, with a good degree of accuracy within the range of data used for developing ANN models. Table below show the robust of the two models. Table 2.robust of the two models Single oedemeter test

Double oedemeter test

Criteria training set

testing set

training set

testing set

0.88

0.85

0.94

0.87

RMSE

0.85%

1.02%

0.946%

1.286%

MAE

0.66%

0.77%

0.73%

1.02%

r

6. Validity of the ANN Model Equation To assess the validity of the derived equation for the collapse potential of single oedemeter test (CPS) and the collapse potential of double oedemeter test (CPD) models,

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the equations are used to predict these values on the basis of all, training, and validation data sets used. Table 3.Validity of the ANN Model Equation Single oedemeter test

Double oedemeter test

Set r2

r2

Training set

0.701

0.7

Testing set

0.788

0.864

Validation set

0.89

0.711

7. Conclusions ANNs have the ability to predict the collapse potential of single oedemeter test and double oedmeter test in gypseous soils, with a good degree of accuracy within the range of data used for developing ANN models. The models performance indicates that ANN performance is sensitive to the number of hidden layer nodes, momentum terms, learning rate and transfer functions. The ANN model could be translated into simple and practical formula from which collapse potential of single oedemeter test or double oedmeter test may be calculated.

References

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[1] M.K., Abood, Treatment of Gypseous Soil with Sodium Silicate, M.Sc. Thesis, Building and Construction Department, University of Technology,1995. [2] M.D., Al-Agaby, Effect of Kerosene on Properties of A Gypseous Soil, M.Sc. Thesis Department of Civil Engineering, College of Engineering-University of Baghdad, 2001. [3] S. N. Al-Ani, M. M. and Seleam, Effect of Initial Water Content and Soaking Pressure on the Geotechnical Properties of Gypseous Soils, Journal of Al-Muhandis,(1993) No.116.

[4]

M.K.A., Al-Gabri, Collapsibility of Gypseous Soils Using Three Different Methods, M.Sc. Thesis,

Department of Building And Construction Engineering, University of Technology,2003. [5] I.H., Nashat, Engineering Characteristics of Some Gypseous Soils in Iraq, Ph.D. Thesis, Department of Civil Eng. University of Baghdad,1990. [6] I.H., Nashat,ΔϴδΒΠϟ΍ΏήΘϟ΍ϝϼϐΘγ΍, Conference of Gypseous Soil, NCCL,1993. [7] M. Y., Al-Shahwani, A Non-Destructive Technique to Evaluate the Geotechnical Properties of Gypsiferous Soils, M.Sc. Thesis Department of Building and Construction , University of Technology,1994. [8] S. N., Seleam, and M. M., Al-Ani, Effect of Initial Water Content and Soaking Pressure on the Geotechnical Properties of Gypseous Soils, Journal of Al-Muhandis,1993, No.116 [9] R.K. Mohammed, Effect of Wetting and Drying on Engineering Characteristics of Gypseous Soil, M.Sc. Thesis, Department of Building and Construction, University of Technology,1993. [10] J. E. Jennings and K., Knight, The Addition Settlement of Foundations Sandy Subsoil on Wetting. Proceeding of 4th International Conference on Soil Mechanics and Foundation Engineering Vol.1, 1957. [11] J. M. Zurada, Introduction to artificial neural systems, West Publishing Company, St. Paul,1992 [12] M.A, Shahin, M.B., Jaska and H.R., Maier, Application of Artificial Neural Networks in Foundation Engineering, Australian Geomechanics.2003. [13] M. Caudill, Neural networks primer, Part III.,AI Expert, 3(6) (1988), 53-59. [14] G. D., Garson, Interpreting Neural-Network Connection Weights. AI Expert 6(7) ( 1991),47-51.

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[15] M.A, Shahin, M.B., Jaska and H.R., Maier, Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications, Department of Civil and Environmental Eng., University of Adelaide,2008. [16] M.A, Shahin, M.B., Jaska and H.R., Maier, State of the Art of Artificial Neural Networks in Geotechnical Engineering Department. of Civil Engineering, Curtin University of Technology, Perth, WA 6845, Australia,2008.

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Discussion about Data Mining Application in Civil Engineering Deformation Measurement Analysis a

Y.J. TANGa,1 Department of Geotechnical Engineering, Tongji University, Shanghai, China

Abstract. This paper presents the data mining application in the civil engineering deformation measurement. The data mining process is described based on a case study. This case is about deformation of residence buildings caused by one of Shanghai tunnel constructions in the vicinity of these buildings. The steps of data cleaning, data Integration, data mining, model assessment, and analysis results expression are given in detail according to the specific condition in the case. The findings in this article provide reference for deformation measuring data analysis, especially for the situation which limited measuring network or relative unstable region. Keywords. Settlement measurement, tunnel construction, data mining

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Introduction Public transport facility demand increases with the development of urbanization in China. Consequently tunnel construction in dense residence buildings region carries on frequently in the large or medium-sized cities in China. Therefore, the major concern is how to decrease the deformation caused by tunnel construction to adjacent existed buildings [1]. However, the real condition is short construction period, expensive construction cost, and difficult building removal in adjacent to tunnel worksite. It results in excessive deformation or excessive crack width in the existing buildings during adjacent tunnel construction, especially in soft soil areas [2]. The deformation measurement is necessary before, during and after tunnel construction for tunnel excavation and adjacent existing buildings in order to protect them from damage. When excessive deformation or cracks occur, the owners of the existing buildings ask for compensation from construction party, while construction party always refuses giving compensation. To reach agreement, reasonable data mining is important to evaluate deformation caused by different parties in order to make responsibilities reasonable. The data mining process is described based on the deformation of residence buildings caused by a Shanghai tunnel construction in the vicinity of the buildings. The steps of data cleaning, data Integration, data Conversion, data mining, model assessment, and analysis results expression are given in detail according to the specific 1 Corresponding Auhtor: Y. J.TANG, Department of Geotechnical Engineering, Tongji University, Shanghai, China,No.1239 Siping, Shanghai 200092, China; [email protected] Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

Y.J. Tang / Data Mining Application in Civil Engineering Deformation Measurement Analysis

145

condition [3]. The security of existing buildings and the influencing extend caused by tunnel construction are evaluated based on data mining.

1. Engineering Background A Residential district, including six five-story buildings for residence, is located in the south of Shanghai in China. It was made from brick and concrete in 1993 and 1994.Their foundation is shallow style with raft. The geometry size and initial condition is shown in Table 1. The layout is illustrated in Fig.1. Table 1. Geometry size and initial condition of buildings Serial number of building

Height of story (m)

Width

Length

Depth

(m)

(m)

of raft(m)

No.1

22.0

60 square meters located on the dark creek processed

No.2

40.2

Normal

No.3

53.7 2.8

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Initial condition

12.3

Normal 2.0

No.4

31.5

It includes two parts which are not built at the same time. The settlement joint between two parts is 280mm width

No.5

43.0

Normal

No.6

34.0

Normal

Walls below the ground level were built by standard brick MU10 and cement mortar M10. Walls on the first floor and the second floor were built by porous brick MU10 and admixture mortar M10. Walls from on the third floor to the fifth floor were built by porous brick MU10 and admixture mortar M5. Columns, beams and plates were built by concrete C20.

2. Data Mining Process From Fig.1, we can see it is not easy to find stability around the tunnel and the resident buildings because the roads and tunnel surrounding the residence buildings are in vibration situation. Under this condition, a high precise measurement network is required. According to the code for deformation measurement of building and structure [4], the precise connecting transverse shown in Fig.1 consists of 14 points. The elevation of point 1 is assumed to be 10.000m. The other points, according to Table 2, in the connecting transverse system are 10.1260m,9.0966m,7.9751m,7.9745m, 7.9217m,7.9411m,7.9666m,7.9733m,7.7911m,7.8359m,7.8521m,7.9477m,7.8545m and 7.8777m from point 2 to 15, respectively.

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Figure 1. Layout chart of buildings

Table 2. Precise connecting transverse observation(unit: m)

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Date of 12ĺ 7ĺ 8ĺ 8ĺ 8ĺ 9ĺ 1ĺ2 1ĺ 3ĺ 4ĺ 4ĺ 5ĺ 5ĺ 7ĺ 4 measureme

May 24,06

forwar 0.125 -0.903 -1.121 -0.138 -0.000 -0.052 -0.034 -0.088 d 8 1 1 7 5 6 2 1

— 0.0258

backw d

—

— 0.9030 1.1212 0.1386 0.0004 0.0524 0.0339 0.0882

-0.175 5

-0.026 0.1753 1

— 0.0064

—

-0.025 6

-0.006 0.0255 4

forwar 0.126 -0.903 -1.121 -0.138 -0.000 -0.052 -0.033 -0.089 -0.175 -0.088 -0.025 0.0025 0.0254 0.0071 1 6 5 6 6 8 2 1 4 6 6 May d 31,0 6 backw -0.002 -0.025 -0.006 — 0.9038 1.1214 — 0.0006 0.0528 0.0335 0.0889 0.1754 0.0890 0.0258 d 5 6 9

June 7,06

forwar 0.126 -0.903 -1.121 -0.139 -0.000 -0.052 -0.033 -0.089 -0.175 -0.088 -0.025 0.0025 0.0254 0.0069 d 0 4 7 2 6 8 4 0 8 8 6 backw d

June forwar 20,0 d 6

— 0.9038 1.1218 0.1392 0.0006 0.0528 0.0335 0.0890

—

-0.002 -0.025 4 4

— 0.0888

-0.007 0.0258 0

-0.905 -1.119 -0.139 -0.000 -0.053 -0.033 -0.089 -0.176 -0.089 -0.025 0.0022 0.0256 0.0068 2 8 7 6 2 2 2 1 0 7

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Y.J. Tang / Data Mining Application in Civil Engineering Deformation Measurement Analysis

backw d forwar d July 28,0 6 backw d

— 0.9053 1.1194 0.1396 0.0006 0.0532 0.0336 0.0884

147

-0.002 -0.026 -0.006 0.1758 0.0890 0.0260 0 3 4

-0.140 -0.000 -0.053 -0.033 -0.088 -0.176 -0.089 -0.025 0.0020 0.0262 0.0060 5 5 0 6 6 6 2 6

—

—

—

—

—

— 0.1404 0.0002 0.0530 0.0332 0.0894

-0.002 -0.025 -0.006 0.1767 0.0892 0.0260 0 6 6

Different 0.126 -0.903 -1.121 -0.139 -0.000 -0.052 -0.033 -0.089 -0.175 -0.088 -0.025 settlement 0.0024 0.0255 0.0067 0 4 5 2 6 8 4 0 5 9 6 (mm)

2.1. Data Cleaning The elevation of each measure points on the six buildings based on Table 2 and the original records omitted in this article can be easily calculated, as shown in Table 3. Table 3. Relative elevation and accumulating settlements of buildings from May 2006 to July 2006

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No. of measure point

Accumulating settlement (mm)

Relative elevation(m) May 24,06

May 31,06

June 7,06

June 20,06

July 28,06

50

8.394

8.3937

8.3938

8.39345

8.3927

1.3

51

8.3434

8.343

——

8.3429

8.3422

1.2

52

8.3902

8.3902

8.39025

8.38965

8.3889

1.3

53

8.3845

8.3842

8.38425

8.38405

8.3834

1.1

54

8.4407

8.44025

8.44085

8.44015

8.4396

1.1

55

8.4371

8.43685

8.43725

8.43675

8.4362

0.9

56

8.3362

8.33625

8.3366

8.33595

8.3354

0.8

57

8.3526

8.3525

8.3522

8.35245

8.3517

0.9

58

8.4029

8.40235

8.40245

8.40215

8.4011

1.8

60

8.2354

8.23495

8.2354

8.2343

8.2336

1.8

61

8.2108

8.2107

8.21095

8.2102

8.2095

1.3

61N

8.1129

8.1128

8.1125

8.1118

8.1107

2.2

62

8.3324

8.3317

8.3319

8.33095

8.3299

2.5

62N

—

—

—

8.23175

8.2315

0.2

63

8.3665

8.3666

8.36655

8.36635

8.3659

0.6

64

8.3048

8.3052

8.305

8.3048

8.3045

0.3

65

8.2684

8.269

8.2683

8.26835

8.2679

0.5

66

8.2527

8.25225

8.2523

8.25205

8.2518

0.9

67

8.2837

8.2838

8.2839

8.2832

8.2827

1.0

68

8.2432

8.2434

8.24345

8.2431

8.2417

1.5

69

8.1877

8.18805

8.18775

8.18715

8.1855

2.2

70

8.3534

8.3532

8.35335

8.35295

8.3525

0.9

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Y.J. Tang / Data Mining Application in Civil Engineering Deformation Measurement Analysis

71

8.3513

8.35115

8.35115

8.3507

8.3504

0.9

72

8.3259

8.32585

8.3259

8.3256

8.325

0.9

73

8.2712

8.2712

8.2711

8.2708

8.2705

0.7

74

8.2489

8.24865

8.24855

8.24845

8.2481

0.8

75

8.1997

8.19975

8.1998

8.19935

8.1985

1.2

76

8.4741

8.4736

8.4737

8.47305

8.4727

1.4

77

8.3739

8.37355

8.3734

8.37265

8.3718

2.1

78

7.3427

8.34215

8.3419

8.34125

8.3401

2.1

79

8.2128

8.2124

8.2123

8.21205

8.2116

1.2

80

8.0974

8.0971

8.0971

8.09615

8.0961

1.3

From table 3㸪No.78 measure point value on May 24 is obviously wrong, called coarse error. Generally, it is cancelled or restored. If it is cancelled, the accumulated settlement is shown in last column of the table 3. If it is restored, we can get more reasonable results, described in the 2.3. 2.2. Data Integration

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In order to find the most stable point in the measuring network, get initial deformation of the residence buildings just when the buildings construction completed, and get additional deformation of the residence buildings during the tunnel excavation, data integration is done. The deformation datum measured in the period 1993-1994 2004-2006 is gathered, as shown in Table 4 and 5. The datum in Table 4 is provided by Shanghai building office archives. During the period 1993-1994, the buildings construction was carried on. The datum in Table 5 is provided by Shanghai No.2 construction limited company. During the period 2004-2006, the tunnel construction carried on. Table 4. Deformation measurement from 1993 to 1994(bench mark elevation is 4.800m) Serial Number of number measuring points of building

Accumulating settlement (mm)

Relative elevation (m)

8 Apil 30 April 22 May 21 June 20 Sept. 1994 1994 1994 1994 1994 No.6

50

4.8300

4.821

4.817

4.812

4.872

48

51

4.8350

4.829

4.824

4.818

4.786

49

52

4.8500

4.842

4.836

4.831

4.801

49

53

4.8400

4.831

4.826

4.821

4.793

47

4 March 15 March 28 March 20 April 3 May. No.4

60

1994 4.8660

1994 4.861

1994 4.857

1994 4.851

1994 4.847

19

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Y.J. Tang / Data Mining Application in Civil Engineering Deformation Measurement Analysis

61

No.2

No.1

4.8500

4.849

4.844

4.840

4.838

12

15 Dec.

2 Jan.

72

1993 4.8150

1994 4.814

1994 4.806

1994 4.800

1994 4.766

49

73

4.7890

4.787

4.779

4.773

4.734

55

74

4.8230

4.820

4.806

4.795

4.753

70

75

4.815

4.812

4.797

4.785

4.740

75

19 Dec.

9 Jan.

76

1993 4.8400

1994 4.838

1994 4.833

1993 4.827

1993 4.819

21

77

4.7879

4.788

4.784

4.777

4.774

15

78

4.7960

4.795

4.790

4.786

4.768

28

80

4.7970

4.793

4.779

4.759

4.669

128

8 March 11 April 20 April

3 March 11 April 20 April

For No.1 building, it was reported that the maximum settlement were 128mm in the east on 20 April 1993.That is shown that it has about 5‰ inclination towards to the west when it was final completion. From No.2 of the buildings to No.4 of the buildings, they have 0.12‰, 0.04‰, and 3‰ inclination, respectively, according to the Table 4 and Fig.1. After the excavation finished, the buildings have 3.76‰, 2.33‰, 2.31‰, and 2.66‰ additional inclination from No.1 to No.4 building, respectively, according to Table 5 and Fig.1. Table 5 Deformation measurement from 2004 to 2006(bench mark elevation is 4.800m)

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Accumulating settlement(mm) Serial

Number of

number of

Measuring

building

points

No.4

No.3

10 Nov.

14 Nov.

20 Nov.

28 May

29 Aug.

14 Dec

2004

2004

2004

2005

2005

.2005

59

20.76

25.20

30.77

74.1

81.84

——

——

60

37.03

40.64

47.15

87.57

94.3

100.41

105.01

61

74.45

77.49

82.98

109.86

113.51

116.92

120.45

62

48.87

51.72

55.20

72.12

75.24

75.04

78.64

63

18.65

18.29

18.93

26.93

29.18

29.83

30.67

64

18.95

20.44

21.76

30.65

31.47

32.34

32.03

65

35.29

37.42

39.87

53.58

55.62

56.90

58.89

66

40.93

42.74

45.56

65.12

68.21

70.59

71.77

67

61.30

64.23

70.97

96.74

101.63

104.86

109.33

68

79.64

82.68

89.87

122.87

129.73

134.52

138.40

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23 Feb. 2006

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Y.J. Tang / Data Mining Application in Civil Engineering Deformation Measurement Analysis

No.2

No.1

69

90.55

94.92

102.37

138.10

146.22

153.77

159.07

70

17.32

19.60

21.31

34.80

36.41

36.67

36.27

71

20.32

23.05

24.57

36.45

38.73

——

——

72

20.79

23.15

24.64

37.88

39.76

40.52

39.92

73

34.35

37.19

38.89

55.83

58.50

60.57

60.37

74

60.97

63.42

65.44

89.67

94.49

98.15

99.42

75

89.05

91.16

63.10

123.89

128.50

134.24

137.41

76

52.57

54.41

54.81

64.94

67.66

67.09

69.19

77

55.24

56.98

57.29

57.81

61.25

63.56

64.08

78

62.05

65.47

65.94

67.85

71.23

76.00

77.11

79

90.61

89.65

94.41

101.37

106.73

112.55

114.76

80

130

129.35

134.72

147.37

156.61

166.91

171.42

2.3. Data Mining Table 6. Relative elevation and accumulating settlements of buildings from May 2006 to July 2006 after excellent data mining Serial number of building

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No.6

No.5

No.4

No.3

Relative elevation (m)

Accumulating

Number of

settlement

24 May

31 May

7 June

20 June

28 July

2006

2006

2006

2006

2006

50

9.9566

9.9572

9.9568

9.9566

9.9559

0.7

51

9.9059

9.9065

——

9.9060

9.9054

0.5

52

9.9529

9.9537

9.9533

9.9530

9.9523

0.6

53

9.9472

9.9477

9.9473

9.9474

9.9468

0.4

54

10.0036

10.0034

10.0036

10.0033

10.0034

0.2

55

10.0000

10.0000

10.0000

10.0000

10.0000

0.0

56

9.8992

9.8997

9.8995

9.8991

9.8989

0.3

57

9.9149

9.9160

9.9152

9.9149

9.9148

0.1

58

9.9655

9.9657

9.9655

9.9652

9.9643

1.2

60

9.7983

9.7982

9.7979

9.7969

9.7963

2.0

measuring points

(mm)

61

9.7737

9.7740

9.7734

9.7728

9.7722

1.5

61N

9.6758

9.6763

9.6754

9.6749

9.6742

1.6

62

9.8950

9.8952

9.8948

9.8940

9.8934

1.6

62N

——

——

——

9.7949

9.7942

0.7

63

9.9289

9.9298

9.9294

9.9294

9.9293

-0.4

64

9.8672

9.8684

9.8679

9.8679

9.8679

-0.7

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Y.J. Tang / Data Mining Application in Civil Engineering Deformation Measurement Analysis

No.2

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No.1

65

9.8316

9.8322

9.8312

9.8314

9.8313

66

9.8159

9.8155

9.8151

9.8152

9.8154

0.5

67

9.8469

9.8470

9.8467

9.8463

9.8463

0.6

68

9.8064

9.8067

9.8064

9.8059

9.8050

0.3

69

9.7509

9.7514

9.7506

9.7499

9.7488

2.1

151

0.3

70

9.9164

9.9164

9.9163

9.9159

9.9158

0.6

71

9.9143

9.9143

9.9140

9.9138

9.9137

0.6

72

9.8890

9.8890

9.8888

9.8885

9.8884

0.6

73

9.8342

9.8344

9.8340

9.8337

9.8339

0.3

74

9.8119

9.8119

9.8114

9.8110

9.8113

0.6

75

9.7630

9.7629

9.7627

9.7618

9.7617

1.3

76

10.0369

10.0368

10.0366

10.0360

10.0359

1.0

77

9.9367

9.9366

9.9363

9.9356

9.9350

1.7

78

9.9050

9.9050

9.9048

9.9042

9.9033

1.7

79

9.7762

9.7761

9.7752

9.7745

9.7735

2.7

80

9.6608

9.6608

9.6600

9.6585

9.6580

2.8

According to Fig.1 and Tab.2-5, the author finds that: Four points with number 54,55,56,57 are stability;Their heights are: H54=10.0035m ± 0.0003m, H55=10.000m ± 0.0003m,H56=9.8992m ± 0.0003m,and H57=9.9150m±0.0003m.After the height of point 55 is chosen to be a reference mark (datum point), the heights of all measured points on buildings are calculated as shown in table 6.The height of control points are determined by standard deviation Ȫ=± 0.2mm. Transit survey and level survey for inclinations of buildings have been employed. By level survey, inclinations of buildings can be predicted with the ratio of difference settlement to distance between two measure points. Combining Table 4-6, we can predicted that the maximum inclination is 11.72‰ (including initial inclination, construction error and measurement error). Inclination 11.72‰ towards west (tunnel line) or excavation in building No.1 has exceeded the limit permitted by foundation code (GBJ7-89) which 10.00‰. However, the building 1# has about 5‰ inclination when the building is completed. From the table 2 and table 3, we can see that the maximum inclination happens in buildingNo.1, and the second maximum inclination happens in building No.4, which is 7.705‰.That proves that the more initial inclination of building is, the more the inclination increment caused by excavation surrounding. 2.4. Model Assessment Two different reference points assumed produce two different settlement results. When reference point is No.1 point illustrated in Fig.1, the settlement is presented in Table 3 and Fig.2(before excellent data mining), while No.55 on the building 5 as reference point, the settlement is shown in Table 6 and Fig.2(after excellent data mining). From

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Fig.2, we can see obviously that the settlement given after excellent data mining is more reasonable because it is more stable.

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153

Figure 2. Part of measure points settlement curves with time before and after excellent data mining

After data mining, the different responsibilities of different parties have been presented according to Chinese code for design of building foundation [5]. About 40% responsibility is for residence buildings’ initial deformation, belonging to construction quality, 50% responsibility is for the tunnel construction, while 10% is for natural disaster (soil softening caused by water from Typhoon and raining in 2006 summer, omitted here).

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2.5. Analysis Results Expression In this step, the proper results should be given. Figures and Tables are commonly used to express the results. But they should be concise. From the Figures and Tables, some clear summary should be described. In this case, all measure points settlement curves with time before and after excellent data mining can be expressed by Figure easily. Only part of them is presented in this article because of article length. From the Tables and Figures, two buildings (No.1 and 3) are tendency to continue settlement. Therefore it is necessary to continue this control measure. Next measure should be take place every three months. The settlement ratio is less than 0.04mm/d.

3. Conclusion Data mining is also popular in civil engineering, not only in other filed. To solve specific problem effectively, we should focus on data mining steps combined civil engineering professional knowledge. Each step during data mining process should be used properly. When we meet gross error in the data cleaning, restoration should be the firstly considered. In this article, a gross error appeared in the original measurement

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data was restored reasonably according to an assumption that the deformation of one measure point between seven days is zero. In the step of the data integration, put some historical measurement datum and reference datum if have any together in order to better data mining. The step of data mining, stable deformation point should be determined based on professional knowledge and datum cleaned and integrated, especially when measurement worksite is limited by surrounding vibrating car roads. In this article, the relative stable point is analyzed on the measured building according to the measurement data and the data integration, not like convension. The model assessment step is to see if the results are reasonable according to our professional knowledge and if the results are stable. Finally, analysis results expression step presents concise Figures or Tables, and clear summary.

Acknowledgements This article was supported by funds from NSFC Project titled Ancient tower structure stability evaluation (Grant 50878153) and national Key Technology R&D Program (grant 2006 BAJ27B02-02).

Reference

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[1] GB50202-2002,Code for acceptance of construction quality of building foundation. People’s Republic of China Ministry of construction,2002. [2] Z.D.Shao and Y.J.Tang, Inclination of buildings with foundations by soft clay rheology. Proceedings of Geo-Chiangmai, 2008. [3] D.D Li, Data mining technology and development trend,Computer Application Technology 69(2007),38-40. [4] JGJ8-2007, Code for deformation measurement of building and structure. People’s Republic of China Ministry of construction, 2007. [5] GB 50007-2002, National Standard of the People’s Republic of China. Ministry of Construction of the People’s Republic,2002

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Data Acquisitions and Monitoring

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157

3D Reconstruction of Rock Cracks CT Image and Fractal Damage Study Fei ZHANGa,1, Haidong ZHOUa and Yunxia ZHAOb College of Mining Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China b College of resource and environment engineering, Shandong University of Science and Technology, Qingdao 266510, China a

Abstract. Based on the CT image of rock, we come up with a new method of CT image analysis and make the best use of information of CT images. Accomplish reinforcement of CT image of rock according to regulating contrast of CT image. The real microstructure, cranny and hole could be extracted through the technique of image segmentation. On the basis stated above, Marching Cube algorithm is applied to conduct a 3D reconstruction of successive CT tomographic images, visualization is implemented on rock cracks distribution during different damage stages, In this way we can get not only the directly visual course of damage propagation but also a digital representation of the actual spatial distribution of different materials in the rock. And we define the damage variable about damage volume and obtained the relation between damage variable and damage deviatoric stress which correspond exactly to the damage propagation characteristics. Lastly, we calculate the box counting dimension of the rock sample, and through observation of the dimension curve, we summarize the changes of rock damage under stress.

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Key words. CT image, digital image processing, Marching Cube algorithm, 3D reconstruction, rock damage, box counting dimension

Introduction Since the 1990's, CT technology had been used for research of identifying the characteristics of rock cracks [1]; a growing number of scholars use CT technology to study the micro-mechanical behavior of geotechnical materials under different loads. Currently, the research in and abroad, focused on the mechanism and the constitutive relation of damage, defects or cracks. With the constantly developing of computer technology, CT recognition technology becomes the most advanced non-destructive testing means; it can continuously scan the structural changes of the material in multi-directional loads and without disturbance, in order to obtain the changes of the specimen: the microstructure change, micro-particles movement, fracture development, density changes of parts. The CT images can reflect the rock mass distribution of various substances through the way of reproducing gray degree of the images; the images reflect the micro-structure of the materials very well.

1 Corresponding Author: Fei Zhang (1959-),Professor, majored in mining engineering and rock mechanics; email:[email protected]

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Digital image processing technology has become a separate academic subject and has been widely used in all fields, the objects studied will be changed into digital images and stored in computer, by using computer analysis and processing image information we obtain the results needed. At present, the research of digital image is still relatively weak, especially on the CT image and the methods of digital image processing; although there are many scholars make great effort on applying digital image processing technology on micro-cracks and make quantitative description reveals. Such as: there are more scholars abroad research on CT image interpretation and 3D reconstruction (Kawakata.H, 1999,2000), and the indoor rock X-ray CT test to simulates the rock mass movement becomes the tendency (Ueta, 2000); domestic researchers make much effort on analyzing CT distribution rule of the CT image, but the quantitative analysis of the crack is approaching only theoretically (DING Wei-hua, 2001). The paper is based on CT scan images of rock, using digital image processing technology in MATLAB to extract useful information in CT images and make rock’s 3D crack reconstruction. The paper makes in-depth study on the damage activities of rock under stress.

1. The Basic Principles of Micro-CT Images 3D Reconstruction 1.1. CT scanning Based on the Load Test of Rock At first the load test and corresponding CT scanning should be done to obtain the fracture distribution information of two-dimensional CT images. The test is based on the uniaxial stress loading experiment, stratified X-ray scanning of the core approaches at different loading stages, the distance between layers is 20mm, and 5 two-dimensional CT images of different heights will be obtained during each stress stage, as the basis for later image processing.

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1.2. Two-dimensional CT Image Processing According to the characteristics of CT images, apply digital image processing theory and technology, the pre-process of two-dimensional CT images including image enhancement, resulting the middle layer in image interpolation, as well as image segmentation. Image pre-processing can reduce the random perturbation of imaging equipment as electronic device ,and also can reduce the interference and quantization noise inevitably brought by surrounding environment [2]; image interpolation will bring good isotropic volume data, so that the quality of 3D reconstruction can be improved [3]; image segmentation will bring the value target of the rock mass and cracks in CT images, to further reduce the background interference and improve the quality of image reconstruction [4]. 1.3. Three-dimensional Image Reconstruction [5] After the image segmentation, the segmented CT slice data will be set up to constitute a three-dimensional data field, and further more applying 3D visualization technology to achieve three-dimensional reconstruction.

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Firstly, build up a voxel model. During rendering three-dimensional images, we need to get sample of the designated 3D space, if the sampling points is uniform in the x, y, z direction, can be expressed as data m(i, j , k ) . Make the eight adjacent sampling points of adjacent slices as vertices, and the cube region constitutes a voxel, as shown in Figure 1. kz y

v4

v6

v5 P0

P4

z

P3

P6

P1

ix

v7

P5

v1

v3

j y x

P2

v2

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Figure 1. Voxel model

In this paper, Marching Cube algorithm is applied to obtain middle layer of the CT image interpolation. Marching Cube algorithm generated triangle patches in the voxel’s border to fit the isosurface, the main calculation is shown as follows. First of all the eight vertex of the cube should be classified, and then determine the dissection model of iso-surface according to the status of the eight vertex. And then calculate the intersection point of the iso-surface and the voxel’s border. When the three-dimensional discrete data is high-density field, that is, when the voxel is very small, it can be assumed that the function value linearly changes along with the voxel’s border values (this is the basic assumption of MC algorithm), so the intersection point of iso-surface and the voxel side can be directly calculated by linear interpolation. According to the triangulation dissection that is fixed in index table, connect these points into a triangular piece to obtain the iso-surface slice of the voxel.

2. Three-dimensional Reconstruction Examples of Micro-CT Images The experiment equipment is in the CAS Cold and Arid Regions Environmental and Engineering Research Institute, Frozen Soil Engineering National Key Laboratory of the frozen soil and rock mechanics tests. We can obtain a set of the CT scan images of sandstone (core samples is cylindrical with diameter of 50 mm, height of 100 mm) under uniaxial compression conditions. When scanning the axle load is fixed, each scan proceeds at 5 different height levels with distance of 20mm, the numbers were: CT/-63, CT/-83, CT/-103, CT/-123, CT/-143. In the experiment each section was scanned 13 times including the initial scan, stop the experiment until the loaded rock specimen is destroyed [6]. On this basis, 9 set of the CT images are integral, and make image processing of these 9 sets of images, and then interpolating the intermediate layer, obtain fractures and porosity of the rock through image segmention, and then we can proceed three-dimensional reconstruction and realize three-dimensional visualization.

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2.1. Two-dimensional Micro-CT Rock Fault Image Processing The experiment can bring us CT images of different loading stages, when the crack of sandstone in initiation, expansion, through, and destruction. This experiment completed scanning 13 times at different loading stages, 9 sets of the CT images is relatively integral, they were at 0MPa, 8.14MPa, 18.85MPa, 19.93MPa, 20.59MPa, 21.45MPa, 23.05MPa, 25.88MPa, 31.03MPa, each set contains 5 images of different height. Take the crack initiation stage as an example( V = 20.59MPa), we can obtain 5 CT images with 20mm height distance, the size of the images are 512 × 512 pixels, as shown in Figure 2[6].The white part shows rock particles, and the dark part shows cracks and pore spaces.

a

c

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Figure 2.

b

d CT Images when

e

V

=20.59MPa

Through observation, the original CT images are fuzzy and not obvious comparison, so image preprocessing is need. Figure 3(a) is the original CT image, and Figure 3(b) is the image after pre-processing operation of brightness adjustment and contrast adjustment. After pre-processing operation, the image quality is obviously improved. Based on the Otsu threshold segmentation theory (OSTU method), make segment of Figure 3 (b) and then the anti-color operation, the effect is shown in Figure 3(c),(d), in Figure 3 (d) the white part shows rock particles, and the dark part shows cracks and pore spaces. As can be seen from the images, Otsu threshold segmentation method shows better segmentation effect.

(a)original CT images

(b)after brightness adjustment

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F. Zhang et al. / 3D Reconstruction of Rock Cracks CT Image and Fractal Damage Study

(c)images after segment Figure 3.

161

(d)images after anti-color operation

The brightness transformationࠊimage segmentation and reverse drawings

According to the method above, make each set of five CT images brightness transform processing, and carry out inter-layer interpolation operation, then we obtain 128 CT tomography interpolation images for each load stage. 2.2. The Realization of Three-dimensional Reconstruction

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With the method above, we could build up three-dimensional data field with 128 micro-CT interpolation images of different destruction stages, and by using Otsu threshold segmentation method we could obtain the images of cracks and pores. On this basis, we use the Marching Cube algorithm surface rendering method to achieve three-dimensional reconstruction of the rock cracks. The results of three-dimensional reconstruction of the cracks are shown in Figure 4 (a) ~ (i), pink shows the low CT value; blue on the top shows the trend of cracks.

(a) cracks when

V

=0MPa

(c)cracks when

V

=18.85MPa

(b)cracks when

(d)cracks when

V

V

=8.14MPa

=19.93MPa

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(e)cracks when

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(g) cracks when

V

V

=20.59MPa

(f) cracks when

=23.05MPa

(i)cracks when

(h)cracks when

V

V

V

=21.45MPa

=25.88MPa

=31.03MPa

Figure 4. Images of sandstone cracks 3D construction in CT experiment at different stages

By observing the two-dimensional segmentation images and the three-dimensional reconstruction images, the experiment phenomenon is as follows: 1. The crack seems not connected in the two-dimensional images, is connected to each other in the corresponding three-dimensional image. So it is not difficult to see that the two-dimensional image simply reflects the distribution of crack from one plane. In comparison, the three-dimensional reconstruction images could reflect crack’s spatial structure during the rock loading test more intuitively and more accurately. 2. Crack appears very small in two-dimensional segmentation images may be part of a wide range crack with connectivity, because of the connectivity is different in three-dimensional space. However, by simply observing

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163

two-dimensional segmentation image we mistakenly considered these cracks are scattered.

3. Research of Damage Characteristics of 3D Reconstruction Images In the process of rock deformation and failure, at first appears a micro-crack, and then accumulation of nucleation, and then gradually become a macro-crack with connectivity, followed by macro-crack grows and eventually run-through. Volume compression and expansion is associated in the process, the macroscopic deformation is essentially created by the damage can be fully recovered; damage can not be fully recovered and damage can not be recovered. In CT experiment the damage can be reflected and divided with the CT number, so the volume deformation of the rock can be regarded as the density changes. In this paper, based on the CT image segmentation, we make extraction and statistics of rock and damage region. Applying damage mechanics theory, we defined number of pixels as damage variable to reflect the damage evolution process:

D

A A

n1 n

(1)

In this formula, A represents the area of injury in the rock; n1 represents the number of pixels which the gray value equals to 1 in the binary image after

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segmentation; A represents the total area of the rock, n represents the number of all pixels in the image. We can directly calculate the area of cracks and voids according to the segmented image, so the damage variable based on volume of damage can be calculated V=V*/V =n1/n. In this formula V* represents the volume of the damage in the rock; A represents the total volume of rock; n1 represents the number of pixels which the gray value equals to 1 in the binary image after segmentation and three-dimensional reconstruction; n represents the number of all pixels in the binary image after segmentation and three-dimensional reconstruction. In this paper, when proceeding image segmentation we classify the micro-damage, which is out of naked eye observation, into cracks and gaps, so damage variable calculated with this method truly reflects the extent of rock damage.

Table 1. Statistics of pixel of reconstructed 3D images V / MPa

Z

0

8.14

18.85

19.93

20.59

21.45

23.05

25.88

31.03

0.3052

0.2206

0.2378

0.4890

0.6052

0.3690

0.4294

0.4317

0.4605

From the data in table 1, we could obtain the relation curve of damage variables and the deviatoric stress, as shown in figure 5:

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F. Zhang et al. / 3D Reconstruction of Rock Cracks CT Image and Fractal Damage Study



'DPDJHYDULDEOH

      















'DPDJHGHYLDWRULFVWUHVV

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Figure 5. The relation curve between damage variable and damage deviatoric stress

As is shown in figure 5, the initial damage variable is not 0, as the initial injury caused by the existence of micro-cracks and holes in the rock. When the deviatoric stress is 8.14MPa, most of the initial micro-cracks and holes are compressed, and the speed of the main flow grows is less than the speed of the micro-cracks is compressed, so the damage variable decreases at this stage. Since then the damage variable increases, indicating that the microscopic damage is growing. From 8.14MPa to 18.85MPa, the damage evolution begins and keeps steady development; when the damage deviatoric stress increases to 20.59MPa, the damage variable grows rapidly, this is the stage that the damage variable grows fastest, and also is the precursor of rock breaking; in the subsequent stage, the damage variable will constantly grow until the rock breaks. Along with the increase of damage deviatoric stress, there is jumping in the loading procedure, such as the sudden decrease of damage variable when the deviator stress exceeds 20.59MPa, which is probably caused by the cracks space formed at last stage were suddenly compressed. To sum up, the curve reflects that, in the increasing loading procedure, the rock goes through the stages of the initial damage closing, the beginning of damage evolution, damage stability development, and dramatically increases to rock breaking, this is in keeping with damage characteristic of rock.

4. Research of Fractal Characteristics of 3D Reconstruction Images The method of box counting dimension (BCD) has been greatly applied in the study of rock damage field, and the box counting dimension exactly reflected the damage inside of the rock. We use the cubic covering method to cover the 3D reconstructed rock sample, in order to calculate the box counting dimension of the rock in different stages of the test. The result of the calculation is show in table 2. Table 2. The box counting dimension of reconstructed 3D images

V / MPa Box counting

0 2.6828

8.14

18.85

19.93

20.59

21.45

23.05

25.88

31.03

2.6789

2.6873

2.7332

2.8440

2.7659

2.7935

2.7940

2.7995

dimension Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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165

%R[FRXQWLQJGLPHQVLRQ

From the data in table 2, we could obtain the relation curve of box counting dimension and the deviatoric stress, as shown in Figure 6:                   

'DPDJHGHYLDWRULFVWUHVV Figure 6.

The relation curve between box counting dimension and damage deviatoric stress

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As is shown in Figure 6, the initial box counting dimension is 2.6828, as the initial injury caused by the existence of micro-cracks and holes in the rock. When the deviatoric stress is 8.14MPa, most of the initial micro-cracks and holes are compressed, and the speed of the main flow grows is less than the speed of the micro-cracks is compressed, so the box counting dimension decreases at this stage. From 8.14MPa to 18.85MPa, box counting dimension begins and keeps steady development; when the damage deviatoric stress increases to 20.59MPa, the box counting dimension grows rapidly, and also is the precursor of rock breaking; in the subsequent stage, the box counting dimension will constantly grow until the rock breaks. The curve reflects just the same changes which are shown in the damage study above.

5. Conclusions (1) Applying OSTU method to segment CT images of rock fractures and holes, so we can obtain the distribution of fractures and holes in these two-dimensional CT images. And then we proceed the three-dimensional image reconstruction with the method of iso-surface rendering reconstruction. In this way, we achieve three-dimensional visualization, and overcome the limitations, that two-dimensional images can only show the distribution of the cracks in one plane. So we can intuitively reproduce structural information of the cracks during the loading process, this is very significant for judging the time when cracks appear and the direction cracks grow. (2) The relation curve between damage variable and damage deviatoric stress could be obtained simply and accurately by CT image segmentation technology, which shows the characteristics of damage evolution reasonable.

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(3) By calculating the box counting dimension of the reconstructed 3D rock sample, combining with damage theory, we could do more research of the fractal damage characteristics of rock. As the advantage of the fractal theory can be combined with microscopic and macroscopic physical characteristics; in this paper we provide an effective way of using three-dimensional fractal theory combined with the microscopic damage mechanics to quantify the development of damage in process. With the raising precision of the equipment, this method of using fractal theory to study meso-damage will be more accurate, this is very important to reveal the law of cracks development under uniaxial loading.

References

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[1] Geng-she Yang, Ding-yi Xie, Chang-qing Zhang, Yi-bin Pu. CT Identification of Rock Damage Properties. Chinese Journal of Rock Mechanics and Engineering, 15 (1)(1996), 48̾54. [2] Zheng ZENG, Fang-hua DONG. Three Dimensions Reconstruction of CT Image by MATLAB. CT Theory and Applications 13(2),(2004), 24̾29. [3] Peng-cheng ZOU, Xue-song YIN. 3-D Matching Interpolation Based on Images Segmentation. Computer Engineering and Application 40(24) (2004), 80̾82. [4] Jia-wen WANG, Yang-jun LI. Matlab7.0 graphics image processing. Beijing, National Defense Industry Press, 2006 [5] Ze-sheng TANG. Visualization of 3D Data Fields. Beijing, Tsinghua University Press, 1999. [6] Xiao-Tao YIN. Study on the breakage meso-mechanism of the sandstone through the image quantitative analysis. Xi’an, Xi’an University of Technology, 2005.

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167

Application of Geological Radar in Health Diagnosis of Nanjing Gulou Tunnel Yuan WANG a,b,1, Songyu LIUa, Yuehu TAN b, Jianli DUANb, Jing ZENG b and Lei GAO b a Institute of Geotechnical Engineering; Southeast University, Nanjing, Jiangsu 210096,China; b Engineering Institute of Engineering Corps, PLA University of Science and Technology, Nanjing 210007

AbstractTaking health diagnosis of Nanjing Gulou tunnel as background, the principles and methods of making non-destructive testing on the tunnel lining quality by using geological radar were expounded. General rules on the radar images, the thickness of formwork concrete and sprayed concrete, metallic doors and windows, the dense dripping section and the leaking water cranny were interpreted. The detecting results of geological radar show that bugs existing behind the tunnel lining is serious with a consecutive leaking water cranny every 5~7 meters, and the concrete thickness is uneven from 27cm to 37cm. When the tunnel is repaired, it is found that the chiseled structure state and the geological radar detecting results are unanimous. Keywords Geological radar, health diagnosis, Nanjing Gulou tunnel

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Introduction The traditional detection of tunnel lining mainly includes core drilling, excavation sampling and other methods. In addition to these breakages detection, the heavy workload, low efficiency, poor representativeness, high price, weakening the structural strength, its application has been limited. At present, the advanced non-destructive testing methods both at home and abroad are: geological radar method, acoustic method and micro-resistance point and method, of which geological radar is economic, rapid, continuous, intuitive, of high-resolution, and of strong anti-interference ability, etc. Geological radar method is widely used in engineering geology survey, civil engineering testing, mineral resources exploration, hydrological and ecological environmental investigation, underground cover reasonable and many other fields[1-5]. Taking Nanjing Gulou tunnel health diagnosis as an example, this paper introduces the tunnel geological radar detection technology programs, analyses radar images of sub-interpretation, forms a comprehensive analysis of the geological results㸪gives the corresponding detection of the conclusions and recommendations, which provides useful lessons and reference for the future detection of similar projects and research.

1 Corresponding Author: WANG Yuan, Engineering Institute of Engineering Corps, PLA University of Science And Technology, Nanjing 210007 Tel: 13951989919; Email㸸[email protected] Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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1. Overview of the Project Nanjing Gulou tunnel is part of Zhongshan Road. The tunnel length of Nanjing Gulou tunnel is 750 meters, north-south length of approach road is 402 meters, tunnels center's depth is 21 meters, and the grade is 35%. It is a two-way four-lane tunnel. The part between the two tunnels is column-plus stringer for the load-bearing structure. The design and construction of the tunnel were carried out in 1994, and water seepage often happened after its putting into use. After several destructive digging open the pipe handling for crack, little effect was made. Although changing to the drainage pipe cited, the cracks in the tunnel seepage water was still serious. In order to analyze the tunnel structure state, a more comprehensive examination was carried out, including application of ground penetrating radar of the tunnel lining thickness and the lining of the completeness of the non-destructive testing.

2. Tunnel Geological Radar Detection Scheme and Implementation

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2.1. Detecting Principle of Geological Radar Geological radar is an active electromagnetic detection system, by recording the strength of electromagnetic wave signal reflected and arrival time to determine the electrical anomaly zone geometry, characteristics and location. Medium reflected wave form radar images. Through the anomalies reflected wave travel time, amplitude and phase characteristics, the target body and its location and geometric patterns are identified. From the geometry point of view, underground anomalies can be summarized into two categories as point-like body (such as holes, pipelines, etc.) and surface-like body (such as cracks, dimensions, etc.). In the radar images they have their own characteristics, that is, point-like body is characterized by hyperbolic reflex arc, and surface-like body is linear reflection. The characteristic of anomaly region can be judged by the reflected wave amplitude, and its position can be reflected from travel time determined at the time, seeing equation (1):

h v

v 2t 2  x 2 / 4 c /[

(1)

In the equation: h stands for the depth of geological Hugh; t is the reflected wave arrival time; x is the distance from the antenna; v is the propagation of electromagnetic waves in the medium speed: c = 0.3m/ns is the transmission of electromagnetic waves in the air speed; ȟ is the dielectric constant, which can be found by relevant books or by determination[8]. 2.2. Antenna Frequency Selection Geological radar’s actual use effectiveness depends largely on its resolution. Resolution can be divided into vertical resolution and horizontal resolution.

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Table 1. Resolution of antenna of 400MHz and 1000MH in different detecting depth Antenna Frequency

Corresponding Deepness of Detect / m

00MHz

1

Upright Resolution

Horizontal Resolution

/m

/m 0.14

0.075 400MHz

2

1000MHz

1

1000MHz

2

0.1 0.06 0.03 0.09

Combining the factor of the object with the medium around, and taking into account the detection resolution and depth requirements, in Table 1 as the basis, according to experience, to take concrete, when the electromagnetic wave velocity is 0.12m/ns the corresponding depth of the geological radar resolution, and by the radar detection accuracy and depth of requirements, 400MHz and 1000MHz shielding antenna radar are selected. In this paper, geological radar produced by Swedish GEOSCIENCE (RAMAC / GPR) is used. 2.3. Tunnel Detecting Program

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The lighthouse at the middle top of tunnel can’t be detected. Therefore, three detecting lines were arranged in a tunnel. On the left 1, right 1 and right 2 we adopted multi-frequency coverage, using 400MHz and 1000MHz shield big line detection; left 2, left 3 and right 3 used 400MHz shielded antenna detection. Detecting line layout is shown in Figure 1.

Figure 1a.. Transect disposal of detecting line

Figure 1b. Plane section disposal of detecting line

During exploration, the height of the left 1 Right 1 are both about 1.4m, adopting artificial manner; left 2, left 3, right 2, right 3 were carried out in the car platform.

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3. Sub-radar Image Interpretation 3.1. The Whole Rule Interpretation of Radar Image Take the right line 1radar detection image which is length of 16 ~ 30m as an example, the length within the sampling interval 0.20862309m was shown in Figure 2. On the Timeline the first arrives is direct wave, following a direct wave added to the oscillation; the outer edge of formwork concrete, there is formwork concrete and sprayed concrete reflection interface. If consecutive reflection interface is stable, the reflected wave indicating the outer edge of formwork concrete. Sprayed concrete and in situ rock mass interface also have a clear reflection interface. But because the interface is irregular, the reflected wave with the phase axis is undulating. The thickness of the formwork concrete is calculated with experience point 0.12m/ns. formwork concrete thickness is about 35-38cm, sprayed concrete’s thickness is about 10-14cm.

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Figure . The whole rule interpretation of radar image

In general, for well-built formwork concrete, the reflection bands are continuous. Among the reflected signals there is a quiet background area. Once the fissures or empty other abnormalities appear, there will be reflection. In figure 2, it is assumed there is a hydraulic conductivity fracture, the cracks disconnect direct wave, direct wave Continues to oscillations and formwork concrete outer edge of the reflected signal phase axis, in formwork concrete flat background region there are reflection consecutive signals too. 3.2. Thickness Identification of Formwork Concrete and Sprayed Concrete In the practical detection, we found the interface of formwork concrete and sprayed concrete is not always clear. This is because that they are both concrete which the electromagnetic parameters are small and they combined well, as a result, reflection interface does not exist. As shown in figure 3. In the left part there is a greater interval between formwork concrete and sprayed concrete's reflective bands which can distinguish the two. But in the right part they can't be easily distinguished. It's difficult to distinguish between the two in the case of the reflecting interfaces; Geological interpretation will not be able to obtain their

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171

thickness, but only to provide a total thickness of the concrete. In addition, the external interface of formwork concrete is regular, and concrete's thickness changes in the scope of 36 within plus or minus 2 centimeters; While sprayed concrete's reflective interface is relatively large fluctuations, changes in the scope of 12 within plus or minus 8 centimeters.

Figure 3. The identification of thickness of concrete

3.3. Aluminum Doors (Window) In the left one and right one test section, there are aluminum doors (window).For radar detecting, the presence of metal have kinds of strong interference. Because of weak electromagnetic wave's attenuation in the air, when the electromagnetic waves transmitted to the interior there will be multiple reflections happens. Reflection of electromagnetic waves from the cave and wall is superimposed. It's characterized by close to seismic exploration in "ringing" phenomenon.

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3.4. .Intensive Drip Zone and Hydraulic Conductivity Fracture Intensive large drip area, on radar cross-section is reflected as clutter signal in formwork concrete reflection region. The appearance of water changes dielectric properties of concrete, this reduces the speed of electromagnetic waves; The cranny which appears inside of formwork concrete has changed its magnetic property, and the reflecting interface appears; In the chase dripping area, some sections (point) have been repaired, which also changes the composing of the reflecting medium, where the rate of the electromagnetic wave will turn exceptional. These complications make the image's character of radar comparatively complex. The cranny inside the concrete may make the lining connective inside and outside, and form a dripping channel, reduce the concrete's holistic intension. When fathering we should pay attention to them [6~11].

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4. Geological Interpretation of Exploration Results 4.1. The Thickness of Concrete From the practical test results, the thickness of formwork concrete and sprayed concrete to left2, left 3, right 2, right 3 are the same with the left 1, right 1. Therefore, at this time, the explanation of thickness is mainly about the left 1, right 1 test sections . and the explanation of the thickness of formwork concrete and sprayed concrete are shown in table 2 and table 3. Table 2. geological detecting statistic of left tunnel Location

Formwork/cm

sprayed concrete /cm

0+50ᨺ0+30

32ᨺ36

12ᨺ16

0+30ᨺ0+10

32ᨺ37

0+10ᨺ0-10

8ᨺ15 40ᨺ44

0-10ᨺ0-30

39ᨺ42

0-30ᨺ0-50

40ᨺ42

0-50ᨺ0-65

35ᨺ42

0-65ᨺ0-90

40ᨺ43

0-90ᨺ0-100

40ᨺ43

Table 3. geological detecting statistic of right tunnel Location

Formwork /cm

Sprayed concrete /c m

0+50ᨺ0+30

32ᨺ36

12ᨺ16

0+30ᨺ0+10

32ᨺ37

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0+10ᨺ0-10

8ᨺ15 40ᨺ44

0-10ᨺ0-30

39ᨺ42

0-30ᨺ0-50

40ᨺ42

0-50ᨺ0-65

35ᨺ42

0-65ᨺ0-90

40ᨺ43

0-90ᨺ0-100

40ᨺ43

4.2. The Integrity of the Lining Dripping zone or seepage areas inside of the tunnel are prevalent, some of governance has been conducted, where media is complicated, and more fractured, the radar reflectivity profile to reflect the area for the concrete clutter signal, which is due to the following factors: x Cracks, as a typical linear reflector, reflected into banded reflection signals in the radar image: x In the dense dripping zone, some sections (points) have been patched also the material composition of reflective dielectric have changed. Such the speed of electromagnetic waves turned abnormal in this place.

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x

Water is of the most critical factor which affect the speed of electromagnetic waves in the medium. Once soaking, the permittivity of the medium will be increased, and the speed of electromagnetic waves will be greatly reduced. In the seepage and drip site, since the emergence of water, electromagnetic wave speed reduces, which makes the reflection phase axis intermittent discontinuity. The result of the factors mentioned above makes characteristics of radar images of the dense dripping zone more complex. To this end, the reflection interface location inside of the tunnel lining and preliminary judgments summary, together with on-site characterization are provided to the tunnel management departments.

5. Conclusions

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There are obvious defects on the top of Gulou tunnel, where a crack exists through the vertical length at intervals of 5 ~ 7m, and the phenomenon of water leakage is serious; The thickness of concrete lining is uneven㸪where 37cm thickest and 27cm thinnest. Tunnel vault is undulating. Longitudinal stiffness is not balanced, which is different significantly from the original design requirements .Concrete cracks may make lining inside and outside connect and form a drip channel, which reduce the concrete's overall strength, which should be concerned about in governance[12~14]. In addition, as a non-destructive testing method of geophysical, geological radar determine the reflection of electromagnetic waves through the media characteristics. This requires explanation dealt with the existing data, coupled with adequate pit exploration drilling or work, in order to enter the stage of quantitative interpretation. As to this study there is non-relevant drilling data to compare to interpret, media electromagnetic wave speed cannot be accessed directly. This paper determined the electromagnetic wave speed 0.12m/ns as taking the experience value, thus the corresponding interpretations of thickness of layers are obtained.

References [1] Li-wei CHEN, Application of penetrating radar to detecting concrete quality in tunnel engineering, Rock and Soil Mechanics 24(add 1) (2003),146㸫149. [2] Li-ming ZHOU, Fa-gang WANG, Test and Analysis Testing of the quality of tunnel-lining concrete by a geological radar, GEOTECHNICAL ENGINEERING FIELD 6(3) (2003),74㸫76. [3] Xiuqin Ni, Yunquan Wang, Guoqun Wang. Study on Thickness Measuring of Tunnel Lining-wall by Geologic Radar Method , Modern Transportation Technology 3(2006), 50㸫53. [4] Ziqi Li, Yanyan Fan, Application of the Ground Penetrating Radar in Detection of the Tunnel Lining Quality, Journal of Lanzhou Jiaotong University 25(3) (2006), 48㸫51. [5] Fang LUO, Application of Geological Radar in Health Diagnosis of Tunnel, Journal of Chang’an University: Natural Science Edition 26(3) (2006), 51㸫54. [6] J L Davis, Annan A P, High resolution sounding suing ground probing radar. Geoscience Canada,1986. [7] Hui-min FENG, Application of geo-radar to tunnel inspection , MODERN TUNNELLING TECHNOLOGY 41(4) ( 2004), 67㸫71. [8] Hua LI, Cai-chu XIA. Optimization of GPR Exploratory Parameter in Quality Detection of Tunnel Secondary Lining, West-china Exploration Engineering 1 (2006), 169㸫171. [9] Jin-ping JIA, Applied Technique and Effect of Geologic Radar in Tunnels, Coal Geology of China17(3) ( 2005),50㸫52.

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[10] Yang ZHOU, Yuan-bao LENG, Sheng-li ZHAO, Research progress in GPR technology applied in pavement testing, Progress in Geophysics 18(3) (2003),481㸫482. [11] Mellett S, Ground penetrating radar applications in engineering, Journal of Applied Geophysics,1995. [12] The Engineering College Of Engineer Of PLA University Of Science And Technology. Test Reports of Nanjing Gulou Tunnel,2004. [13] The Engineering College Of Engineer Of PLA University Of Science And Technology. Test Reports of Nanjing Gulou Tunnel (accessories 1), 2004. [14] The Engineering College Of Engineer Of PLA University Of Science And Technology. Test Reports of Nanjing Gulou Tunnel (accessories 2)], 2004.

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175

The Development and Application of Automatic Monitoring System for Dam Seepage Xiuguang SONGa, Hongbo ZHANGa, 1, Yaoting CHENband Xin ZHUANGa a School of Civil Engineering, Shandong University, 250061 Jinan, China b School of Hydraulic Engineering, Hohai University, 210098 Nanjing, China

Abstract. Serious seepage can influence the stabilization of reservoir dam and accessory structure. In order to monitor the seepage phenomenon of Wohu-Mountain reservoir exactly at real-time, automatic monitoring system for earth dam is developed. For the hardware system, sensors on site and control computer in center control station are connected effectively with four-core cable, data acquisition device and signal conversion equipment. For the software system, advanced hierarchical and distributed structure is adopted in the system to manage and control monitoring sensors. By developing the data base, seepage curve and related analysis can be shown on the system at real-time. And if monitoring data surpass the limited value, the warning information will be given by adopting different ways according to alarming grade. In general, the automatic monitoring system can play an important role in the protection of the safe of reservoir, and exert economic and social benefits of water reservoirs at mostly.

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Keywords. Dam seepage; automatic monitoring system; hardware constitution; software design

Introduction In China, there are many reservoirs that have been used for several decades. However, because of structure ageing, poor construction quality, human destroy or natural influencing, many reservoir structures, such as dams and spillways, have potential safety concerns in different levels, which would endanger the safety of people’s life and property seriously nearby. Thereby, the information engineering for hydraulic work, include dam safety monitoring, has been developed widely all over the world. As the only large reservoir of Ji’nan City, Wohu Mountain reservoir has undertaken many important tasks, which include water supplying, irrigation, flood prevention, etc. So far, it has been used for almost fifty years and exist much engineering faults. According to the demand of related state codes and the practical condition of Wohu-Mountain Reservoir, it is imperative to develop the automatic safety monitoring on the running of the dam, spillway and other additional structures.

1 Corresponding Author: Hongbo ZHANG, School of Civil Engineering, Shandong University, 250061 Jinan, China; Tel.: +86 0531 88399080; E-mail address: [email protected].

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1. General Introduction on Wohu-Mountain Reservoir Wohu-Mountain Reservoir was built originally in September 1958, and finished in July 1960. The top elevation of the dam is 131.5m. It reaches the present state after the reconstructed project in 1966, ensuring security project in 1976, and continued construction project in 2002. First-stage project of continued construction of Wohu Mountain Reservoir was completed finally in August 2002. After that, the designed beneficial reservoir capacity raises from 37,000,000 m3 to 5,298,130,000 m3. The designed beneficial water level rises from 126m to 129m. The basin area is 557km2. The total holding capacity of the reservoir is 117,140,000 m3. According to the national code, Hydraulics and Electric Power Project Rank Division and the Flood Standard (SL252-2000), the project scale of Wohu Mountain Reservoir is large, and belongs to grade II. Because of the influence of the geology of the dam foundation, spillway foundation, the bank foundation and the bad construction quality, the seepage flow in the body and base of dam and floodgate are serious. Therefore, in order to protect the normal running of the reservoir, according to the demand of national code, Earth and Rock Filled Dam Safety Monitoring Technical Specification, it is imperative to perform the automatic safety monitoring on the operation of the dam and floodgate. According to the practical condition, the plan of automatic dam seepage security monitoring system include four different contents as below, x Firstly, the monitoring of seepage saturation line of the dam body; x Secondly, the monitoring of seepage saturation line of the dam foundation; x Thirdly, the monitoring of seepage saturation line around the dam at the junction of the right dam end and mount; x Fourthly, the monitoring of seepage saturation line around the gate piers of floodgate.

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2. Design of Automatic Monitoring System for Dam Seepage 2.1. Design Monitoring Sections of Dam According to the practical seepage problems in dam and floodgate, seven monitoring sections were selected, which include five sections in dam (location: 0㸩020, 0㸩135,0 㸩415, 0㸩635and 0㸩835m) and two sections in fooldgate. And the location of embedded piezometer tube is shown in Figure1.

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177

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Shale 亥ዙ rock

എປ⸲⹮⸣ sand and gravel 儈〻˄㊣˅ 哴⎧䴦⛩

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water

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Piezo

D

Ⲯᒤ䇮䇑⍚≤ս

Piezo

⍻ ঻ ㇑ ⭢

Figure 1. Location of embedded piezometer tube in the dam

2.2. Design Goals of Automatic Monitoring System In order to determine the seepage condition at real-time, the design goals of automatic monitoring system can be listed as follows, x Firstly, collect the data of saturation line of the dam and spillway at real time or occasionally. x Secondly, make a brief and real-time analysis on the dam seepage. For example, display the saturation line of each section, data report and so on. x Thirdly, make a real-time prediction and alarming on the dam seepage condition.

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2.3. The Specific Principle of the Automatic Monitoring System The seepage monitoring system adopts the advanced hierarchical and distributed structure for automatic control. Thereinto, the first hierarchical consist of all piezometer tubes, the second hierarchical consist of two equipments for data acquisition, and the third hierarchical indicates the computer net constituted by PC computer and service equipment. According to the basic principle of automatic monitoring system, the specific ways can be listed as follows: x Firstly, piezometer tubes distributed in all monitoring sections are connected with two equipments on site for data acquisition(MCU1 and MCU2) by using special shielding cable with four cores. x Secondly, the two equipments are connected with PC computer in central room by using optical transmitter and receiver and optical fiber cable. x Thirdly, data acquisition equipments can be controlled by PC computer to monitor and save data of all piezometer tubes timely or untimely. x Fourthly, these monitoring data can be transmitted to the database in service equipment.

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In order to achieve the goal of automatic monitoring, the dam seepage monitoring system should consist of the hardware system and the software system. Thereinto, the former system include seepage pressure monitor, sensors, data acquisition system, specialized cable, optical transmitter and receiver, optical cable, PC computer and service device. And the latter system is constituted much software, which include data acquisition, communication, data base and related hydraulic software. 2.4. Hardware Constitution in the Automatic Monitoring System The general constitution of the automatic monitoring system is shown in Figure2. As shown in Figure2, the osmometers in monitoring sections of the dam and the spillway are respectively attached to the MCU1 and MCU2 data acquisition instrument on-site via special four core shield cable. Then the data gathered is linked to optical transmitter and receiver and PC computer in Central Control Station. The MCU1 and MCU2 data acquisition instrument on-site will carry on automatic circuit testing and data storage to each osmometer at definite time or occasionally under the commands, and send the data to the database in sever. As a result, it will realize automatic inspection control of certain observation point and saturation line of certain monitoring section. Service device (data acquisition)

㸦Center Control Station㸧 PC computer

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Optical transmitter and receiver

㸦dam on site㸧

Optical transmitter and receiver

Optical transmitter and receiver

Data acquisition device MCU 1

Data acquisition device MCU 2

smometer㸦sensors㸧

Figure 2. The constitution of automatic monitoring system

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2.5. Software Design of Automatic Monitoring Systems 2.5.1. The Structure Design of Software System By considering the reliability, economically and securely of monitoring system, the C/S double-level structure is adopted to develop the software. Thereinto, the client-side finishes the display of the UI and the Business Logic Service. And the sever-side finishes data services. Compared with the conventional single-level system, the C/S system has a good stability, reliability and flexibility. At present, most of monitoring systems adopt this structure, which can be shown in the Figure 3. In the Figure 3, data collected by MCU is stored in GEONET database through GEONET engine. The application of Delphi software that runs in monitor unit will carry on data conversion, data checking and send the data to MS SQL SERVER.

MONITORING COMPUTER

GEONET GeO

NETEngin ENG

GEONET

MS SQL SERVER

DATA BASE

GEONET DATA BASE

Figure 3. System diagram

As shown in Figure 4., the saturation line can be displayed at real-time. In the figure, the blue horizontal line on the left represents water level in the upstream, the thin red cure represents seepage saturation line and the thick line with red color represents the water level of piezometer tube. In addition, it also offers estimated saturation line corresponding to water level, which is shown by thin green line.

Water Level/m

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2.5.2. Data Analysis and Alarming of Monitoring System

Figure 4. Saturation line displayed at real-time

Location /m

When the monitored water level of piezometer tube surpasses the warning value, the monitoring system will give some warning information. According to different Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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warning grade, corresponding information with different colors will be displayed, which can be shown in Figure5. 2009-08-08 14:32:48 Warning information with grade III: current water level is 125.9m and approach the limited water level 126m in the flood season. Please pay attention to the change of water level and seepage condition of spillway.

Figure 5. Warning messages

3. Example Analysis In order to certificate the efficiency of the automatic monitoring system for dam seepage, select monitoring data in 0+415 cross section at real time as an example, which can be shown in Table 1. By dealing with water level in different monitoring locations, the hydraulic slope and saturation line can be shown in Table 2 and Figure6. Table 1. Monitored water level and temperature for 0+415 cross section at real time

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Time

Monitored locations

Temperature (0)

Water level(m)

2009-09-11 10:02:02

Water level in the reservoir

128.34

2009-09-11 10:00:16

P415-BD1

122.579

14.25

2009-09-11 10:00:24

P415-BD2

120.118

14.35

2009-09-11 10:00:27

P415-MD1

110.687

14.43

2009-09-11 10:00:31

P415-MD2

101.536

13.51

2009-09-11 10:00:39

P415-BJ1

102.510

13.99

2009-09-11 10:00:57

P415-BJ2

102.489

14.00

Table 2. Hydraulic slope of saturation line at real time Piezometer tube

Hydraulic slope

From P415-BD1 to P415-BD2

0.2813

From P415-BD2 to P415-MD1

0.4287

From P415-MD1 to P415-BJ1

0.2973

From P415-BJ1 to P415-BJ2

-0.0347

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Water Level/m Location /m

Figure 6. Saturation line of 0+415 cross section at real time

4. Conclusions After the application of automatic monitoring system, the management of Wohu Mountain Reservoir realizes the informationization, which indicates that managerial staffs can monitor and deal with the data automatically at real-time. According to performance effects, the monitoring system plays an important role on the safety of reservoir, and can adjust the utilize ways of water resources.

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References [1] P.R.China. Ministry of Hydraulic. SL60-94 Code for the safety monitoring of earth-rock fill dams. Beijing, China Hydraulic Press, 1994. [2] P.R.China. Ministry of Hydraulic. SL252-2000 Hydraulic and hydro-Power engineering grades and waterflood criterion㸬Beijing, China Hydraulic Press, 2000. [3] jie Liu. Analysis of dam break of Bayi Reservoir, Journal of China Institute of Water Resources and Hydropower Research 3(2004) , 161-166㸬 [4] qi yue Zhang, Observation techniques of earth and rock filled dams. Beijing, China WaterPower Press,1993㸬 [5] zumin Liu㸪zhiming Zhang, Approach to the New Way of Safety Monitoring for Small Earth Dam. Pearl River 2 (2003), 43-44㸬

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Development of Real-Time Soil Deformation Monitoring System (RSDMS)

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M.A. Mohd Dina,1 , Z. Harun a,2 and L. Kang Wei a,3 a Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur

Abstract. This research study has identified four contributing factors to the success of monitoring tasks activity by a Real-time Soil Deformation Monitoring System (RSDMS). The first factor is the ability to achieve highly accurate observation. Secondly, it is the maximum reliability of the system. Thirdly, the automatic measurement and computation factors and the least is RSDMS has emails alert function to alert undersign engineer with up to date deformation data. To meet this objective the RSDMS is being developed using Microsoft Visual Basic 6.0 which systematically measure and trace for any alteration in the coordinates of monitoring prisms which are potentially caused by soil movements. The RSDMS equipped with functions of logging measured values used for post processing, carry out deformation analysis, targets health checks and events triggering alarm thus will provide a simple, low cost and functional way to record the absolute 3-D displacements especially for a large number of monitoring points. The TM30 robotic total station is used as a geodetic measuring device in RSDMS. All the collected data are then transferred back to the server by using Files Transfer Protocol (FTP) method subsequently processed with STAR*NET, the program embedded in the system for data analysis by Least Squares Adjustment. Any adjusted coordinate differences from the initial survey data will be analyzed further by the targets health check function of the RSDMS thus sending alert emails with event details to the designated authority. Keywords. Real-Time Monitoring, Robotic Total Station, GeoCOM, Visual Basic 6.0, Least Squares Adjustment

Introduction The measurement technique for spatial data collection for industrial measurement and deformation detection usually employed by surveyors are based on the geodetic method. Industrial measurement and deformation detection commonly consists of data collection and processing modules. Data collection is one of the important aspects where all the spatial information collected must not contain gross error (Wolf and 1

M.A. Mohd Din: Researcher, Department of Civil Engineering,Faculty of Engineering,University of Malaya, 250603 Kuala Lumpur.Tel: 03-7967 5232, Fax:03-7967 5318, e-mail: [email protected] 2 Z.Harun: Researcher, Department of Civil Engineering,Faculty of Engineering,University of Malaya,50603 Kuala Lumpur.Tel: 03-7967 7355, Fax:03-7967 5318, e-mail: [email protected] 3 L. Kang Wei: Researcher, Department of Civil Engineering,Faculty of Engineering,University of Malaya,50603 Kuala Lumpur.Tel: 017-200 9072, Fax:03-7967 5318, e-mail: [email protected]

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Ghilani, 2006). Gross error is normally contributed from surveyor or observer blunders. Observer blunders are usually caused by misunderstanding of the problem, carelessness, fatigue, missed communication or poor judgment (Mikhail, 1976; Wolf and Ghilani, 2002). Software implementation during data gathering can reduce and avoids gross error or observer blunders. This paper deals with real time data acquisition for industrial deformation application. RSDMS has 2 computerized programs where installed in remote terminal unit (RTU) at monitoring site and in processing server in laboratory, the first one is RSDMS Measurements Control, functioned as a interactive software to send and receive commands from Total Station. It has to established communication with robotic total station and carry out data accquisition work. The database management system will be implemented to support huge amount of raw measurement data. Open Database Connectivity or ODBC will be used for this software system and linked to Microsoft Access, all measured data will be store in Microsoft Access database for further processes. The measurement data collected from InDA was verified by commercial software Leica GeoMos. The second program is RSDMS Process and Analysis, its able to receive raw data from RTU with FTP technology and perform post processing, subsequently send alert emails to end users. The raw data will be process by Star*Net software using an embedded program initialized function within SDAnaS program. With integration of these 2 programs, RSDMS has become interacting, interrelated, or interdependent software and hardware elements forming a complex whole for deformation monitoring that, once set up, does not require human input to function. The motivation inside this research have considered several factors such as expensive price and licensing for commercial software, error in measurement data issues and analysis aspect in commercial software. Due to this problem, RSDMS is developed as a low cost software system, to minimize the error in measurement, and provide robust analysis.

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1. Research’s Objectives There are three objectives to be achieve in the research. The first objective is to develop a control program to communicate with total station for taking measurements. Subsequently, a program needed to process raw data from total station in order to get resultant coordinates for each measurement cycle. Finally, an analysis program used to detect alert value and generate alarm or warning emails to end users. With completion of these three objective, users are able to get informed with real-time soil deformation value.

2. Methodology Surveying technology allows the determination of 3-dimensional (3D) coordinates and movement. Current technology provides robotic total stations (RTS) that are able to measure angle with an accuracy of r 0.5” (0.15 mgon) and distance with an accuracy of r 1mm+ppm in standard measurement mode (Leica Geosystems, 2000). For example, TCA1800 produces by Leica Geosystems AG, which is designed for conducting deformation-monitoring survey. Many researchers have used the TCA2003

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model for industrial measurement like automatically search and lock target prisms within seconds see Dünish and Kuhlman (2001) and Kuhlmann (2001). Their studies claimed that tracking moving target is possible with RTS in setting out rail geometry. Radovanovic and Teskey (2001) used Leica TCA2003 for measuring several points in continuous mode and the results were compared with GPS technique. Lutes et al. (2001) implemented DIMONS software (and supported by Leica TCA2003) for monitoring a water reservoir dam in Canada. With this latest technology, the RTS allows the measurement of many points on a surface. Then the points will be monitored within a short period of time. All the operations are done using Automatic Target Recognition (ATR) technology (Leica Geosystem, 2000), where each prism can be found automatically. The instrument selected in this study is Leica TM30. It has 1 second of Angle measuring accuracy with 1mm + 2ppm distance measuring accuracy. It also have motor driven mechanism which can be controlled using the software to perform data recording at predefined time intervals or manually should the situation requires. Measurements can be carried out remotely from the computer. Hardware communication is a crucial part for this research, communication protocols needed to be comprehenced before enter software developing state. GeoCom protocols will be implemented in RSDMS Measurement and Control program using Visual Basic computer language. GeoCom is implemented as a point to point communication system the two communication participants are known as the client (external device) and the server (TM30 total station). The cummunication unit consists of a request and a corresponding reply. Hence, after communication takes place when the client sends a request to the server and the server sends a reply back to the client. RSDMS system operation procedure are illustrated as Figure 1 A control program will be develop as a component to RSDMS named RSDMS Measurement & Control program (SDMonS). The duty of this program are to establish communication with robotic total station from remote terminal unit (RTU) and carry out data accquisition procedure. The raw measured data will first stored in RTU Microsoft Access database before being FTP through broadband network back to processing server. In processing server, RSDMS Process & Analysis program (SDAnaS) will take charge of the raw data. SDAnaS will first convert raw data into Star*Net input format follow by initiate Star*Net least square processing functions for data adjustments. After least square processing procedure, the deformed coordinates of each monitoring point will be extracted to compare with initial coordinates to get actual deformed measurements. SDAnaS also embeded with soil deformation alert function. The program will check throug the deformed measurements with a preset allowable deformed value, if any deformation exceeded the allowable value alert emails will be send to designated emails address for alert purposes. Engineers or researchers may spare time to take mitigation strategies to counterfeit soil deformation situation. 3. Concept’s of RSDMS RSDMS is developed for use on personal, laptop computer and Industrail PC with Microsoft Window Me/2000/XP operation system. The monitoring system allows users to perform a complete deformation monitoring in real-time data acquisition and analysis. RSDMS monitoring procedure consist of 2 core programs named SDMonS and SDAnaS where SDMonS is install in RTU on site to perform robotic total station controlling and data accquisition works, SDAnaS is install in server to perform post processing and alert functions.

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Figure 1. Diagram of RSDMS Configuration

Literature Review

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Understand and make use of GeoCOM protocols communication with TM30 Robotic Total Station operations

Develop RSDMS Measurement & Control program (SDMonS) for communication between computer system and Robotic Total station TM30 Develop RSDMS Process & Analysis program (SDAnaS) for coordinates adjustments and act as data delivering and publishing tools

Setup a system testing site and carry out testing on RSDMS.

Prepare documentation and presentation for RSDMS Figure 2. Methodology Research Flow Chart

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3.1. RSDMS Measurement & Control program (SDMonS)

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Only Leica instruments in TPS1000 and TPS1100 system software family (e.g. Leica TCA 2003, Leica TCA 1800 and TM30) can be integrated with computer. The TPS system software is built around the sensor element (On-board software), organizes and control interplay of several sensor elements. It provides a set of function to access sensors. Figure 3 shows architecture of communication between TPS 1000/1100 software system with computer. All these functions can be manipulated and controlled form GeoCOM Client (i.e. software packages that developed by Microsoft Visual Basic 6.0 and VBA).

Figure 3. Client/Server Applications and GeoCOM Function (Leica Geosystems, 1999)

The functions (Figure 3) are grouped and organized as subsystems, the functions are:•

AUT for Automation – Function to control ATR, change face and do positioning.

•

BAP for Basic Application – Function used to get measurement data.

•

BMM for Basic Man Machine – Function to control basic input/output

•

COMF for communication – Function to handle basic communication parameter.

•

COM for communication – Function to access some aspect of TPS 1000 control which are related to communication.

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•

CSV for Central Services – Function to get or set central/ basic information about TPS 1000.

•

CTL for Control Task – Function contain system control task.

•

EDM for Electronic Distance Measurement – Function module which measures distance.

•

SUP for Supervisor – Function to control general values of TPS 1000.

•

TMC for Theodolite Measurement and Calculation is a core module for getting measurement data.

•

WIR for Windows Registration – Function for GSI recording.

The communication module links the client to the server with serial communication connection (RS232) by send and receive communication protocol called GeoCOM command set. GeoCOM is based on SUN Microsystem’ Remote Procedure Call (RCPC) protocol thus its able to recognize and act on certain sequences of character (commands) that sent via serial port. With the low level of implementation, each procedure, which is executable on the remote instrument, is assigned a remote procedure call identification number. This number is used internally to associate with a specific request, including the implicit parameter to a procedure on remote device (Leica Geosystems, 1999). GeoCOM provides an ASCII interfacs for low level design, on the other hand, GeoCOM has provides normal function call interfaces for high level design such as Microsoft Visual Basic, Visual C/C++ and VBA software developments.

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3.2. RSDMS Process & Analysis program (SDAnaS) SDAnaS (Soil Deformation Analysis system) is given an objective to detect soil deformation. SDAnaS developed using Microsoft Visual Basic 6.0 programming language, and uses robust method of Iterative Weighted Similarity Transformation (IWST) for deformation detection computation. SDAnaS consist of three parts, the first part is integration module, it convert raw data from measurement cycles into STARNET (commercial LSE software) process data. The program provoke STARNET processing via OLE (Object Linking and Embedding) and convert LSE output to SDAnaS format for second part of the program. The second part is deformation detection module, it will compare output from each cycle to the initial coordinate for each point in the loop before produce a numerical real-time result of deformation. The third part of the program is the function to send alert via emails to undersign users for deformation result that over the preset threshold. 3.3. Verification Between RSDMS & Leica GeoMos This research will adopts Leica GeoMos software to check the consistency, reliability and capability of RSDMS Measurement & Control program (SDMonS). Fifty monitoring points will be setup around the building with ten more reference points will be located at the edge of group of monitoring points. ATM30 robotic total station with IPC (industrial PC) which installed SDMonS will be setup on the building top as RTU (Remote Terminal Unit). Further more, a computer with SDAnaS installed will setup in office for receive and process data.

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Figure 4. Flow Diagram for Proposed RSDMS software system - Architecture of Instrument Control and Data Acquisition Module

4. Conclusion This research focused on the development of RSDMS software for real-time automated data capture and analysis for soil deformation monitoring applications. In order to accomplish the research objectives, RSDMS has able to deliver real-time deformation results with consistency. The approach of unique technical design in RSDMS guarantees the integrity that is high accuracy, overall availability of the system and continuity of the instrument function for rough construction needs. RSDMS has

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deployed on site to carry out monitoring measurement for a month for data collection and system durablility test. test result showed that RSDMS is able to deliver data according to the preset measurement cycle with consistancy during testing period.

References [1] [2]

[3]

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[4] [5]

Leica Geosystems. GeoCOM Reference Manual. Switzerland: User Manual, 1999. A.L. Allan, Practical Surveying and Computations Revised Second Edition. Amimprint of ButterworttHeinemann, Linacre House, Jordan Hill, Oxford OX28DP: A Division of Reed Educational and Professional Publishing Ltd, 1997. Khairulnizam M Idris, Halim Setan. Automation in Data Capture and Analysis for Industrial/Deformation Surveying Using Robotic Total Station. Msc Thesis. University Teknologi Malaysia, 2008. G. M.Miller, Modern Electronic Communication. 5th Edition. New Jersey: Prentice- Hall, 1996. Muhammad Asyran Che Amat. Implementasi Pengoptimuman Komputer Dalam Pembangunan Perisian Analisis Pelarasan Kuasa Dua Terkecil. Msc. Thesis. Universiti Teknologi Malaysia, 2007.

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Diagnosis of Ginza Line Subway Tunnel, the Oldest in Asia, by Acquiring Data on Deterioration Indices Tsutomu YAMAMOTO a,1, Shunsuke MATSUKAWAa and Haruo HISAWAa a Infrastructure Maintenance Dept. Tokyo Metro Co., Ltd

Abstract. The Ginza Line subway tunnel was opened for service in 1927. A large amount of data on various deterioration indices was acquired. This report describes the results of the diagnoses by utilizing these data. The results of the visual inspection, strength test, and structural analysis indicated the tunnel proved highly satisfactory in terms of load bearing capacity. As regards the durability, the tunnel was almost free of progressive corrosion because of limited water supply if there was no water leakage. Our projection of the progress of deterioration confirmed that there was no possibility of reinforcement corrosion and resultant cracks even 140 years later. Maintenance and repair work, including leak repair and recovery patching, will be implemented for those sections around water-leaking locations where the water content was high and thus the possibility of corrosion was high. We think that it is extremely significant that the maintenance and repair policy could be clearly defined on the basis of quantitative evaluation by acquiring deterioration index data.

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Keywords. Subway tunnel, diagnosis, deterioration indices, carbonation, reinforcement corrosion, water content of concrete, structural analysis, prediction of progress of deterioration

Introduction The Ginza Line subway tunnel, which opened for service in 1927, was the first subway in Asia. Conventionally, efforts have been made to maintain the subway by corrective repairs based on engineers’ personal experiences derived from visual inspections and hammer tapping. Recently, however, concern has risen about a decrease in residual load-bearing capacity due to time-course material deterioration and due to change in external environment. An evaluation was undertaken using various deterioration indices based on the “Standard Specifications for Concrete Structures, Maintenance Edition” established by the Japan Society of Civil Engineers in 2001. This approach was used to predict the progress of deterioration and for structural analysis and a review of countermeasures. The objective was to perform a comprehensive evaluation of current and future integrity and to clearly define a maintenance policy for the future, thereby ensuring safe tunnel use for many years to come. 1 Corresponding Author: Tsutomu Yamamoto, Infrastructure Maintenance Dept. Tokyo Metro Co., Ltd, 19-6, Higashi-ueno 3-chome, Taito-ku, Tokyo, Japan; E-mail: [email protected].

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1. Overview of the Tunnel 1.1. Tunnel Structure The Suehirocho Station to Asakusa Station section (3.3 km) of the Ginza Line covered by this inspection is a box-section tunnel of steel-framed reinforced concrete. The tunnel structure is shown in Figure 1. The overburden depth of this tunnel section ranges from 2 to 5 m. 1.2. Tunnel Design Reference documents from the time of tunnel design describe the allowable stress design method that was employed. The allowable compressive stress due to bending of concrete was specified at 3.5N/mm2, which suggests that the concrete used was expected to offer about one half of the strength that is expected today. On the other hand, the allowable stress of steel was 120 N/mm,2 which is roughly equivalent to the present standard. We also found that the design road traffic load was substantially less than the current standard value. 1.3. Materials Used

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Materials used as described in the reference documents are listed below: x Portland cement x 13-mm and 16-mm plain round steel bars for reinforcement x Steel spacing: 100 to 150mm x Concrete cover for embedded bars: 50 to 70mm x Both steel space and cover were equivalent in design to the present ones. x

Figure 1. Tunnel structure.

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Table 1. Inspection items and No. of inspection points. Inspection items

No. of inspection points

Visual and hammering checks

12 points

Compressive strength test of concrete

105 pieces

Tensile strength test of reinforcement

9 pieces

Measurement of carbonated thickness of concrete

261 points

Measurement of concrete cover on embedded bars

261 points

Inspection of corrosion of reinforcement

17 points

Inspection of corrosion of steel frame

5 points

Measurement of water content of concrete

24 points

Estimate of concrete mix proportion

5 points

Component analysis of leaking water

3 points

Humidity and temperature measurement in tunnel

20 points

2. Acquiring the Deterioration Indices 2.1. Inspection Items Inspection items and the number of inspection points for acquiring of deterioration indices are shown in Table 1.

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2.2. Result of Visual and Hammer Tapping Inspections Defects observed in the section included cracks, peeling concrete, honeycomb, water leakage. There was no rust fluid indicating corrosion of reinforcement. The crack widths were about 0.1 to 0.3mm None of these defective elements differed significantly from those cited in inspection results 15 years ago. 2.3. Strength Tests Concrete compressive strength was measured by a core-sampling test. In spite of a downward trend in strength in parts of the section, the average strength was 21.2N/mm2, and the measured values exceeded the estimated design standard strength of 10N/mm2 in all locations. No strength deterioration was observed from the inspection results 15 years ago. The tensile strength of steel frames and reinforcement was also found to be sufficient. 2.4. Measurement of Carbonated Thickness and Concrete Cover on Embedded Bars The results were as follows: x The averaged carbonated thickness was 18mm.

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x x x

193

The averaged concrete cover on embedded bar was 43mm. Carbonated thickness data was distributed within an approximate range of 2 mm to 40 mm, with certain data exceeding 100 mm in a part of section. The concrete cover on embedded bar data was distributed within an approximate range of 20 mm to 70 mm.

2.5. Inspection for Corrosion of Reinforcement We inspected for corrosion of reinforcement according to the self-potential method and by directly viewing the covering concrete that was partially chipped. The results showed that, in spite of the more than 75 years that had elapsed since construction, there was almost no corrosion proceeding in most locations. The state of reinforcing bar corrosion is shown in Figure 2. The condition was actually, “no corrosion” to “slight rust spots.” However, in certain locations there were signs of water leakage inside the tunnel, and some thin loose rust was detected in a few spots. Generally, it is said that corrosion of reinforcement proceeds when the carbonated thickness exceeds the depth of reinforcement. However, in the section inspected, there were many location points where corrosion was limited in spite of propagation of carbonation.

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2.6. Measurement of Water Content of Concrete In this context, concrete water content, which influences reinforcement corrosion speed, was measured by using an electric resistance moisture meter. The results indicated that the concrete was considerably dry with both the surface and internal water content being extremely low, about 1-2% and about 0-1% respectively when the concrete was not affected by leakage. In other words, the tunnel was almost free of corrosion because of limited water supply in spite of propagation of carbonation beyond the reinforcement position. The concrete had remained in a dry condition because there was naturally no rainfall inside the tunnel and because of the firm waterproofing layer provided at the time of construction.

 Sound portion. (no corrosion)

 

Leakage points.(thin loose rust)

Figure 2. Corrosion state of reinforcing bar.

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Figure 3. Water content of concrete measured near water leaking points

However, around the points showing leaking of water, the internal water content was high at 5% or more at the source of the leak, and at the wet surface area below the source. It could be confirmed that corrosion of reinforcement there had proceeded because of the water supply. Figure 3 shows an example of water content measured near the water leaking point. The water content exceeded 5% immediately near the water leaking point, which decreased as the distance from the leaking point increased. The water content measured at a distance of 1 meter away was 0%.

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2.7. Considerations on Acquiring Deterioration Indices Data This was our first attempt to acquire many deterioration indices for diagnosis. The considerations pertaining to data acquisition are described below. x Data on carbonated thickness was dispersed over a wide range, and it was impossible to simply determine a representative value for judgment. x The Standard Specifications basically indicated that reinforcing bar corrosion is to be determined by referring to uncarbonated cover depth. In the course of inspection this time, an inconsistency in the results was that, in many locations, no corrosion was observed, even when uncarbonated cover depth was identified as zero. x This can be attributed to the extremely dry state of the subway tunnel concrete. It was concluded that this was an indication that concepts for Standard Specifications covering mainly above-ground structures could not be directly applied here. x The above results show that it is essential, in the case of subways, to take into account the water content and actual reinforcing bar corrosion data, instead of only relying on uncarbonated cover depth. x Although data on deterioration indices was acquired, some data proved to be only rarely helpful in determining the deterioration state during measurement. It is therefore essential to specify items to be measured that are related to the mechanism of deterioration in the tunnel concerned.

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3. Structural Analysis 3.1. Purpose of Analysis A considerable number of years have passed since this tunnel was constructed, and environmental conditions have changed. In addition, the original design calculation sheets have been lost. Therefore, structural analysis was implemented using current load conditions and material physical values to assess the existing proof stress. 3.2. Analytical Model A two-dimensional frame linear analysis with a good track record was adopted as the analytical model. For analysis and verification we used a three-dimensional solid model that is an assembly of hexahedron solid elements, to faithfully reproduce the complicated structure of steel-framed reinforced concrete. An example of output of each analytical result is shown in Figure 4. In the actual field, partial loss or honeycomb were also observed in the covering concrete, which meant that the three-dimensional-model analysis involved an assumed case of loss or rigidity deterioration in a part of these concrete elements. The results of the test implemented this time were used for property values, such as the material strength, etc. in analysis and verification.

3.3. Results of Analysis and Verification

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In the two- and three-dimensional analyses, the response values IRd for flexural strength, axial strength, and shear strength were below the design strength ILd. The verification result from a three-dimensional model analysis was approximately IRd / ILd = 0.3 to 0.6, which proves sufficient allowance in strength. The strength allowance could also be confirmed for cases in which the concrete element was partially lost.

 Two-dimensional frame liner model.    

Three-dimensional solid elements model.

Figure 4. Typical analytical output results.

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4. Prediction of the Progress of Deterioration 4.1. Purpose of Prediction We first predicted progress of carbonation. In this section where the concrete water content is low, however, it was presumed that the corrosion speed of reinforcement would be extremely low if there was no water leakage even when carbonation had progressed. Therefore, using the water content determined from inspection, the time period from onset of carbonation at the reinforcement position up to occurrence of cracking caused by reinforcement corrosion was predicted. 4.2. Prediction of the Progress of Carbonation Predicting the progress of carbonation was done by referring to the square-root relationship between time and carbonation depth. When viewed as the average between stations, deterioration progress could be predicted not to reach the reinforcement position in the five decades to come, that is, even 130 years after construction. Note, however, that in some locations where carbonation had progressed considerably, it was predicted that carbonation had already reached the reinforcement position or would reach it in the near future. 4.3. Prediction of Time of Crack Occurrence Due to Reinforcement Corrosion

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Prediction involved calculation of the corrosion speed from the concrete water content and calculation of the period up to occurrence of cracks by setting the corrosion amount at which cracks appear and the current corrosion amount. The prediction results were as follows: the period up to occurrence of cracks is 140 to 200 years, provided there is no water leakage. It was predicted that, if water leakage occurs, cracks could be expected in about ten years. The underlying concept for the prediction results is shown in Figure 5.

Figure 5. Conceptual view of predicting the timing of crack occurrence due to corrosion of reinforcement bar

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5. Evaluation and Judgment On the basis of the inspection, analysis, and prediction results, the present and future of condition of the tunnel concerned could be evaluated as follows: x In regard to load bearing capacity, visual inspection and structural analysis yielded sufficiently satisfactory results because there were no adverse effects from external forces such as overload or fatigue. x As regards durability, various inspection results indicated that deterioration was mainly due to carbonation, not deterioration induced by chloride, alkaliaggregate reaction, or chemical corrosion. x In almost all locations, the progress of deterioration due to carbonation had not yet reached the stage of reinforcement corrosion starting. In addition, as indicated in the prediction of deterioration progress, it could be confirmed that, in locations without water leakage, the possibility of reinforcement corrosion, and thus crack occurrence, would be low, even 140 years from now.

6. Selection of Remedial Measures

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It was determined from the above results that the tunnel in its present state does not require any reinforcement to recover or enhance load-bearing capacity or rigidity. Countermeasures against deterioration for the entire tunnel were also judged to be unnecessary. Since cracks or portions where concrete was peeling were observed near localized water leakage, however, maintenance and repair works of those sections, including recovery patching leak repair will be implemented without fail. Research is still in progress in the fields in which analysis and inspection were conducted. For this reason and because each category of data contained unconfirmed elements, we decided to continue regular monitoring to follow the diagnostic results over a long period of time.

7. Conclusion It was extremely meaningful to be able to define a maintenance policy for the future for the subway, the oldest in Asia, with 80 years of history. This was done by making a transition from conventional subway tunnel maintenance that depends on visual inspections and hammer tapping to quantitative evaluation used to obtain deterioration indices data. This was an alternative to relying on visual inspections and hammer tapping. Currently we are attempting diagnosis through data acquisition, primarily based on conventional visual inspections, for lines other than the Ginza Line. In particular, tunnels running under rivers near the sea suffer from leaks of highly saline water. Since deterioration induced by chloride may proceed in the future, inspection and diagnosis in this respect is essential. We strongly hope that the results of the diagnosis of the Ginza Line this time will contribute to practical maintenance of subway tunnels all over the world.

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References

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[1] Historical materials of Tokyo-Chikatetsudo, Tokyo Chikatetsudo Co., Ltd, 1934. [2] T. Nishimura et al., Study of Safety Evaluation of Steel Frame Structure Using a Three-Dimensional Nonlinear FEM Based on On-Site Survey Results, Doboku Gakkai Ronbunshu F65, No.1, JSCE (2009), 38-49. [3] T. Sasaki et al., Study on Corrosion of Reinforcing Steel Embedded in Concrete Suffering Chloride Attack and Carbonation, Proceedings of the Concrete Structure Scenarios, Vol. 10, JSMS (2004),1116

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Exploratory Drilling with Recorded Parameters using Wireless Technology Carla ALKASSISa, Eliane NASSIFb, Imad ELHAJJc, Shadi NAJJARd, 1 and Salah SADEKe a Engineer, Electrical & Computer Engineering, American University of Beirut - AUB bEngineer, Electrical & Computer Engineering, American University of Beirut – AUB cAssistant Professor, Electrical & Computer Engineering Department - AUB dAssistant Professor, Civil & Environmental Engineering Department – AUB eProfessor and Chair, Civil & Environmental Engineering Department - AUB

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Abstract. Instrumented exploratory drilling in rock has been widely used in geotechnical engineering and in the oil and gas industry to characterize geological formations and to investigate the presence of subsurface cavities and soft spots. Instrumented drilling involves monitoring the drilling process by measuring a number of drilling parameters using sensors that are mounted to the drill rig. The data collection process used in conventional instrumented borehole drilling suffers from several limitations that are associated with the use of cables. The cables are susceptible to damage during drilling and could cause disruption to the drilling process. The objective of this paper is to present an enhanced system for instrumented borehole drilling that was developed to achieve (1) wireless data acquisition that would eliminate the need for cables and allow for remote access and control of the data, (2) automated real-time detection of cavities and soft spots, and (3) enhanced measurement of the rate of penetration using a laser sensor. Field implementation and testing of the wireless drilling system indicated an adequate functionality of the sensors used and their proper communication with the wireless data acquisition system and offered a realistic demonstration of the system. Keywords. Wireless drilling, drilling recorder, cavity and soft spot detection

Introduction Instrumented exploratory drilling is widely used in geotechnical engineering and in the oil and gas industry to characterize geological formations and to investigate the presence of subsurface cavities and soft spots. The process, which is sometimes referred to as “measuring while drilling (MWD)” involves monitoring the drilling process by mounting several sensors on the rotary destructive drilling rigs to measure drilling parameters such as fluid pressure, torque, drilling speed or rate of penetration, thrust on drill bit, and rotation speed [1]. The outcome of the process is a set of logs that describe the variation of the drilling parameters with depth. For geotechnical engineering applications, these drilling parameters could be monitored and interpreted 1

Corresponding Author: Assistant Professor, E-mail: [email protected].

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in real time or after the completion of the drilling process to characterize the subsurface conditions which include defining the stratigraphy, getting an indication of quality, and identifying locations of weak zones, fractures, or cavities [2]. In the oil and gas field, the drilling parameters could be used to provide early warning on rock type, hydrocarbon saturation, and the location of potentially dangerous zones of high pressure [3]. In addition to their application in the above fields, instrumented borehole drilling is becoming common in areas involving, ground water development, mining, geophysical and geological research, in addition to the environmental field. The focus of this paper is on applications of instrumented borehole drilling in the geotechnical field. Several studies have reported on practical projects that involved the use of instrumented borehole drilling in subsurface investigation for different geotechnical systems ranging from old tunnel linings [1], high rise buildings [2], bioremediation techniques for rock aquifers [4], soil nailing in weathered slopes [5], characterization of cavities in karst terrains [6], and mining applications [7]. Most of these studies were implemented using one of the two most common drilling parameter recorders that are available in the market. The first system is called Explofor and is manufactured by the company Apageo. The main parameters measured and recorded by Explofor are the rate of penetration, the rotational torque pressure on the drilling rotary head, force of penetration of the drilling rod and the pressure of the injection fluids. These are obtained through the use of pressure sensors and a spooler system for the rate of penetration. The other system is designed by Jean Lutz. It measures and records the depth of penetration, instantaneous advance speed, thrust and resistant pressure, water pressure, torque, rotary speed, reflected percussion wave and the flowrate of water, mud or air. The data collection process used in conventional instrumented borehole drilling suffers from several limitations that are associated with the use of cables which extend between the sensors on the drill rig and the drilling recorder placed at a distance from the rig. The cables are susceptible to damage during drilling and could cause disruption to the drilling process. Disruption also can occur due to damage to the spooler system caused by the spoil and mud resulting from the drilling process getting stuck on the spooler cable. In addition, most current systems for instrumented drilling do not have the capability for real-time analysis of the data collected and do not generally allow for remote access and control of the data and the drilling process itself. The objective of this paper is to present an enhanced system for instrumented borehole drilling that was developed to achieve (1) wireless data acquisition that would eliminate the need for cables and allow for remote access and control of the data, (2) automated real-time detection of cavities and soft spots, and (3) enhanced measurement of the rate of penetration using a laser sensor. Field implementation and testing of the wireless drilling system was conducted on a geotechnical exploration project in Beirut, Lebanon. The results of the trial will be discussed in the next sections.

1. Hardware Design The diagram shown in Figure 1 describes the different components of the proposed system which is comprised of three pressure sensors, one laser sensor, a wireless data acquisition system, and an on-site control box. The three pressure sensors (Figure 2a) were used to measure the rotational torque on the drilling rotary head, the force of penetration, and the pressure of the injection fluids. The sensors (ECOS250.0A) have a

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capacity of 250 bars and are manufactured by Trafag. These sensors can withstand strong vibrations and harsh environmental conditions expected during typical destructive drilling jobs.

Figure 1. Schematic diagram of the system.

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In conventional drilling parameter recorders, the depth of drilling is typically measured using a pulley system mounted on the rig mast and attached to the drill head, with the rotation speed monitored by an electromagnetic sensor to determine the number of revolutions per minute. In the proposed system, a laser sensor (LDM41A by ASTECH) which is highly accurate (± 2 mm) and reliable is used to measure the depth and monitor the rate of penetration (Figure 2b). The laser sensor can be easily mounted on the drill rig without major modifications. The laser enclosure is rated IP65, which provides dust proofing and protection from low pressure water jets from all directions. The maximum range of the sensor is 30m, which is higher than that required in real field drilling applications.

Figure 2. (a) ECOS250.0A pressure sensors and (b) LDM41A laser sensor

The three pressure sensors and the laser sensor are connected to the wireless data acquisition device (NI WLS 9205 by National Instruments) shown on Figure 3. This wireless DAQ provides the connectivity between the sensors on board of the driller and the laptop on site. The NI WLS9205 provides IEEE 802.11g (Wi-Fi) wireless and Ethernet communications interfaces, 32 single-ended or 16 differential analog inputs, 16-bit resolution and 250 kS/s aggregate sampling rate. This wireless DAQ and the

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required circuitry are housed in a box on the rig. This box, shown in Figure 3, is rated IP55 which provides protection from dust and low pressure water jets from all directions.

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Figure 3. Wireless data acquisition system (DAQ) and control box

Also shown in Figure 3 is the control box developed to allow flexibility in controlling the recording process by operators close to the rig. The box, which is also connected to the wireless DAQ, provides two release buttons for Start and Pause and two LEDs to provide the operator status information as described in the next section. The first button is pressed at the beginning of the process and whenever a new rod is added, and the second button is used to stop or cancel the recording process. This ensures that the process is controlled either through software on the laptop away from the rig or using the control box by the rig.

2. Software Implementation The software used for data analysis and hardware integration is LabView. The main function of the software is to acquire the data from the different sensors and tabulate the parameters collected while drilling to be used for further analysis. It saves an excel sheet for each drilling process. The latter contains the information related to each exploratory hole: project name, operator name, location, an identification number, time and date of drilling as well as the name of the contracting company. The file saved includes a table with measurements for the rate of penetration, injection fluid pressure, pushing force pressure and rotation pressure. In real time, four graphs will be displayed showing the four listed parameters versus depth. Two additional graphs showing the interpreted depths of expected soft spots and cavities are displayed on the main menu.

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The first tab in the Software is the “Welcome” tab. It provides the user with two main types of operations, indicated by the “New” and the “Load” buttons. The “Load” would be used mainly by the consultant to regenerate the graphs which were previously obtained from the drilling process and saved on file. The “New” would be used by the site engineer or technician to start and save a new exploration project. When choosing “New”, the second tab, named “Input File”, becomes visible. The date and time of drilling is set automatically according to the time/date settings of the computer used. Pressing “OK” will automatically create an excel file according to the number of the current exploratory hole and the file is saved in the created project folder. After the completion of all drilling activities, the user can press “Stop” to terminate and close the entire project. After setting the appropriate filename and pressing save in the dialog box, the user is transferred to the third tab named “New File”. In this tab the recording and monitoring process can be controlled. The pressure measurements are obtained directly from the pressure sensors through the wireless data acquisition system, which in turn supplies LabView with voltage readings that are proportional to the actual pressures. The pressure is then calculated in LabView. On the other hand, the rate of penetration is calculated in the software using the depth readings provided by the laser sensor through the same wireless DAQ, by subtracting two consecutive readings of the depth, and dividing by the time difference, dt, separating them. dt is the inverse of the sampling rate N and is calculated by LabView as dt = 1/N seconds. In order to increase the accuracy of the readings and minimize measurement noise, the samples are averaged to get a smoother graph. The sampling rate is set to 50 samples per second, and every 10 samples are averaged and plotted as one point. Thus the effective rate becomes 5 samples per second. This averaging is also implemented to filter the readings of the pressure sensors. It is worth noting that the process of destructive drilling includes periodic cleaning of the hole. This involves repeated backward and forward motions, that are recorded by the system but have no practical use. Therefore, it was decided to include a filter for these motions and only save the values that correspond to increasing depths. Another main practical component that was added to the system involved indicators for soft spots and cavities. Given measured values for the rate of penetration and fluid pressure, a soft spot could be detected at depths where the rate of penetration increases beyond a user configurable pre-set value, while a possible cavity could be detected where the rate of penetration increases with an associated drop in the injection fluid pressure. These preset values are entered by the user on site before the start of the exploration and could be modified when the file is loaded again for analysis. These indicators are displayed by adding two graphs that plot Boolean charts of the cavities and soft spots over the cumulative depth, in addition to instantaneous visual alarms on the front panel. A plot showing the six logs (four sensors and two soft spot and cavity indicators) that are displayed in real-time during drilling is presented in Figure 4. The Labview interface with the Record, Pause, and Exit buttons, in addition to the tabs assigned for specifying the critical values for rate of penetration and injection fluid pressure needed for soft spot and cavity detection are also shown on Figure 4. Furthermore, this tab communicates via the wireless DAQ with the control box described previously. When the “Start” button is pushed from the control box by the rig, or the “Record” button is activated from the front panel,the blue LED on the control box will light-up and remain lit and as long as the system is recording. When the “Pause” button is pressed (on the control box or front panel), the red LED will light-up. The data saved in excel can be reloaded in the fourth tab named “Uploading File”, as

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shown in Figure 6, to obtain the above mentioned graphs. Also, the critical values that were used on site for soft spots and cavities detection are displayed. Here, the user can choose to keep the existing graph, or enter different indicator values to obtain a new detection graph.

Figure 4. Screen shot of application interface.

Lastly, LabView provides the functionality of publishing the application on the web in order to be viewed and controlled remotely. Using the Web Publishing tool, the HTML document is configured to allow the engineer (on another remote computer) to monitor the recording process. The HTML page is saved on a directory on the local host with a file name and URL link to access it as one would open any webpage.

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3. Field Implementation Field implementation and testing of the wireless drilling system was conducted on a geotechnical exploration project in Beirut, Lebanon. The objective was to test the adequate functionality of the sensors used and their proper communication with the wireless data acquisition system. The drilling rig used for the field tests was a hydraulically operated rotary crawler CMV-MK 420 type rig from Geomechanica. The three pressure sensors were installed at various locations through the hydraulic circuits of the machine using tee-connectors onto the appropriate hydraulic or water/mud lines (Figure 5). The laser sensor was mounted at the top of the mast of the drill rig as indicated in Figure 5. The required range for the laser sensor in this job was around 10 meters, since the maximum length of each drilling rod was 6m, and the distance from the top of the mast where the laser was located to the location of the drilling head was about 4m. Drilling parameters were recorded for several boreholes using the configured system. The results obtained indicated the correct functionality of the sensors used and their proper communication with the data acquisition (Figure 6). The use of the laser sensor proved to be a viable and practical alternative for monitoring the penetration rate as indicated by the high accuracy and reliability of the measurements obtained. Moreover, the Software interface was found to be very user friendly and efficient in

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controlling and monitoring the drilling process in real time. The indicators of soft spots and cavities proved to be a valuable addition to existing drilling parameter recorders.

Laser

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Figure 5. Pictures of the rig and sensors used in the field implementation

To illustrate the functionality of the software interface, the variation of the drilling parameters that were recorded with depth from one borehole location is presented in Figure 6. In addition to the drilling parameters, the plot showing possible locations of cavities and soft spots is presented. To illustrate this function, critical values for the rate of penetration (150 m/hr) and injection fluid pressure (2.0 bars) were selected and the resulting locations of possible soft spots and cavities were determined by the software. Results indicated soft spots at the depths of 1.2 m, 1.5m and 1.85m and a cavity at a depth of 0.2 m (Figure 6). It should be noted that this particular plot is interactive in the sense that the operator/engineer can change the criteria used to define these anomalies by changing these criteria in real time on the software interface. Once these criteria are changed, the plot will be updated showing new locations for possible cavities and soft spots.

4. Conclusions This paper presents the detailed design of a drilling parameters recorder which uses state of the art instrumentation and wireless data acquisition to measure several important drilling parameters that are beneficial for subsurface characterization in the geotechnical field. In addition to three pressure sensors that are used to record the rotation torque pressure, the force of penetration and the pressure of the injection fluids, a laser sensor is utilized to record the rate of penetration and depth. The proposed system could be remotely accessed and monitored and could perform real time analysis

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of the data collected to provide warnings for the presence and location of cavities and soft spots.

Figure 6. Output with critical Rate of Penetration=150 m/h and critical Injection Pressure= 2 bar

Acknowledgments

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The project was partially funded by Ericsson, Lebanon and the Civil and Environmental Engineering Department at the American University of Beirut. The authors would also like to acknowledge the technical help provided by Mr. Maroun Khadra and Mr. George Nohra from Bardawil Company, Lebanon.

References [1] [2] [3] [4] [5] [6] [7]

M.W, Gui, K. Soga, M.D. Bolton, and J.P. Hamelin, Instrumented borehole drilling for subsurface investigation, Journal of Geotechnical and Geoenvironmental Engineering 128(4) (2002), 283–291. S.S, Sadkowski, K.P. Stetson, J. Benoit, Investigating rock quality using drilling parameters in Boston, MA, USA, Proceedings of the 3rd International Conference on Geotechnical and Geophysical Site Characterization (2008), 1359–1364. J. Lutz, Retrieved July 28, 2009, Website: http://www.jeanlutzsa.fr/default.aspx?lang=en J. Benoit, S.S, Sadkowski, and W.A. Bothner, Rock characterization using drilling parameters, Proceedings of the 2nd International Conference on Geotechnical and Geophysical Site Characterization (2004), 665–670. Z.Q. Yue, C.F. Lee, L.G. Tham, Automatic drilling process monitoring for rationalizing soil nail design and construction, Proceedings of the 2004 Annual Seminar of HKIE Geotechnical Division, Hong Kong, China, (2004), 217-234. W. Gao, J. Chen, and Z.Q. Yue, Characterization of cavities in marble from automatic monitoring of hydraulic rotary drilling in ground investigation, The 42nd U.S. Rock Mechanics Symposium (USRMS) , San Francisco, CA (2008). P. de Groulard, Drilling parameters and their application in mining and exploration, Proceedings of Exploration 97: Fourth Decennial International Conference on Mineral Exploration, (1997), 721–722.

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Information Monitoring on Surrounding Rock of Tunnel and Its Application a

Yankai WU a,1 and Xiaohua XI b,c Shan Dong university of Science and Technology Qingdao 266510 b XiƍDQ8QLYHUVLW\RI6FLHQFHRI7HFKQRORJ\;LƍDQ c Jiangxi Traffic Academe Nanchang 330038

Abstract. As an important part of the New Austrian Tunneling Method construction (NATM), the processes of monitoring and measurement are important and indispensable in the tunnel construction, which effectively reflect the rock deformation and its mechanical behavior, and provide a suitable installation time for tunnel lining structure. Scientific analysis and forecast real-timely on monitoring data provide a precondition for supporting the strategic decisions of construction organization, as well as dynamic design. In this paper, the tunnel monitoring method and datum analysis are illustrated through an example of the port tunnel monitoring in Jing wuhuang (chang) highway, Jiangxi province china. Many items monitoring data (vault sinking, peripheral convergence, surface settlement, bolt axial force and drawing force, internal force of steel timbering, internal force of secondary lining) acquired from the field monitoring have been analyzed. The appropriate opportunity of surround rock stabilization after tunnel excavated and second lining are proposed by the analyzed the monitoring datum which can provide some lesson for the future tunnel construction.

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Keywords. Tunnel, new Austrian tunneling method, fielding monitoring, surface settlement

Introduction Tunnel monitoring measurement in construction filed is an important component part of New Austrian Tunneling Method which not only be regarded as the guide of safe tunnel constructing but also a basic method of knowing and understanding of the performance of tunnel surrounding rock.. It provides a scientific basis for surrounding rock stability and retaining structural safety judging to ensure the tunnel construction safety. Since New Austrian Tunneling Method appeared in 20 century 60s, tunnel monitoring measurement is widely spread in lots of underground engineering projects in Europe and the United States, Japan and many other countries. In China, although the monitoring method has been improved a lot during the last 20 years, the monitoring measurement has not really combined well with construction and design. The monitoring and measurement method should not only be able to reflect the problems during the construction process but also guide the construction when problems appear.

1 Corresponding Author: Yankai WU, No.579 Qianwangang Road, Qingdao Economic Technical Development Area, Shandong, P.R.China 250061; E-mail: [email protected] Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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This paper analyzes the monitor and measure process of port tunnel field construction in Jing wuhuang (chang) highway, Jiangxi province china. Definite the directive function of the measured data in field to be regarded as the reference for future project practice.

1. Project introduction The tunnel entrance is located 200 meters form Port village, Jiangwan town, Wuyuan county. The tunnel with the mileage from K15+180 to K15+487, 307 clear width. It has not been found the big fracture in the tunnel field. The core sample is broken and the rock is soft. The tectonic deformation, for joint crack and fold, is more development. It has a corrode fracture zone in the tunnel import. The axis of tunnel import is a angle of 45 degree with the Ground contour. The rock in the tunnel import is more loose and soft, which make a poor condition for construction.

2. Monitoring Scheme in Tunnel Construction Field

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10

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In order to ensure the safety of tunnel construction, there’re several items that must be under monitoring during construction: (1) Geological and retaining conditions observation (2) Surrounding convergence monitor (3) Vault sinking monitor (4) Bolt internal force and withdrawal resistance monitor (5) Ground settlement monitor (6) Surrounding rock pressure and pressure between supports monitor (7) Internal force in steel support monitor (8) Internal force in secondary lining monitor During the construction process, the eight monitor items mentioned above are carried out, and the site plan of monitor points are showed in figure 1 and figure 2.

ൠ䶒㓯





Figure 1. Ground settlement measurement range and monitor points site plan

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Ground line

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4 point for measuring the bolt drawing force

7 point for measurin the vault sinking

6 point for mesauring the bolt axial force

13 point for measurin the Peripheral convergence

Figure 2. Multiple arch tunnel construction section monitor site plan

3. Field Monitoring Data Analysis

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settlement (mm)

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In the excavation process, all monitor points are under field monitoring and the data are analyzed as follows: (1) Vault sinking For the ZK15+190 section, the deformation curve, variation of displacement rate, deformation acceleration curves are shown in figure 3 and figure 4. Figure 3 is the accumulated vault sinking curve of section ZK15+190. From figure 3, in the primary deformation period, the deformation variance is large and the accumulated deformation is ascending. About 60 days later, the deformation rate slows down and deformation develops slowly. Displacement variance rate is converged to 0 after 60 days as shown as in figure 4. It indicates that the wall rock deformation becomes stable after 60 days excavation.

-2  -6  -10 -12 

Figure 3. ZK15+190 accumulated settlement curve at vault

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sinking velocity (mm/d)

1.5 1.1 0.7 0.3 -0.1 -0.5 -0.9 Aug-05

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Figure 4. ZK15+190 accumulated variation of displacement rate curve at vault

According to the analysis of deformation rate, it can determine whether the rock is stable so as to reflect the construction situation in time which can be a safety guide to reasonable construction. At the same time, analyze the rationality of support scheme refer to the deformation grade, and determine whether there’s need to strengthen the support. From the field monitoring data, this support scheme satisfies the supporting conditions very well which guarantees the overall stability of rock. Therefore, the construction can be carried out according to the original design proposal. (2) Peripheral convergence The peripheral convergence of section ZK15㸩220 versus time curve is shown in figure 5. date

Peripheral convergence (mm)

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Sep-05

Oct-05

Oct-05

Nov-05

Dec-05

Dec-05

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0 -5 -10 -15 -20 -25 -30

Figure 5. Peripheral convergence versus time of section ZK15㸩220

From the peripheral displacement variance curve, the peripheral displacement is increasing in accordance with the deformation of intrados settlement deformation before November (in excavation process). After November (excavation is ended), the peripheral convergence enters the relative stable period. (3) Surface settlement The monitoring points are buried at the entrance of the tunnel. But according to limit of the terrain, the observed section is buried at the entrance of the tunnel, and there’re 7~10 observation points. The surface settlement versus time curves are shown figure 6 and figure 7. From figure 6 and figure 7, in the process of excavation, there’s no sudden increase of ground settlement at the entrance of the tunnel and the surface

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settlement is relatively stable. In the first 18 days, the variance is relatively smooth but undulates during 18 to 28 days and after 28 days the variance becomes stable. date Aug-05 0

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-10 -20 -30  1# measuring point 2# measuring point 3# measuring point PHDVXULQJSRLQW 5# measuring point 6# measuring point 7# measuring point

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Figure 6. Settlement deformations versus time at the entrance of tunnel slope date Aug-05 10

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0 -10 -20 -30  -50 -60 -70 

1# measuring point 2# measuring point 3# measuring point PHDVXULQJSRLQW 5# measuring point 6# measuring point 7# measuring point

Figure 7. Settlement deformations versus time at the exit of tunnel slope

The maximum settlement is 78.3mm but the actual measured settlement is 74.7mm that 95.4% of the final prediction which means the settlements is tending to converging. Figure 8 is the maximum settlement convergence prediction curve at the entrance of tunnel slope. It can be concluded that the settlement deformation can be predicted from the field data that considered as precious material as construction guide.

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Oct-05

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measuring curve in-situ



settlement(mm)

Oct-05

convergence prediction curve

      

Figure 8. Maximum settlement convergence prediction curve at the entrance of the tunnel

(4) Bolt axial force and drawing force Figure 9 is the relationship curve between bolt axial forces versus time of the tunnel section YK15+240.

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D[LDOIRUFH˄N1˅

60 50  30 1# measuring point 20

2# measuring poin 3# measuring poin

10 0 Feb-06

Mar-06

Apr-06

Apr-06

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Jun-06

date Figure 9. The axial force versus time curve of section YK15+240

The axial force variance among 3 points at the left side of section haunch are similar which are all be pulled first (positive value) and then transferred to be pressed (negative value) gradually. The axial force at 2.3m monitoring point is small and stable as well while the axial forces of other 2 points increase fast at the primary buried period. On the tenth day after buried, the measured value (pulling force) of monitoring points at 0.5mࠊ1.4mࠊ2.3m reach 9.206kNࠊ10.190kNࠊ6.901kN respectively and become stable soon. Along with the excavation of step sulcus, the measured value decrease gradually. Eventually, the monitoring points become being pulled after the excavation of abutment walls and stable after two weeks. Because of the influence of excavation method, it takes a long time for the axial force to reach stable (approximately 40 days). (5) Internal force of steel timbering

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Eight sections are arranged in Wangkou Tunnel, Kangkou Tunnel and Shangtan Tunnel and 120 reinforcement meters in total to measure internal force. Figure 10 is the axial force of steel lagging jack versus moment tense of section ZK15+450. Point B and C are located at arch springing while point A is near vault. The variation tendency of date in figure 10 shows that the stress in earlier stage (about 15 days) changes a lot which means the existence of steel timbering restrict the relaxation of surrounding rock and expanding of plastic zone. The convergence tendency in later stage (about 45 days) illustrates that the design load of steel timbering could satisfy the demand that the stress on vault is relatively large which also fits the loading feature of tunnel.

Internal force of steel timbering(kN)

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20 15 10 5 0

A B C

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Figure 10. Internal force of steel timbering variance curve of section ZK15+450

(6) Stress of surrounding rock In the stress measurement of surrounding rock at section ZK15㸩450, the stress at point A in all eight buried points is the biggest but still under 0.3Mpa, while point B located at left hance is pressed by small value about 0.1 MPa, and point C on the right hance is pulled by 0.05 MPa as showed in figure 11. It can be concluded from the measured results that the pressure variance of surrounding rock in deep buried section and shallow section are in one order of magnitude. The distribution rules of contact pressure between primary lining and secondary lining are similar with surrounding rocks. The measured values in shallow buried tunnel are 0.13-0.36MPa while 0.3㹼 1.0MPa in normal conditions.

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surrounding rock pressure(kPa)

214

Nov-05 200

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Date Jan-06 Jan-06

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160 120  A B C

 0  

Figure 11. The variance curve of c versus time of section Zk15㸩450 (B and C at the arch springing, A around vault).

(7) Internal force of secondary lining The construction of secondary lining is largely influenced by time which means neither too early nor too late is good for the stability of tunnel. The surrounding rock can not display its self load-bearing capacity which make the secondary lining bear too large loading if construct too early while too late is not good to the stability of primary support. Thus, the field monitoring data can reflect the reasonability and safety of secondary lining design.

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internal forces of secondary lining (KN)

7 6 5

A B



C

3 2 1 0 Feb-06

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Figure 12. The variance curve of internal force of secondary lining at section ZK15㸩245 (B and C at the arch springing, A around vault).

Figure 12. is the variance curve of secondary lining at section ZK15㸩245 which shows the stress of secondary lining is small. The moment and axial force becomes stable after the instrument is settled. The maximum convergence value of moment is 1.26 kN·M and for axial force is 5.95kN. Therefore, the safety factor can be decreasing properly in the process of secondary lining design to avoid waste of material.

4. Conclusion and Suggestion It can be concluded from the field monitor that the surrounding rock become stable after 45~60 days of excavation, i.e. the secondary lining can be under construction after 60 days of primary support. In the actual field monitoring process, the surrounding rock

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is regarded as vary rapidly when the displacement rate is bigger than 1mm/d. Protect proposal should be prepared if the displacement keeps increase. In the process of construction as well as monitoring, the variance information and analysis results should be feedback to construction organization in time to ensure the smooth and safety of tunnel construction. In other words, the monitor is a key factor of tunnel construction safety. Therefore, the field monitoring date should be made full use of to provide information to design and construction to prevent accidents.

References

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[1] Jianbo, CHEN. The field monitor and analysis of shallow buried tunnel. Shanxi Architecture. Vol.33 No.9( Mar 2007). 318-319. [2] Ning YU, Hehua ZHU. Application of monitoring and prediction in construction of tunnel. Technology of modern tunnel. Vol.40 No.5 (Oct.2003), 59-66. [3] Gengye CHEN, Bin LIU etc. Stress monitor analysis of Hanjialing tunnel. Chinese Journal of Rock Mechanics and Engineering. Vol.24 Supp.2 (Nov 2005), 5509-5515. [4] Jinxing LAI, Yongli XIE. Safe construction and monitor of soft broken wall rocks of double-arch tunnel. Journal of Engineering Geology 14(04) 1004-9665 (2006), 513-517 .

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Mesoscopic Test Study of the Interface between Geogrid Transverse Rib and Sand Jiaquan WANG a,1, Jian ZHOU b, Xianyuan TANGa and Liuyun HUANGa Department of Civil Engineering, Guangxi University of Technology, Liuzhou, China b Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China a

Abstract. A series of pullout tests are carried out in different vertical pressure by using pullout model test equipment. Using image visual tracing technique to track the interaction process of the interface of the geogrid transverse ribs and sand, and the rule of interface particles movement is further studied through quantitative analysis. The results show that: The standard sand/transverse ribs upper interface region is about 10 times the average particles size, lower interface region is about 6 times the average particle size, in which the particles take place displacement and rotation during pullout process. The transverse rib interface maximum shear strain field along the pullout direction and the upper and lower areas take place expansion, forming a shear strain concentration zone, and are basically consistent with the transverse ribs pullout displacement field. The upper interface thickness is larger than the lower interface thickness, and with the normal pressure to increase, the interface thickness increased slightly. Keywords. Pull-out test, Interface, Geo-synthetics, Meso-mechanics

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Introduction Pullout tests are widely used to interpret the interaction mechanisms and to evaluate the interface interaction parameters between geosynthetics and soil. Pullout test of geosynthetics has been studied by many researchers for understanding various factors affecting the pullout results, i.e., box size, sample size, sleeve length, front as well as side wall conditions, test speed, etc. (Palmeira and Milligan, 1989; Sugimoto et al., 2001; Palmeira, 2004). Experimental results (Moraci and Recalcati, 2006) showed that the reinforcement extensibility influences the peak pullout resistance. In particular, extensibility effects were more evident in long reinforcements and in high vertical confining stresses. Many researchers also conducted the interaction on the soilstructure interface from micro-view. Guler (1999) and Hryciw (1996) have developed several algorithms to track soil particles and measure their movements by detecting the edges of individual soil particles. Zhang et al. (2006) used a newly developed micromeasurement techniques for measuring the shear interface test particle movement and pointed out that the movement of soil particles significantly reduced with increasing distance from the structure. It is a new way of thinking that studies the reinforced soil interface properties from the micro-level, which helps to further understand the interaction mechanism of reinforced soil.

1

Corresponding Author, email: [email protected]

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The purpose of this paper is to study the micro feature of the soil/geogrid transverse rib interface by conducting a series of pullout tests. Using Olympus stereomicroscope (magnification 10 to 220 times), associating with image visual tracing technique and non-target measuring technique, the influence scope of size soil/geogrid transverse rib interface and the law of sand particles movement and interface stain field are studied from mesoscopic view.

1. Experimental Overview 1.1. Pullout Test Apparatus and Image Capture Device The visual model box size of 60cm × 40cm × 40cm (length × width × height), frame with angle iron welded together. In order to reduce the side wall friction, the tempered glass of 5mm thick was glued in the three iron inner side, and glass surface coated with a layer of silicone. To facilitate shooting micro-and macro picture of soil/geogrid interface during the course of pullout, the obverse side of model box is 12mm thick tempered glass. Loading system consists of vertical load and horizontal loading system, as shown in Fig.1. The images of pullout test contain the macro and micro pictures. Macroscopic features was observed by using high-resolution digital photo capture, as shown in Fig.2, with 8 million pixels digital photos, 8 optical zoom Canon SLR 350D (image maximum resolution of 3456×2304) shooting. Auxiliary camera equipments are news lights, tripod, remote controller. The micro-observation system consists of the stereomicroscope, iodine-tungsten lamp light source, camera and computer, for observing soil particles characteristics. reaction frame for loading

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reaction frame

Load plate

Pressure Sensor Hydraulic jacks

Flexible Rubber

geogrid

YE2539 Static Strain Gauge

stereomicroscope

light source Image Acquisition

Video Capture Device

Sleeve Tension Sensors

Motor and gearbox

stereomicroscope stand

Organic glass sheet

Figure 1. Pullout tests apparatus.

Figure 2. Image capture device.

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J. Wang et al. / Mesoscopic Test Study of the Interface Between Geogrid Transverse Rib and Sand

1.2. Soil Samples Preparation and geogrid Soil samples of pullout test are used standard sand in Fujian,china. Sand specific GHQVLW\ȡ s PD[LPXPGU\GHQVLW\DQGPLQLPXPGU\GHQVLW\UHVSHFWLYHO\ȡ dmax = 1.74g/cm3 ȡ dmin = 1.43g/cm3; the maximum and minimum void ratio, respectively e max = 0.848, e min = 0.519; size composition of characteristic parameters: d 50 = 0.34mm, C u = 1.542, C c WKHIULFWLRQDQJOHRIVDQGij ƒ Standard sand with falling rain method (falling height 60cm) layered into model box in order to prepare a homogeneous sand sample. Dense sand samples with a mean relative density of 66% by this method. After placing sand up to the pullout opening level, geogrid was placed on the top of the leveled sand and the front end of the geogrid was firmly connected to the loading shaft by the clamp. Sand was then placed in layers up to the top level. Using the digital camera to shoot continuously the entire pullout test from the front wall of the model box, received a series of high-resolution digital photos that reflect the process of tests. At the same time, the micro-images of the reinforced soil interface particles can be obtained by using stereomicroscope. The biaxial geogrids were used in the test series(see Fig.3), and the geogrid properties is shown in Table 1.

Figure 3. Geogrid.

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Table 1. Geogrid physical characteristics. Unit Weight 0.45kg/m2

Tensile strength of 2% 13.5KN/m

Tensile strength of 5% 16.5KN/m

Yield tensile strength 35KN/m

Yield Minimum elongation carbon content 10.5%

2%

2. Micro Test Results 2.1. The Law of Sand Particles Movement Using stereo microscope to take a photograph of transverse rib interface particles movement, and setting the window size is about 6mm. Taking the standard sand under WKHQRUPDOSUHVVXUHı v = 30kPa) conditions for analysis: Fig.4 and Fig. 5 are respectively showed the displacement evolution of transverse rib upper and lower interface particles, combined with the video captured by stereomicroscope, we can observe: (1) The transverse ribs on the left side constantly squeeze sand in the pullout process, and the sand particles dramatically rotate, at the same time the sand particles move towards the upper left and lower left rib of the

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transverse rib. (2) The void is generated on the right side of transverse ribs in the pullout process caused, the upper particle in that region continue to fall, resulting in a larger range of the top-right dramatic displacement of particles. (3)In the transverse ribs upper (lower) interface region of about 10 (6) times the average particle size, the particles arises obviously displacement and rotation during pullout process, and the particles displacement of upper interface more obvious than lower interface particles. (4) In Fig.4, we can find that mark 1 on the upper interface does clockwise rotation and upper-left corner displacement with the ribs pullout, and mark 2 does horizontal displacement and clockwise rotation; In Fig.5, we can find that mark 1 on the lower interface does counterclockwise rotation and lower left corner displacement, and mark 2 is mainly as translational motion, and a little rotation.

Figure 4. The particle displacement evolution map of grid transverse upper interface.

Figure 5. The particle displacement evolution map of grid transverse lower interface.

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2.2. Displacement Field and Strain Field of Interface Sand The displacement and strain process of transverse ribs interface can be acquired by using non-target measuring technique to deal with continuous digital photos. Fig.6 shows the displacement field evolution law of the interface particles: (1). At the beginning stage of pullout, the transverse rib moves from the right end to the left end, and the interface sand particles are squeezed, and then occur rotation and displacement. (2). When the pullout displacement 3mm, the relative displacement between the transverse ribs and sand have been obvious, the contact interface regions have been developed, the interface profile significantly. (3). When the pullout displacement is 5mm or greater, the upper and lower interface regions of transverse ribs form a stable displacement Zone. (4). The upper interface regions displacement are higher than the lower interface regions displacement, mainly under normal pressure, the sand particles of transverse ribs upper interface are more dense than the particles of lower interface, so the sand particles of upper interface are more easy movement than the particles of lower interface. Fig.7 shows the strain field evolution law of the interface particles: (1). In relatively small pullout displacement, the maximum shear strain region is not law, when pullout 1mm, the sand shear strain on the interface first appeared concentration. (2). With the pullout progresses, the maximum shear strain to the pullout direction and

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the upper and lower regional expansion, when pullout 3mm, the maximum shear strain expansion of the region have been obvious. (3). When the pullout is even greater displacement of 5mm, the maximum shear strain expansion region has stabilized, forming a shear strain concentration zone, the upper interface region is greater than the lower interface region, and basically consistent with the transverse ribs pullout displacement field. (4). When pullout displacement continues to develop, a wedgeshaped area of shear strain begins to appear in front of the transverse ribs.

Y

X

(a) Pullout 1.5mm

(b) Pullout 3mm

(c) Pullout 5mm

(d) Pullout 14mm

Figure 6. Displacement field evolution map of transverse rib interface under normal stress 30kPa.

Y

X

(a) Pullout 1.5mm

(b) Pullout 3mm

(c) Pullout 5mm

(d) Pullout 14mm

Figure 7. Strain field evolution map of transverse rib interface under normal stress 30kPa.

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2.3. The Interface Thichness of Transverse Rib and Sand Fig.8 show the maximum shear strain of Y-section of the standard sand under the normal stress 10kPa. We can see that the interface region is the highest concentration region of shear strain. The interface shear strain of Y-section has formed two distinct peaks region. The peak region is the location of the interface region, interface thickness of transverse rib and sand can be acquired by quantitatively analyzing the region. Based on the test results of the analysis, under the normal stress 10,30 and 50kPa, the transverse ribs upper interface thickness were 3.3 ~ 3.8mm, 3.5 ~ 4.1mm, and 3.5 ~ 4.1mm, were approximately equal to the standard sand average particle size D 50 of 10.3, 10.9 and 11.2 times; lower interface thickness were 2.1 ~ 2.5mm, 2.5 ~ 2.8mm, and 2.5 ~ 2.9mm, respectively, were approximately equal to standard sand average particle size D 50 of 6.8, 7.7, and 7.4 times.We found that as the normal load increases, a slight increase in the thickness of the interface.

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Y

X

Figure 8. The change of maximum shear strain along Y-profile under normal stress 10kPa.

3. Conclusions The main conclusions that can be drawn from this investigation are as follows: (1) Standard sand/transverse ribs upper interface region is about 10 times the average particle size, lower interface region is about 6 times, and interface region particles occur displacement and rotation during pullout process. (2) The transverse rib interface maximum shear strain field along the pullout direction and the upper and lower areas take place expansion, forming a shear strain concentration zone. (3) The upper and lower interfaces thickness of standard sand/transverse ribs is asymmetric, and the upper interface thickness is larger than the lower interface, and with the normal pressure increase, the interface thickness increased slightly.

Acknowledgements The authors appreciate the support of the Natural Science Foundation of China, Grant No.50879059.

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References [1] M. Guler, T.B. Edil, P.J. Bosscher, Measurement of particle movement in granular soils using image analysis, Journal of Computing in Civil Engineering, 13(2) (1999), 116-122. [2] R.D. Hryciw, S.A. Raschke, G.W. Donohoe, Microdeformations in sands by digital image processing and analysis, Transportation Research Board, 1 (1996), 31-37. [3] N. Moraci, P. Recalcati, Factors affecting the pullout behaviour of extruded geogrids embedded in a compacted granular soil. Geotextiles and Geomembranes, 24 (2006), 220-242. [4] E.M. Palmeira, G.W.E. Milligan, Scale and other factors affecting the results of pull-out tests of grids buried in sand. Geotechnique, 39(3) (1989), 511-524. [5] E.M. Palmeira, Bearing force mobilisation in pull-out tests on geogrids. Geotextiles and Geomembranes, 22 (2004), 481-509. [6] G. Zhang, D. Liang, J. Zhang, Image analysis measurement of soil particle movement during a soilstructure interface test. Computers and Geotechnics, 33 (2006), 248-259. [7] M, Sugimoto, A.M.N. Alagiyawanna, K. Kadoguchi, In uence of rigid and exible face on geogrid pullout tests. Geotextiles and Geomembranes, 19 (2001), 257-277.

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The Application of Modified Gaussian Model in Hyperspectral Image Analysis a

Caixia YANG a, Yibo HAN a,b and Pu HANa Nanyang Institute of Technology, Nanyang Henan b China University of Geosciences, Beijing

Abstract. According to the feature of hyperspectral image spectral curve and the morphological characteristics of Gaussian curve, in this paper, a pixel(always mixed pixel) was chose as the analysis object and used the modified Gaussian model (MGM) with a number of improved Gaussian curve fitting of the real partial spectral curve, then compared the parameters(Center, FWHM, Amplitude) of each Gaussian curves to the parameters which comes from the standard spectral library of mineral samples, so that classified the species of the mineral and determined the ingredient. In this paper also made further research directions: using the modified Gaussian model to determine the proportion of different minerals in a pixel. Keywords. Hyperspectral image, Modified Gaussian model, mixed pixel, Mineral Identification

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Introduction Since the concept of imaging spectrometer was put forward in the early 1980s, the hyperspectral remote sensing technology had always been in the forefront of remote sensing field. With the aerospace hyperspectral systems running successfully㸪new and high-performance hyperspectral system being fully practical 㸪 the rapid development of computer technology, the study of hyperspectral have been widely used in fields like geological prospecting and mapping, atmosphere and environment monitoring, agriculture and forest surveys, marine biology and physics. Therefore, mining and using remote sensing information resources with the use of remote sensing technology have become a major research topic. Two features of hyperspectral remote sensing image data are super-multi-band and large volume of data, its dealing has become one of the key issues for its successful application. In view of such a mass of data and information, it makes the method of hyperspectral image processing and analysis be different from the previous general remote sensing image processing methods. However, image classification has always been dealing as the basic research methods in remote sensing image analysis, so that the classification technology of hyperspectral image has become one of the most important technical in this field. There are many common methods for hyperspectral remote sensing image classification, this paper mainly focus on the modified Gaussian model and the modified Gaussian in actual hyperspectral remote sensing image classification and composition estimation of mineral.

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1. Introduction of Gaussian Model(GM) Method The hyperspectral image is a special image whose data is collected by the spectrometer and reflected by the minerals in the visible or near infrared light. The image collection is based on the characteristics of the minerals or rocks scattering and absorption spectra. the model classification method is a simulation for the scattering and absorption characteristics of the minerals or rocks. The hyperspectral image model method is a classification method which based on pixel classification method. Each pixel (mostly non-pure pixel) always contains rich information of mixed mineral which provide important evidence for us in the diagnosis of pixel, and these diagnostic information can also reveal the proportion of a single mineral composition. The application of hyperspectral classification with Gaussian model is a kind of methods which are based mainly on research of Sunshine (1990) on the olivine and several pyroxene. In hyperspectral image each pixel corresponds to the reflection data of many discrete bands, which means each pixel corresponds to a series of discrete points, so we can draw a continuous spectrum curve with these points. The Gaussian model is a solution that is used to simulate the spectral by spectral curve. According to the consistency on the shape between the Gaussian curve and the absorption curve which is in the visible spectrum and near-infrared bands, so a Gaussian deconvolution can produce a unique fit for a spectrum characteristics of a given mineral(under certain reasonable restraints)(Sunshine), this method could dispose the hyperspectral image classification. Under the central limit theorem of statistics, the Gaussian or normal distribution can be used to describe any random distribution if a large number of samples are given.

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­   x  P 2 ½ x ~ s ˜ exp® ¾ (Variable x represents energy) 2 ¿ ¯ 2V

(1)

The absorption spectrum of mineral is caused by the electronic transition and vibration, and the process can be assumed as a random process caused by electronic vibration. On this basis, we deem that the absorption spectrum can be described by the dispersion energy, and in this case the absorption band can be regarded as a random distribution of energy. In order to apply the central limit theorem to spectra curve analysis, the energy e which cause the various electronic transitions and vibration absorption process of the absorption bands should be seen as a random variable, and the energy is a random variable of Gaussian distribution. If these assumption hold, the absorption can be considered to be randomly distributed in energy and the band can be model with Gaussian distribution where the random variable x is energy. However, according to the result that Sunshine and his research on types of pyroxene (Clinopyroxene, Orthopyroxene) by GM model, they find that it is not appropriate for Gaussian model to describe electronic transitions, such as Fe2+, which cause the absorption spectra of the minerals pyroxene. Particularly, when we fit a spectral curve by Gaussian model method, usually it needs many Gaussian curves in a diagnostic absorption band. In that case, there is no doubt to increase the number of parameters (center, amplitude, width) and the scale of calculation, thus it increases the difficulties of image analysis. Furthermore Gaussian model does not comply with the characteristics of physical absorption of minerals, and can not provide a feasible

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solution to estimate the unknown samples for its component. Therefore, the modified Gaussian model is followed.

2. Modified Gaussian Model (MGM) and Its Assumptions Whereas the application of GM in the classification of hyerspectral image, the energy reflected in the spectrum is regarded as a random variable and it is assumed that the energy can be described by Gaussian distribution. But it was pointed out that the assumption is not consistent with the characteristics of physical absorption of minerals in Sunshine and others’ evaluation of this method, and indicates the average bond lengths is Gaussian distributed. According to conclusion that the crystal field theory description of electronic transition absorption (e.g. Burns,1970;Marfunin,1979),suggest n

that the absorption energy ( e ) is related to the average bond length( r ): e v r , on this basis, we can modified the Gaussian Model and find another mapping between the energy and average bond length:

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g x



n n ­ ° x  P s ˜ exp ® 2 2V ° ¯



2

½ ° ¾ ° ¿

(2)

.

Figure 1. Modified Gaussian Distribution



n

n



Compare with the GM(Figure 1. Sunshine,1900), the exponent of x  P changes the symmetry of the distribution, and rectifies the slope of the left and right wings. In this case, it will reach the actual characteristics of the spectral curve. While the exponential of n is usually determined by experience. According to Sunshine and others’ result and combining the optimal RMS residual error, the value of n is chosen 1.

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3. Application of MGM in the Classification of Hyerspectral Image The main idea of MGM which is realized by computers: First, analyze an actual spectra and identify the obvious absorption bands; Second, according to the beforehand rules, which include the number of curves, center position of the curves, error and so on, calculate the parameters of each MGM-band absorption curve; Third, compare the parameters calculated in the second step with the standard data in the spectral library and distinguish the mineral with the degree of similarity. The process is illustrated in the Figure 2(Susan Cook, 2006):

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Figure 2. the Process of Spectrum Fitting

In normal condition, an actual spectral curve usually responds to a mixture of several minerals and contains a number of absorption bands. In this paper ,we fit the discrete spectral curve by nonlinear least squares fitting iteration, the bands of spectral absorption curve are broken into many Gaussian curves until the error between the sum of the Gaussian curve superimposed and the actual value of the curve reaches to the permitted extent. Then analyze the single Gaussian curve of absorption band and get the degree of similarity, then the different minerals will be distinguished from the image. The MGM method is different from the GM method when classify hyerspectral images. The GM method often uses two or more curves to fit an absorption band, while there is a separate curve to fit an absorption band in the MGM, that is to say each absorption band corresponds to only one modified Gaussian curve. This method will overcome many shortcomings, such as decreasing the number of parameters in image classification by GM, the MGM method’s making the fitting degree more stable, the above will make it more realistic.

4. Experiment Analysis and Results Comparison We chose some mineral samples, such as alunite, needle iron ore, and hematite, illite. In this experiment, the result of fitting is listed in the tables followed:

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C. Yang et al. / The Application of Modified Gaussian Model in Hyperspectral Image Analysis

Table 1. Fitting Parameters of Alunite (1391-1809nm) Initial value

Fitted value

Bands

Center(nm)

FWHM(nm)

Strength

Center(nm)

FWHM(nm)

Strength

1

1425±100

20±1000

-0.4±10

1425.1

15.5

-0.1698

2

1458±200

30±400

-0.1±10

1456.3

19.5

-0.0294

3

1475±300

20±400

-0.5±10

1475.7

11.9

-0.1957

4

1504±200

50±400

-0.05±10

1466.8

62.5

-0.1104

5

1707±200

120±200

-0.05±10

1740.9

99.9

-0.0334

6

1764±200

60±200

-0.4±10

1762.9

45.0

-0.1187

estimative intercept: 0.85±10

intercept:0.6548

estimate slope: -1.0E-6±1.0E-3

slope: 2.1115E-5

RMS

RMS

9.008%

0.2328%

Table 2. Fitting Parameters of Needle Iron Ore (717-1574nm)

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Initial value

Fitted value

Bands

Center(nm)

FWHM(nm)

Strength

Center(nm)

FWHM(nm)

Strength

1

702±10000

200±10000

-0.5±100

712.0

50.0

-0.3696

2

764±100

20±50

-0.1±10

753.3

26.1

-0.0622

3

910±200

250±800

-1.0±10

886.0

457.4

-1.1675

4

1148±300

550±1000

-0.2±10

1345.3

329.9

-0.1892

estimative intercept: 0.85±10

intercept: -0.0152

estimate slope: -1.0E-6±1.0E-3

slope: 9.2154E-5

RMS

28.416%

RMS

0.5039%

Table 3.Fitting Parameters of Hematite (717-1645nm) Initial value

Fitted value

Bands

Center(nm)

FWHM(nm)

Strength

Center(nm)

FWHM(nm)

Strength

1

709±1000

100±1000

-0.8±100

580.1

173.5

-1.0318

2

775±200

80±400

-1.0±10

821.2

41.0

-0.0354

3

861±300

300±400

-1.0±10

887.4

218.8

-0.6499

4

1651±10000

700±800

-0.2±5

1643.9

635.4

-0.2295

estimative intercept: 0.85±10

intercept: -0.6538

estimate slope: -1.0E-6±1.0E-3

slope: -2.3354E-5

RMS

53.119%

RMS

0.8402%

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Table 4.Fitting Parameters of Illite (896-2266nm) Initial value

Fitted value

Bands

Center(nm)

FWHM(nm)

Strength

Center(nm)

FWHM(nm)

Strength

1

1409±50

40±100

-0.6±10

1407.2

26.6

-0.2877

2

1910±400

60±100

-0.4±10

1926.1

110.7

-0.0754

3

2118±100

120±200

-0.4±10

2256.1

278.5

-0.1813

4

2200±50

100±100

-1.0±10

2198.7

43.6

-0.3645

estimative intercept: 0.85±10

intercept:0.6983

estimate slope: -7.46E-6±1.0E-3

slope:9.8127E-6

RMS

18.625%

RMS

0.6074%

Table 1 lists the fitting parameters of alunite, Table 2 lists the fitting parameters of needle iron ore, Table 3 lists the fitting parameters of hematite,Table 4 lists the fitting parameters of illite(FWHM: Full Width Half Maximum㸪RMS: Rights Management Services ).

5. The Effect of Identification

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In this paper, we use a hyperspectral image of an area, which is an emendated image and includes 199 bands whose band range is between 399.37nm and 2477.08nm. Then classify the minerals with the foregoing fitting result, the result are as follows: Figure 3 is the original image, Figure 4 is the result of alunite, Figure 5 is the result of needle iron ore, Figure 6 is hematite, Figure 7 is the result of illite

Figure 3. Original Image

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C. Yang et al. / The Application of Modified Gaussian Model in Hyperspectral Image Analysis

Identification of the minerals:

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Figure 4. the Result of Alunite

Figure 6. the Result of Hematite

Figure 5. the Result of Needle Iron Ore

Figure 7. the Result of Illite

The MGM can also be used to estimate the proportion of the minerals with the parameters of the spectral curve. It is meaningful to researcher in the practical mineral exploration, according to the relationship between the proportion and the MGM parameters of relevant bands (Sunshine, 1990), we can estimate the concentration of the minerals. Although, since the samples Sunshine chosen in his experiment is not large enough, while just includes pyroxene and olivine minerals, which means the MGM has many restriction in estimating another mineral proportion. That is what we should do in future study.

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6. Conclusion The MGM provides a useful method for identifying the spectral characteristic which is caused by the electronic transitions. That is an advantage of using one MGM curve to fit an absorption band and reduce the number of unknown parameters and simplify the process. The MGM provide a powerful tool for analyzing the spectrum of pyroxene and olivine.

References

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[1] Rui-liang PU, Peng GONG. The Remote Sensing and Application of Hyperspectral. Higher Education Press.(in Chinese) [2] Qing-xi Tong, Bing ZHANG, ZHENG Lan-fen. Hyperspectral Remote Sensing- Principle,Technology and Application. Higher Education Press.(in Chinese) [3] Yu-qing WAN, Ke-long TAN, Ri-ping ZHOU. The Research and Application of the Hyperspectral Remote Sensing. Science press.(in Chinese) [4] Susan M. J. Cook . Rock type classification using MGM peak-fitting analysis. 2006. [5] Sunshine J M, Pieters C M, Pratt S F. Deconvolution of Mineral Absorption Bands An Improved Approach. Geological Research, 1990 [6] Sunshine J M, Pieters C M. Estimating model abundance from the spectra of natural and laboratory pyroxene mixture using the modified Gaussian model, J. of Geophysical Research, 1993. [7] Sunshine. J M. Determining the composition of olivine from reflectance spectroscopy. Journal of Geophysical Research, 1998, 6.

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Information Technology in Geo-Engineering D.G. Toll et al. (Eds.) IOS Press, 2010 © 2010 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-60750-617-1-230

The Role of DInSAR Techniques in the Analysis of Ground Deformations Related to Subsidence and Landslide Phenomena Leonardo CASCINIa, Settimio FERLISI a,1, Gianfranco FORNARO b and Dario PEDUTO a a Department of Civil Engineering, University of Salerno, Italy b Institute for Electromagnetic Sensing of the Environment (IREA-CNR), Naples, Italy

Abstract. This work presents some successful applications of Differential SAR Interferometry (DInSAR) techniques to both subsidence and slow-moving landslide phenomena at different scales, also giving perspectives of future valuable developments. Keywords. subsidence, slow-moving landslides, DInSAR

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Introduction Subsidence and slow-moving landslides are widespread all over the world where systematically cause damage to facilities. A comprehensive study of these phenomena calls for the availability of displacement measurements which can be expensive and time-consuming if carried out via conventional monitoring techniques. In this regard, the use of advanced remote sensing data, such as those obtained via Differential SAR Interferometry (DInSAR) techniques, can be extremely useful. DInSAR is widely used to complement with topographic methods thanks to its comparable accuracy, large area coverage (around 80 x 80 km for a single image) and the availability of image archives since 1992. Moreover, the continuous enhancement in image processing algorithms allows analyses of ground surface displacements with different periods and levels of details, according to the study scale, as shown in the following with reference to some case studies.

1. DInSAR Techniques and the Adopted Processing Algorithms Since the first description of the technique [1], most of the DInSAR applications were based on single interferograms (i.e. using an image pair) or few interferograms. The advantage of these simple configurations is the flexibility to provide (qualitative) information on deformations, even with a reduced SAR data availability. However, standard two pass DInSAR is limited by the presence of at least two error sources: the APD (Atmospheric Phase Delay) variation and the inaccuracies of the external Digital 1

Corresponding Author: Settimio FERLISI, Department of Civil Engineering, University of Salerno, Italy

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231

Elevation Model (DEM) involved in the cancellation of the topography component from the signal interferences. These limitations were overcome for the first time by [2] via the Persistent Scatterers (PS) technique that exploits long acquisition sequences, characterized by view and temporal diversity. At present two classes of techniques are available for the analysis of phase signals in interferometric stacks: persistent scatterers interferometry (PSI) [3] and small baseline approaches [4]. In the first class, the analysis is carried out at full resolution on stable scatterers in order to separate the atmospheric, topographic and deformation components. Key assumption is the stability of the radar response, which occurs mainly in the presence of dominant point scatterers. In the case of small baseline techniques, the scattering is supposed to be distributed within the resolution cell and spatial multilooking is implemented to enhance the phase stability. As a consequence of this operation, the spatial resolution is degraded with respect to the PSI approach. In this sense, small baseline approaches are more suitable for analyses over wide areas. Nevertheless, a product of the small scale analysis is the estimate of the atmospheric phase delay (APD), which allows the implementation of a subsequent large scale analysis carried out at full-resolution. The radar analysis carried out in this work is based on a two step approach. In particular, the low-resolution analysis (with pixel spacing of almost 80 x 80 m, is performed via both the original Small Baseline Subset (SBAS) algorithm [4] and the Enhanced Spatial Differences (ESD) approach [5], which represents an upgrading of SBAS. Differently from SBAS algorithm, which performs the phase unwrapping on each interferogram independently of the others, the ESD algorithm carries out a preliminary estimation of the mean deformation velocity and residual topography via modelling of the spatial phase differences of the whole interferogram stack. The model assumes the phase to be linearly related to the mean deformation velocity and residual topography via the temporal and spatial baseline, respectively. During this step the selection of the sparse grid of coherent pixels is refined according to the degree of fitting of the signal to the model. Once the residual topography, temporal deformation and APD variations at small scale have been separated, the full resolution analysis (with pixel spacing of 20 x 5 m , range-x-azimuth) is carried out according to [6] and the tomographic analysis [7]. This latter has been proven to constitute an extension of the PSI techniques; in particular, by using the amplitude and the phase of the received signal, it allows achieving a higher robustness degree in the detection of persistent scatterers and the separation of possible scatterers interfering in the same resolution cell. Interference of target in the same pixel generally occurs in the analysis of urban areas due to the steep topography. For the applications under investigation such a super-resolution analysis was not applied; however, based on the higher degree of detection robustness, the dominant scatterer were only located, as usually done for PSI.

2. The Application of DInSAR Data to Subsidence Phenomena As far as the subsidence phenomena are concerned, DInSAR analyses were carried out with reference to the well-documented case study of Sarno town (Campania region, southern Italy) where ground surface settlements, ascribed to groundwater withdrawals, caused serious damages to numerous buildings by the end of 1980’s [8].

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For this case study, low- and full-resolution analyses were performed via the use of SBAS algorithm. The dataset consisted of 83 images acquired by the ERS-1/2 and ENVISAT systems, spanning the time interval from June 1992 to October 2004 (descending orbits; track 36, frame 2781). The adopted procedure furnished low-resolution SAR pixels exhibiting a regular spatial distribution, while the full-resolution SAR pixels had an improved localization of the measures on elements where the signal attains the highest coherence, mainly buildings in urban area [9]. The validation process of both low- and full-resolution DInSAR data started with a point-wise comparison with traditional ground levelling (mean square deviation of 0.3 mm/km) data referring to six sets of topographic measurements (carried out from 1992 to 2006) for 18 benchmarks [9]. 2.1. Low- Resolution

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The ground surface settlements, corresponding to the measured radar line of sight (LOS) displacements for each available SAR pixel, have been computed assuming that ground displacements mainly occur along the vertical direction [10]. This hypothesis was confirmed by comparing settlements values obtained, for the time period 1992– 1993, via the DInSAR technique and the analysis of ground levelling data [9]. Considering the reliability of radar data analysis, the DInSAR results were interpolated and a three dimensional map (Fig. 1a) of cumulative settlements was generated, referring to the period June 1992-November 1993 [10]. The obtained DInSAR subsidence patterns seem to confirm the knowledge on the spatial distribution of the geological strata and their thickness. In fact, the highest ground settlement values are achieved where the thickest continuous layers of highly deformable peat soils were recorded by [8], near the “Cerola” spring.

Figure 1. a) 3D settlement map obtained via the DInSAR technique (June 1992–November 1993); b) deformation gradient map of the investigated area and spatial distribution of damaged buildings.

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As far as the relationship between the magnitude of settlements (both absolute and differentials) and the building damage occurrence is concerned, a first attempt was made by [9]. In particular, the deformation gradient magnitude, computed on the ground pixel grid starting from DInSAR measured settlements, is compared in [10] with the localization of damages (Fig. 1b) recorded to some buildings before either DInSAR and ground levelling measurements started [11]. The recorded damages essentially consist of i) noticeable rigid tilts between adjacent buildings or ii) subvertical cracks – whose width increases from the bottom to the top. It is interesting to point out that both cracks and axes of building rotation are almost always normal to gradient deformation directions, shown in Figure 1b as arrows, in agreement to basic physical considerations.

A key point of the full-resolution DInSAR data is the precision in estimating the residual topography that allows accurate geo-localization of the pixels monitored by the DInSAR technique. Moreover, the presence of more than a single phase centre, located on building roofs [9], allows the detection of local effects induced by the subsidence phenomenon, as described in the following with reference to a building within the urbanised area of Sarno town. This building (Fig. 2a) was constructed before 1900. It is a masonry structure composed by two attached blocks corresponding to a Church and the Town Hall; the foundations are of shallow type. Their bases are not horizontal in the case of the Town Hall because the back part is founded on the calcareous bedrock whereas the front one rests on pyroclastic soils with a total height difference of 4 m. Focusing the attention on the recorded settlement-induced damage [11], it can be noticed that the cracks, wide up to few millimetres, are aligned along a single vertical section of the Town Hall, following the east-west building orientation and 25 m far from the northernmost bound of the building (Fig. 2b). On the basis of this information and starting from June 8th, 1992, the available DInSAR data over the building were analyzed and interpolated on a grid of 5 x 5 m in order to obtain settlements along sample cross-sections (longitudinal and transverse profile), respectively parallel (Fig. 2b) and perpendicular (Fig. 2c) to the principal building façade. The computed settlements were obtained assuming a pure shear mode of deformation of the building, without any strain in the vertical direction [9]. Town Hall

Church

settlement (cm)

0

20/12/1993

-1 12/04/1995

-2

07/10/2004

-3

Church border

-4 -5 -6 0

20 40 60 distance along building façade [m]

80

0

a)

settlement (cm)

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2.2. Full- Resolution

b) 20/12/1993

-1

12/04/1995 07/10/2004

-2 -3

c)

-4 -5 -6 0

5

10

15

20

25

30

distance from building façade (m)

Figure 2. a) Longitudinal and transverse profiles on the Church (left) and Town Hall building (right). b) settlements computed along the longitudinal profile (2) farthest from the principal façade of the building (observation period starting from 8th June 1992 and referred to three significant time intervals); c) settlements computed along the transverse profile (2) on the Town Hall building.

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Settlements show a well-defined trend along the longitudinal profiles where sagging and hogging zones can be observed (Fig. 2b). Referring to this profile - around 14 meters far (along the north-south building direction) from Town Hall section where the damages concentrate - the sagging zone where the maximum settlements occur is located just in correspondence of the above mentioned weak section, along the eastwest building direction. As for the transverse profile (Fig. 2c), the Town Hall building undergoes a rotation southward, as it was already supposed by [11].

3. The Application of DInSAR Data to Landslide Phenomena According to the procedure described in details in [12] the first step of DInSAR data analysis at different scales consists of the generation of the a priori DInSAR landslide visibility map [13, 14]; this can allow distinguishing in advance whether an area is expected to be visible from space-borne SAR sensors, thus driving data-users through the image dataset selection. Once SAR images have been processed, if an adequate knowledge of landslide phenomena is available, a procedure for 1D-LOS DInSAR data projection can be implemented to generate the advanced DInSAR landslide velocity map [12]. As for the scale of the study, low-resolution DInSAR data can be used for landslide analyses at 1:25,000 scale, according to the dimension of both the landslide phenomena and the coherent DInSAR pixels on the ground. Conversely, full-resolution DInSAR data allow studies at more detailed scale (i.e. 1:5,000) according to the almost point-wise information and the dimension of both single portions of landslides and structures/ infrastructures interacting with the displaced masses.

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3.1. Low-Resolution The above procedure was firstly applied to a study area extending for around 500 km2 in the territory of National Basin Authority of Liri-Garigliano and Volturno (NBALGV) rivers (central-southern Italy). Within this area accurate inventory maps at 1:25,000 scale have been provided by the NBA-LGV that, in accordance with the Act of Italian Parliament (L. 365/2000), zoned both landslide hazard and risk all over its territory [15]. Within the test area, the low-resolution analysis was performed for 553 slowmoving landslides (classified according to [16] as rotational slides, earth flows and rotational slides-earth flows) of which 185 (around 33%) resulted covered by DInSAR data. The analysis highlighted that almost 84% of the DInSAR covered dormant landslides (144) exhibit evidence of no-movement; on the other hand, the percentage of active landslides (25) with moving coherent DInSAR pixels resulted about 24%, on the average [17]. In Figure 3 a simplified version of the so-called “advanced low-resolution map” shows the potentiality of DInSAR technique in detecting new landslides by extending the analysis of moving/not moving coherent pixels on those portions of the territory mapped as hollows by NBA-LGV in the geomorphological map at 1:25,000 scale. These zones (1,263 in the investigated area) are characterized by geomorphological settings quite similar to landslide-affected areas, also exhibiting the same landslide predisposing factors. Indeed, as for 63 hollows a clear evidence of movement was

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recorded; this can provide elements for a check/update of the landslide inventory map that represents the starting point for the landslide risk analysis as described in [18].

Figure 3. An example of low-resolution moving DInSAR coherent pixel detection within portions of the territory mapped as hollows in the NBA-LGV. (1) Hollow with moving DInSAR coherent pixel; (2) hollow not covered or with not-moving DInSAR coherent pixel; (3) dormant rotational slide; (4) active rotational slide; (5) dormant earth flow; (6) active earth flow; (7) dormant rotational slide-earth flow; (8) active rotational slide-earth flow; (9) creep phenomenon (after [14]).

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3.2. Full-Resolution Analyses of landslide phenomena at more detailed scale (i.e. 1:5,000) can exploit fullresolution DInSAR data. However, since these analyses call for significant computational efforts they can be concentrated on limited areas, pursuing two main goals: the preliminary analysis of landslide features (i.e. check of mapped boundaries; detection of ground displacement out of mapped areas); an insight into different kinematic behavior characterizing different portions of the same phenomenon. In order to check possible changes in landslide boundaries, the full-resolution coherent pixel data can be projected assuming translational movements along the steepest slope direction for the pixels out of the mapped landslides. Some examples are reported in [12]; here the case study of “La Consolazione” landslide (§12 ha), located within the area considered for low-resolution analysis purposes is presented. In particular, the recovered documents report that in 1986 only the narrow upper portion reactivated; then, in the 90s subsequent reactivations involved the central portion of the landslide. The above information allows the identification of three main portions: the old main scarp and the old terraced accumulation zone with evidence of cracks; the recent minor scarp bordering the reactivated earth flow; the old accumulation zone. Figure 4a shows the advanced full-resolution DInSAR velocity map superimposed to both the building map and the landslide inventory map at 1:5,000 scale. DInSAR data concentrate on 4 buildings within the landslide affected area. The analysis of mean velocity values for 1995-2000 period highlights that the full-resolution coherent pixels located near the old scarp exhibit no evidence of movement, whereas those located

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within the reactivated portion are moving with mean velocity values higher than 0.5 cm/year. As for the landslide mechanism, Figure 4b shows the longitudinal cross section of the instability phenomenon and the DInSAR velocity vectors. Finally, referring to the buildings in the area, it can be noticed that: i) the evidence of movement recorded to two buildings, framed with the circle in Figure 4a and located in the central portion of the reactivated landslide, matches the damage occurrence as observed by the damage survey; ii) the unchecked buildings located in the lower portion of the landslide exhibiting evidence of movement need a damage survey in order to assess their structural integrity.

Figure 4. La Consolazione landslide: a) Map of the landslide with buildings and advanced full-resolution DInSAR velocity data; b) Longitudinal cross-section of the landslide with buildings and DInSAR velocity vectors. 1) terraced old accumulation zone; 2) reactivated old accumulation zone; 3) old accumulation zone; 4) old scarp; 5) recent scarp; 6) cracks; 7) reactivated earth flow; 8) dormant earth flow; 9) dormant rotational slide; 10) cross-section; 11) not moving DInSAR coherent pixel or moving coherent pixel on flat areas; 12) DInSAr coherent pixel moving on vertical direction; 13) not projected translational displacement owing to high condition number; 14) damaged building; 15) building without damage survey; 16) building without damage survey (modified after [12]).

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4. Concluding Remarks In this work some applications of DInSAR techniques to both subsidence and slowmoving landslide phenomena are presented. With reference to subsidence phenomena, the performed analysis of low-resolution DInSAR data at municipal scale highlights the possibility of detecting the most critical areas. This can address further detailed studies, furnishing inputs to the analysis of the phenomenon and the related effects. As for full-resolution DInSAR analysis, measurement accuracy enables: i) the investigations on the building response to ground movements; ii) the use of damageability criteria usually adopted in engineering practice in order to avoid building damages due to ground movements [19,20]. As regards slow-moving landslides, innovative procedures are proposed for the detection of DInSAR visible areas and the generation of advanced DInSAR landslide velocity maps at different scales of analysis (i.e. 1:25,000; 1:5,000). These procedures can be used for: i) checking/updating landslide inventory maps over large areas; ii) providing an insight into kinematics of single phenomena; iii) allowing the monitoring of single exposed elements interacting with them.

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References [1] Gabriel A.K., Goldstein R.M., Zebker H.A., Mapping small elevation changes over large areas: differential radar interferometry. Journal of Geophysical Research, 94(B7) (1989), 9183-9191 [2] Ferretti A, Prati C, Rocca F Permanent scatterers in SAR interferometry. IEEE Trans. Geoscience and Remote Sensin,. 39(1) (2001), 8–20. [3] Kampes B.M., Radar Interferometry: Persistent Scatterer Technique. Springer, 2006. [4] Berardino P., Fornaro G., Lanari R., Sansosti E., A New Algorithm for Surface Deformation Monitoring based on Small Baseline Differential SAR Interferograms. IEEE Trans. Geoscience and Remote Sensing, 40(11) (2002), 2375-2383. [5] Fornaro G., Pauciullo A., Serafino F., Deformation monitoring over large areas with multipass differential SAR interferometry: A new approach based on the use of spatial differences. International Journal of Remote Sensing. 30(6) (2009), 1455 - 1478 [6] Lanari R., Mora O., Manunta M., Mallorqui J.J., Berardino P., Sansosti E., A Small Baseline Approach for Investigating Deformations on Full Resolution Differential SAR Interferograms. IEEE Trans. Geoscience and Remote Sensing, 42(7) (2004), 1377-1386 [7] Fornaro G., Reale D., Serafino F., Four-Dimensional SAR Imaging for Height Estimation and Monitoring of Single and Double Scatterers. IEEE Trans. Geoscience and Remote Sensing, 47(1) (2009), 224-237 [8] Cascini L., Di Maio C., Emungimento delle acque sotterranee e cedimenti nell’abitato di Sarno: analisi preliminare. Italian Geotechnical Journal, 28(3) (1994), 217-231 [9] Cascini L., Ferlisi S., Peduto D., Fornaro G., Manunta M., Analysis of a subsidence phenomenon via DInSAR data and geotechnical criteria. Italian Geotechnical Journal, 41(4) (2007), 50-67. [10] Cascini L., Ferlisi S., Fornaro G., Lanari R., Peduto D., Zeni G. Subsidence monitoring in Sarno urban area via multitemporal DInSAR technique. International Journal of Remote Sensing, 27(8) (2006), 1709-1716. [11] Nigro E., Field survey report. Unpublished,1992. [12] Cascini L., Fornaro G., Peduto D., Advanced low- and full-resolution DInSAR map generation for slow-moving landslide analysis at different scales. Engineering Geology, 112(1-4) (2010), 29-42, [13] Peduto D., Analysis of ground deformations related to subsidence and landslide phenomena via DInSAR techniques. Ph.D. Thesis (In English). University of Salerno, Italy, 2008. Tutor: Cascini L; cotutor: Fornaro G. [14] Cascini L., Fornaro G., Peduto D., Analysis at medium scale of low-resolution DInSAR data in slowmoving landslide-affected areas. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), (2009), 598-611. [15] Cascini L. Applicability of landslide susceptibility and hazard zoning at different scales. Engineering Geology. 102(3-4) (2008), 164-177. [16] Varnes D.J. Slope movements. Types and processes. In: Landslides: analysis and control, (Schuster R.L. & Krizker R.J. Eds.) Nat. Acad. of Sciences, Transp. Res. Board, Washington, 1978. Special Report 76,11-35. [17] Cascini L., Ferlisi S., Peduto D., Pisciotta G., Di Nocera S., Fornaro G., Multitemporal DInSAR data and damages to facilities as indicators for the activity of slow-moving landslides. In: Landslides and Engineered Slopes. From the Past to the Future. Chen Z., Zhang J., Li Z., Wu F., Ho K. (eds.). Proceeding of the 10th International Symposium on Landslides and Engineered Slopes, Xi’an (China), Taylor and Francis Group, London, Vol. II (2008), pp. 1103-1109. [18] Fell R., Corominas J., Bonnard C., Cascini L., Leroi E., Savage W.Z., Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology 102 (3-4) (2008), 99-111. [19] Skempton A.W., Mac Donald D.H. Allowable Settlement of Structures. Proc. Institute of Civil Engineers, Part III, Vol. 5(1956), 727-768 [20] Burland J.B. Assessment of risk of damage to buildings due to tunnelling and excavations. Invited special lecture. Proc. 1st International Conference on Earthquake Geotechnical Engineering. IS-Tokyo, (1995) ,1189-1201.

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A Study of Using Wireless Sensoring Network(WSN) to Improve Tunnel Disaster Prevention and Rescuing Scheme Dave Ta Teh CHANGa1, Horng-Cheh LEEa2, Ming-Ru LEEa3 and Liang-Tso WANGa3 a Professor , Department of Civil Engineering, Chung Yuan University ,Taiwan

Abstract. This study focuses on the using WSN in tunnel environmental temperature monitoring systems. Due to complex terrain and tunnel environment, conventional monitoring and alarm systems have severe restrictions on data transmission range and power supply. The inherited low-power and rechargeable battery module empowering the WSN based environmental surveillance system to overcome the complex power distribution and high maintenance problems. Through the fire exam testing, tunnel fire is simulated by using the fire dynamic simulator (FDS) program. Results indicate that the distribution of fire temperature along the tunnel significantly provide the people moving direction during fire. It is expected that the management units can be utilized as a base to establish an intelligent WSN early-warning notification system that can be truly applied in the current disaster prevention engineering. Keywords. Wireless sensor network, tunnel, disaster prevention and rescuing, fire Dynamic Simulator

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Introduction The WSN system was used in the research to process the on-site temperature measurement of full size Brazier test in Taiwan's Hsuehshan Tunnel. And the FDS (Fire Dynamic Simulator) was used which was developed by NIST and BFRL to do the fire simulation test directly to the Hsuehshan Tunnel and was compared the results of on-site stimulation test. The WSN monitoring the research was used that monitoring the variation of the environment temperature in the tunnel. The major goal was to provide a series of early warning and monitoring system which could transmit the timely temperature change in human activities space. To help the Disaster Response Centre controlled the temperature distribution and provided the accordance for police slipped into the tunnel 1

:

Corresponding Author: Professor , Department of Civil Engineering, Chung Yuan University ,TaiwanΙ

E-mailΚ[email protected]

2

Assistant professor, Department of Civil Engineering, Chung Yuan University ,TaiwanΙ

E-mailΚ[email protected]

3

Master students, Department of Civil Engineering, Chung Yuan University, TaiwanΙ E-mailΚ [email protected], [email protected]

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disaster rescue position. It also could warn the refugees escaped quickly according to the safe refuge device to reach the function of disaster prevention and reduction. In the highway tunnel fire accident, most of the accidents were caused by the vehicle traffic accidents. In the report of reason investment for vehicle fire accident by, the vehicle fire accident including the features of preventing difficult, rapid combustion or explosive and rescue difficulty. Review of tunnel safety facilities Consideration the necessary of tunnel safety facilities which was ensured the passenger traffic were safe and flowing. Especially for driving in the long tunnel, the ventilation, disaster prevention and lighting equipment were indispensable. The refuge contact channel (Pedestrian and motors contact channel) was the only which could evacuation and stay away from the disaster spot in the tunnel, and it also was the way to process the disaster relief. The settings which maintained the shelter was shown as Figure 1. Emergency telephone Lane control signal Horn and trumpet

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Escape signs

Escape led

Variable speed limit signs

Single-line contact tunnel

Emergency stop bending

Fire hydrant

Figure 1. The tunnel relative escape facilities.

Wireless sensor network (WSN) WSN is constructed by the Miniature sensor (MEMS) which has the sensoring capability, computing capability and communication capability. With the trend of diversification and ministration in the application area, WSN could apply at ambient temperature monitoring, structural safety monitoring, health care services and defense military security, etc. Therefore, it become the research priorities in all research area in recent years, even The National Science Council in Taiwan has set up a prospective

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research project specially. The WSN was the network system, composited by thousands of Sensor nodes collocated with the base station in random distribution way. And the Sensor node was composited by power, perceptual component, embedded processor, storage communication components and software which were shown as figure 2. Power

Software

Perceptual component

Embedded processort

The communication component

Storage

Figure 2. Construction of sensor node.

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The basic principle of WSN was used the sensor node which distributed in the network and composite to the network in the Ad-hoc way. Each sensor node data link layer was formatted by star topology, as shown in figure 3, 4. The monitoring data was transmitted to the node of base station by the way of relay cooperation to reduce the power consumption rate in transmission. At last the whole area of data was transmitted to the Observer to process by the long distance or temporary base station.

Figure 3. Star topology.

Figure 4. Ad-Hoc network layer.

1. The presearch program and methods Due to the limited number of WSN test (The Brazier test could only test once) and the small section of on-site temperature measurement, for understanding the application of WSN in tunnel to enhance the disaster relief results, the computer simulation was used in the research to predict the tunnel fire accident results in any disaster situation. According to the result of simulation controlled how to set up the WSN sensor node in the tunnel. The simulation program was CPD (Computational Fluid Dynamics)

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software which with fully functional and was designed specially for the fire flow field. In the calculation of transient flow in the fire, besides the advantages of save memory and short calculation time, there was a processing tool, Smokeview, major in after fire design. And the graphics was presented by 2D or 3D animation by drawing software-OpenGL. 1.1. Testing methods and spot location

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The WSN equipment was used in the research developed by Disaster prevention technology centre in CYCU and the corporation organization. Also, the control program was written by JAVA software to process the tunnel temperature measurement. The process of test included sensor node layout and record the time of ignited the fire, fighting fire and born out the fire, read the temperature signals in the sensor node at the tunnel walls and finally captured the measurement temperature signal to process the research. The research was proposed get the best layout height and distance from sensor nodes of temperature measurement. Especially it sited a group of Sensor node and Gateway in the left and right side of tunnel wall, totally sited 14 sensor nodes and 2 gateways. In the left side was included the sensor node No.5, 11, 25, 7, 19, 23, 17. And in the right side was included the sensor node No.24, 18, 14, 28, 8, 16, 22. The gateways were sited respectively in No. 24 and 7 m from the upstream. The sensor node had space 5 m and respectively had distance 1, 2, 2.5 m from the ground. The spot layout and on-site setting was as shown in the figure 5.

NodeΚ14 GatewayΚ2 The distance of left and right sensor nodeΚ5m The height from the groundΚ1Ε2Ε2.5m

ʳ

Figure 5. Planning of sensor node layout.

1.2. The features and specifications of WSN sensor node The WSN sensor node equipment was used in the research, the tolerance temperature was Ё40°C to 123.8°C, and the functional specifications was shown in table1. And the major functions of sensor node could be divided into the data receiver, data transfer-end and USB data capture-end three parts. As shown in figure 6. 1.3. FDS fire disaster simulation software FDS was the simulation software which was used the governing program of LES (Large Eddy Simulation) by Weakly Compressible to descript the phenomena of buoyancy drover the gas flow. The functions could be used by simulation of three-dimensional fire situation. It segmented the building space into many small

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lattices and solved each conservation governing equation by the Numerical analysis. By the process of repeating calculated the gas flow speed, pressure, temperature and concentration values on the small lattices in the simulation building could estimate the physical data of the gas flow speed, pressure, temperature and the flow of smoke stream while the fire occurred. Table 1 Specifications of WSN Sensor Node. Parameter

Resolution

Min

Max

Units

0.04

0.01

㷄㩷

0.07

0.02

̧

12

14

Bit

-40

123.8

㷄㩷

-40

254.9

̧

Range

The data receiver USB data capture-end

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Data transfer-end

Figure 6. The function of WSN sensor node.

2. WSN test and the results of FDS simulation 2.1. WSN measurement temperature and the FDS simulation results Compared with the WSN measurement values and FDS simulation results, the figure 7 to 10 showed that there was the same trend between the monitoring point temperature of FDS simulation and the WSN measurement temperature. The results of FDS simulation temperature analysis and the results of WSN temperature test were all presented the obvious reaction for the Brazier test on the fire and downwind 5 m of fire. And the temperature variation was little on the downwind 10 meter of fire.

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Figure 7. The temperature comparison for FDS and WSN upwind 5 m.

Figure 8. The temperature comparison for FDS and fire location.

Figure 9. The temperature comparison for FDS and WSN in the downwind 5m.

Figure 10.The temperature comparison for FDS and WSN in the downwind 10 m.

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2.2. Temperature changes in critical period It could be know in the figure 11 for the variation figure of temperature changes in critical periods. The critical period is the moment of temperature changes was on the 35 second between the time of lightening the fire and alert warning, and the sensor point was one meter from ground in the test. The temperature was rapid rise in the fire and leeward side of 5 m. And there were little sensor point variation on the windward side of 5 m and leeward side of 10 m. From the results of analysis, while the accident was occurred in the tunnel, the application of WSN could send the temperature signal to the control centre. To warn the passenger escaped from the upper drive way by the broadcast equipment and variable flags of inside and outside tunnel information.

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Figure 11. Critical Periods for the Temperature Changes.

Therefore, used the WSN system could send out the early warning before the alert system, and confirm the location of fire accident to improve the time of passenger escaped and relieved and to reach the effect of disaster prevention.

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3. Conclusions As the presented from the value of WSN measurement temperature, the temperature on the upper surface was rise little after the fire start growing. It could determine that the upper surface was much suitable for escaped and relieved. As the presented from the WSN test results, there was the obvious reaction for the Brazier tests on the fire and downwind of 5 m. And the relative sensor point variation was little on upwind of 5 m and the downwind of 10 meter in from fire. As the result, the idea layout distance was around 5 m. As the presented from the WSN test results, the fire reaction was fast on the sensor point from 2 m to the ground, which is near the height of human moving for escaping. Using the WSN temperature sensor system could timely monitoring the temperature variation in the tunnel, once the temperature is unusual would send out the early warning and process the confirmation of disaster work. To provide the information for passenger was crucial. The application the timely temperature information provided from WSN system and sent out the early warning could process the escape action persuasion by the human perception of temperature in the early stage of disaster which could reach the effect of relief.

References [1] Chinese Institute of Civil and Hydraulic Engineering, "Tunnel Engineering Design Criteria and Descriptions" China , 1999.

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[2] Tunnel Engineering Committee of Institute for Civil Society in Japan, Japaniess Tunnel Standards and Descriptions - Mountain Tunnel chapter, Research and Development Foundation of Technology Translated, Taiwan, 2001. [3] Huo-Yan Chen , The Causes Investigation of Vehicles Fires, Vol. 79 (1997), 23 ~ 48. [4] Eric W. Marchant, Modeling Fire Safety and Risk, in Fires and Human Behavior, Edited by David Canter, John Wiley & Sons Ltd. , New York, USA ,(1980),302. [5] Jack Pauls, Movement of People, in SFPE Handbook of Fire Protection Engineering, Society of Fire Protection Engineering & NFPA, Maryland, USA(Sep. 1995),269. [6] Shen,Zih-Sheng (2002), Determinants of the Evacuation Time on the Investigation, Police Research Series, No. 25, No. 1, 139 ~ 154. [7] Yu-Chiun Chiou, Kai-ling Chang, Yen-Fei Huang, Pei-Shan Wu Long Road Tunnel Accident Rescue Strategy for Multiple Criteria Decision Model, Road Safety and Law Enforcement International Conference (2004), 245 ~ 256. [8] Gregory J. Pottie, Wireless sensor networks, IEEE Information Theory (June. 1998),139-140. [9] Sameer Tilak, Nael B. Abu-Ghazaleh, Wendi Heinzelman, A Taxonomy of Wireless Micro-Sensor Network Models, ACM Mobile Computing and Communications Review (MC2R), Volume 6, Number 2 (April. 2002), [10] K.B.McGrattan, Fire Dynamics Simulator (Version 4)-Technical Reference Guide, NIST Special Publication 1018, National Institute of Standards and Technology, 2005. [11] K.B. McGrattan, H.R. Baum , R.G. Rehm, G.P. Forney, J.E. Floyd, K. Prasad, and S. Hostikka,“Fire Dynamics Simulator (Version 3)-Technical Reference Guide, NISTIR 6783, National Institute of Standards and Technology, 2002

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Application of Two Different Temperature Monitoring Systems in Liquid Nitrogen Ground Freezing Construction Xiangdong HUa,b, Wangguoa,b,1 and Jun ZHANGa,b Department of Geotechnical Engineering, Tongji University, Shanghai 200092, P. R. China b Key Laboratory of Geotechnical Engineering, Tongji University, Shanghai 200092, P. R. China a

Abstract. On the background of liquid nitrogen freezing method applied in a shield tunnel recovering project, this paper introduces the system composition of the thermocouple temperature monitoring system and “1-wire bus” temperature monitoring system for liquid nitrogen freezing process. Based on temperature monitoring data, the advantages and disadvantages of the two monitoring systems are summarized in the cryogenic measurement scope of the systems, the accuracy of monitoring data, the resistance to interference of the system, etc. The most important advantage of thermocouple temperature monitoring system is the wide measurement range in low temperature of -200°C, whereas for the “1-wire bus” temperature monitoring system, the most important advantage is that the system is insensitive to external working conditions and environmental temperature.

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Keywords. Liquid nitrogen freezing, temperature monitoring, thermocouple, digital temperature sensor, “1-wire bus” system

Introduction Liquid nitrogen freezing (LNF) is an artificial ground freezing (AGF) technique, which has the features of easy system, fast freezing, low temperature, high strength of frozen soil, etc. As the structure state of frozen soil formed by LNF and the frozen soil properties are functions of temperature, and temperature field of the frozen soil changes with freezing time [1], it is necessary to gather all related parameters in time, especially the timely variation of frozen soil temperature, to ensure safety and validity of frozen soil. On the background of LNF applied in a shield tunnel recovering project, this paper describes the system composition of the thermocouple temperature monitoring system and the “1-wire bus” temperature monitoring system for LNF process in detail. The advantages and disadvantages of the two monitoring systems are also summarized on basis of temperature monitoring data.

1 Corresponding Author: Wang GUO, No. 1239, Siping Rd, Yangpu District, Shanghai, China; E-mail: [email protected] Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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1. System Composition of Temperature Monitoring System 1.1. Thermocouple Temperature Monitoring System

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A thermocouple is a junction between two different metals that produces a voltage related to a temperature difference. T type thermocouple was applied during the LNF period in this project, the material of which is copper/cupronickel (constantan), with a scope of temperature measurement from -200qC to -350qC and sensitivity of 43PV/qC. The secondary instrument used in the data acquisition is DT80G Geologger universal data acquisition instrument, produced by Data Taker Company. DT80G Geologger plays the role of reference end of thermocouple. This instrument could be connected to computer through the data bus or Ethernet. Then, with the Geologger data monitoring software installed in the computer, the real-time and visualized monitoring of the frozen soil temperature variation could be realized (see Figure 1).

Figure 1. Interfaces of temperature real-time monitoring

1.2. “1-wire bus” Temperature Monitoring System In this recovering project, automatically continuous computer monitoring is realized by the freezing-method remote monitoring system which is developed by Tongji University. Both hardware and software are included in this system. The system hardware contains digital sensors, the “1-wire bus” network, data collection and transmission modules, RS485 bus and a computer, as shown in Figure 2[3-5]. RS485 bus outlaid distributed data acquisition system is adopted in this monitoring system. Using LTM-8520 isolated RS232/485 transducer, the RS485 network interface could be changed into RS232 interface which would be identified by computer. Utilizing the LTM-8303 intelligent temperature collection module (see Figure 3) in the monitoring system [4-6], this temperature collection module could be

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intercommunicated to the computer by RS485, and the data transmission rate can reach to 10Mbps and the transmission distance to 1200m in theory.

Figure 2. Scheme of freezing temperature monitoring system

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Measurement of frozen soil temperature is realized with the “1-wire bus” digital temperature sensor produced by DALLAS Company, USA[7, 8]. According to the measuring point location, a series of sensors are enveloped in low temperature resistant suit, so that the frozen soil temperature measuring cables can be made by special factory. On the basis of measuring point design, the temperature measuring cables are put into the temperature measuring holes. Finally, temperature measuring cables are accessed to “1-wire bus” system through special interface, by which the frozen soil temperature measuring net is formed.

Figure 3. LTM-8303 intelligent temperature collection module

The operating principle of “1-wire bus” temperature monitoring system can be summarized as follows. First, by guide of the comprehensive monitoring scheme designed with freezing purpose, the numbered and connected sensors in cables are buried in drillholes. Followed, the signal wires of sensors are connected to the 1-wire bus and then to the signal collection module, and, finally, to the computer, with all kinds of information and functions set in the monitoring management software. Then, as the freezing system works, the monitoring system automatically monitors the freezing process by the management software with its checking, reporting and display functions. Based on “1-wire bus” temperature monitoring system, the fully automatic temperature field monitoring is realized, which achieves timely feedback the temperature changes of the measuring points in all the temperature measuring holes.

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This monitoring system can set temperature monitoring frequency on computer and show the change curves of measuring points in mode of time and space. In addition, this system also has functions like data storage, information maintenance of measuring point sensor, etc. The interface is shown as in Figure 4.

Figure 4. The interface of the management software

2. Analysis of Temperature Monitoring Data

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These two types of temperature monitoring system applied in a shield tunnel repairing project. Vertical freezing using liquid nitrogen is adopted in this project. The freezing holes and the temperature measuring holes are arranged as shown in Figure 5. The temperature data, gathered from these two systems, is analyzed in following aspects.

Figure 5. Arrangement of the freezing holes and the temperature measuring holes in LNF period (unit: mm)

2.1. Cryogenic Measurement Scope of Monitoring System The lowest temperature that can be measured by thermocouple in this project is nearly 200qC. So in order to test this temperature limitation, thermocouple was set in freezing pipe BK8 as shown in Figure 5. Compared the temperature data measured by the thermocouple at bottom of the freezing pipe (in active freezing period, the temperature is -190qC ~-195qC) with the theoretical temperature of liquid nitrogen in this place (the

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theoretical temperature in active freezing period at bottom of the freezing pipe where the liquid nitrogen is gasifying is nearly -195qC), it is concluded that thermocouple made by the copper and constantan alloy can measure the lowest temperature is nearly 200qC. Regarding the “1-wire bus” system, the manufacturer stipulated the temperature measurement scope of DS18B20 from -55 to +125qC. Therefore, it is to be tested whether the system is able to measure the temperature lower than -55qC in LNF condition. As is well known, the lowest temperature in brine freezing process is higher than -40qC. In the monitoring process, it was found that the “1-wire bus” system could still work in temperature measuring holes BC1~BC12, C3~C5, C7, C10 and C11 where most of the monitoring points are measured below -55qC. However, we found a “hibernation phenomenon” that the sensors might stop giving normal value when the temperature reached -120qC. This phenomenon occurred in monitoring points BC1-1, BC1-2, BC1-3 and BC4-5, probably taking 1% of the total measuring points. Temperature data monitored by these 4 measuring points dropped to -120qC after 9 freezing days and kept the same degree for 12 days. In this process, whether the strata zone was still being frozen or not, the data did not change. The temperature data of these 4 measuring points did not start to rise until the 7th day after freezing was stopped for this zone, whereas the actual temperature started to rise immediately after freezing was stopped (see Figure 6). That means that the recovery time of the “hibernation phenomenon” could delay for some days. During the period of “hibernation phenomenon”, the lowest temperature measured by thermocouple reached -125qC. On the other hand, for the measuring points without “hibernation phenomenon”, the lowest temperature measured reached -116qC (in monitoring point BC4-4). -90

Temperature/°C

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-100

-110

-120

-130 7-17

7-19

7-21

7-23

cable measuring point

7-25 Date

7-27

7-29

7-31

8-2

thermocouple measuring point

Figure 6. Comparison of temperature variation at monitoring point BC4-5 measured by thermocouple and "1-wire bus" systems during "hibernation"

Therefore, it can be concluded that “1-wire bus” system can normally read temperature above -120qC and the “hibernation phenomenon” might occur when temperature drops below -120qC.

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2.2. Accuracy Analysis of Monitoring Data The previous analysis shows that the “1-wire bus” system can read the temperature under the ultra low temperature field in LNF condition, but the accuracy of the data needs more analysis. Here, the temperature data taken by measuring cable and thermocouple in the same position in temperature measuring hole BC10, as a typical example, are chosen for analysis to see the range of error. As shown in Figure 7 and Figure 8, difference of temperature monitored by the two systems is small, less than 2qC, and sometimes even smaller (as shown in Figure 8). So it can be concluded that the “1-wire bus” system is accurate enough at least in the range of -110qC ~-55qC. -65 -67 -69 -71 -73 -75 -77 -79 12:00

16:48

21:36

2:24

Time

cable measuring point

7:12

12:00

16:48

21:36

thermocouple measuring point

Figure 7. Comparison of temperature variation at elevation -20.417m in temperature measuring hole BC10, measured by thermocouple and "1-wire bus" systems -55 -60 -65 -70

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-75 -80 -85 12:00

16:48

21:36

cable measuring point

2:24

Time

7:12

12:00

16:48

21:36

thermocouple measuring point

Figure 8. Comparison of temperature variation at elevation -17.967m in temperature measuring hole BC10, measured by thermocouple and "1-wire bus" systems

2.3. Resistance to Interference Analysis of Monitoring System Invariability of reference end temperature is very important for thermocouple temperature measurement system. It is necessary to consider the temperature stability of the environment where DT80G works. As a case, in the afternoon on July 27, 2009, the electric power of working site failed due to circuit fault and the air conditioner stopped working, leading to a temperature variation of the monitoring room. Although the DT80G could still work by the external battery, the data read by the thermocouple temperature measurement system had abnormal variation in 5 temperature measurement holes. Taking the data of BC7 for example, the data jumped between -

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200qC to -10qC at that time. In the evening, when the electric power applying was recovered, the data became normally. This event shows that variation of the reference end temperature has a strong impact on the accuracy of the thermocouple system. The temperature measuring cable and the intelligent temperature collection module LTM-8303 of the “1-wire bus” system was exposed in outdoor conditions over a long period. Although the weather varies, the data read by the system did not have any abnormal variation. So it is seen that the “1-wire bus” system can not be impacted by circumstances temperature. Moreover, the thermocouple temperature measurement system was impacted by the variation of electromagnetic field. In this project, about 20m of thermocouple wires were exposed in outdoor conditions. When the electrical equipments near the monitoring area operated, the data read by the system became abnormal, while the “1wire bus” system in the same condition had no abnormal reaction. So, it can be believed that the resistance to interference of “1-wire bus” system is better than the thermocouple temperature measurement system.

3. Conclusion Application of visualized real-time monitoring by two sets of temperature measurement system on the temperature field of frozen soil for the rehabilitation works using LNF, has demonstrated the advantages and disadvantages of thermocouple and 1-wire bus temperature measurement system as follows: Advantages of thermocouple temperature measurement system: x

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x x x x

Wide measurement range. The measurement range of thermocouple type adopted in this project can cover the -200qC ultra low temperature, which meets the requirements of temperature measurement for LNF; Simple structure and easy system; DT80G GeoLogger Data Acquisition Equipment is an easy and stable automatic continuous monitoring instrument; Visualized real-time monitoring can be realized by DT80G GeoLogger Data Acquisition Equipment; With the external battery of DT80G GeoLogger, the monitoring work can also be implemented when power is cut off.

Disadvantages of thermocouple temperature measurement system: x x

x x

Existence of nonlinearity; When the environmental temperature is adopted as the reference end temperature, the environmental temperature must be stable or the position of instruments must be fixed, it is inapplicable for the fast moving monitoring; With long-wire distribution, interference signals results in more errors; Inconvenience for wire arrangement, system maintenance and trouble shooting due to large amount of thermocouple wires.

Advantages of “1-wire” bus temperature measurement system: x

With a multi-functional software for automatic continuous monitoring;

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x x x x x

253

The monitoring frequency can be easily adjusted according to the project requirements for improvement of the monitoring accuracy; Simultaneity of data monitoring, to avoid the time difference; The temperature, temporal and spatial variation process of each measuring point can be found and output in any time; It is insensitive to external working conditions and environmental temperature; With the temperature monitoring of this project, it is found that the measurement range of 1-wire bus temperature measurement system can cover -110qC with high accuracy.

Disadvantages of 1-wire bus temperature measurement system: x

The measurement range is not wide comparatively, the ultra low temperature below -120qC can’t be measured;

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Reference [1] Jiajie Weng. Theory and Practice in Liquid Nitrogen Freezing in Soil Ground. COAL SCIENCE AND TECHNOLOGY 22 (9) (1994), 11-16. [2] Wenxu Song, Fan Yang. Sensor and Measurement Technology. Bei Jing, High Education Press, 2009 [3] Zhiyong Zhou. Study on Network Information System for Artificial Ground Freezing Construction. Tongji University, 2009.3. [4] Xiangdong Hu, Ruifeng Liu. Temperature monitoring system for freezing method based on “1-wire bus”, Chinese Journal of Underground Space and Engineering 3(2007), 937-940. [5] Zhonghui Huang, Xiangdong Hu, Jiyun Wang, Hongbao Lin, Ruizhi Yu. Key techniques in cross passage construction of Shanghai Yangtze River Tunnel by artificial ground freezing method. The Shanghai Yangtze River Tunnel–Theory, Design and Construction, Complimentary Special Issue to The Sixth International Symposium on Geotechnical Aspects of Underground Construction in Soft Ground (IS-Shanghai 2008), Shanghai, 10-12 (April, 2008), 205-210. [6] Min Li, Meng Chen. Digital Temperature Measurement Module LTM8003 and Its Application, International Electronic Elements (2002), 50-52. [7] Yuedong Chen. Principle and applications of DS18B20 IC temperature transducer. Journal of Anhui Institute of Mechanical & Electrical Engineering 17 (2002)4, 34-38. [8] Dallas Semiconductor/Maxim. DS18B20 Programmable Resolution 1-Wire Digital Thermometer [M], http://www.dalsemi.com/, 2001.

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Detection of tunnel water leakage based on image processing a

ChuanPeng HUa,1, HeHua ZHUa and XiaoJun LIa Department of Geotechnical Engineering, Tongji University, Shanghai, P. R. China

Abstract. In this paper, a novel method of tunnel water leakage detection and recognition method is proposed. It combines the analysis of leakage intensity feature and the application of Artificial Neural Networks (ANNs) algorithm. Firstly, the original color image is transformed into a gray value image. Then, the canny filter is used to extract the ragged edge. After that, the non-maximum-suppression method is adopted to remove the noise and hysteresis threshold is utilized to obtain the accurate edge. An experiment was conducted to test the accuracy and stability of the proposed method. Keywords. Tunnel leakage detection, image processing, Artificial Neural Networks (ANNs)

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Introduction Tunnel water leakage detection is one of the important jobs that ensure the safety and normal working of a tunnel. Through checking the existence of the leakage area, the working status of the tunnel could be estimated. Presently, the detections of the leakage and other damages of the tunnel are mainly performed manually. The method is difficult and inefficient due to the large number of data. Besides that, the results of manual inspection are subjective and not always reliable due to their heavily reliance on inspectors’ personal experience and knowledge to make evaluation. Based on these reasons, an automatic technique of the tunnel leakage detection is being desired for a long time. Digital image processing technology has been widely used as a solution to damage detection in many areas including roads(J. Pynn et al, 1999), bridges(Ikhlas Abdel-Qader et al, 2003), fatigues(Young-Suk Kim et al, 2000), and sewer-pipes(Sunil K. Sinha et al, 2006), as well as the operation and management of a tunnel. This method is also used to detect the tunnel crack by Zhiwei Liu, et al (2002). An auto inspection system using a mobile robot for detecting concrete cracks in a tunnel is built by Seung-Nam Yu et al (2007). However, there is no such method with respect to the detection of tunnel leakage yet. The main purpose of this study is to propose a method aiming at the detection of the tunnel leakage based on image processing. Cameras and lasers are frequently used to acquire images for the inspection of structure surfaces. The cost of the laser-scanning device is relatively high; additionally, it has a problem of heat dissipation that affects system maintenance. Both attributes 1

Corresponding Author. ChuanPeng HU, Department of Geotechnical Engineering, Tongji University, Shanghai, P. R. China, [email protected] Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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make it an inefficient system for use in a wide array of fields (Seung-Nam Yu et al, 2007). Compared with laser device, the camera-scanning device is more cost-effective though in some cases it has illumination problems. On the whole, camera-scanning device is the better choice as a faster, cheaper, and more efficient solution to automatic inspection. Leakage detection and recognition are object detection problems, the same as crack detection which has been widely studied. Object detection algorithms can be categorized into two technical approaches: feature-based approach and image-based approach (E. Hjelmas and B. K. Low, 2001). In a tunnel scene, there are different leakage shapes and also many other objects. Single feature-based approach is difficult to discriminate leakage from its background. Therefore, image-based approach will be used to build a trainable leakage detection and classification system. But during the image pretreatment, the intensity feature of leakage edge pixels is utilized. That is, the two kinds of approaches are combined together. 1. Intensity features of leakage region Features of tunnel water leakage are clarified before the detection. Some images of the water leakage in a boring tunnel, like figure 1 and figure 4, are used. Through analyzing these images, it is easy to find the differences between leakage area and the other area, which are just the features we need. Based on these images, the following three features can be noticed easily.

x

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x x

Leakage areas are darker than the rest areas due to a low reflectance, which means leakage areas have relatively lower gray value. The edges of leakage areas have larger gradient of gray value. The edges are irregular, but closed.

However, there are also some exceptions, such as partial region in the water leakage area probably has a higher gray value due to the specular reflection. Therefore, some preprocessing procedures have to be done before the detection. In order to inspect the edge of leakage area, definition of the edge is supposed to be defined first. It can be seen from the figure 1 that the edge is an irregular curve, which should be considered in two dimensions.

Figure 1ˊThe edge of leakage area.

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So, the edge of leakage area is fit to be defined by using the definition of the two-dimensional edge (MVTec Software GmbH, 2008) as follows: a tow-dimensional edge is regarded as the points in the image where the directional derivative in the direction perpendicular to the edge is locally maximal. From the differential geometry we know that the direction n(t) perpendicular to the edge curve s(t) is given as follows: (1) n(t ) s '(t ) A || s "(t ) Where, n(t) perpendicular to the edge can be determined from the image itself (MVTec Software GmbH, 2008). It is given by the gradient vector of the image, which points into the direction of steepest ascent of the image function ƒ(r,c). The gradient of the image is given by the vector of its first partial derivatives as follows: wf (r , c) wf (r , c) (2) ’f ’f ( r , c ) ( , ) ( fr , fc ) wr wc The Euclidean length of the gradient vector is defined as follows, which is also called the amplitude:

|| ’f ||2

fr 2  fc 2

(3)

The direction of the edge can also be calculated by following formula: (4) I  tan 1 ( f r / f c ) According to the definition above, the leakage edge can be extracted from images effectively. Firstly, the original color image can be transformed into a gray value image. Then, the canny filter (MVTec Software GmbH, 2008) can be used to extract the ragged edge. After that, the non-maximum-suppression method (MVTec Software GmbH, 2008) is adopted to remove the noise. Finally, hysteresis threshold (MVTec Software GmbH, 2008) is utilized to obtain the accurate edge. Unfortunately, in the tunnel surface, some objects own the same intensity features, the edges of which will also be extracted. In order to recognize the water leakage part exactly, a classifier is built to recognize these patterns.

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2. Leakage detection As can be seen from the analysis above, the problem of leakage detection is considerably complicated. The edge of leakage area can't be readily discriminated from the original image due to complex background of the tunnel, such as the pipelines, segment joints, and so on. It is impossible to merely utilize a simple classification method to solve this problem. So, artificial neural network which is widely used in the domain of complicated pattern recognition is adopted. 2.1. Artificial neural network Neural network models in artificial intelligence are usually referred to as artificial neural networks (ANNs). These are essentially simple mathematical models defining a function f : X o Y . Each type of ANN model corresponds to a class of such functions. The word network in the term ‘artificial neural network’ arises because the function f(x) is defined as a composition of other functions g i (x), which can further be defined as a composition of other functions. This can be conveniently represented as a

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network structure, with arrows depicting the dependencies between variables. A widely used type of composition is the nonlinear weighted sum, where f ( x) K (¦i wi gi ( x)) , where K (commonly referred to as the activation function) is some predefined function, such as the hyperbolic tangent. It will be convenient for the following to refer to a collection of functions g i as simply a vector g ( g1 , g 2 ,..., g n ) .

Figure2.

ANN dependency graph.

This figure depicts such a decomposition of f, with dependencies between variables indicated by arrows. These can be interpreted in two ways. The first view is the functional view: the input x is transformed into a 3-dimensional vector h, which is then transformed into a 2-dimensional vector g, which is finally transformed into f. This view is most commonly encountered in the context of optimization. The second view is the probabilistic view: the random variable F = f(G) depends upon the random variable G = g(H), which depends upon H = h(X), which depends upon the random variable X. This view is most commonly encountered in the context of graphical models.

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2.2. The ANN model constructed for leakage detection ANN model varies differently depended on the object to be studied. Therefore, it is important to construct a suitable ANN model for your research. Taking the characteristics of this study into account, the following model is proposed. A frequently used formula to determine the optimum hidden nodes is as follows:

­m ° ®m ° ¯m

nl a log 2 n nl

(5) Where, m is hidden nodes, n denotes the nodes of the input layer, l is the nodes of output layer, a is a constant between 1 to 10.

Figure 3. A typical model of ANN.

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In the multi-layer neural network, a single neuron can percept the input parameters in different ways. In this study, the response z j of the node of the hidden layer neuron to the input parameter x i is as follows: ni

¦w

1 j

a

1 ji i

x  b1j ,

j 1,..., nh

i 1

zj

(6) 1 j

tanh(a )

(7) Where, w ji and b j are the input layer weights and offset values separately, n i is the number of the input layer nodes. The calculation process from the value z j of the hidden layer to the value a2 k of the output layer is as follows: 1

1

ak2

nh

¦w

2 kj

z j  bk2 , k 1,..., no

j 1

(8)

Where, w2 ji and b2 j are the input layer weights and offset values separately, n 0 is the number of input layer nodes. There are a number of options for the calculation of the output result according to the output layer. Here, the output result y k is calculated as follows: 2

yk

e ak 2

¦e

, k 1, 2 al2

l 1

(9)

Where, if y k equals 1, the object being detected is the edge of leakage area, otherwise it is not.

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2.3. The selected parameters for leakage detection It is known that the leakage area has some features which are different from the rest part of the image and can be utilized to discriminate the leakage area from its background. In order to realize such objective, suitable parameters need be selected to describe these features. Since the leakage area is usually darker, with an irregular but closed edge which has a relatively larger gray value gradient, the following parameters are selected as inputs of the ANN model:

x x x x x x

The mean value and the variance of the gradient along the line, which can be calculated by formula (3). The mean value and the variance of the gradient direction along the line, which can be calculated according to formula (4). The mean value and the variance of the RGB spatial gray value. The mean value and the variance of line width. The trace length, which denotes the true length of the edge. The chord length, which denotes the length of the straight line joining the two end points of the edge.

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3. Experiment 3.1. Experiment setup The purpose of this experiment is to ascertain the accuracy and stability of the proposed method under different circumstances. Various kinds of tunnel leakage images (figure 4) are used during this experiment, which are acquired from the tunnel of NO.1 line of Shanghai Metro. According to different backgrounds, these images can be categorized into four types. The first type is ideal leakage images with a very simple background and without any occluders and light reflection like (a) in figure 4. The second type is images like (b), with some occluders over the leakage area. The third type is that the leakage area is very bright due to the light reflection like (c). The last type is such kind of leakage image which is taken in an antiquate tunnel segment, with a considerably complex background due to the dated lining (d).

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(a)

(c)

(b)

(d)

Figure 4. (a) Ideal leakage near the longitudinal tunnel joint. (b) Leakage covered by occluders such as electric wires. (c) Leakage with light reflection near the bolt hole of the tunnel segment. (d)Leakage image taken in the antiquate tunnel segment with a complex background.

The proposed method will be applied to each of the four cases to see if it is good enough for the detection of various kinds of tunnel water leakage. 3.2. Experiment results Detection results in the four cases mentioned above of the experiment are displayed below.

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x

Case 1: As shown in figure 5, the leakage area can be basically discerned when the leakage image is relatively ideal, even though there are also a few detection errors. On the whole, the result is desirable.

(a) original image.

(b) sub-pixel edge which is detected. Figure 5.

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x

Case 2: Figure 6 indicates that when the leakage area is covered by occluders such as electric wires, the detection result is not so ideal because the electric wires discerned contribute much to the influence of the result.

(a) original image.

(b) sub-pixel edge which is detected. Figure 6.

x

Case 3: Sometimes a light reflection may exist in the leakage area, as shown in figure 7, which will lead to a loss of the detection accuracy by changing the gray value of leakage area.

(a) original image.

(b) sub-pixel edge which is detected. Figure 7.

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x

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Case 4: As for images taken in an antiquate tunnel such as figure 8, the detection would be considerably troublesome due to ruleless distributions of the image features. So, the detection result is not so good.

(a) original image.

(b) sub-pixel edge which is detected. Figure 8.

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4. Conclusion and future work In this paper, an automatic tunnel leakage detection method based on image processing has been introduced. By analyzing the features of the leakage area, the artificial neural network model is employed to inspect the leakage area in the image. Proper parameters which could clearly describe the features of the leakage area are selected as the input values of the ANN model. Finally, the experiment was conducted to test the accuracy and applicability of the proposed method and the results in different cases are displayed. According to the experiment, the following conclusions can be drawn. The detection result is satisfactory when the image background is relatively simple and there are few occluders or other interference factors, as shown in case 1. Whereas, when the image is influent by occluders, light reflection, or other complex interference factors, the result is not so good, as shown in case 2, case 3 and case 4. The recognition accuracy of our work is not good enough to precisely verify the leakage or non-leakage image if the background of the image is too complicated. So, better recognition accuracy is desired. In order to realize this aim, some appending processes, such as histogram equalization, to stretch the image contrast, will be proposed during our future work. Besides, the parameters selected as the input values of the ANN model will be optimized since some other features of leakage area will be found and utilized. A better choice will be sought to improve the recognition accuracy in the future work. Acknowledgments This research has been supported by National Nature Science Foundation of China with Grant No. 40802071 and Shanghai Municipal Science and Technology Commission with Grant No. 9231200800. The financial support is gratefully acknowledged.

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References

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[1] Sunil K. Sinha and Paul W. Fieguth, Automated detection of cracks in buried concrete pipe images, Automation in Construction 15 (January 2006) 58–72. [2] Ikhlas Abdel-Qader, Osama Abudayyeh and M. ASCE, Michael E. Kelly, Analysis of edge-detection techniques for crack identification in bridges, Journal of Computing in Civil Engineering 17 (October 2003) 255–263. [3] Pi-Cheng Tung, Yean-Ren Hwang and Ming-Chang Wu, The development of a mobile manipulator imaging system for bridge crack inspection, Automation in Construction 11 (October 2002) 717–729. [4] Young-Suk Kim and Carl T. Haas, A model for automation of infrastructure maintenance using representational forms, Automation in Construction 10 (November 2000) 57–68. [5] J. Pynn, A. Wright and R. Lodge, Automatic identification of cracks in road surfaces, image processing and its applications, Conference Publication, vol. 465, IEEE, 1999. [6] Seung-Nam Yu, Jae-Ho Jang and Chang-Soo Han, Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel, Automation in Construction 16 (2007) 255–261. [7] Zhiwei Liu, Shahrel A SUANDI, Takeshi OHASHI and Toshiaki EJIMA, A Tunnel Crack Detection and Classification Systems Based on Image Processing, Machine Vision Applications in Industrial Inspection X, Martin A. Hunt, Editor, Proceedings of SPIE Vol. 4664 (2002) © 2002 SPIE · 0277-786X/02. [8] E. Hjelmas and B. K. Low, “Face detection: A survey,” Computer Vision and Image Understanding 83, pp. 236–274, 2001 [9] MVTec Software GmbH. HALCON 9.0[DB/CD]. Muich, Germany: 2008.

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Data Standardization

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Standardization and Digitization of the ISRM Suggested Methods on Rock Mechanics Tests Ming CHIa, 1, Zuyu CHEN b and Yufei ZHAOc Geotechnical and Structural Engineering Research Center, Shandong University b Department of Geotechnical Engineering,china institute of water resources and hydropower research c Department of Geotechnical Engineering,china institute of water resources and hydropower research

a

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Abstracts. With the rapid development of Geotechnical Engineering now, enough rock parameter is indispensable for geotechnical design, numerical analysis, and so on. However, data 㸪saved as the specific parameter, such as , will be highly different in the rock test with different methods. Consequently, it’s becoming hard to using and managing data generated in rock mechanical test of Geotechnical Engineering, and the data won’t play their role well in geotechnical engineering without useful storage, retrieve or collaboration. For this reason, it is very important to research how to obtain these parameter. The Joint Working Group on Representing ISRM Suggested Methods in Electronic Form (RISMEF) was established after the Rison ISRM Congress in 2007 under the Auspice of ISRM TM and JTC2.2. On the basis of the 52 ISRM suggested methods for rock mechanics test, and close discussions of members of the RISMEF working group, 7 suggested methods have been chosen for conducting the procedures of standardization and digitalization. The primary purpose of establishing RISMEF is to ensure that the ISRM Suggested Methods to be conducted in standard and electronic procedures. This work will result in a series of spreadsheets as appendices to the ISRM Suggested Methods and a database documenting the applications of these methods and the related data. The database will be attached to the ISRM website that will be shared on a non-commercial basis. The XML technology is widely used in recent years, it’s because that XML carries the inherent advantages of being a international-accepted, self-validating, data separated from representation. XML technology provides a solution for data storage, data retrieval, and data shared . so, in this paper, The application of XML technology combined with EXCEL sheets in the rock tri-axial compress test is described, as a pilot work. The test data in XML document can be shown and analyzed conveniently in EXCEL by VBA. Keyword. RISMEF, XML technology, rock tri-axial compression test.

1 Corresponding Author: Ming CHI, Geotechnical and Structural Engineering Research Center, Shandong University.

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Foreword

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Over the past few decades ISRM has been dedicating on publishing suggested methods for rock mechanics testing. This work has achieved great success and has been compiled into a book with the permission of Elsevier. However Literal descriptions of testing procedures cannot assure different people doing the same thing. It would be much beneficial if the testing data following these procedures are documented in a standard spreadsheet. Standardizations of the documented testing results will make it possible to establish a database shared on a non-commercial basis. On 12 July, 2007, JTC2 and ISRM CCHRE 1 jointly organized Specialized Session 2 entitled ‘Rock Mechanics Data: Representation and Standardization’ during the 11th International Congress on Rock Mechanics to be held in Lisbon. The idea that to establish an Joint Working Group on Representing ISRM Suggested Methods Data in Electronic Form was discussed in the session. At last year, the Working Group was organized and entitled 'Joint Working Group on Representing ISRM Suggested Methods in Electronic Form (Abbreviated to JWP/RISMEF or RISMEF)'. The primary purpose of establishing JTC/RISMEF is to ensure the ISRM Suggested Methods to be conducted in standard and electronic procedures. This work will result in a series of spreadsheets as appendices to the ISRM Suggested Methods and a database documenting the applications of these methods and the related data. On the basis of the 52 ISRM suggested methods for rock mechanics test, and close discussions of members of the RISMEF working group, 7 suggested methods have been chosen for conducting the procedures of standardization and digitalization. As an example, the rock tri-axial compress test method was firstly selected to perform the standardization and digitalization by using XML technology. With the advent of modern digital technology, the database created and shared by international geotechnical community appears possible. Now㸪XML is the most popular tool to storing and transfering the data of database. In this paper, on the rock tri-axial compress test, the Excel combined the XML technology is used to deal with the data of the test.

1. The Introduction of the XML Technology XML is a markup language and similar with HTML in programming, a subset of SGML (the Standard Generalized Markup Language to generate the standardize document of ISO8879 published in 1986). It inherits the self-defined markup character, and changes the deficiency of HTML in function, to have more extendable characters [1]&[2]. The XML technology has some features: x Extendable. XML is a language to create markup, to create new markup to use. Thus its use level can be extended finitely; x Simple to understand. XML code is text-based, unlike other ASCII language. So it can be edited by usual edit software. And it expresses directly and easily understands; x Information exchanged with different platform. As XML is simple to understand, its format can be used to mark different data type. Only if there

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has a XML decode system between the exchanging plats, the right information can be obtained by interpreting the marked data; x International. At the beginning of XML proposed, the international was considered. So it is founded on Unicode. XML’s files can be shown by IE with the aid of CSS (cascading style sheets) or the extensible stylesheet language. Because XML-file is only used to store data, not including other information such as format, et. al., it is generally used to process the data. First an ‘.xsd’ XML Schema file is created, to be judged between the style and element character. So that to determine the requirement of the XML document, that is to say the ‘.xsd’ files descript the character of them. It is necessary to use same style material file in database storage. Another ‘.xsl’ file is defined how the XML document is shown in browser. The relation of the XML file, the ‘.xsd’ document and the ‘.xsl’ document is expressed as Figure 1㸸

Figure 1. The relationship of XML file, XML Schema and XSL document.

2. The Application of XML Technology in the Rock Tri-axial Compress Test

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2.1. The Brief Introduction of the Rock Tri-axial Compress test Generally, three different types of tri-axial compress test are intended to measure strength of cylindrical rock specimens as a function of confining pressure. The rock mass tri-axial compress test method suggested by the ISRM introduced these three types of operations, there are the individual test, the multiple failure state test and the continuous failure state test. Combined the Chinese rock test specification, specifications for rock tests in water conservancy and hydroelectric engineering (SL264-2001), the ECXEL table be designed to record the test data [3]&[4]. 2.2. The EXCEL Form and the XML Document According to the rock tri-axial compress test and the Chinese rock test specimens, we design the EXCEL form to record the rock test data. The form is shown in figure 2. The information of the rock test data form as following:

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Figure 2. The EXCEL table for the rock mass tri-axial compress test.

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x

Some information of rock specimen. For example the name of geotechnical project, the source of the rock specimen, the number of rock specimens and rock test, and so on; x The description of the rock specimen. The description can be the word, image and video. This information includes the lithology of rock, the moisture of rock test, the simple geology introduction of rock, and the size of rock specimen. x The equipment of rock tri-axial compress test, the specifications of rock test used in this rock test; x The data of rock tri-axial compress test. The most important information is the data of the rock test, the rock strength parameters should be decided from these data. x The operator name in the rock tri-axial compress test. Finished the EXCEL form, we can click the “transfer to XML” button on the EXCEL document, the EXCEL form can be saved as the XML data document without any format. In this process, the structure of the XML data document is decided by the .xsd document or DTD document. The XML data document should be generated according to these scheme documents. The .xsd document for this EXCEL form shows in the appendix.

3. The Application of the XML Document The XML document has several benefits, for example, the XML document can be transferred between the different platform; the XML document only has the most

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important data without the style information; the XML can be shown in different style according to the specifically style document programmed by the user’s require. In the XML, the CSS and XSLT technology are often used to define the display of the XML document. The figure 3 shows the relation of the XML document, the EXCEL document and the XSLT document. In this paper, the XSLT document is used to show the data in the XML document in the EXCEL style, as the figure 7 showed.

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Figure 3. The construction of the application of XML technology and EXCEL.

By using the VBA technology, we can do some analysis on the data in the EXCEL from. In this paper, the Mohr Circle can be drawn by loading the Marco program of VBA. And the same way, the envelope of Mohr Circles can be calculated and the rock specimens strength parameters can be obtained through the regression analysis by using the VBA technology.

4. The XML Technology and the Database XML has many advantages in database application. First, Cross platform. XML file is text-based file, not only restricted to OS and software plat. Second, it is simple and straight-forward. XML has the ability of Schema’s self-description which can be understudied and auto-processed by computers. Third, XML describes not only the structural data, but also the sub-structural, even non-structural data. Now we are constructing a slope database based on Internet share, combining the SQL Server 2000 and XML, incorporating the network programming technology. Figure 4 is the develop structure of SQL Sever2000 and XML combination system. According to the different system of structure and service, different visit component or protocol is used. Considering the capability and programmable reason, it is often used

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to couple the logic and data visit. Thus we can using the standard components to realize it, such as OLEDBࠊADO and NET API and so on.

Figure 4. SQL Server’s XML visit system.

Through above analysis, based on the rock test database, we can add, search or browse the rock test data file by internet. From the internet, we can share the cherish resource of our world, and every engineers of each country can obtain their rock test data file concerning. This job is complicated, much quality, and many people to cooperate. It has been carried on. Near the future, we believe it can share on the internet.

5. Examples

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In the rock tri-axial compress test of a hydropower station, there are four rock specimens in this test. These rock specimens are mica schist㸪 and from No. 28 testing tunnel of the hydropower station. These rock specimens have conserved in water for 48 hours and its moisture is saturatedࠋThe rock tri-axial compress test according to the “specifications for rock tests in water convervancy and hydroelectric engineering(SL264-2001)”ࠋThe figure 5 shows the rock specimens’ photo after test, the figure 6 shows the stress-strain curve of rock specimens and the table 1 shows the result of the rock tri-axial test.

㸦1㸧

㸦2㸧

㸦3㸧

㸦4㸧

Figure 5. the photos of the rock specimens.

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㸦1㸧

㸦2㸧

㸦3㸧

㸦4㸧

Figure 6ˊThe stress-strain curve of the rock specimens. Table 1.The result form of the mica schist tri-axial compress test

Mica

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schist

Specimen diameter

Specimen length

Saturated unit weight

Axial stress

Confining stress

(mm)

(mm)

(kN/m3)

(ı1 , MPa)

(ı3 , MPa)

1

48.14

101.99

29.4

35.2

5

2

48.25

101.78

29.4

56.1

10

3

48.21

102.16

29.5

60.5

15

4

48.27

102.44

29.3

56.8

20

Remarks

From this rock tri-axial compress test, the EXCEL form is shown in figure 7, and the XML document is be generated from the EXCEL form. And the XML document shown in the EXCEL, and the Mohr circle and the envelop are also shown in this EXCEL sheet. And the rock strength parameters, c and f, was calculated from this rock test data. the strength parameter c is 9.24 MPa and the parameter f is 0.39. Figure 8 shows the result of this rock test.

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Figure 7. The XML document of rock test data is shown in the EXCEL.

Figure 8. The Mohr circle and the envelop of the rock tri-axial compress test.

6. Conclusions The XML technology can bring many benefits in the rock rest. And this technology can improve the standardization and digitization of the rock mechanics test. The application

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of the XML technology in the rock tri-axial compress test verified the feasibility and effectiveness of this method. The advanced network and computer technology have made a database of collected rock mechanics test information possible. Now the generalized XML technology can realize the data transfer cross platforms. It has simple readable and extensible characters. By the relate knowledge of database, it can provide the network share of the database of rock mechanics test login using XML. The Chinese committee of rock mechanics and engineering is working on the establishment of rock mechanics test database under the help of international committees. We believe, in the near future, the information of geotechnical engineering over the world can be searched and browsed on the internet with the rock test login files. Endotes: JTC2 is a Joint Technical Committee on Representation of Geoengineering Data in Electronic Form of the International Society for Soil Mechanics and Geotechnical Engineering, International Association for Engineering Geology and the Environment and International Society for Rock Mechanic. ISRM TM is a Commission on Testing Method under the International Society for Rock Mechanics.

References: [1] [2] [3]

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[4]

Bing Gu. XML practical technology course. Beijing. Tsinghua university press, 2007. Chunxiao Xing et al. XML data management. Beijing. Tsinghua university press, 2006. Resat ULUSAY, John A. HUDSON.. The complete ISRM suggested methods for rock characterization, testing and monitoring( 2007), 1974-2006. Commission on testing methods international society for rock mechanics. The ministry of water resources of the People’s Republic of China. specifications for rock tests in water convervancy and hydroelectric engineering(SL264-2001). Beijing, 2001 .

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The STREAM’s Testdefinition Facilitates Type of Test Independent Database Storage Paul E.L. SCHAMINÉEa,1 and Ardt A. KLAPWIJKb a Deltares, Delft, the Netherlands b NCIM B.V., Leidschendam, the Netherlands

Abstract. Results of many different types of physical experiments have to be stored. To facilitate efficient archiving and exchange of test results a new method, STREAM, has been developed. This paper describes a database that utilizes the characteristics of STREAM, in order to create a flexible, robust and incorruptible tool to store data from a broad range of geotechnical experiments. The result is a type of test independent database. All information stored in the database is checked to be correct and complete. The principle structure of the database is a searchable set of both general metadata as type of test specific metadata to identify, to query and to select the data file. Using STREAM’s standardized data description, this type of database is ‘prepared’ to accept any new type of test with corresponding data files, without specific programming. The described storage process is unique in its kind and currently operational at Deltares. Keywords. Standardize the description of a test, store results of experimental testing in a database, Testdefinition, STREAM.

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Introduction In any field of engineering and research, data are gathered, analysed and reported. These data are transferred from one user to another, several times. In some cases the data remain on one location for a longer period: it is stored. In this paper the focus is on data obtained by experimental testing. At the start of an experiment only data is gathered about the setup of the experiment. When the actual experiment is performed the actual measurement results are obtained. After performing the actual experiment the obtained results are analysed and reported. Finally the results are presented to a client and stored for future reference. Concerning the information available two observations were made: it differs (i) at different stages and (ii) for the different types of tests. Deltares was confronted with the problem to store and exchange data from numerous types of tests, varying from simple moisture content tests to complex geotechnical centrifuge tests. No appropriate method was found that covered this broad range of tests. Therefore in the last fifteen years Deltares has autonomously solved this problem by developing and implementing a methodology, called STREAM. In this method each type of test is treated independently and for each single type of test five chronological so-called SMARF - phases involving any form of experimental activity are 1 Corresponding Author: Deltares, P.O.Box 177, 2600 MH, Delft, The Netherlands; E-mail: [email protected].

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distinguished. In this method all information available after a phase of an experiment is stored in a data file, so five data files for one single test are created. This set of five data files contains all meta data and test results of a single experiment. A database was designed, which was able to store and retrieve all data files created during the STREAM process. The developed database can be easily extended by new types of test without any programming and still tests can be found by using type of test specific metadata. Now users, i.e. operators and researchers, can define their own set of relevant type specific metadata. This feature appears to be very powerful in research environments where many different (or modified) types of tests are performed and need to be stored securely. Of course general metadata such as filename, date of creation, project identification are also available for queries. This database, designed to facilitate STREAM for a broad range of geotechnical tests, is described in the next sections. In Section 1 the underlying concepts are described and in Section 2 the functionality and design of the database are described.

1. The Underlying Concepts The database described above requires chronological structuring of the measurement process and standardization of the descriptive information of corresponding data files. The recently developed method fulfils these requirements [1]. This method is centered around two elements (i) a chronology that captures the activity involved in any form of experimental activity: SMARF and (ii) a method that structures information generated throughout the SMARF process: STREAM.

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1.1. The Concept of SMARF The SMARF concept identifies five chronologically-ordered phases from the design of a test to its factual reporting. These phases lead to the SMARF acronym – set-up, measurement, analysis, reporting and filing. x Set-up phase in which the equipment and samples are prepared; x Measurement phase in which the actual experiment takes place, i.e. the gathering of sensor readings; x Analysis phase in which the recordings are analyzed and new derived quantities are calculated or key values at particular times are extracted; x Reporting phase in which both the measured and calculated results are presented attractively by means of figures, tables, etc; x Filing phase in which the results are prepared for long term storage and future accessing. The SMARF concept provides the moments to transfer a well defined phase data file by storing it in the database, to make it available for the users in the next phase. 1.2. The Concept of STREAM The method called STREAM is developed to facilitate efficient exchange and archiving of test results. STREAM is an acronym for Standardized Test Results Exchange and Archiving Method. Documentation of exchanged or archived results is an integral part

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of the process. Tests of the same type share the same procedures and therefore share the same documentation with respect to the description of quantities and equipment. Each quantity or item is referred to as ‘element’. The precise descriptions of all these elements (i.e. an explanation in a standard way, comprehensible by any user) for a type of test are stored in a so-called ‘Testdefinition’, which is a document that is formatted in a standard way. Only one Testdefinition is created for all tests of the same type, illustrated in figure 1. All values, parameters and readings from each single test, are stored in a separate data file.

N tests of one type of test

One Testdefinition

N data files

Figure 1. A Testdefinition and the corresponding data files

In STREAM the five SMARF-phases are defined separately. In the Testdefinition it is clear in which of the five SMARF phases an element is created. Using the Testdefinition each data file can be checked for completeness and correctness before entering the next SMARF phase.

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1.3. Data File A data file consists of a header and a data section. The data section contains quantities with more than one observation: these elements are referred to as columns. Typically columns contain the series of registered and analyzed values. The header contains the context information, referred to as metadata: x General metadata. This general information is common to most types of tests and is therefore predefined. These general information elements are well defined. They can be grouped in two categories: (i) file tracing, i.e. information to identify for example the organization where the test was performed, the person responsible for the test (ii) test description, i.e. the reference to the corresponding Testdefinition. x Type of test specific metadata, or typespecific metadata. These elements are used to store information that is only relevant to describe the context of one specific type of test, for all other types of test this information is meaningless. All elements in the data file are described in the Testdefinition. For STREAM it is essential that the relation between the value in the data file and the Testdefinition is maintained continuously. The interaction between the Testdefinition, the test data and the software is based on an unique key for each element, referred to as ‘shortname’. Although a shortname is defined for one type of test, its value assigned will be different for each single test performed in most cases.

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Currently STREAM is implemented with a GEF formatted data file [2] and an MS-Excel data file, but could be easily extended for example an XML data file. Conversion between different types can be performed easily. 1.4. Testdefinition

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The Testdefinition turns out to be the central part in the STREAM process. Only with a completely defined Testdefinition, test results can be acquired, validated and stored. A Testdefinition can be created using the Testdefiner®. For each SMARF phase the elements to be acquired can be defined with a distinguishable shortname and adequate properties. This results in the Testdefinition, which is an XML-file. Besides a detailed description of each element, a Testdefinition also offers the opportunity to enter reference data. This additional information is crucial for comprehensive description of a test and its procedures. Examples are: x Coordinate Systems. Coordinate systems are used to be able to work with sensors in mounted in modular test set-ups. A sensor location is referenced by a position relative to one defined coordinate system. In one Testdefinition all coordinate systems are related, and software has been developed to change the coordinate systems in which locations are expressed originally. x Input Editors. Each element can be assigned to a person or role. This user is responsible to enter these elements. A generic application has been developed to allow a specific editor to enter information using specific dialogs based on the Testdefinition. This appeared to be very powerful and by combining the advantages of the STREAM concept and the type of test independent database storages. x Applied Procedures. Each element can refer to an applied procedure, which itself is a reference to a procedure that is applied, stated in another document. This connects the Testdefinition to existing standard procedures. x Enumerations. General en typespecific metadata elements can be assigned a list of enumerated values or strings. These enumerations can be used to create an input program and forces the user to choose an item from a limited list.

2. The Database A database is a common way to store data permanently. This is done to keep the data available for a longer period of time on a centralized location, but also to be able to query the data in the defined entities (tables) in order to gain statistical information. In most situations when large amounts of information need to be stored a relational database system is used, with a limited number of specified entities. However, storing test results from a data file in a database is not so straightforward. The general metadata can be stored in prescribed entities, but the typespecific metadata can vary from type to type. It varies in the number of elements and the type of the elements. On the other hand, only storing the data files doesn’t offer the opportunity to query the results very easily and negates the possibility to check the validity of the data in the files. Deltares has developed a mixed solution in which a set of the general and type of test specific metadata is stored in a relational database and the data files itself are stored on a central location. The relation between the relational data and the data files is taken

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care of by central services. Additionally, important general and type specific metadata is extracted from the data files and stored in an entity. To apply the STREAM methodology, a test should be related to a Testdefinition and all metadata should be referenced by a shortname. This is done by putting the Testdefinitions in the database too, both as a file and extracted in relational entities. In this way, type specific metadata can be stored.

Figure 2. Relation between Testdefinition, SMARF phases, database and FTP server

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2.1. Data Types and their Properties The Testdefinition describes all elements that might occur in the data files. These elements are classified as: x Numerical elements (Number), x Textual elements (Text) x System elements (System); these are predefined elements included for meta information like project identification, file owner and file date, and test information like start date and location. These elements can have a composite structure. x Columns of numerical values (Column). In the Test definition, the elements of different data types are defined by several properties, depending on the data type of the element. In table 1 the most important properties are listed, related to the data type. It should be noted that a Column does not have the property “Searchable”, this is because the values of a Column can not be used as a parameter in a query. Furthermore, a Column element can be enhanced as a sensor, which provides additional options to enter information regarding the sensor calibration and location.

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Table 1. The properties for data type Number

Text

Column

System

Shortname

x

x

x

x

Required

x

x

x

x

Documentation

x

x

x

x

Searchable

x

x

Quantity

x

x

Unit

x

x

Minimum/maximum value

x

x

x

2.2. Identification and Versioning A Testdefinition is identified by a definition code. This is combination of a label, including the owning company and the type of test, and a release, version and update number (version for short). For example: Deltares.nl:ConePenetrationTest, 1, 0, 1. The first part of the identification of a Testdefinition is the internet address (without the preceding ‘www’) of the company responsible for all security aspects of the Testdefinition, i.e. uniqueness, version management. Also each of the five SMARF-phases is identified by a phase code. These codes are also structured as a label and a version number. This gives the opportunity to copy definitions of phases between various versions of a type of test or even between different types of test. Whenever something changes in a certain phase, the phase version of that phase needs to be increased. Also the definition code needs to be increased, because a Testdefinition is uniquely identified by its definition code. Table 2 presents an example when changing the definition of the analysis phase. It is common practice that also the report and filing code are increased although nothing has been changed to those phases. This guarantees that the software is backwards compatible. Copyright © 2010. IOS Press, Incorporated. All rights reserved.

Table 2. Version before and after applying a small change in the analysis phase Definition

Setup

Measurem.

Analysis

Report

Filing

Before

1, 0, 0

1, 0, 0

1, 0, 0

1, 0, 0

1, 0, 0

1, 0, 0

After

1, 0, 1

1, 0, 0

1, 0, 0

1, 0, 1

1, 0, 1

1, 0, 1

2.3. Checks at SMARF Phases Transition After finalizing a SMARF-phase the file is put in the database after validation. In the same process the values of the metadata elements indicated by the Testdefinition as ‘searchable’ are copied into the relational database. To keep the database up to date and incorruptible, some portal software has been developed. Its main function is to check all files that are submitted to the database and store them in the right location. The extracted metadata are put in the relational tables. A file that doesn’t pass the validity checks based on the Testdefinition or checks against reference data (e.g. does the project exist?) is rejected and cannot enter the database. The user should correct it and retry.

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2.4. Design of the Database Figure 3. shows the structure of the database, which is explained in the next paragraphs.

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Figure 3. Key entities and their relations in the database

The left block - the Testdefinition related part - applies to a specific type of test. It contains all relevant information from each single Testdefinition. Entity Definitions contains the definition code (DS_LABEL, DS_VERSION) and the link to location of the actual Testdefinition file (DS_LOCATION). Entity DefinitionPhases contains information the separate parts of the Testdefinition for the five SMARF-phases. This is the phase code (DP_LABEL, DP_VERSION). It also contains a link to location of an extract of the Testdefinition file that contains the checks for the specific SMARF-phase (DP_LOCATION). These comprise the checks whether an element is present, if it is of the right type and if its value lies between the specified minimum and maximum value. Entity DefinitionElements contains the general and typespecific metadata elements from the Testdefinition which are marked as ‘Searchable’ (not for Column). Note the special relationship between Definitions and DefinitionPhases. As DefinitionPhases can be shared among more than one Definition, entity DefinitionPhases acts as a parent, where logically Definitions is the parent. The right block - the test related part - consists of the entities that contain data of a each single test. Entity Tests contains the specific tests that are carried out. Each test has an identifying label and can be related to a Definition. It can also be related to certain reference data in the database, like Projects, Locations and Samples. Entity TestPhases contains the separate data files for each of the five SMARF-phases of each test. Finally entity TestData contains the extracted values for the general and typespecific metadata elements in DefinitionElements which were present in the data file. Depending on the type one on of the value fields is filled.

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3. Conclusions

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The STREAM concept offers the possibility to store and exchange test results of all types of test in a way that is both effective and flexible. The Testdefinition plays a central role in this concept. STREAM can only function properly if the Testdefinition is complete, which requires quite some effort. Therefore it is less suitable for volatile tests. Test independent database storage creates a centralized location for storing test results of all types. The data files itself are stored, but general and typespecific metadata are stored in relational tables. With these two concepts Deltares is able to put all acquired data in a single database, which is effective and flexible. The result is a database that: 1. Permits deploying new types of tests without any database programming. 2. Is suitable for most geotechnical experiments from a simple laboratory test as the moisture content, field tests as CPT, but also complex geotechnical centrifuge tests. 3. Can find files based both on general metadata (project, sample and test identification, etcetera) as well as on information available for certain types of test only (typespecific metadata). 4. Validate each file stored, by checks on completeness (are all required elements present) and correctness (are all numerical elements between the specified limits). 5. Is accessible by all standard means, i.e. SQL. 6. Supports an external web portal to allow (external) clients direct and secure access to their own test results. The implementation of the STREAM methodology in combination with the type of test independent database has lead to a flow of well defined intermediate data files during the test procedure itself and to a collection well described of test results in the permanent archive.

References [1] E.L. P. Schaminée et. al., STREAM: a method to facilitate efficient data exchange and archiving, Proceedings ICPMG 2010, Zürich 2010, to be published. [2] E.L. P. Schaminée et. al., Geotechnical exchange format language a base for physical modeling data standard?, Physical Modeling in Geotechnics – 6th ICPMG ‘06Taylor & Francis Group London. (2006), 241-246.

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Geological Modeling and Integration with Numerical Model

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Building a Geological Model of the Copenhagen area using HoleBASE, MIKE Geomodel and KeyHOLE Sanne Louise HANSONa, 1 and Ole Frits NIELSENa COWI A/S, Parallelvej 2, 2800 Kongens Lyngby, Denmark

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a

Abstract. In connection with the Copenhagen Metro 'Cityringen' project, a geological model of the Copenhagen area has been created by the software applications of HoleBASE, MIKE GeoModel and KeyHOLE. The project involved some 1000 project boreholes located in close proximity to the alignment combined with another about 4000 existing boreholes further away from the alignment. The project borehole data were imported to HoleBASE via AGS files. Geological layer codes were defined in HoleBASE for each layer. With all project borehole data collected in HoleBASE, the data were exported to MIKE GeoModel for building a 3D geological model. In MIKE GeoModel the borehole data were combined with existing borehole data located further away from the alignment. Areas between the boreholes were interpolated and the geological layer interfaces created. The layer interfaces were interpolated as surfaces to give information on layer thicknesses and depths to top and/or bottom of each defined layer. The total 3D geological sequences of layer boundaries were imported to KeyHOLE for creation of 2D vertical profiles. KeyHOLE is the CAD connection program between the planned constructions and the geological 3D model. Once all layers were imported to KeyHOLE, a cut was made along the alignment through the 3D model layers. This process has resulted in a 15.5km long vertical geological profile along the Cityringen alignment shown together with planned constructions. Additionally the surfaces were imported to MapInfo for making a number of detailed geological surface maps. The geological model confirms already known data and furthermore gives an improved and more detailed picture of the Quaternary deposits as well as the limestone surface in the Copenhagen area. Keywords. Geological model, Copenhagen, geo engineering

Introduction A geological model was set up for the Copenhagen area in connection with the construction of the existing Metro. Now, a new 15.5 km long metro with 17 stations has been planned to be integrated with the existing metro and the city 1 Corresponding Author. Sanne Louise HANSON, COWI A/S, Parallelvej 2, 2800 Kongens Lyngby, Denmark

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trains in Copenhagen. In connection with the new Cityringen metro an extensive amount of geotechnical borings have been made. These borings and other relevant existing data have been used to create an updated geological model for the Copenhagen area. The model extents to relatively unknown areas of Copenhagen. The computer applications HoleBASE, MIKE GeoModel, KeyHOLE, MapInfo and Geoscene 3D have been utilized to create a number of geological cross sections and surface maps. This paper describes the use of the software applications and how the updated geological model of Copenhagen was created.

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1. Geology of Copenhagen Copenhagen is located on the Eastern side of the Danish island Sjælland, see Figure 1. The Copenhagen area is tectonically situated in the eastern part of the Norwegian-Danish Basin which is a WNW-ESE trending basin constricted by the NW-SE trending Sorgenfrei-Tornquist zone to the north, and by relatively high lying basement blocks (Ringkøbing-Fyn High) to the south (Lund et al. 2002) The island has a gentle undulating landscape formed by glaciations during the three latest Scandinavian ice ages. The geological framework for the Copenhagen area describes a stratigraphy consisting of a Quaternary sequence comprising two layers of glacial till, an upper and a lower Copenhagen till, separated and underlain by melt water deposits of sand and gravel. The Quaternary sequence is resting on Danian limestone, and is overlain by fill and post- and late glacial deposits with varying organic content. The pre-Quaternary limestone surface is dominated by subburied melt water made valleys whereof the Rådhus-valley is the most pronounced. The limestone surface may be locally disturbed by glacial activity. Selandian Greensand deposits (Middle Paleocene) are found locally subjacent to the Quaternary deposits. These deposits indicate a glacially undisturbed limestone surface. Only a few geological structures are present in the project area. The most significant structure is the Carslberg Fault, a normal fault, located around 300 m west of the westernmost part of the Cityringen alignment. The Carlsberg Fault has, however, not been detected in the Quaternary strata, but is clearly seen in the limestone strata. Otherwise the project area is dominated by gentle folding along WNW to NW trending fold axis.

2. Building a Geological Model The geological model of Copenhagen is based on borehole data, geophysical data and a comprehensive logstratigraphical analysis for the whole Cityringen area. In order to form the foundation for a hydrogeological model the geological model area is somewhat bigger than the Cityringen area itself. All together the model holds information from about 5000 boreholes, 13 km of refraction seismic data

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and 257 geophysical borehole logs integrated in a detailed logstratigraphical analysis.

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Figure 1. Overview of the Cityringen alignment in Copenhagen and the utilized data in the proximity of the alignment

Figure 2. Workflow with the different software applications utilized in the geological modeling and for the presentation of the results

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The geological model of the Copenhagen area has been created using different software applications. An overview of the workflow using the different software applications is given in Figure 2. 2.1. HoleBASE All site investigation- and laboratory data were received from the contractor as geoform files. These files were transformed to AGS files which can be directly imported to the geotechnical data management system HoleBASE. This application serve as a borehole database holding detailed geological and geotechnical information. Geological data supported by interpretations made from borehole logs were entered directly into HoleBASE. The layers were named with geological codes referring to the geological origin eg. till (ML) or meltwater sand (DS). Furthermore the interpreted layers were correlated to the already known existing stratigraphic model and given a 2nd geological code referring to the actual stratigraphical level, eg. ML1 for 'upper till'. Legend codes refer to the type of soil; clay, sand etc. This work was done for some 500 geotechnical boreholes.

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2.2. MIKE Geomodel The borehole information including the stratigraphical interpretations in HOLEbase was imported into MIKE Geomodel. Also other borehole databases were imported, amongst these the Danish national borehole database JUPITER. Where possible the boreholes were separated into different databases according to type, quality or credibility. The different borehole databases were hereafter imported as separate databases making it possible to differentiate between the different borehole types or qualities during the interpretation process. Different types of interpretation profiles were made in MIKE Geomodel making it possible to focus in different levels of detail. For instance long, regional profiles were given a broad buffer zone and a high vertical exaggeration while short, local profiles had a buffer zone of a few meters and a low vertical exaggeration. Interpretation profiles were placed along the alignment as well as perpendicular to the alignment. In the GIS section in MIKE Geomodel thematic maps covering type, quality and drilling depth of the boreholes made it possible to choose the right placement of the interpretation profiles with respect to relevant boreholes. 2.3. GeoScene 3D A 3D visualization tool was utilized to validate the final interpolated geological surfaces in space against the borehole information. In this process COWI used the

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software Geoscene 3D. The adjusted geological surfaces together with the borehole databases were imported into Geoscene 3D. The planned tunnel alignment was also imported to visualize the geological setting around the tunnel in 3D space. Furthermore the 3D visualization software was used to get a better understanding of the composition of the geological model and for presentation purposes.

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Figure 3. Screendump from the software Geoscene3D showing an underground view of boreholedata, geological surfaces and planned tunnel

2.4. KeyHOLE KeyHOLE is the AutoCAD modeling tool that has been used for drawing the vertical geological profiles. KeyHOLE also makes it possible to draw technical drawings from the underground stations and shafts directly on the geological sections. When all geological layers are made in MIKE Geomodel, the layers are imported to KeyHOLE as .xyz files. These files are then transformed in KeyHOLE to KGM files, one KGM file is made for each layer. The KGM files can then be transformed to .SEK files, again one for each layer. The Cityringen alignment was made into a polyline from where a vertical geological profile was made showing the different geological layers. Each cross section is about 2 km long. Fig. 4 shows a geological cross section from the north of the project area, where the sub-buried melt water made valleys incised into the limestone surface are visible. The tunnel running in the Quaternary layers and two stations shafts are also seen on the profile.

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Figure. 4. A geological cross section from the north of the project area

2.5. MapInfo The geological surfaces were imported in the GIS software MapInfo. The layers were imported as grids using the software utility Vertical Mapper and different thematic maps were produced such as level or depth of the different layer interfaces and thickness maps. In MapInfo relevant information was added such as background maps, planned tunnel alignment, constructions, data points etc. MapInfo was further used in the planning and execution fase of the geological model to keep track on the status of the data.

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3. Discussions and Conclusions Cityringen is a large urban, underground construction project with a strong demand for detailed geological information. The geological model forms the basis for a wide range of further planning and work in the project and it was therefore essential that the best possible conditions were given in the making of the geological model. Furthermore the model had to be presented for the future users in a way, that ensures a good overview and at the same time the necessary details. To meet these demands COWI has applied a powerfull combination of software applications. The software applications HOLEbase, MIKE Geomodel, KeyHOLE, Geoscene 3D and Mapinfo have been utilized to gather, interpret and present geological, geotechnical and geophysical information in a way that can meet the demands. All project borehole information has been interpreted in HOLEbase from the available geological, geotechnical and geophysical information. Further borehole databases have been added after being divided into different categories making it possible to differentiate between them during the geological interpretation made in MIKE Geomodel. Validation and presentation

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has been carried out in a 3D environment using Geoscene 3D. Detailed presentation has been given using MapInfo and the CAD software KeyHOLE. This includes surface maps and geological profiles shown with detailed drawings of the planned constructions. The geological model confirms already known data and furthermore gives an improved and more detailed picture of the Quaternary deposits as well as the limestone surface in the Copenhagen area.

Acknowledgements We wish to thank 'Metroselskabet' for the use of the huge amount of borehole data.

References

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[1] N.S. Lund, L.H. Nielsen, & C. Knudsen: Københavns underground med focus på Danien aflejringerne, dgf-Bulletin 19 (2002), 5-18.

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XML-Based Approach for Reporting and Exchanging Experimental Data Sets Using Metadata Model Fang LIUa,b, 1, Jean-Pierre BARDETc and Nazila MOKARRAMc a Geotechnical Engineering Department, Tongji University, Shanghai 200092, China b Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University, Shanghai 200092, China c Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, Los Angeles, CA 90089-2531, USA

Abstract. Growing collaborative research projects prompted the development of metadata models for documenting voluminous data sets produced by experiments and computer simulations. Herein we demonstrate the usefulness of a recently proposed metadata model for reporting and exchanging experimental data sets. The object-oriented metadata model is expressed in terms of XML schemas, which allows researchers to exchange their data sets with XML metadata explaining how data were obtained. The XML metadata can also be used to generate automatically reports which present data comprehensively. The present approach, which fully takes advantage of XML technologies, helps pave the ways for the acceptance of metadata in earthquake engineering.

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Keywords. Web report, metadata model, XML

Introduction Growing collaborative research projects promotes large data sets from experiments and computer simulations in earthquake engineering. Based on a previous reference data model (Bardet et al. 2004; Peng and Law, 2004), Bardet et al. (2010) proposed a metadata model for documenting such data sets. This past work underlines the need for demonstrating the usefulness of metadata before they become accepted and used by earthquake engineers. Nowadays, earthquake engineers commonly exchange data from experiments and computer simulations through websites. However most of these websites are made up of HTML (HyperText Markup Language) pages with static data reports generated case by case and highly specific to research programs. They require time-consuming maintenance when data change frequently, and could benefit from metadata for more efficient data dissemination. The present paper demonstrates the usefulness of metadata by showing how to generate automatically data reports and exchange documented data sets using XMLbased technologies.

1 Corresponding Author: Geotechnical Engineering Department, Tongji University, Shanghai 200092,China; Email: [email protected] Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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1. Metadata Model

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Metadata, which means literally "data about data," take different meanings depending on professional communities. Hereafter, the term metadata refers to the data that are added to document the data produced by experiments and computer simulations. For small data sets with a limited number of files, metadata could be reduced to basic annotations describing how data files were generated. For instance, metadata could be made up of hand-written notes from laboratory notebooks. For large data sets involving experiments, computer simulations and many persons and pieces of equipment, metadata can only be represented using database techniques. Bardet et al. (2010) proposed a metadata model based on the object-oriented framework for archival and exchange of data generated in collaborative projects in earthquake engineering. This model is applicable to various fields in earthquake engineering as well as other research projects in engineering and sciences. The metadata model was constructed using a development tool called Protégé with a convenient graphical user interface as shown in figure 1 (Protégé, 2004; Rothenfluh et al., 1996; Gennari et al., 2002). This model consists of 16 classes (i.e., Organization, Person, Worker, Publication, Activity, Project, Task, Event, File, EQMotion, Configuration, Software, Equipment, Sensor, Label, Specimen), among which Project is the root of the metadata model and invokes directly or indirectly all other objects. The metadata model systematically reuses objects, which practically eliminates repetitive and conflicting information. Detailed definitions about those classes used in the metadata model can be found in Bardet et al. (2010).

Figure 1. Display of the metadata model using Protégé

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2. Automation of Data Reports Using Metadata Model and XML-Based Techniques The eXtensible Markup Language (XML) extends the capabilities of HTML by separating data and formats. XML data are well-structured and can be tailored for diverse specific applications. Nowadays XML is a common solution for exchanging data over the internet. The eXtensible Stylesheet Language (XSL) is often used to express how XML documents should be styled, laid out, and paginated as HTML documents. A XSL processor, which is specific engine to convert XML to HTML, is the core for translating XML data into a friendly and readable format to internet users. Figure 2 illustrates the process for generating HTML reports using the metadata model and XML-based techniques. Data is initially archived in a Protégé database in accordance with the data structure defined by the metadata model. Data is then exported in a XML document using XML tab equipped in Protégé. A customized XSL processor calls pre-designed XSL stylesheets, and transform the XML document into HTML pages suitable for presentation. Data exported in XML

Data report generation

Protégé

XML tab

XSL

XML

XSL Processor

HTML

Figure 2. Flow chart for generating data reports using metadata

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2.1. XML Schemas of the Metadata Model The metadata model was intentionally kept simple so that it can be solely expressed in XML schemas. The XML schemas of the metadata model are made of 16 elements corresponding to the 16 classes included in the metadata model. Figure 3 provides a few examples of those elements represented in XML schemas using standard diagrams, the symbols of which are explained in Table 1. 2.2. XML Documents Export for Specific Data Sets Protégé can not only input data to the pre-defined metadata model for specific projects, but also provide a way to export data sets in the format of XML documents using the built-in XML tab. Protégé generates XML documents in which objects are defined only once but may be referenced several times. It uses ID and IDREF to avoid duplication of information. When an object is referenced more than once in a document, it is assigned a unique ID and is subsequently called using IDREF. For example, as highlighted in Figure 4, a person named “Bill Spencer” is called several times in the XML document. This Person is assigned a unique “id” attribute valued “Bill_Spencer” (i.e., ). This person is then called at several other places by using . The use of ID and IDREF eliminates redundant data in XML documents but may create some problems when formatting XML documents as discussed in the next section.

(b)

(a)

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(c)

Figure 3. XML schema diagram of element (a)Project; (b)Specimen; and (c) Equipment

…

…

Spencer Bill …

…

Figure 4. IDREF points to the object identified with ID

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Table 1. Symbolic representation of XML schema diagrams. Symbol

Explanation Global element at the top level of the schema. Child element embedded into another element and referencing a global element with the same name. The small arrow represents referencing. The solid borderline means the element must appear once. Optional child element referencing a global element. The dashed borderline means optional; the child element may appear once or not at all. Optional child element referencing a global element. The numbers below the box mean minimum and maximum occurrences, respectively. As shown, the child element may appear as many times as needed or not at all. Child element referencing a global element. It must appear at lease once or many times. The plus mark means it can be expanded, i.e., it has child element(s). Sequence of child element(s) which must be in a specific order. Choice of child element(s) which must be from a list but in any order. Choice which must appear at least once or many times.

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2.3. Design of XSL Stylesheets XSL stylesheets herein were designed for an example of a HTML web report with a three-frame layout. The top frame is a static header specifying the report name. The left frame is a navigation bar listing various elements of data reports, e.g., activities and inventories. The activities are displayed using a collapsible tree structure with Project as the root, Task as branches and Event as leaves. The inventories consist of pointers to pages of Organization directory; Person directory; Equipment inventory; Sensor inventory; Specimen inventory; Software inventory; and Configuration list. The main frame displays the content called through hyperlinks and navigation bar. Table 2 lists the 14 HTML pages that make up the data report and XSL stylesheets in use. Except the entry page (i.e., index.html) and the static header displayed in the top frame (i.e., header.html), HTML pages are automatically generated using XML documents and pre-designed XSL stylesheets. For instance, the stylesheet content.xslt is applied to generating content.html that displays the navigation tree. Table 2. HTML pages composing a data report HTML Page

Description

Related XSL Stylesheets

index.html

Entry page for a data report with three frames: header.html, content.html and home.html.

static page

header.html

Header in top frame.

static page

content.html

Navigation bar in left frame displaying activity-dependent pages and inventory pages.

content.xslt

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HTML Page

Description

Related XSL Stylesheets

home.html

Initial page of main frame listing all the projects.

home.xslt

Project.html

Page describing individual project. Each project has a separate directory.

Project.xslt

Task.html

All tasks associated with a project.

Task.xslt

Event.html

All events associated with a project.

Event.xslt

OrganizationDirectory.html

All organizations involved in a project.

OrganizationDirectory.xslt

PersonDirectory.html

All persons involved in a project.

PersonDirectory.xslt

EquipmentInventory.html

All equipments used in a project.

EquipmentInventory.xslt

SensorInventory.html

All sensors used in a project.

SensorInventory.xslt

SpecimenInventory.html

All specimens used in a project.

SpecimenInventory.xslt

SoftwareInventory.html

All software used in a project.

SoftwareInventory.xslt

ConfigurationList.html

All configurations used in a project.

ConfigurationList.xslt

2.4. XSL Processor

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A XSL processor, HTMLGenerator, was coded in JAVA to generate those HTML pages listed in Table 2 using the XSL stylesheets. As shown in Figure 5, HTMLGenerator first reads the XML document and parses it into a document object model (DOM, 2004). After the XML document has been loaded as a DOM, all its references to IDREF are recursively replaced by their actual contents with an identifier ID. The inventories and directories of various types of objects (e.g., Equipment) are created by transforming DOM sections into HTML pages using XSL stylesheets. For instance, the person directory (i.e., persondirectory.html) is generated using the three lines of code of Figure 6. As shown in Figure 6, “source” represents the complete DOM, and PersonDirectory.xslt is the stylesheet that formats the XML data of person directory. The HTML output is DirPath+"/PersonDirectory.html" where Dirpath is the output directory. The first line defines tFactory, a new instance of Java class TransformerFactory. Using the new instance tFactory, the second line defines tranPerson, a new instance of Java class Transformer. The third line generates the HTML document. The code of Figure 6 can be easily adapted to generate HTML pages for other inventories and directories. For activity-dependent HTML pages (e.g., project.html), the XML document (i.e., source) is broken into DOM fragments, each of which contains project-specific information. Those fragments are then transformed into HTML pages using Java code similar to the one shown in Figure 6.

3. An Example of Automatic Data Reports Figure 7 illustrates automatic data reports for two particular experimental projects using the approach described herein. As show in this figure, the left frame displays the hierarchy of one of the project using a directory structure and points to the pages of

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Project, Task, Event, inventories and directories. Inventory pages describe and bookmark all objects including Equipment, Sensor, Software, Specimen, Organization, Person, and Configuration. These objects are called by the Project, Task and Event pages through hyperlinks. Start Step 1: Generate inventory pages XML

Read

Parse XML into DOM Substitute IDREF with ID contents

XSL

Read

Generate HTML inventory pages

DOM

Write

HTML

Step 2: Generate activity-dependent pages

DOM

Modify DOM to sort Project Split DOM into fragments describing separate projects Generate activity-dependent HTML pages

Write

HTML

XSL Read End

Web reports

Figure 5. Flow chart of the HTMLGenerator

…

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TransformerFactory tFactory = TransformerFactory.newInstance(); Transformer tranPerson = tFactory.newTransformer(new StreamSource("PersonDirectory.xslt")); tranPerson.transform(source, new StreamResult(new FileOutputStream(DirPath+"/PersonDirectory.html")));

… Figure 6. An excerpt of Java codes for creating person directory

4. Discussion The present approach generates data reports where data are displayed through hyperlinks. It can be extended to generate complete data reports in which data are inserted instead of hyperlinked, similar to technical publications. The representation of metadata using XML schemas is convenient not only to generate reports, but also to exchange metadata among researchers. When researchers distribute their data sets, they can simply attach XML metadata for documenting directories of data files (e.g., zip files). The present method references files using URI; it has been tested using an Apache Server file server, but pertains to all types of file servers. The application of metadata was illustrated by reporting and exchanging data, which does not exclude the exploration of other applications. In the future, metadata models are likely to become accepted in earthquake engineering as they become integrated with electronic

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notebooks and contribute to the development of convenient publishing and analysis tools.

Figure 7. Data report automatically generated by the metadata model

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5. Conclusion A growth of collaborative research projects prompted the development of metadata models that fully document the data generation processes. The metadata model of Bardet et al. (2010) was applied for automatically generating data reports and exchanging data sets derived from experiments and computer simulations. The objectoriented metadata model was expressed in terms of XML schemas, which allows researchers to exchange their data in a complete XML document. The XML schemas were also used to generate automatically data reports which merge comprehensively data and metadata. The present approach, which fully takes advantage of XML technologies, promotes a better documentation of experiments and computer simulations and helps pave the ways for a wide acceptance of metadata in earthquake engineering.

Acknowledgement The first author acknowledges financial support from the Program for Young Excellent Talents in Tongji University and Kwang-Hua Fund for College of Civil Engineering, Tongji University.

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References

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[1] J.P. Bardet, F. Liu, and N. Mokarram. A metadata model for collaborative experiments and simulations in earthquake engineering, Frontier of Architecture and Civil Enging in China, 4(2) (2010), 133-153 [2] J.P. Bardet, J. Peng, K. Law, and J. Swift, Overview of the NEES data/metadata model, in Proceedings, 13th World Conference in Earthquake Engineering, Vancouver, Canada (2004) , 4001. [3] DOM, Document Object Model (DOM) Technical Reports, W3C, 2004. http://w3.org/DOM/DOMTR [4] Gennari, J., Musen, M.A., Fergerson, R.W., Grosso, W.E., Crubézy, M., Eriksson, H., Noy, N.F., and Tu, S.W., The Evolution of Protégé: An Environment for Knowledge-Based Systems Development, Technical Report SMI-2002-0943, Stanford Medical Informatics, Stanford University, 2002. http://www.smi.stanford.edu/pubs/SMI_Reports/SMI-2002-0943.pdf [5] J. Peng, and K. Law, Reference NEESgrid Data Model, report TR-2004-40, NCSA, University of Illinois at Urbana-Champaign, 2004. http://www.neesgrid.org/documentation [6] Protégé, The Protégé Project, Stanford University, 2004. http://protege.stanford.edu [7] T.R. Rothenfluh, J. Gennari, H. Eriksson, A.R. Puerta, S.W. Tu, and M. A. Musen, Reusable ontologies, knowledge-acquisition tools, and performance systems: PROTÉGÉ-II solutions to Sisyphus-2, International Journal of Human-Computer Studies, 44(1996), 303-332.

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Study on the Integration of Digitalization and Numerical Analysis Based on the Digital Underground Space and Engineering H.H ZHU a,1 and X.X LI b Department of Geotechnical Engineering, Tongji University, Shanghai, China b R&D Department, Shanghai Tunnel Engineering and Rail Transit Design & Research Institute, Shanghai, China a

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Abstract. With the development of information technology in geotechnical engineeringˈUnderground Space and Engineering System (DUSES), a digital product was introduced. It is a managerial platform, which can provide information sharing and analysis for the construction, the management, the operation and the maintenance of the underground engineering. Its main functions include 3D modeling, visualization, data query, spatial analysis and so on. However, the supreme deficiency of DUSES lies in the fact that it cannot be introduced into numerical analysis to guide engineering practice. So it is necessary for the underground engineering, the resource sharing of the strata information and the numerical analysis to realize the integration of DUSES and numerical analysis. The paper describes an extension for DUSES that enables to perform finite element analysis by integration. Firstly, the integration mode of the system is researched and a combined type (embedded and loose coupling) is presented. That is to say, the numerical modeling is embedded into the function of DUSES and the modeling module is exploited; the calculation and the result feedback are performed with the loose data conversion module. Secondly, the contents of the integration are discussed in detail from the aspects of function, data and technique. The general framework of implementation is established. Finally, to illustrate the applicability of the proposed method, one example is provided. The results show that the integration of digitalization and numerical analysis can extend the professional analysis functions of DUSES. By employing the integration technique, mechanical analysis in partial region can be optionally implemented and the project design can be optimized. For the numerical analysis system, the automatic modeling can be realized by extracting the data from database. The efficiency of preprocessor and the accuracy of the calculation results can also be largely improved. The proposed technique will give some useful references for the continuous research on this subject. Keywords. Digital underground space and engineering, digitalization of underground engineering, numerical analysis, finite element method, integration

1

H.H Zhu: Department of Geotechnical Engineering, Tongji University, No.1239 Si-ping Road, Shanghai, 200092, China, [email protected]. Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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Introduction With the development of information technology, the digitalized technology is introduced into geotechnical engineering and a new research subject is exploited. Gore (1998) called for an ambitious global undertaking to build a multi-faceted system for education and research called Digital Earth[1]. An European Union EUREKA project CITYGRID was initiated in 2004 and its main objective is to develop a precise mapping of whole cities in form of digital city-models[2]. Marte Gutierrez et al. in Virginia Tech (2003) propose a research project AMADEUS (Adaptive Real-Time Geologic Mapping, Analysis and Design of Underground Space) which exploits new IT technologies such as digital imaging, data management, visualization and computation to improve analysis, design and construction of underground excavations in rock[3]. An European “Technology Innovation in Underground Construction (TUNCONSTRUCT)” research project that promotes the development and implementation of technological innovation in underground construction started from 2005[4]. Myung Sagong et al. in Korea Railroad Research Institute (2006) developed a digital tunnel face mapping system (DiTFAMS) using PDA and wireless Network[5]. There are also many other digital efforts reported to be relating to underground, such as digital strata, 3D digital ground information management system, digital mine and underground spatial information system[6-8]. During the exploitation of underground space and the whole life cycle of underground engineering, an enormous amount of data is gathered. Although a vast amount of data and knowledge exists, such information cannot be easily retrieved, especially in remote sites. To solve these problems, DUSES (Digital Underground Space and Engineering) is proposed by Zhuhehua (2007)[9]. The DUSES is a managerial platform which can provide information sharing and analysis for the construction, the management, the operation and the maintenance of the underground engineering. That is, it is a digital museum of full lifecycle of underground engineering. Its main functions include 3D modeling, visualization, data query, and spatial analysis and so on. It can make current underground space and engineering data into digital format, and summarize these data into useful information. The ultimate goal of this effort is aimed at providing users with DUSES within the full service of underground engineering. The information visualization of engineering geology is an important research subject in geosciences. AQGthe geology simulation has the powerful function of 3D geological modeling. However, the 3D geological model only can provide the function of visualization, and it can not implement the numerical analysis. Considering the characteristics of the geologic model and the numerical model, a new modeling method of numerical analysis is proposed and the integration of digitalization and numerical analysis is implemented. In this method, the geologic model can be transformed into numerical analysis model, and its data can also be directly introduced. The proposed technology will give some useful references to the continuous research on this subject.

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1. Integration of Digitalization and Numerical Analysis 1.1. Basic Conception In the paper, the study of the integration of digitalization and numerical analysis is called IDNA. The IDNA based on the DUSES is the integration of DUSES and numerical analysis. In this study, the DUSES platform is used to the new founded software platform, the IDNA technique is the main way, two techniques of DUSES and numerical analysis is integrated by a certain integration mode. The aim is that respective advantages are represented. To the DUSES, its function of professional analysis is extended. To the numerical analysis system, the pre-processing efficiency and the result accuracy are improved. In general, inter-operation of two systems is realized by integration.

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1.2. Integration Mode The system integrated level and operational efficiency is influenced by the construction of integration system. The different sorting modes of integration are introduced in large documents[10-12]. At present, there are four types used in system integration extensively: loosening integration, embedded integration, dynamic-link integration and tightening integration. By comparison these four modes, the paper presents a composite system mode to realize the IDNA based the DUSES. This composite mode combines the loosening integration and the embedded integration at the same time. The detailed procedure of composite integration includes: • In the platform of DUSES, the pre-processing modeling of numerical analysis system is re-exploited using the embedded integration; • In the numerical analysis system, the numerical calculation is completed using the loosening integration by the transfer of data file; • In the platform of DUSES, the modular of data transformation is exploited. The calculation results in the numerical analysis system are imported into the database of DUSES using the loosening integration. The integration mode of IDNA is showed in Figure 1.

Figure 1. Integration mode of IDNA

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1.3. Integration Contents The integration contents of IDNA include the function integration, the data integration and the technique integration. • Function integration The function integration is the aim of integration. It decides the having functions and the development trend of integration system. In the paper, it is that the function of FEM numerical analysis is realized in the DUSES platform. The preprocessing, numerical calculation and post-processing of numerical analysis are included. The composite system mode is applied. • Data integration The data integration is the basis of integration. In the guide of function integration, it presents the information needed by organize function modular through the data manager service layer. To implement the resource sharing, the space data and the attribution data of the DUSES and the geometry data, material attribution data, result data of the numerical analysis system are managed uniformly by the mode of data file or database. • Technique integration The technique integration is the core of integration. It decides the whole performance and the advantage of the integration system in the basic of data integration. In this paper, it integrates the modeling technology of two systems. In order to get the uniform model, the digitalization geologic model in DUSES is transformed into the numerical calculation model used to precede the FEM analysis by the Geologic Model Transforming Method[12]. 1.4. Process flow

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To the operational process of engineering model numerical analysis in DUSES, it is different from the operational process directly in the numerical analysis system. The process flow of IDNA is showed in Figure 2.

Figure 2. Process flow of IDNA

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• Building the 3D geologic model Using the space borehole database established by the fact geological information in DUSES, the 3D geologic model of investigation subject can be built by applying for the geologic modeling technique. • Element mesh modeling The established 3D geologic model can be transformed into the element mesh model needed by the technique integration. • Assigning attribution data By the data integration, the attribution data needed in calculation can be extracted from the borehole database and are assigned to the elements, such as the material parameters. At the same time, the modeling is finished. • Importing model into the numerical analysis system By the data transfer modular, the data file of the established numerical model can be formed. And it is also imported into the numerical analysis system subsequently. • FEM calculation According to the requirement of the FEM analysis, the calculation condition is selected and the whole calculation is begun. • Outputting calculation results When the calculation is completed, the calculation results are outputted according to the certain data format from the numerical analysis system. • Saving results into the database By the data transforming modular, the results are saved into the result database of the DUSES. At the same time, it can be saved into a data file. • Displaying the results The results of numerical analysis are displayed in the DUSES platform.

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2. Implementation of IDNA Due to the inter-dependence of Digital Underground Space and Engineering System in the facts of data manage, 3D modeling, visualization, space analysis and professional application, it is very difficult to operate the secondly development in 3D drawing software or GIS software[10]. If it is developed from the bottom layer by the secondly development tools, such as programming language, visualization, GIS or virtual reality, the spare time will be long. However, it can satisfy the function need, and it has better expansion. In this paper, the Windows software acts as the basic platform to implement the DUSES. Depended on the ACCESS database, Visual C++ and OpenGL, it is developed from the bottom layer by using component technique. Its system interface is showed in Figure 3.

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Figure 3. Platform of DUSES

3. Application To illustrate the application of the IDNA, it is successfully used to a foundation pit engineering in Shanghai. 3.1. 3D Geologic Modeling An underground transformer substation in shanghai of four-layers cylinder structure is adopted. The bracing structure is constructed by the frame-shear wall structure. The underground continuous wall of which the thickness and height are 1.2m and 57.5m, respectively, is introduced into the foundation pit enclosure structure. During the process of geologic modeling, the number of boreholes and SPT holes are 26(Fig.4), and the type of strata is 13. The 3D geologic surface model which is constructed by the Delaunay algorithm is shown in Figure 5. 



 

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Figure 4. Distribution of boreholes

Figure 5. 3D stratum surface model of transformer substation

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3.2. Numerical Modeling Because the initial boreholes only lie around the underground transformer substation, the geologic model should be expanded to meet the demands of FEM. The surface model of whole extension region is shown in Fig.6. The underground continuous wall is the key structure during the excavation of foundation pit. According to the message of segment of underground continuous wall, the meshing are automatically performed by the MTR method and the MSR method respectively (Fig.7).

Figure 6. Surface model of whole extension region

(a) Finite element mesh

(b) Excavation region

(c) Continuous concrete wall

Figure 7. FEM model

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3.3. Results The 3D finite element model generated with the GMTM is introduced into MARC to simulate the excavation of foundation pit. A nonassociated elastic–plastic soil model with the Druck-Prager failure criterion was adopted for the soil and the material in the shearing zone. When the excavation is finished, the plastic zone induced by excavation is shown in Fig.8. Fig.9 shows the distribution of the principal stress.

Figure 8. Plastic zone induced by excavation

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 (a) The maximum principal stress

 (b) The minimum principal stress

Figure 9. Distribution of the principal stress of continuous concrete wall

4. Conclusions This paper has demonstrated the feasibility of the integration of digitalization and numerical analysis based on the digital underground space and engineering. The approach is particularly attractive for numerical analysis which adopts the excellence of digital technology. For DUSES, the module of numerical analysis is introduced and the function of DUSES is expended. In this proposed method, the numerical modeling can be implemented based on the database of visualization, which improves the modeling efficiency and the accuracy of the results.. Then, aiming at different calculation region, the technology of dynamic extraction can be adopted and the repeated modeling can be avoided. The proposed technology will give some useful references to the continuous research on this subject.

References

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[1]

Gore A. The Digital Earth: Understanding our planet in the 21st century [OL]. http://www.digitalearth.gov/VP19980131.html. 1998. [2] CITYGRID Project [OL]. http:// www.citygrid.at. 2004. [3] AMADUES Project [OL]. http://amadeus.cee.vt.edu. 2003. [4] TUNCONSTRUCT Project [OL]. http://www.tunconstruct.org. 2005. [5] Sagong Myung, Jun S. Lee, Kwangho You, et al.(2006). Digitalized tunnel face mapping system (DiTFAMS) using PDA and wireless network. Safety in the Underground Space - Proceedings of the ITA-AITES 2006 World Tunnel Congress and 32nd ITA General Assembly. Seoul, Korea. 22-27 (April 2006). Edited by In-Mo Lee, Chungsik Yoo and Kwang-Ho You. [6] Zhu Hehua. From digital earth to digital stratum—a new idea in the development of geotechnical engineering. Geotechnical Engineering World, 1(12) (1998), 15–17. [7] Wu Lixin Yin Zuoru and Deng Zhiyi㧘et al. Research on the mine in the 21st century㧦 digital mine. Journal of China Coal Society, 25(4) (2000), 337̄342. [8] Zhou Cuiying, Chen Heng and Huang Xianyi 㧘 et al. Developing trends of underground spatial information system in major projects, Journal of Sun Yat-sen(Zhongshan) University(Natural Science), 43(4) (2004), 28̄32. [9] Zhu Hehua, Li Xiaojun. Digital underground space and engineering. Chinese Journal of Rock Mechanics and Engineering, 26(11) (2007), 2277-2288 [10] X.X. Li. Study on key techniques on the integration of digital underground space and engineering (DUSE) and numerical nanlysis, Ph.D Dissertation, Shanghai: Tongji University, 2008. [11] X.X. Li, H.H. Zhu and Y.L. Lin. Geologic model transforming method (GMTM) for numerical analysis modeling in geotechnical engineering. Geotechnical aspects of underground construction in soft ground (2008), 791-797. [12] X.X. Li, H.H. Zhu, Y.C. Cai, X.J. Li. An automatic modeling method of numerical analysis in geotechnical engineering based on 3D geologic model, Chinese Journal of Geotechnical Engineering, 2008, 30(6), 855-862.

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Information Systems and Application

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A Design and Construction Database for Cut-and-cover Tunnel Maintenance Using 3D Models Takashi ARUGA a,1, Yasushi ARAI, Ph.D. b, Hideya KAMACHI c and Keiji OISHI d a Conport Co., Ltd. 3-8-3,Hino-honmachi,Hino-shi,Tokyo,Japan [email protected] b Railway Technical Research Institute, 2-8-38, Hikari-cho, Kokubunji-shi, Tokyo c JR Souken Information System Co.,Ltd., 1-7-23, Kita, Kunitachi-shi, Tokyo d Tokyo metro Co., Ltd., 3-19-6, Higashi-ueno, Taito-ku,Tokyo

Abstract. Maintenance and management have become increasingly important. In particular, underground structures in urban areas such as subway tunnels require suitable maintenance. It is vital to manage specific information for maintenance and management to ensure permanent use. In this study, we focused on cut-andcover tunnel structures. We searched wide-ranging reference materials for maintenance and management, and developed an information management system using 3D models and a database. Keywords. 3D model, Cut-and-cover tunnel, maintenance, database

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Introduction In recent years, development of infrastructure in Japan has almost been completed. Many businesses are shifting their focus from new construction to maintenance of their existing facilities. For suitable maintenance and management, it is essential to identify time-course changes in structures by using reference materials (e.g. design calculation sheets and design drawings) made at the time of completion as initial values. In the case of shield tunnels, Sugimoto et al [1] have summarized the tunnel quality and the relationship between loads during construction and durability. With regard to cut-andcover tunnels, Tanabe et al [2] have explored the consistency between actual on-site conditions and the theory when cracks are caused by external forces, and Morohashi et al [1] have examined how the theory supports the data when cracks are attributable to materials. In short, their view point is that cracks are a determining factor in tunnel quality and durability. In order to maintain the tunnel structures by using those findings, it is important to understand the process of defect development based on original reference materials made during the construction stage. This study focused on the cut-and-cover tunnel as typified by subway station. Firstly, we searched wide-ranging reference materials that are required for maintenance 1 Corresponding Author: Takashi ARUGA, Conport Co., Ltd. 3-8-3,Hino-honmachi,Hino-shi,Tokyo,Japan; [email protected]

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and management made during the design and construction stage. Secondly, we developed an information management system using 3D models and a database to use the reference materials efficiently. Thirdly, the applicability of this approach was confirmed in actual cut-and-cover tunnel work.

1. Information for Maintenance Work

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1.1. Necessity of Information for Maintenance Work Most cut-and-cover tunnels currently in service in Japan were constructed during the years of high economic growth from the 1950s to the 1960s. Some of these structures have deteriorated in recent years. However, in urban areas, these tunnels are being used for subway lines or trunk roads, and they are hard to replace or reconstruct. The administrators of those structures make every effort to use their structures as long as possible. At the same time, they are required to be accountable to society in explaining the safety structures and facilities in the case of disaster. The grasp of the current state of the structures and the state at their time of completion are indispensable for those requirements. However, the state of the structures cannot be fully ascertained, due to the contexts of the years of high economic growth: many construction methods were employed, and records related to inevitable defect in construction were not saved. Unfortunately, this problem also remains in the present, as information technology has progressed. A wide range and variety of reference materials are prepared to ensure smooth progress and problem solving in design, construction, and other stages. Generally, they are stored as a record of the construction work once the structure is completed. Therefore, they are seldom used for maintenance and management purposes although it is well known that a grasp of the initial condition of the structure is needed [4]. The vast amount of stored reference materials cannot be narrowed down to the reference materials that are pertinent to maintenance and management use, as all of them are basically needed to maintain the structure. This results necessarily in large amounts of reference materials to be handled, making their control troublesome. In this context, we proposed the storing of a selected minimum amount of required reference materials that will contribute to maintenance. 1.2. Definition of Maintenance Information We defined information related to maintenance as Maintenance Information. Figure 1. shows a conceptual view of maintenance information. Maintenance information is classified into Structure Information and Defect Information. Structure information refers to information on design and construction as initial condition. Defect information refers to information on defects that are attributed to time-course changes as present condition. The actual reference materials are normally Design Document and Construction Document for the structure information and Inspection Document for the defect information.

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Maintenance Information

Structure information

313

Design Document Construction Document

Defect Information

Inspection Document

Figure 1. Conceptual view of maintenance information

1.3. Selection of Maintenance Information We selected the reference materials that can retrieve the maintenance information based on the research of Tanabe [2] and Morohashi [3]. Table 1 shows selected reference materials. These were selected on the assumption that all were prepared in the design and construction stages. The design documents provide the specifications of the structure concerned, shape and dimensions. The construction documents provide the construction method used for the structure concerned, including actual information on materials, strength, and environmental conditions. The inspection document provides the state of structure at the present. A crack development diagram is particularly important in the inspection documents. Additionally, it is preferable that openings on the wall or slab, job division boundaries, concrete joints, marks of intermediate piles, marks of forms, and inducing joints are also represented on the crack development diagram [3]. Table 1. List of reference materials Item

Type of reference materials Design specification, Construction specification.

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General view of site, General view of structure, Bar arrangement drawing, Design document

Design calculation sheet General view of trench timbering, Plan view of piling Geological profile, Geological survey report Completion drawing Concrete placement report, Layout of Crack inducing joint, Construction photograph

Construction document Construction plan, Report of concrete mix design, Report of test of compressive strength of concrete, Material catalog Groundwater level observation record Inspection document

Crack development view, Inspection record

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2. 3D model 2.1. Selection of 3D CAD Software 3D models are seldom used for maintenance and management in Japan. This may be attributed to the fact that 3D CAD software still involves a high introduction cost and a considerable period of time to master it. In particular, maintenance and management services, in which many company are involved, may commonly use software (twodimensional CAD). Cost considerations can deter these enterprises from aggressively introducing expensive 3D CAD software. In this study, the 3D model was positioned as a communication tool for engineers to communicate and served as an interface for the maintenance information. In this context, we attempted to use Google SketchUp [5] (hereinafter, SketchUp), which is a free and inexpensive 3D CAD software provided by Google Inc. 2.2. Features of Cut-and-cover Tunnel A cut-and-cover method is used to build structures by excavation from the ground surface to specified locations while installing an earth retaining system. This method is often employed in subway construction. During excavation, earth-retaining walls are built to prevent collapse of wall surfaces and inflow of groundwater. Most of these walls are left surrounding the main structure after completion. The main structure is mostly made of reinforced concrete, and the body is divided into multiple blocks in which concrete is placed according to a planned sequence. In the light of the features of the cut-and-cover method, a 3D model was created in parts such as the main structure, earth-retaining walls, ground materials, and including defects such as cracks, and assembled to use them as a package. The main structure was composed of multiple blocks.

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2.3. Definition of Model Elements In this study, we defined the 3D model as two separate elements, the physical element that could be represented physically, and a spatial structure that was an assembly of physical elements prepared independently from each other. Figure 2 shows the composition of the 3D model. Physical elements were those expressed specifically by edges and surfaces on this model, indicating the Stratum, Slab, Wall, Beam, Column, and Defects. The spatial structure is the element expressed by the assembly of physical elements, indicating the Ground, Main structure, Placement block, placement lot. SketchUp was used to create ͆Edges͇ and ͆Surfaces͇ representing the physical elements, and ͆Component Instances͇ were used to present the spatial structure. Defects include such aspects as cracks, water leakage and deformation. In this study, cracks were chosen as typical examples in the 3D model on the basis of the consideration that identifying cracks is crucial in the maintenance of cut-and-cover tunnels. Information on crack occurrence timing and other defects are assigned as attribute data. For SketchUp, it was decided to use layers to represent the time-series. In addition, these definitions are provided in the light of the process of defect development, and used for the creation of the 3D model for maintenance and management.

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Figure 2. Conceptual view of the 3D model

3. External Database Reference materials must always be prevented from being scattered and lost. In addition, they must be controlled in a state ready for rapid searches to obtain necessary information. For this purpose, we stored information that was retrieved from reference materials and used reference materials themselves in a database. The database was developed in distinction from SketchUp in consideration of the future extendibility. Information and reference materials were classified according to the contents, intended use, or the way of screen display. Storage of information in the database was made in the following formats as shown in Table 2; Text, PDF, JPG, and Others. Database tables were prepared corresponding to each category. The database was developed by Microsoft Access.

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Table 2. Example of storage formats and corresponding reference materials Storage format

Corresponding reference material

Display form

Text

Concrete placement report, Inspection record, Groundwater level observation record

Spreadsheet

Design specifications, Construction specifications, Design calculation sheet, Geology survey report, Construction plan, Construction plan, Report of concrete mix design, Report of test of compressive strength of concrete, Material catalog

Adobe Reader [7]

PDF

JPG

Construction photograph

Image

Others

General view of site, General view of structure, Bar arrangement drawing, Geological profile, General view of trench timbering, Plan view of piling, Layout of Crack inducing joint,

Original application

(included in link program)

4. Development of Link Program SketchUp does not have a function that links models and an external database in the default configuration. Therefore, we developed a new program (hereinafter, Link Program) that can link those using the extended functions of SketchUp. Major functions of the Link Program are to link those, to retrieve information from the database, and to display information on the screen. The link program was programmed

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by using Microsoft Visual Basic and Ruby [6]. Ruby is an object-oriented programming language; it is supported in SketchUp for user extensions. Figure 3 shows a conceptual view of the composition of the link program. Ruby scripts provide operation command and generate GUID (Globally Unique Identifier) in SketchUp. The Link program functions to link SketchUp and the external database and shows information on the screen. The external database functions to store the maintenance information. The method of displaying information is as follows: firstly, select one of the 3D models that require information. Secondly, right-click on the model and choose the command to display information from context menu. Then, information that links to the selected model will be displayed on the screen. The display form is changed depending on the storage format of reference material. The relations between the storage format and display format are shown in table 2.

Figure 3. Conceptual view of link program

5. Confirmation of New Method 5.1. Objected Structure We confirmed the applicability of the method provided in this study in a subway site under construction. The objected structure was a reinforced concrete box tunnel of a two-layer single-span and two-layer two-span structure, having a length of 52 meters. The concrete surface condition of inner surfaces in the objected area can also be observed, as the area will not be provided with advertising displays even after the subway is open for service.

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5.2. Collection of Maintenance Information We searched design and construction documents as reference materials for maintenance information, with the support of a business proprietor and construction companies. As a result, the reference materials as shown in Table 1 could be collected within a relatively short period, since most design and construction documents have been made using personal computers in recent years. The reference materials were stored into the database according to a classification described in chapter 3. In addition, immediately after completion of the structure, we conducted an inspection similar to the initial overall inspection, identifying the location of job section boundaries, concrete joints, and marks of intermediate piles. 5.3. Creation of 3D Model

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Figure 4. 3D model

Figure 5. Interior view provided by 3D model

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We created a 3D model using the design drawings according to the model definition shown in figure 2. Figure 4 shows the 3D model of the objected structure. Figure 5 shows an example of representing 3D cracks in the main structure.

5.4. Summary In regards to collecting reference materials for maintenance information, they could be collected and rearranged from design and construction documents that were prepared in the design or construction stages. The result indicates that all of the necessary reference materials could be collected from documents prepared in design and construction stages. It was also confirmed that plotting of cracks inside the main substructure could be done in a manner similar to that conventionally required to prepare a crack development view at completion of the work.

6. Conclusions

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x

All maintenance information proved to be rapidly and logically collectable and controllable from design and construction documents that were normally prepared. x A connection can be easily drawn between the main structure with a complicated shape and a condition of defect development using the 3D model. x Relating the model with the database containing the maintenance information made it possible to access this information from the model by generating a link program. Extended SketchUp functions and the program language Ruby were used to obtain this. This scheme allows all businesses to use 3D models for their maintenance work. Introductory prices of this system are very low. However, its functions are similar to expensive software with equivalent functions. This can be expected to provide many engineers with an opportunity to use 3D CAD software at a reduced cost, and may result in the use of the 3D model as an influential tool for control of information in maintenance and management. It is concluded that information control using a 3D model as a platform is efficient and rational in order to provide a precise explanation of facility safety in the event of an accident or disaster. Future tasks include training engineers to handle this type of 3D model, and setting a new production rate for preparing 3D models, something not previously expected.

Acknowledgements We would like to express our deepest gratitude for those concerned who were kind enough to cooperate in in-situ surveys and in the presentation of reference materials.

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Reference

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[1] Review Group on the Load during Shield Tunneling, Technical Subcommittee, Tunnel Engineering Committee, Japan Society of Civil Engineers; Load during Shield Tunneling (Tunnel Library No.1 [7], 2006 (in Japanese) [2] M. Tanabe; K. Oishi, T. Yamamoto, K. Oishi, T. Yamamoto, M. Honma and S. Matsukawa: Considerations on the Shape and Dimensions and Load Conditions and Flexural Cracks Occurring in Tunnels; Tunnel Engineering Report Collection, 16 (2006), 455-460. [3] Y. Morohashi, K. Ishikawa, S, Sezutsu, Y. Arai and T. Aruga; A Consideration on Material Cracks in Cut-and-cover Tunnels, Tunnel Engineering Report Collection, 17 (2007), 349-354. (in Japanese) [4] Railway Technical Research Institute: Standard for Management and maintenance of Railway Structures, and Explanation (Tunnel), 2007.1. (in Japanese) [5] http://SketchUp.google.com (accessed May 3rd) [6] http://www.ruby-lang.org/ja (accessed May 3rd) [7] http://www.adobe.com (accessed May 3rd)

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A Knowledge Base System to Support Emergency Response of Geo-hazards under the conditions of Extreme Snow and Ice Disasters a

Haifeng HUANG a, 1 and Shimei WANG a Key Laboratory of Geological Hazards on Three Gorges Reservoir Area, Ministry of Education, China Three Gorges University, Yichang 443002, Hubei, P R China

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Abstract. Rapid and effective emergency response of geo-hazards is an important means to reduce disaster losses. Geo-hazards under the conditions of extreme snow and ice disasters are more complicated than conventional geological hazards. Therefore there’s urgent need to build a knowledge base system for emergency response on geo-hazards under the conditions of extreme snow and ice disasters (KBS-ERG/ESID). Based on the analysis of specificity of geo-hazards and emergency response under the conditions of extreme snow and ice disasters and in accordance with the targets of knowledge base system, we put forward and elaborate a layered system architecture of KBS-ERG/ESID, which includes knowledge base layer, core control layer and graphical user interface layer. Then, some geographic information system (GIS) components are introduced into the system architecture due to the needs of displaying and analyzing spatial data when necessary. Finally, the operation steps of KBS-ERG/ESID are introduced by showing some critical system interfaces; meanwhile the effectiveness of the KBSERG/ESID is demonstrated. Keywords. Knowledge base system, emergency response, geo-hazards, snow and ice disasters, geographical information system

Introduction With the change of globe climate, more and more extreme weather disasters and secondary disasters such as flood, geological disasters and so on occurs, for example, in early 2008, an extreme snow and ice disaster occurred in southern China, which was lasted for nearly a month, affected 19 provinces (autonomous region, municipality) and 100 million people[1]; besides, the geological disasters triggered by extreme snow and ice disaster brought serious damage to people's lives and property safety, slope engineering, power transmission project, and so on. As an important means to reduce disaster losses, rapid and effective emergency response of geo-hazards is based on lots of qualitative and complex professional experience and knowledge, so there’s urgently need to build a knowledge base system

1 Haifeng HUANG: College of Civil Engineering and Architecture, China Three Gorges University, Yichang 443002, Hubei, PR China; E-mail: [email protected]

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for emergency response of geo-hazards under the conditions of extreme snow and ice disasters (KBS-ERG/ESID) to assist decision-makers in dealing with the emergency.

1. Specificity of Geo-Hazards and Emergency Response under the Conditions of Extreme Snow and Ice Disasters As we all know, rainfall is the major triggering factor in conventional geo-hazards. In fact, snow and ice can also be a trigger of geo-hazards because the influencing mechanism of water comes from melted snow and ice for rock and soil is similar to the water from rainfall. Furthermore, the choice of emergency response routes and measures would be severely affected and restricted because snow and ice disaster might disrupt traffic, electricity and communication facilities in surrounding areas of geohazard. Emergency rescue decision-makers need to consider fully the specificity of geohazards and emergency response under the conditions of extreme snow and ice disasters mentioned above, before making more accurate judgment and adopting more targeted measures, the same is true in the design and development of KBS-ERG/ESID.

2. Software System Architecture of KBS-ERG/ESID In order to solve large number of semi-structured and unstructured problems about geohazards emergency response, variety of knowledge include rules, causal relationship, experience of decision-makers and so on should be stored in KBS-ERG/ESID, and more important, the system must provide decision support strategies and advices of emergency response measures according to different condition combinations by some reasoning methods.

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2.1. A Layered System Architecture

Desktop Application

Knowledge Representation

Network Browser

GUI LAYER

Inference Engine CC LAYER

Knowledge Management

Incident identification knowledge

Impact estimation knowledge

Emergency actions knowledge

KB LAYER

Figure 1. The layered system architecture of KBS-ERG/ESID.

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The KBS-ERG/ESID is a complex integrated software system, which involves in many functional units and software technologies, in order to outline its components and their mutual relationship, a logical layered system architecture is described in detail, and this architecture enumerates all the logical functional components to ensure the achievement of the system targets adequately while it ignores the details of final construction. The system architecture of KBS-ERG/ESID consists of 3 layers which are knowledge base layer, core control layer and graphical user interface layer, shown as figure 1.

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2.1.1. Knowledge Base Layer The knowledge base layer (KB layer) is mainly used for storage knowledge. According to the characteristics, content and process of the emergency response of geo-hazards under the conditions of extreme snow and ice disasters, the knowledge can be divided into three parts in KB layer: incident identification knowledge, impact estimation knowledge and emergency actions knowledge [2]. (1) Incident identification knowledge It saves and provides knowledge about identifying the basic properties of geological disasters, for example, the judgments “if [landside mass thickness] < 10m then it’s a shallow landslide” or “if [landslide volume] < 100,000m3 then it’s a small landslide”[3] are incident identification knowledge. The knowledge can help decision maker to grasp the basic situation of geo-hazard on first time, so as to formulate pointed emergency response plan quickly. (2) Impact estimation knowledge It is critical in make a decision for emergency response, it consists of two aspects: on the one hand is situation estimation knowledge such as loss assessment, risk assessment, for example, the judgment “If [death toll] Ӌ 30 or [direct economic loss] Ӌ 10 million RMB then it’s an oversized geo-hazard”; the other hand is development trend prediction knowledge of geo-hazard such as stability evaluation, vulnerability assessment and so on. In addition, impact estimation knowledge must also include some evaluation knowledge of local relevant factors which mainly are weather conditions, traffic, communication, electricity supply, etc. under the conditions of extreme snow and ice disaster. (3) Emergency actions knowledge It is based on incident identification and impact estimation and used to generate proposals of decisions to be taken in the following aspects: assignment of response task, technical control, and road network management. 2.1.2. Core Control Layer The core control layer (CC layer) is the running central of KBS-ERG/ESID, it achieves three major functions: (1) Knowledge management It has dual role, one is to manage and maintain the knowledge base that includes add, modify, delete and check knowledge, the other is as a bridge and intermediary between user interface, knowledge base and inference machine during the course from inputting parameters to outputting conclusions, in order to ensure the normal operation of reasoning process.

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(2) Knowledge representation It is to study how to express the knowledge and expertise in related fields as computer software operation modes effectively, which is one of the key technologies of KBS-ERG/ESID design. Production rule method is one of the simplest of knowledge representation methods[4,5], the method represents knowledge using the sentence structure of “if-then”: the part of “if” represents reason, status, conditions and the part of “then” represents action, result, conclusions. The examples in the description about the knowledge base layer above have proved the usage of production rule. (3) Inference engine It is a core functional component that responsible for the normal operation of the reasoning process from getting specific characteristics of geo-hazard under conditions of snow and ice disaster to providing the strategies and advices of emergency response measures. The KBS-ERG/ESID uses forward reasoning control strategy (Fig.2), its process is as follows[6]: first, users provide a group of initial data and put these data into a dynamic database; then, inference engine search all match knowledge in knowledge base to establish a match knowledge congregation according to the existing knowledge of dynamic database; next, the inference engine choose a knowledge for reasoning in the knowledge congregation and save the new knowledge to the dynamic database; at last , if there is conclusion available in the dynamic database, the inference engine can output the conclusion and then end the process, otherwise, continue the reasoning process. Start

Obtain initial data to establish an initial dynamic database

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Whether the problem is solved ? Y

Y

Output conclusion

N End

Can add new evidence or facts ? N No solution

End

N

If there is a new opening knowledge for reasoning ? Y Reasoning using the knowledge

Whether the conflict occurs ?

Y

Continue reasoning using conflict resolution strategies

N Save the new reasoning facts to dynamic database

Figure 2. The forward reasoning schema of KBS-ERG/ESID.

At the same time, the KBS-ERG/ESID uses conflict resolution strategies which to activate according to the priority level, it includes as follows:

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x

x

When storing knowledge, users set a priority level for each knowledge according to some principles[7], such as fact knowledge in preference to rule knowledge, fine granularity of knowledge in preference to coarse granularity of knowledge, and so on. When conflict occurring, the system would automatically select the highestlevel rule to continue reasoning. If there are two or more same priority level knowledge rules, the system would continue reasoning after providing user interface and accepting user manual settings.

2.1.3. Graphics User Interface Layer The graphics user interface layer (GUI layer) provides interactive tools between users and the KBS-ERG/ESID system, which not only receives the input of initial parameters, but also displays the output of emergency response conclusions. The graphic user interface can possibly be a desktop application in client/server (C/S) architecture, or be a network browser (internet explorer, firefox, etc.) in browser/server (B/S) architecture, or be a variety of user interfaces in hybrid software architecture.

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2.2. System Operation Process There are two aspects of system operation process because of two main functions in KBS-ERG/ESID: (1) Establishment and maintenance process of knowledge base Users give a command of add/modify/delete knowledge by GUI; the knowledge management components convert the new additional or modificatory knowledge in accordance with the knowledge representation method, and then to check the consistency, redundancy and integrity of knowledge; if passed the check, save the knowledge to corresponding knowledge base, or else passed the failure signal to GUI, and GUI show the failure information to users. As for the knowledge that need to be deleted, knowledge management components would delete the knowledge directly after received the command of confirm by GUI. (2) Reasoning process Users input initial parameters of geo-hazard under conditions of snow and ice disaster by GUI; then inference engine start to work according to the reasoning process of Fig.2 assisted by the knowledge management components and knowledge representation module; until getting the final conclusions of emergency response or no conclusion, the inference engine return to GUI and then output the conclusions.

3. GIS Components of KBS-ERG/ESID In some cases, analysis and display of spatial data is necessary for emergency response of geo-hazard under conditions of snow and ice disaster, for example, when decision maker need to select the route of emergency rescue or transferring the affected people, they must find the answer through network analysis on the basis of fully understand some spatial information in the disaster area such as road map, extreme weather map, etc., so geographic information system (GIS) technology that deal with spatial information specifically should be introduced into the knowledge base system.

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However, the KBS-ERG/ESID is not a pure GIS software system and its functional emphasis lies on knowledge base and inference engine, we introduced the GIS components into the system by loosely coupled means [8]. According to system’s functions and the layered architecture, some GIS components were introduced into different system layers, shown as figure 3. In GUI layer, there are map control, output control, toolbar control and table of contents (TOC) control, and these controls are responsible for display and output of map data in GIS. Toolbar Control TOC Control

Map Control

GUI LAYER

Output Control

Display/Output spatial data Statistics Analysis Component Network Analysis Component

CC LAYER

Spatial Analysis Component Analyse/Modeling spatial data Spatial Database Engine Component

DB LAYER

Store/Manage spatial data

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Figure 3. The GIS components distribution in KBS-ERG/ESID’s layered architecture.

In CC layer, some GIS components which included spatial analysis, network analysis and statistics analysis are introduced in order to analyze and modeling with spatial data to solve the spatial decision-making problems such as route selection. And in database layer (note: not the KB layer, because knowledge base is only a logical structure, database is the physical structure of saving all data include knowledge), there is a spatial database engine component to store and manage spatial data in RDBMS.

4. Using the System We developed KBS-ERG/ESID with the layered C/S architecture, the design of inference engine and the coupled GIS components technology. In the following, we illustrate how the system is used by showing some critical system interfaces. The first step is to input the initial parameters about geo-hazard information through a GUI (Fig.4). Mentioned before, these parameters can be divided into: (1) Incident identification parameters: type, properties, scale, slip mass thickness and more other details of geo-hazard. (2) Impact estimation parameters: x Losses: victims, damaged housing area, property and other affected facilities such as highway, communication base station and so on;

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x

Prediction: stability evaluation, vulnerability assessment and more other details; x Local relevant factors: weather, traffic, communication, electricity supply and more other details. The geo-hazard basic information can be saved as a file and then can also be load into the parameters input interface to display and be modified. The next step concerns reasoning according to the geo-hazard parameters which was input or load in first step. Under normal circumstances, the reasoning process carried out automatically under the control of inference engine, but sometimes there

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Figure 4. The interface of parameters input.

needs manual intervention, for example, when reasoning conflict occurs and all conflictive knowledge have same priority level, the reasoning process would be suspended until the users set the knowledge manually by GUI. At last, the emergency response advices would be show and could be saved after the ending of the reasoning process (Fig.5). In addition, if some geographical information about the geo-hazard such as local road map, weather map, etc., or emergency rescue road map by spatial and network analysis was saved in system, users can view the data through the “route selection and spatial information display” button in figure 5. Figure 6 is the interface of route selection advice. In this example, when the landslide disasters occurred, users should choose red route A (S312) in normal state for reasons of distance, however, if route A had disrupted, the system would analyze other available routes automatically by means of GIS components, as a result, green route B (S334) was recommended.

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Figure 5. The interface of emergency response advices. Figure 6. The interface of route selection advice.

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5. Conclusions The occurrence probability of extreme snow and ice disasters and the corresponding geo-hazards increased rapidly, especially in southern China, it is necessary and urgent to study and establish the KBS-ERG/ESID. In this paper, we put forward a layered knowledge base system architecture and introduce some GIS components into the system. The KBS-ERG/ESID can meet the basic needs of the geo-hazard emergency response, but a lot of theories, methods and techniques need to be further in-depth study, at least include as follows: (1) The build and improvement of knowledge base. Knowledge base stored the experience of experts, it’s impossible to build a perfect knowledge base at the beginning and it’s necessary to go through a gradual improvement process that there’s growing number of cases in practice, moreover, the knowledge base is different in accordance with different application region, geo-hazard type, etc. So, as long as the system is running, the build and improvement of knowledge base will not stop, nor can we guarantee the vitality of the system. (2) The reasoning strategy of inference engine. The forward reasoning control strategy that used in this paper is the simplest method in all kinds of inference mechanism, in fact, we need to introduce more reasoning control strategies such as backward reasoning, two-way reasoning, inductive reasoning, deductive reasoning and so on for KBS-ERG/ESID in order to achieve more accurate inference. (3) The relationship of knowledge base system and GIS. In this paper, we introduced the GIS components into the system by loosely coupled means, which can meet the needs of spatial analysis, but at the same time it brings inconvenience to users because knowledge base software technology and GIS technology did not integrate more closely, perhaps spatial decision support system[9] technology is a good way to solve the problems.

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References

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[1] P. Liu, G. Liu, Y. Lian, et al. The elementary analysis of an infrequent low temperature, rain and snow, frost, disaster weather event during the last ten-day of January to the beginning of February in 2008 in China, Journal of Jilin University (Earth Science Edition), 38(2008), 193–195. (in Chinese with English abstract). [2] Z. Josefa. Hernandez, M. Juan. Serrano, Knowledge-based models for emergency management systems, Expert Systems with Applications, 20 (2001), 173–186. [3] The Ministry of Land and Resources P.R.C, Specification of design and construction for landslide stabilization (DZ/T 0219-2006), Standards Press of China, Beijing, 2006. (in Chinese). [4] M.D. Crossland, B.E. Wynne, W.C. Perkins, Spatial decision support systems: An overview of technology and a test of efficacy. Decision Support Systems, 14 (1995), 219–235. [5] S.L. Yang, W.Z. Fu, J. Dong, Research on the incremental knowledge base system in intelligent decision support system, Journal of Hefei University of Technology, 27 (2004), 339–343. (in Chinese with English abstract). [6] M. Cui, Design of inference engine kernel in expert system, China's high-tech enterprises 22 (2008), 298–299. (in Chinese). [7] J. Ma, J.C. Zen, A strategy of conflict resolution for knowledge reasoning based on Rough theory, Mathematics in practice and theory, 37 (2007), 66–72. (in Chinese with English abstract) . [8] Iftikhar U. Sikder, Knowledge-based spatial decision support systems: An assessment of environmental adaptability of crops, Expert Systems with Applications, 36 (2009), 5341–5347. [9] Luc Jouneau and Alexia Stokes, Development of a Decision Support System for Managing Unstable Terrain: Calculating the Landslide Risk of Slopes, Disaster Mitigation of Debris Flows, Slope Failures and Landslides, 2 (2006), 543–552.

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A New Information System for Underground Construction Projects Klaus CHMELINA a,1 Geodata Ziviltechnikergesellschaft mbH Hütteldorferstraße 85, A-1150 Vienna, Austria

Abstract. The contribution introduces the tunnel information system Kronos. It are described its objectives, architecture and its special functions for the analysis and visualisation of tunnel project data. The system description is followed by information on currently running large European metro sites such as Budapest and Thessaloniki where the system is installed and particular functions and services are used such as alarming and reporting. Keywords. Tunnelling, monitoring, alarming, reporting, information system

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Introduction When an underground construction like, for example, a railway tunnel is designed, constructed and finally maintained, huge amounts of data are produced; and the amounts increase with every new project. The data stem from various different disciplines and are acquired, collected and produced in different ways, by different systems and experts and at different times and places. Unfortunately, a data platform or data standard able to store and exchange all these data does not yet exist and the use of standards that would be applicable for at least some of the data is more an exception than a rule in practice. Thus, the data of a tunnel project, until now, are not integrated but stored in arbitrary files and databases (if digitally available at all) of numerous software applications from different vendors. Data exchange between applications is a complicated and often annoying procedure (if possible at all) and means dealing with various proprietary and incompatible data formats. From a data management point of view the situation is chaotic and dissatisfying. As a consequence, the responsible engineers on site quite often have to handle the redundancy, incompatibility, inconsistency, non-actuality, non-availability etc. and even the loss of important project data. And after a project is finished, data are normally lost forever. To overcome these problems and to satisfy the growing demand for a more comfortable data access that should be possible from everywhere, at all time, quickly and at a click of a button, the tunnel information system KRONOS has been developed. In the following chapters the system is going to be described together with implementations done at two currently running European tunnel sites.

1

Corresponding Author. Klaus CHMELINA, Geodata Ziviltechnikergesellschaft mbH, Hütteldorferstraße 85, A-1150 Vienna, Austria, [email protected], www.geodata.com Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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1. Kronos System Description 1.1. System Objectives Fundamental parts of the tunnel information system Kronos have been developed in the course of the European research project Tunconstruct [1] between 2005 and 2009. In this project, the initial objectives have been to develop a system able to: x x x x x x x x x

store and integrate all relevant data of an underground construction project in a single powerful storage platform, integrate the data of all project phases - from design to service, provide authorized users a comfortable 24-hour, reliable and secure data access, allow for a local and remote multi-user access, support computer networks (intranet and internet), allow for data exchange by support of relevant data formats and standards, allow for all relevant data operations and manipulations through easy-to-use graphic user interfaces, provide advanced functions for data analysis and visualisation and provide advanced automatic services such as alarming, reporting, data import/export, online sensor control etc.

After Tunconstruct, the system has been continuously further improved and extended. These further developments have been driven by feedback from Kronos users that work with the system at tunnel sites already day by day.

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1.2. System Architecture Due to the dominance and wide use in practice, Kronos has been based on the database technology of the MS SQL Server, the commercial relational model database server produced by Microsoft. The developed Kronos data model has been designed to cover all relevant data produced for an underground construction project. Figure 1 depicts the currently covered data categories.

Figure 1. Kronos data categories and software components.

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To communicate with the Kronos DBMS (Database Management System) two user interfaces have been developed: x x

a local client application (Kronos Client, see fig. 2) and a web application (Kronos Web, see fig. 3).

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The Kronos Client is a database application software installed on the local PC allowing a user to connect to one or more (remote or local) Kronos database servers hosting Kronos databases (typically via VPN-connection). The Kronos Client can be used to perform all data management operations (i.e. to query, manipulate and input/output data in various data formats), to perform project-specific data analyses, to produce problem-oriented visualisations and to configure and control automatic functions and services vital for underground construction such as monitoring, alarming and reporting.

Figure 2. Kronos Client main window displaying a project map from where underground construction data can be selected and accessed interactively like in GIS software.

Kronos Web users (fig. 3) only need a commercial web browser and internet access. For performance reasons, Kronos Web does not offer the full range of functions of the Kronos Client but important ones such as the selection and up-/download of data and the production of reports and visualisations. All digital data not (yet) directly covered by the Kronos data model can be stored and managed in an own document management system (see column “Docs” in fig. 1) that is fully integrated in Kronos. Such documents are treated as binary large objects that can be linked with any other database entities (e.g. images of bore logs with the further data of the bore logs). This concept allows the data model to be further extended flexibly and at the time needed. More detailed information on the system architecture can be found in [2], [3] and [4].

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Figure 3. Kronos Web displaying a dialogue for the management of monitoring data (e.g. inclinometer measurements).

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1.3. Analysis and Visualisation Features Beside standard functions, the Kronos Client also provides some especially useful features in order to analyse and visualise the integrated tunnel data. In what follows, two such features, the 3D ground model [2] and the Virtual Reality visualisation system [5], [6] are described. 1.3.1. 3D Ground Model In the course of geological investigation for a planned tunnel, various data gathered by various investigation methods have to be combined into a final geological/geotechnical model. For this purpose, a unique 3D ground model has been developed and integrated in Kronos. The model consists of three components: 1.

2.

a geometry model (CAD model, see fig. 4) that can be created by use of commercial 3D modeling software (e.g. by using Autodesk® Civil3D). In the model the 3D geometry of lithological units (represented as 3D solids, see arrow nr. 1 in fig. 5), discontinuities (represented as 3D faces) etc. can be defined together with the geometry of the planned tunnel, the geological and geomechanical data that is associated with each entity of the above geometry model. The data include lithological, mineralogical, rockand discontinuity properties that are classified in accordance to the ISRM/EN

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3.

333

ISO norms and represented as Kronos database records (see arrow nr. 2 in fig. 5) and a link between a specific entity of the geometry model and the corresponding Kronos database records (see arrow nr. 3 in fig. 5).

Once established, the 3D ground model can be flexibly used for mutual queries, the automatic generation of section plots, the generation of voxel models for geostatistical analyses etc. as required by tunnel design specialists.

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Figure 4. 3D ground model (CAD model) with tunnel crossing different lithological units.

Figure 5. Linking and mutually querying geometrical and geological data of the 3D ground model.

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1.3.2. Virtual Reality Visualisation System The Virtual Reality visualisation system allows rendering and animating tunnel data within a virtual tunnel environment. In this way data analysis and interpretation is supported through impressive and highly realistic 3D stereo visualisation techniques. The system basically consists of hard- and software components allowing to: x x x

define and extract a Kronos data set that a user wishes to analyse, generate a VR tunnel geometry model for this data set and display the defined data set within the VR tunnel geometry model (for the visualisations can be used standard PC monitors up to professional 3D projection equipment).

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The visualisations, for example, support the geotechnical interpretation of tunnel deformations occurring during tunnel excavation. The ongoing deformation process can be animated interactively and studied in time and space together with the ongoing excavation progress (and further data if desired) which helps to detect critical and abnormal deformation behaviour. Figure 6 shows the example of a VR scene.

Figure 6. VR scene showing a 3D tunnel model generated from laserscan data, measured deformation vectors (over-scaled) and deformation vectors processed by numerical simulation (blue) for comparison.

2. Kronos Implementations at Tunnel Projects 2.1. Budapest Metro Line 4 (Hungary) The currently constructed new line M4 of the Budapest Metro has a length of 7,2 km and will have 10 new metro stations. The excavation of the two parallel running tunnels is done with two tunnel boring machines. Difficult geological conditions, the underpassing of historical buildings and the crossing of the river Danube with low overburden are big challenges for the engineers making necessary the execution of an

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extensive geotechnical monitoring program. Therefore, more than 10.000 monitoring points (levelling marks, geodetic prisms) and sensors (inclinometers, rod extensometers, tilt meters etc.) have been installed to cover the city area influenced by the tunnel project. Kronos has been installed to manage the monitoring data and, especially, to provide an automatic alarm system. The monitoring data are transferred periodically via file-upload from Budapest (Hungary) to the Geodata office in Graz (Austria), where the database server is run. There, automatically, all incoming data files are qualitychecked and their contents imported into the database. The Kronos alarm system consists of and permanently executes 260 alarm rules of 3 alarm levels (warning, alert, alarm) and, in case, immediately sends alarm messages (via SMS and/or e-mail) to about 30 different alarm receivers. The alarm service in this project is based on a server hosting concept where data is no longer stored locally on site but transferred over hundreds of kilometres (even across a state border) to a remote, sub-contracted data management centre. The whole system is easily maintained by one system administrator who manages the alarms via the Kronos Client (see fig. 7). From March 2006 to March 2010 the Kronos database has grown to 10 GB of size storing more than 7.000.000 monitoring measurements (sensor readings).

Figure 7. Kronos Client displaying the actual alarm status of the tunnel project.

2.2. Thessaloniki Metro (Greece) The currently built Thessaloniki metro network comprises 13 modern center platform stations and 9,5 km of line (with two independent single track tunnels) constructed mostly (7,7 km) by means of two tunnel boring machines. The remaining section of the line is constructed by the cut and cover method.

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In the project, Kronos has been installed to serve as the Geomechanical and Structural Monitoring Database (GSMDB). It manages the data of about 10.000 monitoring sensors installed on site (about 20 different sensor types, some of them online connected to the database), the data of surface structures (e.g. 590 buildings), geological data (e.g. 370 bore logs), the data of two tunnel boring machines (each producing 390 parameters every 10 seconds) etc. and serves as a document management system meanwhile containing more than 350.000 documents. In addition, an alarming and a reporting service are integrated. The reporting service automatically creates predefined alarm and monitoring reports and provides them to the experts on site. With this service it is no longer necessary to search for and put together all data needed for interpretation manually, the data is extracted from the database and delivered by the system automatically. On site, more than 30 users (experts from the client and contractor) work with Kronos regularly. Figure 8 shows the output of a frequently used system function that online-visualises surface settlements occurring during tunnel excavation.

Figure 8. Kronos Client displaying settlements caused by TBM excavation on the city map of Thessaloniki.

3. Conclusion The paper presents a tunnel information system that has been developed to solve the current data management problems in underground construction. The particular objectives have been to save and integrate tunnel project data over the whole life-cycle of the construction and to make these data easier accessible, exchangeable and usable. To meet these goals, the system has been based on latest database technology and on software applications specially tailored for underground construction. It has been used in several large tunnel projects where it proved to successfully contribute to a safe and economic tunnelling. From the lessons learned in these projects, the system has been further improved and optimized. Its features and services have made it a valuable and efficient IT-tool for underground construction projects.

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References

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[1] Homepage of the EU-project Tunconstruct, URL: www.tunconstruct.org, last visited June 2010. [2] K. Chmelina, UCIS – Underground Construction Information System, Technology Innovation in Underground Construction, Gernot Beer (ed.), CRC Press, Taylor and Francis Group, 2010, 9-30. [3] K. Chmelina, Integrated Tunnel Monitoring and Surveying Supported by an Information System, in: Proceedings of the ITA–AITES World Tunnel Congress, Budapest, Hungary, May 23-28, 2009. [4] K. Rabensteiner, G. Krusche, G. Hesina, Integrated Tunnelling Data Management, Analysis and Visualization - New IT Tools for Better Projects, in: Proceedings of the ITA–AITES World Tunnel Congress, Vancouver, Canada, May 17-21, 2010. [5] K. Chmelina, A virtual reality visualisation system for underground construction, Technology Innovation in Underground Construction, Gernot Beer (ed.), CRC Press, Taylor and Francis Group, 2010, 51-61. [6] K. Chmelina, A Virtual Reality System for the Visualisation of Underground Construction Data, in: Proceedings of the II Int. Conference on Computational Methods in Tunnelling, Euro:Tun 2009, Bochum, Germany, Sept. 9-11, 2009.

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A system for remote monitoring information management and risk control in the underground engineering Xiaodong LONG a,1, Rong WANG a, Bo CHEN a and Nan WU a a Shanghai Telsafe Engineering Technology Co.. Ltd

Abstract: A remote supervisory control system for subway construction is introduced. The system provides a way for all kinds of information, such as the geologic, design, construction, environmental information, the monitoring point and monitoring data of projects, to be managed intergratedly and shared with the project parties. The key function of the system is the embedded early-warning mechanism and emergence commanding, which alert management personnel to analyze the potential risk reflected by abnormal data and give the corresponding measures and emergence response plans when necessary. Furthermore, a new mode of project safety management in subway construction is established based on the system. With the application of this system, engineering accidents have been reduced obviously, which proves that it is a powerful tool for engineering risk control during the project construction. Keywords: Remote supervisory control system, subway construction, risk control, early-warning mechanism, emergence response plan

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Introduction Rail transit construction has been developed rapidly since the first subway of Shanghai prepared to be constructed in 1983[1]. Right now, subway construction market in china has become the largest one in the world. However, subway construction is underground engineering with significant risks. One due to unflavored factors themselves, such as the complexity of geological conditions, the accuracy of the survey, the limitation of design theory, the influence of urban environment, etc; the other due to the contradiction between the lack of technical or management personnel who has ability, knowledge, experience and the plenty of projects started simultaneously or poor quality of construction teams. Furthermore, the monitoring data, the main means to control the engineering risk, can’t be used effectively and timely because of the limitation of objective condition. So, the situation of safety is very severe during the period of project construction, especially there are many engineering accidents happened at home and abroad in the past decades. Hence, how to make all kinds of resources optimized integrated and brought favorable factors into full play to ensure the construction of rail transit carried out smoothly, is the problem need most utterly solved. The remote supervisory control system, which combines the network technique 1

Xiaodong Long: Master, Shanghai Telsafe Engineering Technology Co.. Ltd, Kong Jiang Road 1555#, Yangpu, Shanghai, China. E-mail: [email protected]. Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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and database technique based on the engineering practice and theoretical study, is aim to solve the safety problem during the period of construction via the integration of all kinds of sources. It makes the engineering management electronization; informationization, networking and centralization come true; the whole operation process monitored realized and the management level of rail transit improved effectively. The system used in the Shanghai, Wuhan, Hangzhou, Shenzhen, Chongqing and Ningbo proves that the system is a powerful guarantee tool for the safety problem of subway construction.

1. Function of system Remote supervisory control system is a set of dynamic safety management system in underground engineering based on the network technology and database technology. It aims to solve the safety problem of subway construction engineering, which has the characteristics of project scattered, project information content large, project risk high. The functions of the system can be realized as follow: •

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•

•

Management of project information. The system can automatically manage and store the electronic documents of survey report, design proposal, construction organization schemes, monitoring alternatives, project risk prevention, etc to ensure the information completeness of project. Also, the related shared material can be linked through the project informative attributes or key words and then queried, downloaded and printed via the system. So, the system can improve the efficiency of management and reduce the costs of management. Data gather, store, track and analysis. The monitoring data can be inputted into the system in two ways: automatic acquisition or manual entry. The system provides the standardized data entry pattern to avoid the huge data transfer if the data is inputted by manual entry. The dynamic information of projects, such as monitoring data and construction schedule etc, will be overall gathered and analyzed timely through the auxiliary professional data analysis program embedded in the system. So, the system, provides powerful supports for professional staff to analyze data with project background, ensures the analysis work carry out efficiently. Also, the historical monitoring data and construction schedules can be stored by the system and offered to engineering technicians to query or researchers to study if necessary. Especially, the researching finding comes from the case histories can guide the engineering practice effectively and the combination of study and practice is an effect way to ensure the safety during the subway construction [2]. Early-warning mechanism. Early-warning mechanism, which is the key function of the system, is embedded in the system. If the abnormal monitoring data is found during the data check by the system automatically, the system will inform the related professional staff with the short message service (SMS) timely. Also, the system has a function of performance appraisal to ensure the related staff to deal with the early-warning events which are caused by abnormal data in time. Furthermore, if the early-warning events caused by abnormal data don’t handle timely, they will upgrade to be the alarm events.

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•

•

•

•

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•

Video function. The video subsystem consists of two parts: video monitoring and video conferencing. Video monitoring part can provides powerful supervision on site construction and help the manager to master the field situation of project through the videos install on the field. If necessary, the manager can take a picture as a historical data stored in the computer via video in the office. Video conference can overcome the restriction that the technicians’ and specialists’ source distributed in different area can’t be integrated. It can optimize the human resource and improve the management efficiency. Safety evaluation. The related staff, based on the construction information and monitoring data, needs to carry out the specified analysis, give out the evaluation result of project construction safety state and rational proposals with the perfect standardized safety evaluation mode provided by the system every day. Also, the evaluation results will be shown in the security management platform to ensure the manager can master the situation of projects accurately and timely. Furthermore, the related problems and solutions are allowed to be discussed and exchanged by relevant parties on the management platform Risk control. Risks are classified by category and stage and related risk assessment outcomes at each stage are all integrated in the system. The system will remind the binding person to check the related risks automatically and evaluate their performance to ensure the risk control measures are implemented effectively. The risk knowledge base embedded in the system give the detailed engineering risk at each stage and the accordingly dispose measures, similarly engineering accidents are attached to the system to avoid the engineering accidents happen. Check and supervision. The staff register results examine function is embedded in the system to ensure to system can run smoothly with the support of management principles. Online office. The system, the organic integration of the above-mentioned function, is a strong network office platform. Manager can master the situation of project via the manager platform at any place where there is available network. Besides, the system gives the user good feeling of convenient use by providing with friendly user interface.

2. Management mode of system The risks of subway construction are caused by the factors as follow: • • •

The financial loss and social impact is huge if a project accident is happened, but most of the subway projects are constructed in the downtown of city and the neighboring environment of projects is complex. The geological condition in the field is complex and the survey results come form limit samples taken from borehole have great uncertainty. The mechanical behavior of engineering object is not understood clearly. Furthermore, the mechanical assumption of design theory is different with the mechanical behavior of practical object.

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• •

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•

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The construction quality is difficult to control, for it is affected by too many factors. It’s a time consuming and laborious task to find the key data from the huge monitoring data, for a number of projects started simultaneously. So, the construction projects can’t get the feedback from the monitoring data, not to mention guide the project construction. The manager can’t mask the overall situation, because the spatial span is high in different project and the information transfer is difficult. However, the cost of human resource is huge if every project needs a manager.

The scale of subway project is large and the projects are scattered, which means that it needs more engineering technical personnel and it will increase the cost of project. More important, there are no suitable measures to ensure that all technicians can control the engineering risk appropriately for their own deficiencies [2-4]. So, it is a challenge for traditional engineering management modes according to the characteristics of subway project. In order to solve the safety problem, the remote supervisory control system combines the network technique and database technique, based on the engineering practice and theoretical study, to provide an effective information exchange platform for parties in engineering. It makes the engineering management electronization, informationization, networking and centralization come true; makes use of finite resource effectively; decreases the construction cost of project and achieves the goal of control the engineering risk effectively. The management system based on the remote supervisory control system consists of command, middle management and field execution, the detailed relationship of parties is shown in Figure 1. In the course to concretely carry out the project, the detailed information, such as monitoring data, project schedule, of engineering construction is uploading to the system database with special software by construction organization, supervision units and monitoring units. Proper authorization verification embedded in the system ensure the safety of data; early-warning mechanism embedded in the system can check the monitoring data automatically and inform the person in duty via SMS to deal with the abnormal events timely if discovered; the staff in the monitoring center and mass transit railway group can query the project information, analyses the project status and issue the related news via the system. The staff in the monitoring center needs to carry out the professional analysis according to the related uploading project information; seeks out the potential safety hazard and then gives evaluation about the safety of project in the management platform for parties to query. So, most of the unsafe factors will be eliminated in the bud stage. The staff also needs to report to managers about the security situation of the whole engineering and give technical advises to ensure that the managers can make the right decisions. Furthermore, the specialists will give technical advises and processing measures to managers to ensure the safety of the project if there is a very dangerous case. So, the manager can master the situation of project and make the right decisions to guide the project construction via the system. The total data flow mode can be seen in Figure 2.

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Figure 1. Engineering management mode based on the remote supervisory control system

Figure2. Data flow mode based on the remote supervisory control system

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3. Implement effect of system The remote supervisory control system has improved with the development of Shanghai subway construction since the successful test run of in Shanghai metro line 4 lan-cun station in 2000. With the formulation and implementation of “Management Measures of Remote Monitoring and Management System for Excavation and Tunnel Engineering Construction of Shanghai Rail Transit” in 2005 and “Implementation Rules of Remote Monitoring and Management System for Excavation and Tunnel Engineering Construction of Shanghai Rail Transit” in 2006, the safety management of Shanghai subway construction entered into a new stage-network safety control stage[1]. Right now, the system has been applicated in many cities, such as Shanghai, Wuhan, Hangzhou, Shenzhen, Chongqing and Ningbo. According to incomplete statistics, the system has already monitored accumulatively 920 subway construction site until now and initiated 599 early-warning events with all kinds of dangerous situation timely. All the projects with remote supervisory control system applied have no major accidents occurred. The practice shows that the system can effectively control the project risk, improve the level of management and ensure project safety during the period of construction. Here an example is given to introduce the management effect of this system. The related proprietors of project went to site immediately after received the warning message and hold a meeting to communicate with affected parties and decide the corresponding measures local. First, construction unit must erect support timely and apply pre-stress on the support; monitoring unit improves the frequency of monitoring. Second, pouring bottom plate timely. After those measures carried out, the lateral deformation tended to be stable and the foundation pit and surrounding environment are safe. This process sufficiently illustrates that the remote monitoring system is a timely and effective tool to ensure project safety. A foundation pit of subway station entrance-exit excavated using bottom-up method was retained by the Soil-cement Mixed Wall (SMW) plus three levels of internal bracing struts and the section of steel struts is 609˜16. During the period of excavation monitoring, the system discovered abnormal data on many monitoring points on September 30, 2009.Especially, the daily variation of the wall lateral deformation at 10m depth, the monitoring point CX12-21, achieves the maximum of 17.18mm/d, as shown in Figure3. The system gives abnoraml event automatically and informs the related monitoring center staff via SMS. The staff also discovered that the step of a villa nearby the excavation generated a crack of 8mm width during the actual situation verified timely and finds that the very large deformation was caused by the support not erected timely after third soil layer was excavated. The staff initiates early-warning events on the platform and reforms the related managers.

4. Conclusion The remote supervisory control system, which is generated, developed and perfected from engineering practices, is a set of management system for safety control of subway construction. The results of application show that it can discover the risks timely and eliminate them in the bud stage; centralize management of many scattered projects; optimized integrate human’s resource as a whole. It realizes the engineering

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Figure3. Total and diary deformation of the displacement of retaining wall

management electronization; informationization, networking and centralization really, improves the management level and provides strong guarantee for the engineering safety of subway construction projects.

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References [1] C.M. Zhang, F.R. Luo. Technical management system of environmental safety risk for metro construction, Urban Rapid Rail Transit, 20(2) (2007), 63-65. [2] T.H. Bai, X.L. Bi, X.Z. Wang, Safety and risk control in Shanghai URT construction projects, Urban Rapid Rail Transit, 20(6)(2007), 24-28. [3] T. Liu, S.H. Wang, L.N. Sun. Remote monitoring management and risk control in railway transit construction, Chinese Journal of Underground Space and Engineering, 2(5)(2006), 796-799. [4] H.X. Wang, G.B. Liu. Implement remote monitoring and management and avoid risk in advance, Proceedings of shanghai urban rail transit construction, (2002), 482-488.

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An AJAX-based Web Application for Disseminating Site Characterization Data Fang LIU a,b,1, Yaping ZHOU a,b, Mingjing Jiang a,b Geotechnical Engineering Department, Tongji University, Shanghai 200092, China b Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University, Shanghai 200092, China

a

Abstract. AJAX (Asynchronous JavaScript and XML) technique enables asynchronous communication between browser clients and server-side system. It shows advantages in developing responsive, interactive, and customizable webbased applications. One of the most significant users of AJAX technology, Google employs the paradigm in its many recent innovations, e.g., Google Maps. This paper presents a web-based application using Google Maps API incorporated with a customized database for disseminating data on subsurface conditions at key strong motion station sites. The data to be distributed is capsulated in a relational database, and it consists of in-situ test and laboratory test data, which provides useful information for analyzing soil profiles and characterizing the sites. A web interface, coded in JavaScript and XML, bridges the database and web clients. It presents visible data on Google Maps interface, and enables remote users to query and retrieve data from the database. Keywords. AJAX technology, Google Maps API, web-based application

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Introduction The site characteristics of upper soil deposits are essential to civil engineers who design and construct civil infrastructures which rest on those deposits. Site characteristics are usually derived from geotechnical investigation reports based on geotechnical field tests and laboratory tests. This type of information is often overwhelming and comes from different agencies that present data in hard-copy reports using diversified formats. It is a big challenge to digitize such voluminous information, compile them into a seamless data central and share them across different platforms and applications. Geography Information System (GIS) and web-service techniques were proposed for disseminating such geotechnical data to massive potential users (Zimmermann et al. 2006). However the early approach relies on comprehensive knowledge of web service and sophisticated programming skills. Due to advancement of web mapping techniques, a few web mapping tools are gradually available to public and remove many barriers of the entry into the world of web mapping. For instance, Google Map API exposes nearly the entire interface of Google Maps to customization. Employing the technology of Asynchronous JavaScript and XML (AJAX), Google Maps Application Program Interface (API) offers a simplified solution to whoever is interested in presenting his/her own data on top of Google Maps interfaces. 1

Corresponding author: [email protected]

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This paper aims to demonstrate the usefulness of new AJAX technologies in developing responsive and light-weight web applications for the purpose of data dissemination through Internet. Herein presents an example of web application which incorporates AJAX technologies and database techniques using Google Maps API.

1. Emerging Technology: AJAX and Google Maps 1.1. What is AJAX

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AJAX is a standards-based programming technique designed to make web-based applications more responsive, interactive, and customizable. The key of AJAX is the asynchronous interaction between browser clients and web servers, which implies multiple requests can occur in parallel. Rather than the traditional web model of round trips to the server which reloads the entire page for each action, AJAX allows a “behind the scenes” interaction with the server to update portions of the page. As the AJAX’s heart, XMLHTTPRequest JavaScript object sends a request to a Hypertext Transport Protocol (HTTP) server and receives the response. Instead of having a button submit a form requiring a page refresh, any event handle supported by JavaScript can trigger a request over XMLHTTPRequest object to a server-side script, which processes the request and returns a mostly XML-formatted response. AJAX can dramatically reduce the network load of web applications, as it enables them to separate data from the graphical user interface used to display it. Traditional web applications deliver the results data as well as the HTML required to render the user interface, while AJAX application could deliver the user interface once and deliver data only after that. AJAX web pages can appear to load very quickly, since generally only small requests need to be sent to the server, and relatively short responses are sent back. This permits the development of highly interactive applications featuring more responsive user interfaces due to a combination of technologies such as Dynamic HyperText Markup Language (DHTML), Cascading Style Sheets (CSS), and Document Object Model (DOM) interactions. 1.2. Google Maps and its API AJAX technology has been around for several years but did not gain wide appeal until recently it began being used by Internet companies such as Google in web-mapping applications (Wusteman & O’hlceadha, 2006). Google Maps, first announced in February 2005, is a free web map server application and technology, which features a draggable and zoomable map that can be used to locate destinations and create driving directions. Technically Google Maps uses AJAX technology to achieve fast performance (Webber, 2005, Schutzberg, 2005ab). As the Google Maps code is almost entirely JavaScript and XML, some end-users reverse-engineered the tool and produced applications, called Google Maps mash-up, incorporating metadata, markers, and even their own images. In late June 2005, Google released Google Maps API, which exposed nearly the entire interface to customization and led to the explosion of map mash-ups (Berinstein, 2006). Google Maps offers a few new features over typical web-mapping applications. First of all, Google Maps enables fast performance of web-mapping system due to the implementation of AJAX technology. It also offers a quick solution for lightweight

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web-mapping system by removing the need of archival of base data in local server since Google Maps remotely offer aerial imagery and street data. Even though coding is still needed to achieve advanced functionalities, most scripts are based on JavaScript which is commonly used in web-based applications and supported by a huge knowledge base in the Internet. In addition to Google Maps, a few other Internet companies, such as Yahoo Maps, Mapquest, Microsoft Virtual Earth and ESRI ArcWeb, also releases their own APIs to public. It is hard to find the one superior to others in all aspects. However, due to relatively familiarity to Google Maps API, it is employed herein for the purposed of demonstration.

2. An example of data warehouse ROSRINE (Resolution Of Site Response Issues from the Northridge Earthquake) project is a collaborative research project aiming to improve engineering models for estimation of earthquake ground motions. The central component of this project is the collection, synthesis, and dissemination of high quality subsurface data obtained primarily from instrument sites that recorded strong shaking during the 1994 Northridge earthquake. A total of 60 sites have been characterized through site investigation (e.g., drilling tests, and shear wave velocity tests) and lab experiments (e.g. index properties test, and dynamic test). The collected data is tabulated in Table 1. A data warehouse (http://geoinfo.usc.edu/rosrine) was developed using web-based technology for archiving and disseminating RSORINE data in an efficient way.

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Table 1. Summary of geotechnical data disseminated by ROSRINE data warehouse Data Type

File Number

Format

Size (MB)

Boring logs

54

LogPlt Dat, JPG

15.3

Shear Wave Velocity Test

51

MS Excel, PDF

2.2

Index Prosperities Test

21

MS Excel

0.4

Dynamic Prosperities Test

8

MS Excel

2.2

Field Photos

74

JPG

9.8

Site Plans

8

JPG

0.5

Figure 1 illustrates major components making up an early web application designed for the data warehouse. The application consists of a Relational Database Management System (RDBMS) and Internet Map Server (IMS). The data files are stored physically in a file system of a web server. A relational database powered by Microsoft Access 97 carries the metadata of those files, detailing data location, data format, associated sites and investigation operators etc. A GIS dataset is developed in ArcView software, and it includes two portions: (1) base data (e.g., street maps and topographic maps) describing the geographical context of regions of interest; and (2) project-related data specific to the application being developed, e.g., a layer locating project sites. Available data can be queried and retrieved through two types of interfaces, i.e., text-based ASP-driven interface and interactive map-based interface. Although this application, evolved from conventional static HTML pages, has pioneered new ways for releasing geotechnical information over the Internet in the past,

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it confronts a few limitations. For instance, base data has to be loaded into the IMS system to support the project-related data, which leads to a heavy system. The insufficiency of base data in a format compatible to the system often hampers the employment of this approach. Format conversion is another difficult issue. In a case where few detailed geographic maps in digital version are available covering affected area, paper maps are often collected from various sources, scanned, stitched, digitized, and afterward imported into desktop GIS system for geo-referencing based on recognition of a few characteristic geographic features. More efficiency can be achieved if one can shorten the whole process for retrieving base data. Emerging new tools and technologies in web-mapping enable such a lightweight application and still remain or enhance the performance being responsive and fast. Client ……

Text-based query Internet

Interactive map query

ArcIMS

Query Engine Format convert Microsoft Access

Project-related data

Base data

URL Files Win NT Microsoft IIS local web

Figure 1. An example of DBMS-GIS-IMS system for data dissemination

3. An upgrade system

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3.1. System Architecture An improved web application (http://gees.usc.edu/ROSRINE) was developed for the ROSRINE data warehouse based on AJAX-related techniques. Figure 2 conceptualizes major components constituting the upgraded system. It consists of three components: (1) database backend; (2) Google Maps front-end; and (3) AJAX mechanism for message exchange. The application core is a database which works as a data engine and supplies project-related data including the location of the sites and geotechnical data available at those sites. A customized Google Maps interface interacts with clients and handles their requests. Upon users’ requests, data are retrieved from the database backend through AJAX requests and responses, and are ultimately presented in the front end that retrieves the secondary spatial data from remote Google servers.

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AJAX Response Front End: Google Maps Interface

Back End: Database System AJAX Request

Retrieve secondary spatial data to support mapping application

Provide project-related data, e.g., site location, geotechnical investigation data

Figure 2. Major components constituting an AJAX-driven system

Figure 3 illustrates the architecture of the AJAX-driven application. Microsoft Access database is replaced by PostgreSQL, a sophisticated open-source ObjectRelational DBMS with extensible features for spatial data. Comparing to the old application as shown in Figure 1, Google Maps API takes the place of IMS to provide web-mapping service. Base data are provided by remote servers via Google Maps API instead of local server. Project-related data are retrieved directly from the database without format conversion. PHP (Hypertext Preprocessor) pages are constructed to glue Google Maps API and customized dataset together to form an integrated system. Client browser ……

Internet PHP scripts

PostgreSQL

Google Maps

Base maps

……

Base maps

External data source

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Files Linux Apache local web server

Figure 3. System architecture integrating Google Map API

3.2. A customized database Figure 4 illustrates a relational data structure of the database which is composed of five entity tables (i.e., Site, Data, Reference, Organization, Station). Site is the key one that fusions the others. Site refers to a place located at a certain strong motion Station and characterized by Organizations involved in ROSRINE project. Data is collected from the Site and writing reports of site characterization are produced as Reference. Those tables relate to each other directly or indirectly. The relationship patterns (e.g., one-toone, one-to-many and many-to-many) are also indicated in the figure.

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Figure 4. Relation diagram of the database in use

Figure 5 details the communications that take place between web clients and the web server. PHP is employed to developed both client-side and server-side scripts. The web interface mainly deals with three types of request from the web client: (1) onload for initiating the Google Maps and loading markers of project sites; (2) filter for selecting project sites according to some certain search criterion; (3) download for retrieving available data collected from a selected site. Onload is triggered by refreshing web browser. A client-side PHP script, S1, captures the client request, and calls a server-side PHP script, S2, via HTTPXMLRequest. S2 communicates the database using Standard Query Language (SQL) and returns XML-formatted results containing a complete list of project sites to S1. S1 interprets the returned results into geographical features and places them on top of Google maps with tabbed information. The filter function is to select sites of interests. Because all available sites were loaded as JavaScript objects once the browser is refreshed, the filtering only take places in S1 without communicating the sever. A download request is triggered when a specific site is selected by users. The selected site carries a unique identifier, and the identifier is sent by S1 to another server-side PHP script, S3, where a query string is generated to retrieve a list of available data from the connected database. The data in a XML document is then transferred to S1. Afterwards S1 renders HTML pages for presenting query results for users to download data. Onload Flow Filter Flow Download Flow

Web Client

s

JavaScript

DHTML, CSS

DHML, CSS

r

Form,

n

Form,

HML, CSS

n

o HTTP

Client-side PHP Script (S1)

r XML

PHP Script (S3)

Valid API key

o HTTP

Data & JS Class

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3.3. AJAX-based scripting

PHP Script (S2)

p SQL PostgreSQL q Result

qResult p SQL

Apache Web Server

Google

Figure 5. Flow diagram of the communications between clients and server

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3.4. Web Interfaces

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Figure 6(left) shows a client web interface coded in S1. Users are presented with a Google map covering the full screen with a few 3D balloons locating the project sites. The map can be switched among views of traditional street map, satellite imagery and a combination of both (hybrid) through the three buttons on top. Navigation bar is placed on the left upper corner for moving map around or zooming in and out. An information window, initially hidden, is anchored to each 3D marker. Once a marker is clicked, the information window appears with a list of available data followed by a download button. When one clicks the download button, a pop-up window (see Figure 7) appears and provides general information of the selected site as well as entries to downloadable data. As shown in Figure 6(right), two semi-transparent panels, i.e., Map Filter and Search Result, stay on the right hand side of the screen. These two panels can be toggled off when a larger Google map is desired. As enlarged in Figure 7, the Map Filter panel in to narrow down the selection of project sites by specifying the site collaboration, project phase and/or data of interest. The Search Result panel uses DHTML and DOM technique to populate a list of project sites currently displayed on the map. Sites are categorized by different collaboration in this panel, which results in an expandable tree structure. Each leaf of the tree carries a hyperlink, which enable interaction with the Google Map.

Figure 6. Web interfaces of the upgraded data warehouse; an entry page (left) and a popup window upon request of data downloading

Figure 7. Control panels for filtering (left) and listing (right) the resulted project sites

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4. Conclusions AJAX technique enables asynchronous communication between browser clients and server-side system. It shows advantages in developing responsive, interactive, and customizable web-based applications. Google Maps employs AJAX technology and offer new tools for web-mapping application. This paper presented a web-based application using Google Maps API incorporated with a customized database for disseminating data on subsurface conditions at key strong motion station sites. The data to be distributed is capsulated in a relational database, and it consists of in-situ test data and laboratory test data, which provide useful information for analyzing soil profiles and characterizing the sites. A series of web interfaces coded in JavaScript and XML bridge the database and web clients. The application is capable of presenting visible data on Google Maps interface, and enables remote query and data retrieval. The present approach is an efficient alternative to typical GIS-driven web applications especially for the purpose to visualize and disseminate location-related data in a responsive and interactive manner with limited requirement of spatial analysis.

Acknowledgement The first author thanks Prof. Jean-Pierre Bardet for guiding her to a new research direction of information technology in geo-engineering. The support from the Program for Young Excellent Talents in Tongji University and Kwang-Hua Fund for College of Civil Engineering, Tongji University are also appreciated.

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References

[1]

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document,

Information Technology in Geo-Engineering D.G. Toll et al. (Eds.) IOS Press, 2010 © 2010 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-60750-617-1-353

353

Application of Asset Management Technique for Road Tunnel Maintenance Management Bo LIa,1 and Yujing JIANG b Faculty of Engineering, Nagasaki University, Japan b Faculty of Engineering, Nagasaki University, Japan a

Abstract. The number of tunnels in Japan is huge, since 60% of the land is covered by mountains. The maintenance of tunnels has become an increasingly important issue in Japan, where most of the tunnels have been used for more than 40 years. Maintenance helps a tunnel keep in healthy condition so as to have a long service life, which is beneficial for the public budget comparing to a new construction project. When and how to conduct maintenance to the tunnels synthetically taking into account the cost and the performance of tunnels require extensive studies. In this study, a road tunnel database is constructed by means of GIS utilizing the collected data for maintenance management of tunnels in Nagasaki prefecture, Japan. The approach of asset management is introduced into the database for accurately predicting the future conditions of tunnels and searching for the optimal maintenance management method. Using this system, evaluations of the tunnel performances and the maintenance cost for tunnels to reach the required performance level are conducted, which could be a helpful base for the future maintenance management.

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Keywords. Road tunnel, Asset management, GIS, Database, Performance, Cost

Introduction In Japan, subjected to the severe geographical and geological conditions, a lot of tunnels are constructed in mountain areas, some of which are located in the regions with poor ground conditions. These tunnels are supposed to have long service lives due to the high construction costs. However, the degradations of man-made components like lining as well as the ground like rock mass surrounding the tunnels take place as time passes, continuously decreasing the performance of the tunnels. The maintenance of tunnels has become an increasingly important issue in Japan since most of the tunnels have been used for more than 40 years. In this study, a road tunnel database is constructed by means of GIS utilizing the collected data for maintenance management of tunnels in Nagasaki prefecture, Japan. The approach of asset management is introduced into the database for accurately predicting the future performances of tunnels and searching for the optimal maintenance management method. Using this system, evaluations of the tunnel performances and the maintenance cost for tunnels to reach the required performance level are furthermore conducted. 1

Corresponding Author: Assistant Professor, Faculty of Engineering, Nagasaki University, Bunkyomachi 1-14, Nagasaki 852-8521, Japan. E-mail: [email protected]

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B. Li and Y. Jiang / Asset Management Technique for Road Tunnel Maintenance Management

1. Construction of Database A database comprising necessary information of tunnels is essential for the predictions of tunnel performances, which offers the decision-making information, facilitating the introduction of asset management technique. In this study, a database based on GIS was constructed for the evaluations of deformation and current state of the maintenance of tunnels. Fundamental information of tunnels such as tunnel name, length and completed year were firstly input into the database. More information in detail such as crack extension and healthy degree were then input into the database for each span of the tunnels. The digital data concerning the deformation state of each tunnel were linked with GIS to facilitate the evaluation of tunnel performance. Through a userdesigned retrieval item, the information retrieval and view can be easily conducted based on GIS.

2. Factors Involved in Asset Management 2.1. Performance Evaluation Spalling of lining concrete happened in a number of tunnels located in Nagasaki prefecture on the year 2000, therefore, a plan to carry out urgent survey on 100 railroad tunnels in Nagasaki prefecture was proposed. Until the year 2005, the surveys on 37 tunnels were finished, collected data from which were used as basic information for the database. Based on the survey data, present performance level of each tunnel (perfectly healthy 1.0 - perfectly weak 0.0) was estimated. The divisions of performance and safety degree are shown in Table 1. In this study, judgment of the most dangerous element (crack, spalling etc.) at each span based on the survey results was conducted, and the mean value of the judgment results of each span was used to estimate the performance level of a tunnel.

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Table 1. The divisions of performance and safety degree of tunnels. Performance level

Judgment division

Deformation

3A

Large deformation

2A A B S

Medium deformation Medium deformation Negligible deformation

Danger

Measures

Urgent measures are necessary Dangerous if no measure Immediate measures was conducted are necessary Schemed measures are Risk possibility exists necessary

0.5

Dangerous

No danger

Monitor is necessary

0.6 0.7 0.8 0.9

No problem in tunnel function 1.0

2.2. Future Degradation Prediction The degradation curve was assumed for each tunnel to predict their future degradations. The degradation of a tunnel is subject to several factors like natural ground condition, ground pressure change, weather change, and the degradation curves will be different for each tunnel depending on input factors. A former study has conducted deformation

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B. Li and Y. Jiang / Asset Management Technique for Road Tunnel Maintenance Management

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simulation taking into account the time-dependent behaviors of natural ground and lining strengths, obtained the degradation curves of the target tunnels (see Figure 1)[1]. These cases evaluate the relation between the performance level of a tunnel from perfect healthy 1.0 to some value like 0.5, 0.6 after degradation with past year (e.g. a case with performance level dropping to 0.6 costs 30 years). The survey result and one predicted degradation curve (Case 5 in Figure 1) are compared as shown in Figure 2. The degradation curve fits well with the survey result, which was chosen for the estimate of cost-effectiveness of tunnel maintenance by means of asset management.

Performance level

1 0.8 0.6 Case 1:T =50,1.0 Case 2:T =50,1.0 Case 3:T =50,1.0 Case 4:T =30,1.0 Case 5:T =30,1.0 Case 6:T =20,1.0

0.4 0.2 0

0

10

0.6 0.5 0.4 0.65 0.6 0.7

20 30 Past year, T (year)

40

50

Figure 1. Comparison of different degradation curves.

Performance level

1.0

Survey results Degradation curve

0.9 0.8 0.7 0.6 0.5

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0

10

20

30

Past year, T (year) Figure 2. Verification of the validity of the degradation curve (Case 5).

2.3. Decision of Priority Order When several tunnels connect the same route, it is not always correct to primarily take measures to the tunnel with inferior performance (largest degradation level), since another tunnel with better performance but larger importance (e.g. connect high ways) may have higher priority. If the tunnels connect multiple routes, not only individual performance level but also importance of the route should be considered to decide the priority in the maintenance list. At here, the importance of the route a tunnel passes was estimated taking into account the traffic volume and traffic capacity (i.e. road width). The weight for traffic volume is set to 0.7 and for traffic capacity is set to 0.3 as shown in Table 2, where each index was divided into 4 categories with maximum point of 100.

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B. Li and Y. Jiang / Asset Management Technique for Road Tunnel Maintenance Management

Table 2. Decision of the importance of tunnels. Weight

Traffic volume

0.7

Traffic capacity (Road width)

Item 1-5000 5001-10000 10001-15000 15000-4m 4m-6m 6m-12m 12m-

0.3

Direct repair cost Relaitive direct repair cost

Direct repair cost (Hundrad million Yen䠅

4

Evaluation point 50

25

35 Relative direct repair cost (Ten thousand Yen/m䠅

Importance index

30

3

25

2

20 15

Approximation

10

1

5 0

0 0

10

20 Past year

30

Figure 3. Evolution of the direct and relative direct repair costs.

Based on the healthy degree and the importance of a tunnel, the maintenance priority of a tunnel can be estimated by

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P䠙D1 P1  D 2 P2

(1)

where P 1 represents 100 minus the healthy degree of a tunnel, P 2 is the importance degree of a tunnel. The coefficient D i (i=1,2) is decided by the factors like ground condition, environmental condition. At here, D 1 is set to 0.6, D 2 is set to 0.4.

3. Evaluation using Asset Management Technique 3.1. Evaluation of Direct Repair Cost Repair is necessary for 27 tunnels among the total 37 tunnels based on the survey results, and the repair costs for them were analyzed. As shown in Figure 3, three items: past year, direct repair cost and relative direct repair cost are listed. The relative direct repair cost is defined as the direct repair cost divided by the length of the tunnel, which increases as time passes by. The cost could be reduced by inhibiting the deformation in the early stage after a tunnel is put into service. Based on the analyses of the repair cost, the relation of repair cost and performance was obtained as shown in Figure 4.

Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

1.0

60

0.8

50 40

0.6

Performance

0.4

Repair cost

30 20

0.2

10

0.0

0 0

10

20 30 Past year

40

357

Relative direct repair cost (Ten thousand Yen/m䠅

Performance level

B. Li and Y. Jiang / Asset Management Technique for Road Tunnel Maintenance Management

50

Figure 4. Evolution of performance and repair cost.

The evaluation condition was set as follows and the final repair cost was estimated for each case. x 31 tunnels in Nagasaki prefecture carried out investigation in detail are used as objectives. x The cost that used to improve the performance of tunnels is calculated based on the regression analysis result of the direct repair cost. x After repair, a tunnel is certainly improved to performance level 1.0.

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3.1.1. Reference Rank and Priority Order When spalling happens on tunnel lining, the cars passing through the tunnel could surfer damages if concrete blocks falling down, which needs to be considered in the repair reference rank. According to the reference rank and priority order, the long-term change of the direct repair cost by repairing 3 tunnels every year since 2005 is shown in Figure 5. Reference rank is defined according to the current degradation level (in 2005) of each tunnel, where the tunnel with largest degradation has the highest rank for repairing. The priority order is calculated from Equation (1), which synthetically considers the degradation and the importance of each tunnel. High performance and low cost were supposed to be obtained by primarily considering the degradation degree, since in general, a tunnel suffered severe degradation needs to be repaired as soon as possible. However, as shown in Figure 5, the long-term cost is lower by primarily considering the priority order, since in the priority order, high repair rank was estimated for some tunnels with long length and high repair cost but low degradation degree, which helped constrain the total cost. 3.1.2. Evaluation of Repair Time In the maintenance of public facilities, reducing the long-term cost is essential, and one important method is to carry out the repair work at the optimum time. Figure 6 shows the evaluation results that 3 tunnels are repaired every year from 2005 by improving their performance levels from 0.4, 0.5, 0.6, 0.7, 0.8 to 1.0, respectively. The results show that by carrying out the repair in early stage, high performance could be maintained and the long-term repair cost could be reduced. By doing so, however, the costs in the first 7 or 8 years could be very large. Comparing all the cases, it shows that if the repair work is carried out when the performance level of a tunnel drops to 0.7~0.8, stable high performance could be maintained and the final repair cost could be reduced.

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5

25

4

20 Reference rank 15 Priority order Reference rank䠄cumulation䠅 10 Priority oder䠄cumulation䠅 5

3 2 1 0

Cumulative repair cost (Hundrad million Yen䠅

B. Li and Y. Jiang / Asset Management Technique for Road Tunnel Maintenance Management

Repair cost 䠄Hundrad million Yen䠅

358

0 2005

2015

Year

2025

2035

50 40 30 0.4䊻1.0 0.5䊻1.0 0.6䊻1.0 0.7䊻1.0 0.8䊻1.0

20 10 0 2005

(a)

2025 Year

Mean performance level

Cumulative repair cost 䠄Hundrad million Yen䠅

Figure 5. Evolution of the reference rank and priority order.

2045

1.0 0.8 0.6 0.4 2005

(b)

0.4䊻1.0

0.5䊻1.0

0.6䊻1.0

0.7䊻1.0

0.8䊻1.0

2025 Year

2045

Cumulative repair cost 䠄Hundrad million Yen䠅

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Figure 6. The long-term changes of repair cost (a) and mean performance (b).

50 40 30 1 tunnel/year 2 tunnels/year 3 tunnels/year 4 tunnels/year 5 tunnels/year

20 10 0 2005

2015

2025

2035

2045

2055

Year Figure 7. Results by repairing different number of tunnels per year to smooth the budget.

3.1.3. Evaluation of the Budget Smoothing To make a cost-effective tunnel maintenance plan, smoothing the budget for maintenance is an important issue since the fiscal year budget is limited. Figure 7 shows the results of increasing the performance level of tunnels from 0.7 to 1.0 for 1, 2, 3, 4 or 5 tunnels every year, respectively, in which, the smoothest cost is obtained by repairing 1 tunnel every year. However, the long-term repair cost by repairing 1 tunnel every year becomes large since the repair for some tunnels with large degradation are delayed. The long-term repair costs become almost equivalently when 3㹼5 tunnels are

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repaired every year. Repairing 3 tunnels every year is most cost-effective from both aspects of reducing the long-term repair cost and keeping the budget smoothing. 3.2. Evaluation by Life Cycle Cost Life cycle cost of a tunnel comprises several factors such as construction cost, repair cost and maintenance cost. In the maintenance management, the following equation is used to calculate the life cycle cost of a tunnel: (Life cycle cost) = (Direct repair cost)+(Indirect repair cost)+(User loss cost) (2) A tunnel could keep healthy condition by frequently carrying out survey and repair, which however may bring enormous cost. The cost of survey is one of the indirect repair cost, which covers the costs relating to the repair of tunnel. In this study, the indirect repair cost was analyzed from data of 11 tunnels, the mean result of which was assumed to be the indirect repair cost for each tunnel in concern. Calculation of the cost originating from the traffic regulation by tunnel repair work was conducted. Taking into account (1) the time loss cost by stopping vehicles, and (2) the time loss cost by waiting the regulation of traffic, the user loss cost was defined as follows.

Cn

n˜

¦ A

m

˜ N m ˜ 'T

(3)

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m

where, m is the type of vehicles, n is the regulation days of traffic, A m is the time value unit of car type m, N m is the traffic volume of vehicle type m per day, 'T is the lost time per vehicle / total traffic regulation days. Since effective data for vehicle types were not collected in this study, all of the vehicles were assumed to be passenger cars. The lost time was set to 2 minutes depending on the data measured at a tunnel where repair is being carried out. The regulation days of traffic were set to 10 days referring to the collected information in the database. The time value unit was decided referring to the data provided by “Ministry of Land, Infrastructure, Transport and Tourism”, Japan as shown in Table 3[2]. The evaluation result of life cycle cost is shown in Figure 8. In the present evaluation, frequently carrying out repair of tunnels, the repair cost and the user loss cost would increase and finally cause the expansion of the long-term cost. If a tunnel is repaired when its performance level drops to 0.4, the long-term cost can be mostly reduced, which, however, could bring high risk since the performance level as low as 0.4 requires urgent measures. By synthetically considering the performance and cost, the cost-effectiveness is defined as the following equation.

䚷X

p50 ci cmax

(4)

p50 is the mean performance of a tunnel during 50 years of serve life, c i is the life cycle cost in case i, c max is the maximum value of life cycle cost. Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

B. Li and Y. Jiang / Asset Management Technique for Road Tunnel Maintenance Management

Cumulative cost (Hundrad million Yen)

360

80 60

0.4䊻1.0 0.6䊻1.0 0.8䊻1.0

0.5䊻1.0 0.7䊻1.0

40 20 0 2005

2015

2025 2035 Year

2045

2055

Figure 8. The long-term change of Life cycle cost.

Table 3. Calculation of the cost-effectiveness for the cases conducting repair at different performance levels. Performance level

0.4ĺ

0.5ĺ

0.6ĺ

0.7ĺ

0.8ĺ

Mean tunnel performance in 50 years

0.706

0.754

0.824

0.847

0.886

LCC (Hundred million Yen)

48.9

74.0

72.3

72.9

81.7

Cost-effectiveness

1.180

0.832

0.931

0.949

0.886

From this equation, the results of cost-effectiveness are shown in Table 3. Excepting the case that the performance of tunnel drops under 0.4, which is too risky to take, the case that repair is carried out when its performance drops to 0.7 has the highest cost-effectiveness. Therefore, considering the long-term performance and cost of tunnels, carrying out repair work when their performance levels drop to 0.7 is most effective.

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4. Conclusion In this study, a road tunnel database is constructed by means of GIS, and the approach of asset management is introduced into the database for predicting the performances of tunnels and searching for the optimal maintenance management method. Using this system, evaluations of the tunnel performances and the maintenance cost for tunnels to reach the required performance level are conducted. The results show that carrying out repair works for 3 tunnels every year when the performance level of tunnels drops to 0.7 is most cost-effective if the conditions of tunnels are similar to that described in this study. In the future, the effects of various repairing methods need to be introduced into the database to give a better evaluation of the cost-effectiveness of tunnel maintenance methods.

References [1] Y. Jiang, Y. Tanabashi and T. Akihito, Deformation prediction of tunnel and effect evaluation of reinforcing method by considering time dependency of rock strength. The scientific lecture association lecture outline collection, Ϫ-414 (2005), 825-826. [2] The maintenance handbook of the road tunnel. Japan road association, 1993.

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Deep Foundation Pit Construction Monitoring Information System Based on GIS a

Yuan WANG a,b,1, Songyu LIU a, Jianyong LIUb, Jing ZENG b and Lei GAO b Institute of Geotechnical Engineering, Southeast University, Nanjing, Jiangsu 210096,China b Engineering Institute of Engineering Corps, PLA University of Science and Technology, Nanjing 210007

Abstract. At present, the file management model of the deep and large foundation pit construction monitoring data has been unable to meet a large number of monitoring data storage and fast real-time analysis requirementsGeographical Information Systems (GIS) technology has been applied in the management of deep and large foundation pit construction monitoring information. The functions of information management system of construction monitoring to large foundation pit construction based on GIS was designed as followed: monitoring data entry, graphics, pit construction monitoring information query, charts automatically generated curves and deformation simulation, information analysis, monitoring, forecasting and so on. At the same time, based on the three major components, background management, graphical interface and the work of engineering, the basic architecture of the system is designed. This paper has set out a number of key technologies of the system and gives application examples.

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Keywords. Geographic Information Systems (GIS), deep and large foundation pit construction, construction monitoring, information management systems

Introduction Recent years, Geographic Information Systems (referred to as GIS) application in geotechnical engineering, especially in deep and large foundation pit construction increasingly attracted people's attention. GIS is a computer system which can deal with geospatial data input, Output, management, inquiring, analysis and decision support. It has several of characteristics of information systems, with other information systems (Such as office automation system etc.). The main difference is that the objects which GIS research and serving have a certain geo-spatial location. Excavation monitoring points are laid in the foundation pit construction environment to reflect changes. Different geographical spatial location of the monitoring sites reflects different parts of the security status of excavation. Based on GIS, management of monitoring points using the powerful geospatial data analysis and graphical display capabilities, combination of existing technologies of Geotechnical Engineering (In particular, deep 1 Corresponding Author: WANG Yuan, Engineering Institute of Engineering Corps, PLA University of Science And Technology, Nanjing 210007 Tel: 13951989919; E-mail㧦[email protected]

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and large foundation pit Engineering Technology), developing information management system of construction monitoring to large foundation pit construction based on GIS is necessary. Monitoring information can be used for foundation pit construction management, Visualization, analysis and forecasting and utilization. Developing monitoring information system is an important part of information construction process, and has practical value in the foundation pit construction, design and research [1, 2].

1. The Design of Systemic Architecture

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1.1. The Design of Systemic Function Information management system of large foundation pit construction monitoring based on GIS is designed including the following features: (1) Information collection, storage, management and retrieval capabilities of excavation engineering. According to certain norms, standard input system, the relevant data and information of excavation engineering can be input into the system and the new project information can be updated in real-time based on GIS and relational database system. Engineering data mainly includes: excavation basic geographic information (The photo shows the total plan and profile-based), engineering geological exploration information, construction monitoring data etc. These information can be conducted a comprehensive, and systematic summary, built into Database and displayed using GIS technology. Excavation works can achieve digitization and information-sharing. Foundation construction can be provided effective digital information and scientific basis for decision making [3, 4, 5]. (2) Engineering visualization The system takes points, lines as basic form fragmented. Partial excavation exploration data, design, construction, monitoring and interpreting results etc. combined in three-dimensional space and three-dimensional pit information model is reconstructed. Vector form of the construction pit regions is displayed in two-dimensional flat panel and browsed in three-dimensional actively. Excavation complex spatial structure and the relationship expression are reproduced. Namely, the full three-dimensional geographical space can be multi-angle free displayed from surrounding environment, maintaining structure, caving faces to geotechnical engineering geology. The distribution of internal stress, permeability and various attribute information in the engineering geology, excavation longitudinal slice of arbitrary cross section, excavation, virtual drilling and so on can be virtual visualized too [6,7,8]. (3) Query and statistics feature System can realize the system involved in various types of data queries and statistical correlation included: according to geo-morphological units, engineering geology division, hydro-geological district, geological parameters, environmental geological factors, the type of geological disasters, and user-defined query, etc. between graphics / attributes data as well as between graphics / data cross-retrieval, statistics of Attribute data and thematic mapping. System has the ability of multi-parameter statistics and analysis of geological data and can use a variety of ways

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to express the analysis results such as histogram, distribution curve diagram, correlation curve. (4) Engineering monitoring and deformation simulation functions In this part, the name of monitoring points, type, installation time and other static information can be checked, and a variety of dynamic deformation of values, Such as displacement and deformation speed can also be accessed. Various forms of reports and graphs can be automatically generated based on the engineering needs, such as the settlement observation results in table, settlement-time curve, and velocity-time curve. And the deformation process can be carried out on the three-dimensional dynamic simulation, a special pop-up window for deformation process and the development trend of simulation. (5) Monitoring and forecasting capabilities and information feedback GIS-based database system, comprehensive consideration with soil pressure, water pressure, engineering geology, load, building structure and other factors, according to functional relationship of force and deformation, constitutive model analysis for geotechnical engineering, research monitoring results and various influencing factors, make an accurate and timely scientific forecast about security status of the foundation pit construction. Through the analysis of monitoring data, revised design with rock physical and mechanical parameters, Stress, seepage pressure, rock pressure and other basic load; Through monitoring the surrounding rock and the supporting structure of the displacement, stress, strain, to amend the design for using with the base deformation control, security, monitoring methods and monitoring criteria indicators; Based on this, adjust the retaining structure parameters and construction program so as to realize information technology applied to excavation design and construction. 1.2. The design of the system’s basic structure

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According to functional requirements, the system is designed based on three layers: data support layer, basic and advanced application layer, the structure and logical relationship of each layer is shown in Figure 1.

2. Key Technologies of the System Development (1) Geographic Information Systems (GIS) technology Excavation engineering information includes engineering geology, hydro-geology, environmental geology information, also includes engineering design, construction, monitoring data. The large amount of information has complex types and is multi-source heterogeneous. Nearly all of them have geo-spatial data, and with time’s goes by constantly change. These features of excavation engineering information put forward higher requirements to the organization and management of the computer. Geographic information systems take collecting, storing, managing, retrieving, analyzing and describing the spatial distribution of the space objects and associated attribute data as its basic task. Applying geographic information systems technology in excavation engineering management can meet the requirements of organizing and managing excavation engineering information. (2) Visualization technology Based on the excavation geographical information platform, applying computer graphics technology, visual theory technology and so on to show all types of

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System Structure

information of the excavation engineering which include the collectivity conformation, configuration, the geologic structure, drilling information, wade sections and so on. Virtual reality is an advanced form of visualization technology. It can use computer technology to generate a realistic world with visual, auditory, and other sensory. Users can directly review and control the inborn virtual entities. (3) Foundation engineering information modeling technology Based on engineering data, the excavation engineering information model can be founded. The information model is the basis for 3D Visualization model of engineering geology and analysis. The information model includes three-dimensional model of Foundation pit, engineering geology, geological drilling and so on. These models include the characteristics of Geo-spatial location, Geometric shape, functional structure, physical and mechanical characteristics. (4) Geotechnical numerical analysis in excavation engineer Based on engineering information analysis, continue constitutive model analysis for geotechnical engineering, excavation distortion analysis and excavation safety analysis. Due to the immaturity of geotechnical engineering and inaccuracy of rock mechanics constitutive parameters, there is no unified methods for geotechnical

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engineering data calculation and analysis. A platform for numerical analysis of geotechnical engineering should be developed and established. The platform should contain a variety of more mature and advanced numerical analysis methods, and constitutive model library for geotechnical engineering which is easy to expanded, excavation and analysis of monitoring results prediction method library, anti-excavation analysis of monitoring results database, pit deformation and safety analysis methods library. (5) Foundation pit construction decision support techniques Establish evaluation method library, excavation of foundation construction method library and so on. Combining with various types of data, take safety diagnosis about the excavation, draw up the implementation of foundation pit construction programs, and program analysis and evaluation [9~13].

3. Experimental System Development and Application

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In the Windows XP system with Visual C++6.0 for the development language, OpenGL for the graphics development tools, using SQL Server as the database support, application of standard Rose for standard software engineering tools for modeling, developed our own intellectual property rights foundation pit construction safety monitoring information management system. At present, the system development has achieved initial results. And the system applied in retaining structure’s construction monitoring such as the upper reaches Nanjing passage of crossing Yangtze River shield tunnel original wells, receive wells, deep foundation excavation, etc. and has gotten better results. Figure 2. and Figure 3. are monitoring points system based on database-generated Pukou laid envelope diagram of monitoring sections and monitoring of the project, System based on the information generated by monitoring stations measured LK3 +560 the east section of underground continuous wall outside the reinforcement stress map and reinforcement stress profiles.

Figure 2. Schematic of monitoring points laid information

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Y. Wang et al. / Deep Foundation Pit Construction Monitoring Information System Based on GIS

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4. Conclusion and Outlook

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Information management system of construction safe monitoring to large foundation pit construction based on GIS, using the 3D GIS to have efficient management to investigation, design, construction, monitoring, data centralized of the pit in the process, combination the theory of geotechnical engineering information technology and digital aspects to take visual analysis, in order to the foundation pit construction and safety management to provide information sharing and intelligence analysis platform. The system has a friendly and beautiful interface, not only to improve the accuracy of data processing and forecasting timeliness, but also at the same time reducing data processing time. Actually, it improved efficiency of work and the level of information technology of excavation construction.

References [1] Hammock, K. Jon, Using a geographic information system to manage data from a ground-water remediation, ASTM Special Technical Publication, n 1126, 1992㧦247—250. [2] Brokaw, S. William, Using Geometrics in the acquisition and management of field data, ASTM Special Technical Publication, n 1358, 1999㧦298—300. [3] J. CHEN, J. J IANG. Integrated systems foe spatial data production , custodian and decision support. The International Archives of ISPRS , 2002 ,34. [4] Xiao-yang FANG, Environmental geo-technology perspective in the 21st century, Chinese Journal of Geotechnical Engineering(2000), 22(1):1㧙5. [5] Yuan-hai LI, He-hua ZHU, Development of monitoring information system software for geotechnical engineering, Rock and Soil Mechanics(2002), 23(1): 103㧙106. [6] WANG Chun-xiang, BAI Shi-wei, Study on application of 3DSIS to geotechnical engineering, Rock and Soil Mechanics( 2003), 8(4): 614㧙617. [7] Zuo-qiu LIU, Cui-ying ZHOU, Xu-sheng ZHAO, et al, Research on 3D stratum model and its visual technology, Acta Sceintiarum Naturalium Universitatis Sunyatseni(2003), 42(4): 21㧙24. [8] HE Huai-jian, BAI Shi-wei, ZHAO Xing-hua, et al, Discussion on strata partition in three- dimension strata model, Rock and Soil Mechanics (2002), 23(5): 637㧙639.

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[9] Qi-hu QIAN. Modern technology of underground space development and utilization in city and its developing trend, Railway Construction Technology, 2000, (5):1㧙7. [10] Shi-wei BAI, Xiao-hai WANG, Jian CHEN, et al, Visualization and information of geotechnical engineering, Geotechnical Engineering World( 2002), 4(8): 16㧙18. [11] Yun-he XU, Ji-xian ZHAO, Peng-gen CHEN, Construction and visualization of 3D GIS mine model, China Mining( 2003), 12(4): 60㧙63. [12] Chun-xiang WANG, Shi-wei BAI, Study on application of 3D SIS to geotechnical engineering. Rock and Soil Mechanics, 2003, 24(8): 614㧙617. [13] He-hua ZHU, Guo-ping ZHENG, Jiang-bin WU, et al. Study on ground data model bases on drill hole information, Journal of Tong Ji University(2003), 31(5): 535㧙539.

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Development and Study of the Information Management System of Levee Project Based on WebGIS in China

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Bin ZHANGa, 1, Mingyu BIa, Jia LIUa, Xizhong SHENb and Haiting DONGa a School of Engineering & Technology, China University of Geosciences. Beijing,100083, China b Institute of Engineering Mechanics, Water Resources Research Institute of Yellow River. Zhengzhou, 450003, China

Abstract. How to realize the visualization and intelligence of the information management in levee projects is an important prerequisite for disaster prevention and mitigation, especially in China, in which exists ten thousands of kilometers levees with various types in seven major river basins. With the rapid development of Internet Technology, Geographic Information System(GIS) technology has been integrated with WebServices, which has significantly changed the operation mode of traditional GIS. And the so called WebGIS technology is playing a more and more important role in levee projects. In this paper, based on VS.NET and ArcGIS Server technology and combined with the database theory and key technologies of WebGIS, a novel WebGIS model following the MVC pattern was designed and a system called ‘China Levee Project Information Management System(CLPIMS)’ was developed. Specifically, the building of the WebGIS framework on the basis of the ArcGIS Server platform and the realization of its function were expounded; the application of distributed database and ASP.NET technology in GIS development was studied; the real-time data processing technique and the optimization methods to realize the seamless connection between GIS data in VS.NET and the remote database were investigated. Under B\S environment, the functions on real-time enquiry, display, statistics and management of spatial data can be easily realized using this particular system. Moreover, additional applications such as real-time monitoring, safety assessment, early warning and forecasting, and specialized analysis can be further explored through reserved interfaces. The proposed CLPIMS can serve not only as a scientific, systematic, visualized tool for analysis and decision management in levee projects in China, but also as a technical platform for flood control. Keywords. China Levee Project Information Management System(CLPIMS), levee project, Geographic Information System(GIS), WebGIS, B\S

Introduction There are over 250,000 kilometers all kinds of levees in China, many of them exist potential safety hazard, which need reinforce monitoring and reinforcement work. This requires a lot of terrain, basic geology, river section, levee form and structure, and other hydraulic engineering construction information. At the same time, to realize the levee 1

Corresponding Author. Tel.: +86 10 82321196; fax: +86 10 82322624; E-mail: [email protected].

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safety monitoring and early warning also need to collect and manage various types of mass data. For historical reasons, existing levee project data of documented and project files retained are mostly confined to the vulnerable spot paragraph. Data is so fragmented that not possible to share information and data update, which reduced the levee construction and management efficiency. However, Geographic Information System(GIS㸧can be widely used for historical levee project data management because it allows the storage of geographical as well as textual information (Audisio et al., 2009), which enables the integration of geospatial datasets of all kinds of projects and facilitates data sharing via the Internet. Recently, most of GIS applications have been modified from traditional desk to Web-based GIS (Peng, 1999; Richard, 2000; Grunwald et al., 2003; Tang & Selwood, 2003; Chang & Park, 2004). Web-based GIS is the integration product of GIS and Internet technologies, with the advantages of distributed data management, strong independence platform, low system costs, efficient load balancing calculation and so on. In addition, Web-based GIS promotes the sharing and synthesis of multi-source data, and enables widespread sharing of spatial data and geoscience models (Qu et al., 2002). Therefore, Web-based GIS could offer a powerful and effective approach to the efficient management of levee project information and intelligent decision. Currently, some scholars have made so many important results in key technology research and application development of the Web-based GIS. Chang & Park (2004) developed a prototype model of Web-based Geographic Information System application for efficient management of borehole and geological date; Mathiyalagan et al. (2005) established a WebGIS and geodatabase for Florida’s wetlands; Rao et al. (2007) designed and developed a prototype Web-GIS Decision Support System (DDS), CRP-DDS, for use in resource management and assessment of environment quality. Jia et al., (2009) designed and developed a WebGIS-based system to predict rainfall-runoff and assess real-time water resources for Beijing. In addition, Web-based GIS has been applied succesfully to hazard prediction, risk analysis and hazard control (Yin et al. 2007; Pessina & Meroni, 2009; Sun et al. 2008). However, the levee project information involves wide range and more content, all the information distributed in different management and data types vary, so how to achieve a reasonable classification of data, standard importing, interactive query to meet the national-level levee data sharing by permission, to realize the levee project information management and optimical disaster prevention and mitigation, plays an important role. Such research has not to be seen yet. In this paper, based on B / S multi-layer architecture, combines SQL Server database with the ArcSDE data storage model, the magnanimity and various type data can be stored conveniently. A national 1/250,000 topographic was used as a background layer, and 1/10,000 CAD engineering drawing was imported through format conversion software as levee project data layer. ASP technology was used to build server applications system, achieving levee project data share cross-management unit and cross-basin, providing a platform support for the digital levee construction. Integrating above mentioned results, China Levee Project Information Management System(CLPIMS) was developed. Through this system, users can achieve conveniently on-line data query and management, statistical analysis, dynamic update and other functions. Moreover, levees real-time monitoring, safety assessment, early warning and forecasting, special analysis and other functions can also be brought about on Internet.

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1. System Overall Design 1.1. System Overall Architecture Design CLPIMS based on B / S system is multi-layer structure, a MVC pattern (Model-ViewControl) was adopted in this system. The system is divided into four logic layers, i.e. interactive layer, Web layer, logic layer and data layer (Fig. 1).

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Figure 1. System logic structure diagram of CLPIMS

The interaction layer of system is mainly responsible for the user’s request sent to the Web server through the network, and the information server returns information to the user through Web browser after some particular processing. Web layer accepts operating request of system users, shields illegal information, fulfills the non-spatial data processing request. Logic layer based on ArcGIS Server 9.2 is GIS Server Center, system calls AO components of GIS Server Center in this layer to complete the GIS processing through the remote objective access technology. Server object manager assigns equally the complex and costly GIS operation to the single server under its management through the load balancing and computer cluster technology, after single server completed spatial data operation, it will return the results to the Web layer. Data layer based on Microsoft SQL Server 2005 platform manages centrally all the data of system, including the basic database, special database and spatial database. 1.2. System Structure and Realization Approach CLPIMS is comprised of the following 8 major sub-modules: GIS sub-module, statistical analysis sub-module, data query sub-module, data management sub-module, real-time monitoring sub-module, safety evaluation sub-module, flood warning submodule, dedicated analysis module. The system development processes includes three main stages i.e. preliminary data collection and sorting, medium-term system platform development, system operation and maintenance in later period and other key link.

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2. Data Classification and Database Designation of Levee Project Management 2.1. Data Classification There are plenty of data in CLPIMS, including the dike engineering data of the seven major river basins. There are three categories included: GIS vector data, levee engineering management data, real-time monitoring and early warning and forecast data. Each category is subdivided into some subtypes. 2.2. Database Structural Design Based on the SQL Server platform, a three-layer database system is constructed in CLPIMS. Spatial data and property data are divided according to data style. The spatial data is visited and operated by ArcSDE, and the property data is visited and operated by ADO.NET. The system database is divided into three classes based on data content, i.e. GIS vector data, levee project management data, real-monitoring and early warning and forecasting data (Fig. 2). Data is saved in database in a particular encoded rule to facilitate query and calling. 2.3. Data Importing and dyDamic Update

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x Data Importing There are two data importing styles: manual inputting and direct importing. Manual inputting is suitable for small amounts of data storage, the direct importing is used when the amount of data is large. Direct importing is achieved according to the conversion of excel data and SQL data. Appointed Excel file can be downloaded, then imported to database.

Figure 2. System database structure

x Data Dynamic Update There are two dynamic update styles: background updating and onstage updating. Background updating directly revise, add and delete data on the server according to SQL 2005 database software; onstage updating updates module and data using the data that system provides. It requires customers should have relevant data access permission. The newest dike engineering data can be provided to flood prevention as long as the internet is connected and the dynamic updating then is achieved.

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3. System Function and Application

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Eight modules are included in CLPIMS. They are GIS sub-module, data query submodule, data management sub-module, statistical analysis sub-module, real-time monitoring sub-module, safety assessment sub-module, early warning and forecast submodule, special analysis sub-module (Fig. 3). Among them, the former four are basic functional modules, the last four are extended function modules, every module takes different functions. Further more, considering some users or the systematic application range maybe don’t need GIS function during the operation, China Levee Project Database System (the simplified version of CLPIMS, CLPDS) was developed based on VS and SQL. The GIS function is removed, but the other original function included (Fig. 4). GIS sub-module function includes spatial data storage, browse, query, online editing, etc. it’s the center of WebGIS system. Statistical analysis sub-module can display complex data information with specific condition and format, so the query result will be much visualized. Users can obtain any needed data using data query submodule. Two query styles are set: pull-down lists query and fuzzy query. Data management sub-module can realize data addition, deletion, revision, back-up, ect. Real-time monitoring module, safety assessment module, early warning and forecast module, special analysis module, these four modules, are used to save real-time monitoring data, and realize monitoring and early warning through the computational analysis of embedded function analysis program.

Figure 3. Main windows of CLPIMS interface

Figure 4. Main windows of CLPDS interface

3.1. Map Production and Publication x Map production The vector data collected includes the national 1:250000 terrain data with ArcGIS format, CAD data of dike construction, MapGIS format data, paper maps data, etc. An agreed format is needed to meet the system requirement. Format conversion software, DSDC, is developed, so the form transforming from CAD, MapGIS format data into a common data format for ArcGIS can be accomplished expediently. For the paper map, the R2V software is first used to realize data vector preliminarily, then the hierarchical data is imported into ArcMAP software for post-processing. The data is imported into ArcSDE Geodatabase to make mxd file to facilitate map releasing.

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x Map publication The mxd file is passed to a GIS server through ArcCatalog or ArcCatalog software tools, which can release multiple map files simultaneously. The special map service function is provided for seven main river basins in China, i.e. map of the Yangtze River basin, map of Yellow River basin, map of the Huaihe River basin, map of Haihe River basin, map of the Songliao River basin, map of the Pearl River basin and the Taihu Lake basin. 3.2. GIS function x Basic GIS function The basic GIS operations can be realized online, such as zooming, roaming, distance and area calculations, Eagle Eye, and magnifying glass and so on. Distance measurement function can measure the straight line distance between points, it can be accurate to metre. Eagle Eye function can determine the location of certain area in whole map by navigation chart to facilitate the management and operation of the map. Corresponding with the Eagle Eye is the magnifying glass feature, when users roam here, specified area on the map can be magnified to avoid continually zoom in and out in order to find a geographical position. The system response time can be cut down.

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x The use of layers and contents Hierarchical technology is adopted in system spatial data. The visibility of any layer can be set. When the program starts, the layer which is set when the map is published is displayed as the default layers. When the initial state closed layer information need to be view, the corresponding layer in the system can be chosen to satisfy the various personalized needs to GIS layers and levees facilities. One or more layers are contained in each data source; one layer represents a map data type, such as rivers, lakes, typical dike project, cities and so on. x Online editing and buffer analysis functions of spatial data Simple online editing of spatial data can be performed online, including the newly geographic factors creation, moving the existing point, line and surface elements, copying, cutting, merging, deletion, adding and so on, the element's property information can be also modified. The original data edition can be restored using ArcSDE long transaction and version management technology. The element information of certain line or region within a certain range can be sought using the buffer analysis function; the query results will be highlighted. 3.3. Data Inquiry and Statistical Analysis x Mutual visits of multifunctional graph and digit Through this module, the property data sought will appear in the left side of the inquiry window by clicking some of the terrain, ground feather in the map, 1) Range inquiry: the related information of a range query can be selected, such as a city or lake. 2) Single point inquiry: the query information of a single point can be clicked, and all the layers’ information including this point will be displayed, such as drilling survey information, information of elevation point (Fig. 5). 3) Line inquiry: the related information of line inquiry can be clicked, such as dike section information, railways, highways, etc.

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Figure 5. Multifunctional query windows

Figure 6. Multimedia demonstration function windows

x Project management data query Two kinds of data styles are used in project management data, there are text data and multimedia data (Fig. 6). And multimedia data also contains pictures, video, PPT, CAD profile and other information. Data query and fuzzy query can be chosen from the drop-down list to visit these data conveniently. x Statistical Analysis Statistical analysis of levee length, height, historical danger and other data can be easily realized through system statistical functions, and statistical charts, such as bar charts, line charts, pie charts, etc. can be generated automatically. 3.4. User Management To ensure the security of the system, the user management module, including the user login information modification, users adding and users deletion and so on. Basic information and permission information are included in users’ information.

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3.5. Real-time Monitoring, Safety assessment, Early warning and Forecast, Special Analysis Embedded-specific analysis procedures are developed based on WebGIS platform, such as the reservoir bank slope erosion simulation program, piping modeling procedure, dike seepage and stability analysis procedures. The relevant safety criteria are established. The modules are stored in the database automatically to real-time monitor data. Dike safety assessment under certain conditions and early warning and risk prediction of dangerous segment in the flood season is conducted, and personalized thematic analysis is carried on simultaneously. Technical support from system will be provided to government departments for disaster prevention and mitigation.

4. Conclusions In this study, based on VS.NET and ArcGIS Server technology, combining the database theory and key technologies of WebGIS, a prototype for WebGIS application was successfully designed, an information management system named CLPIMS has been developed for efficient management of levee project data on the Web. A platform for information sharing is provided to levee project management staff of the seven

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countrywide major river basins in China. China Levee Project Data Coding System is established during system database designation process to ensure the integrity and standardization of system data. The spatial data translation software DSDC is developed based on VB and ArcObjects considering some difficulties exist when dealing with GIS maps by general users. In addition, considering some users or the systematic application range maybe don’t need GIS function during the operation, China Levee Project Database System (CLPDS) was developed based on VS and SQL. Eight sub-modules are included in CLPIMS, there are GIS sub-module, statistical analysis module, data query sub-module, data management sub-module, real-time monitoring sub-module, safety assessment sub-module, flood warning and forecast sub-module, special analysis sub-module, which constitute the system function framework together. Based on the former four functional modules, data browsing, graph and digit mutual visiting data management and other operations can be achieved by internet whenever and wherever. System data safety is increased by the setting of user permission. Online editing and spatial analysis of spatial data can be realized through advanced GIS function. The later four modules are professional application modules based on numerical simulation and real-time data monitoring to complete the relevant and early warning and forecast. Then technical reference can be provided for the levee project construction, management and flood control.

Acknowledgements The research presented in this paper was supported by China Ministry of Water Resources' Special Funds for Scientific Research on Public Causes (No. 200701022), National Natural Science Foundation of China (No. 40902086).

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References [1] C.G. Sun, S.H. Chun, T.G. Ha, C.K. Chung, D.S. Kim, Development and application of a GIS-based tool for earthquake-induced hazard prediction, Computers & Geosciences, 35(2008), 436–449. [2] C. Qu, H. Ye, Z. Liu, Application of WebGIS in seismological study. Acta Seismologica Sinica, 15 (2002), 99–106. [3] D. Richard, Development of an Internet atlas of Switzerland, Computers & Geosciences, 26 (2000), 45– 50. [4] K.L. Yin, L.X. Chen, G.R. Zhang, Regional landslide hazard warning and risk assessment, Earth Science Frontiers, 14(2007), 85–97. [5] M. Rao, G.L. Fan, J. Thomas, G. Cherian, V. Chudiwale, A web-based GIS Decision Support System for managing and planning USDA’s Conservation Reserve Program (CRP), Environmental Modelling & Software, 22 (2007) 1270–1280. [6] S. Grunwald, K.R. Reddy, V.Mathiyalagan, S.A. Bloom, Florida’s wetland WebGIS. In: Proceedings of the ESRI User Conference, San Diego, CA,2003. [7] V. Mathiyalagan, S. Grunwald, K.R. Reddy, S.A. Bloom, A WebGIS and geodatabase for Florida’s wetlands. Computers and Electronics in Agriculture, 47 (2005), 69–75. [8] V. Pessina, F. Meroni, A WebGis tool for seismic hazard scenarios and risk analysis, Soil Dynamics and Earthquake Engineering, 29 (2009), 1274–1281. [9] W. Tang, J. Selwood, Connecting Our World: GIS Web Services, ESRI Press, Redlands, California, 2003. [10] Y.S. Chang, H.D. Park, Development of a web-based Geographic Information System for the management of borehole and geological data, Computers & Geosciences, 30(2004), 887–897.

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[11] Y.W. Jia, H.L. Zhao, C.W. Niu, Y.Z. Jiang, etc., A WebGIS-based system for rainfall-runoff prediction and real-time water resources assessment for Beijing, Computers & Geosciences, 35(2009), 1517–1528. [12] Z.R. Peng,. An assessment framework of the development strategies of Internet GIS, Environ. Plan. B: Plan. Des, 26 (1999), 117–132.

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377

Development of Tunnel 3D Information Inquiry System Based on ArcGIS Engine a

Fang-hui Jiaoa 1 , Yong-gang Jiaa and Tao Liua College of Environmental Science and Engineering, Ocean University of China, Qingdao, Shandong 266100, China

Abstract. Based on the design and construction data of the Nanjing Yangtze River Tunnel project, a 3D information inquiry system which adopted the ArcGIS Engine techniques was developed. We proposed a classification modeling method which is based on whether or not query the attribute information of the 3D models. We used ArcGIS Engine 3D module to create MultiPatch models so that we can query easily about the attribute, and the other models were generated using 3D modeling softwares. We can change view angles by switching 2D and 3D visual windows to express the plane and spatial relationship of objects at any time. The problem that the query function of the past 3D system was weak was solved by developing the 3D query function of ArcGIS Engine.. Keywords. ArcGIS Engine, 3D, system, model

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Introduction Tunnel project is an important component of modern underground engineering. In the construction progress, a lot of data and information need to be processed and managed in order to optimize the design and manage projects. Therefore, the acquisition and management of tunnel information seems very important and urgent. The traditional tunnel information is limited to two dimensional (2D) graphical display and data management, so it hardly describes three dimensional (3D) objective world. In recent years, with rapid technological advance in geographic information system (GIS) and visualization, 3D GIS has been used extensively in many fields. It provides the condition for us to express the real world. The current research and development on 3D GIS is mainly in three ways. One depends on firmware development which is representative of programming languages and OpenGL; the other depends on virtual reality technology, such as Java3D or Vega; the third takes the secondary development by means of sophisticated GIS software, such as ESRI ArcGIS provides a 3D Analyst extension module [1]. The first method is more efficient and cross-platform compatible, but demands a large amount of code as well as only for an open data format. The second one is focusing on 3D modeling and 3D visualization which has made a great success in making 3D models, but they can hardly provide GIS functions such as spatial queries. The third one has powerful GIS function and is lack of visualization [2]. ArcGIS 3D Analyst extension module show the surface models based on DEM or TIN which are relatively poor visualization. 1

Corresponding Author: [email protected]

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In this paperˈwe analyze the data obtained from the construction process of the tunnel, propose and implement the Nanjing Yangtze River Tunnel 3D information inquiry system using ArcGIS Engine and 3D modeling software 3DMAX and SketchUp. Through building the tunnel and the strata model and developing information system, we browse tunnel intuitively and query information in order to manage tunnel project scientifically and conveniently.

1. Preparation of 3D model 1.1. 3D strata modeling In the ESRI ArcScene environment, we adopt surface-extruding method based on triangulated irregular network (TIN) to build the 3D strata model. The method is that we build the TIN surface model according to the drilling data, and then extrude the upper and lower surface to fill the entity between two TIN surfaces [3]. First of all, we establish the drilling data sheet according to the survey report which stored the dot number and X, Y, Z coordinates as well as the hierarchical information. Then import it into ESRI ArcMap, generate drilling plan and save files for the each layer to shapefile. Call the 3D Analyst module to generate TIN surface models for each layer. In the ArcScene, load the models and open the layer properties box, and select from the surface to obtain a height degree. Therefore, we can get the multi-layered TIN models, and an ArcScene document is created to describe the visualized data layers. The process of extruding surface is implemented by programming with C# and using ILayerExtensions and I3DProperties interface in the ArcGIS Engine.

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1.2. Tunnel modeling Single segment model is build and saved into a .3ds format based on segment design drawing. In order to query the attribute of each segment, the .3ds format needs to be converted to MultiPatch format. We can complete it through the following workflow [4]: x x x x

Firstly, load element from the Geodatabase into the ArcMap; Secondly, import the queried element to Sketch Up using SketchUp6ESRI Plug-in tool and edit the element in the Sketch Up; Thirdly, export the model to MultiPatch format; Finally, open MultiPatch file by Microsoft Access database and edit attribute.

The whole tunnel is composed of a series of the segments which are copied and rotated. Based on the segment reports, the tunnel model can be generated by a great number of single segments which are spliced. 1.3. Other ground objects modeling Other ground objects refer to the toll gate, working well, office buildings, trees and other models. They exist as a whole and there are no needs to query the internal parts. Such models are built by 3DMAX software which supplies powerful 3D basic modeling functions. Save the model to the .3ds format and load it into the ArcScene.

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The models can be opened directly for being browsed. In the ArcScene, according to the coordinate value or the relative location, we create a point file and then replace the point file with the loaded model to complete the loading and displaying of the models. In order to avoid the layer loss when file path changes, we save the model files and ArcScene documents in the same folder. Table 1. Classification modeli Category of model Strata model

Modeling method Extrude TIN surface

Export MultiPatch by Sketch Up 6ESRI Plug-in Other ground Replace point layer with 3D objects model symbols Tunnel model

Query attribute

Storage format

Yes

.shp

Yes

.mdb

No

.3ds

Software and interface ArcGIS Engine Ilayer Extensions SketchUp,ArcGIS Engine ImultiPatch 3DMAX, ArcGIS Engine IMarker3DSymbol

2. Methods

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2.1. System development platform The computer uses Windows XP Professional operating system. The system is developed using Microsoft .NET C# programming language, which requires the Microsoft Visual Studio .NET Framework 2005 and the ArcGIS Engine 9.2 to be installed on computer. The former software provides the application interface and Graphical User Interface (GUI) components of the application. The latter one provides the core function of synchronized visualization in the system. ArcGIS Engine is a simple, application-neutral programming environment using ArcObjects, which are composed of developer kit and runtime library. Using the ArcGIS Engine developer kit, developers can build customized GIS applications with simple interfaces to access the functions provided by ArcGIS. Furthermore, the ArcGIS Engine makes it easier to build reusable program blocks and code basis for future extension. The setup in this work does not require the complete ArcGIS Desktop environment, but only the ArcGIS Engine runtime library [5]. For the 3D development, ArcGIS Engine provides SceneControl to display 3D scenes and Camera control to capture images of the scene, and simplifies program development. 2.2. Design of system structure The information inquiry system combines 2D GIS and 3D GIS. There are three layers: database layer, component development layer, and function module layer. Overall system structure is shown in Fig.1. As can be seen from it, the data have two ways to be stored because there are two data storage types to be used. One way is that data are stored in the Geodatabase using ArcSDE called spatial database engine; the other one is that complex Multipatch models are stored in the Access database [6]. We connect to the database through IPropertySet, IWorkspaceFactory, and IWorkspace provided by ArcGIS Engine in order to implement the reading of data. In the function module, MapControl and SceneControl were used to show 2D view and 3D view.

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Figure 1. Design of system structure.

2.3. System function development

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2.3.1. 3D view function 3D view mainly provides display and browsing of the strata model and the tunnel model, and achieves the basic function including zoom in, zoom out, pan, navigate, and fly. Function implementation is the use of TocControl, ToolbarControl, SceneControl and other controls provided by ArcGIS Engine. ToolbarControl can be added operational tools (zoom in, zoom out, pan, navigate, fly). SceneControl can implement 3D browsing, roaming and control of the thematic maps. TocControl can primarily control hierarchical display and management of thematic maps, and provide illustrations and support it to modify. The complete function includes 3D scene display, view control, 3D roaming of the strata and tunnel model, and hierarchical display. 2.3.2.3D query function The 3D query module of the system is the inter-query opinion between the graph and attribute. It is similar to the Identify control in the 2D GIS [7]. x

x

Query attribute according to spatial location: we select the Locate () method to convert the mouse click position’s coordinates of screen to geographical coordinates, then select Identify () method to get the selected object through the coordinates of the mouse click point. Return this object and transform it into forms showing attribute. Query spatial location according to attribute: we create a query filter, and give the query language. Then call the Select () method to choose the element. The selected element is highlighted in the scene.

Moreover, we can customize the property query box which is more practical than calling the control directly and meet the user needs. Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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2.3.3.2D view function 2D view is not as intuitive as 3D view, it is more accessible to query in practical application because it has been the traditional methods for a long time. The functional blocks include: drilling plane location, strata and tunnel profiles. We can use the MapControl to show the basic map, the ToolbarControl to manage toolbar, and the TocControl to manage the layers. Especially, it is different from the 3D view function that the PageLayoutControl can be used to achieve the drawing and printing map. Drilling plan location is based on drilling X, Y coordinates. Each point represents a drill, so we can protract discrete point distribution map which show the location of the drilling. Strata and tunnel profile is based on the drilling data in the survey report and the tunnel design map. From the map we can clearly see relative position of the tunnel, strata and river, and query the soil around the tunnel segment. 2.3.4. Interactive measurement function The interactive measurement is mainly used to draw line for measuring and calculating the distance or height in the 3D environment. This function is realized by searching the coordinate information of the each point on the line which is drew by a measure tool in the 3D display windows. We select the SketchLineTool and the Measuring Class, between which information can be delivered by the mouse action, such as Click, Move, Double-Click. In the measurement state, the line drawing project is started by a mouse click and finished by a double click. Some mouse moves and clicks are done to draw the line segment of the project. The obtained 2D plane coordinate of each node of line segments can be translated into 3D geography coordinate by which we can calculate the distance corresponding lines whose nodes are in the same Z plane and the height corresponding lines whose nodes are in the different Z plane of the line segment.

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3. An example case study In this paper, the Nanjing Yangtze River Tunnel 3D information inquiry system is the example for a new tunnel 3D system development method using ArcGIS Engine. The main interface of system including main menu, tool bar, table of content, and display window is shown in Figure 2.

Figure 2. System main interface

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4. Conclusions modeling methods and develop a 3D information inquiry system. We use ArcGIS Engine 3D module to create MultiPatch models so that we can easily query the attribute of 3D models. The other models can be generated based on 3D modeling software. The basic functions include 3D display and browsing, 3D spatial and attribute queries, 2D display and queries, data management, and interactive measurement function. The advantages of developing the system using ArcGIS Engine are follows: x

x

In the past, 3D system only had a strong 3D display, but did not have a query function. The problem that the query function is weak was solved by ArcGIS Engine interface; The system does not require the full installation of the ArcGIS and other related packages, which would need more than 1GB of disk space. Instead, only the ArcGIS Engine needs to be installed. By repackaging the function provided by the ArcGIS Engine developer kit, developers can quickly build fairly customizable GIS applications.

However, there are some problems in the system. For example, the color of 3D scene in the ArcScene seems darker than that in the 3D modeling software. When the models are large, they can’t be loaded into the scene. Other 3D GIS functions such as 3D spatial analysis and 3D statistics can’t be developed in the system. To solve these problems, more researches need to be done in the future.

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References [1] J.D. McCarthy, P.A. Graniero, A GIS-based borehole data management and 3D visualization system, Computers and Geosciences 32 (2006), 1699-1708. [2] L.M. Fan, Development and application of 3D GIS system based on ArcGIS Engine: A case of practice in ECNU campus, East China Normal University, Shanghai, 2007. [3] K.X. Zhang, W.B. Wu, Y.F. Bai, 3D geological structure visualization based on ArcGIS, Journal of Liaoning Technical University 26(3) (2007), 345-347. [4] H. Chang, Research of Google SketchUp and ArcGIS in a 3D city underground pipe network, Kunming University of Science and Technology, Kunming, 2008. [5] H. Liang, R. Arangarasan, L. Theller, Dynamic visualization of high resolution GIS dataset on multipanel display using ArcGIS engine, Computers and electronics in agriculture 58 (2007), 174-188. [6] J.C. Li, J.W. Guo, Y.C. Gai, W.P. Fu, Design and Implementation of 3D GIS Based on ArcEngine㸪 Remote sensing technology and application 24(3) (2009), 395-398. [7] M.Apel, From 3d geomodeling systems towards 3d geoscience information system: Data model, query functionality, and data management, Computers & Geosciences 32(2006), 222-229.

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383

Application of 3D Information Technology in Geotechnical Engineering Practice a

Peng ZHANGa, 1, Fang ZHANGa and Xuan HANa National Academician & Masters Research Office, BGI Engineering Consultants LTD, Beijing, China

Abstract. At present, the information technology is profoundly influencing the geotechnical engineering field. Firstly, from the practical perspective, the ideas about design and development of three-dimensional geotechnical engineering information system and subway monitoring and early warning information system by BGI Engineering Consultants LTD are introduced. Secondly, some problems are emphasized separately in the fields of geotechnical investigation, ground movements risk evaluation, groundwater numerical simulation, geo-environmental engineering, urban planning and the subway third-party monitoring, which have been solved by these two systems. With the effective application of information technology, some existing problems are summarized respectively and the development direction is indicated clearly. It has been found that the information technology shows powerful and prosperous vitality in the geotechnical engineering that has special requirements for systematicness, integration and real-time. However, it is a ubiquitous problem that how to refine the professional function to meet the demand of engineering and project management combining with the specific application trend.

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Keywords. Information technology, geotechnical engineering, 3D visualization, engineering practice

Introduction The 3D information technology with powerful and prosperous vitality is extensively influencing the geotechnical engineering fields. In particular, it provides an effective solution to some geotechnical engineering problems which have special requirements in the systematicness, integration, visualization and the real-time performance, such as disaster prevention and mitigation, underground space planning and geo-environmental engineering[1]~[3]. Naturally, it has become a research hotspot how to use the information technology to serve geotechnical engineering effectively [4] ~ [13]. Firstly, this paper introduces the ideas about design and development of threedimensional geotechnical engineering information system and subway monitoring & early warning information system from the practical perspective. Secondly, some problems are emphasized separately, which have been solved by these two systems in the fields of geotechnical investigation, ground movements risk evaluation, groundwater numerical simulation, geo-environmental, urban planning and the subway third-party monitoring. With the application effectiveness of the information 1 Correponding Author: Peng Zhang, National Academician & Masters Research Office, BGI Engineering Consultants LTD, Beijing, China; email: [email protected].

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technology, some existing problems are summarized respectively, and the development direction is discussed.

1. Two Geotechnical 3D Information Systems in Brief Based on the experience in the geotechnical engineering and the accumulation in the computer information technology, the platform of 3D geotechnical engineering information system has been built successfully by BGI Engineering Consultants LTD. (BGI for short) collaborated with Peking University. Additionally, the subway monitoring & early warning information system is also developed used for the efficient management of the third-party monitoring during the construction of Beijing Subway system by BGI[13] ~ [15]. 1.1. 3D Geotechnical Engineering Information System

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The research of this system follows the rules of utility and reality. So-called utility means not to change the original production habits, and to read investigation data directly from the company management system, which is the system data source; Socalled reality means that this system data source comes from the measured data, differs from the general virtual reality, and this system has function of real-time management for real information and real-time analysis and forecast. Based on the known geotechnical engineering data, the system has realized the engineering data management, geological cross-section drawing, 3D geological modeling, coupled visualization of 3D multi-source data, and the preliminary geometric and mechanical analysis features to 3D space. In order to meet the requirements in all aspects of geotechnical engineering, the system’s process design and modules are shown in Figure 1.

Figure 1. Structure and modules of 3D geotechnical engineering information system

1.2. The Subway Monitoring & Early Warning Information System The subway monitoring & early warning information system is timely developed on the occasion of Beijing and other domestic cities large-scale subway construction. Using the OpenGL technology, the system is developed by C# language with a core of specific project and a platform of SuperMap. The system can either display the thirdparty monitoring data on a large-scale field region or the macro-urban subway construction. Coupled with the geological conditions, it can provide information management for the construction of urban infrastructure. The system's main interface is

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shown in Figure2, and the main functions include: basic data management, early warning of monitoring data, and 3D geotechnical engineering analysis module.

Figure 2. The main interface of Beijing subway monitoring & early warning information subsystem

2. Engineering Practice The 3D geotechnical engineering information technology mainly involves three aspects, one is abstract modeling, which is one of the core technology of the whole 3D informatization; the other is 3D computer graphics and display; the third is the specific application of 3D information. In the above-mentioned three aspects, the abstract modeling is the foundation, graphical visualization is the way, and engineering application is the aim. Therefore, the following focuses on specific engineering practice of 3D geotechnical engineering information in various aspects.

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2.1. Geotechnical Investigation Along with the exploitation of the underground space approaching to the deep constantly, the geotechnical engineering investigation will face with complicated shallow and deep, ground and underground, built or under construction or planned various structures, 3D informatization can timely make geotechnical engineering investigation more nichetargeting. Based on design drawings and design documents, a

Figure 3. 3D model showing the foundation excavation and CFG piles of ‘117 Mansion’, Tianjin, China

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Tunnel

Figure 4. The construction environment of subway tunnels

variety of ground and underground structures can be generated directly via 3D information technology, such as residence, foundation, pipelines, tunnels, etc., (Figure 3 and Figure 4) greatly improving the engineers awareness of the project.

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2.2. The Ground Movements Risk Evaluation [16] For a long time, 3D information technology applied in geotechnical engineering focuses on visualization of stratum spatial relationship. Based on the research of ground movements induced by tunneling and their effects on structures deformation characteristic, the practical analysis method is proposed, and then a 3D software system, used for the predicting of dynamic ground movement and building settlement, is developed[17]. The result of this system gives not only an efficient predicting tool for the risk management during the construction of urban subway, but also a successful example for the application of the three dimensional visualization technology in the

Figure 5. Predicted results of tunneling-induced ground movement and building settlement (Xuan Han, 2009)

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engineering analysis and calculation field. It also can be used as buildings safety evaluations along the subway and provide a whole new line of in-depth application for the 3D geotechnical engineering information technology (Figure 5). This example achieved the dynamic simulation process of underground tunneling, gave a new way for underground engineering simulation analysis, and provided an interactive visualized analysis tool for underground engineering construction organization design and management. 2.3. Groundwater Modeling

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Groundwater is one of underground space media, but also media and carriers of engineering and environment interaction, and may also become an incentive of disaster, therefore 3D information visualization analysis for groundwater is necessary. But how to effectively implement 3D informatization of groundwater doesn’t have very mature experience [18]. An active exploration was shown as follows: Figure 6 is threedimensional generalization and visualization for an aquifer in Beijing, from which it can clearly distinguish between aquifer and aquifuge, and provide the foundation of the numerical simulation; Figure 7 is a regional multi-storey groundwater 3D visualization display.

Figure 6. A regional geological model used for groundwater modeling of Beijing area (Zaiming Zhang, etc, 2009)

Figure 7. 3D regional model showing the different types of groundwater distributed in Dongcheng District, Beijing, China

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2.4. Geo-environmental Engineering Geo-environmental engineering is an important branch of geotechnical engineering; we are also actively searching for 3D information technology applications in specific geoenvironmental practice. For the contaminated region of the concentration distribution, three-dimensional property model was established based on investigation data (Figure 8 and Figure 9) and provided an information management tool for the space migration of pollutants.

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Figure 8. The property model of pollutant concentration

Figure 9. 3D model and sectional view of pollutant concentration distribution

Three-dimensional topological model was built for researching on space relationship of informal landfill, and 3D information model was established at last (Figure 10). In order to provide powerful help for management and renovating landfill, the landfill volume was estimated scientifically from the 3D model.

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Figure 10. 3D grid model showing the distribution of construction and household wastes

2.5. Urban Planning

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In essence, planning, investigation, design, construction, operation and maintenance are system engineering of the study of ground and underground space. Their organic integration is one of effective ways to achieve sustainable use of resources. "How the engineering geology services for urban planning?" and " how to avoid the risk of geotechnical engineering when Urban planning?" have always been concerned. Especially after the 5•12 earthquake, WenChuan, China, it is known of the importance of investigation for planning, design, and other initial decision-making. Adhering to this concept, we are actively searching for 3D information technology applying in urban planning.

Figure 11. 3D structural and geological environments developed for urban underground space planning

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Based on the understanding of "take precautions" for servicing planning, it was analyzed that the feasibility of the geological conditions in an administrative region of deep underground space development and utilization, meanwhile, 3D visualization of underground space model was provided by using the information technology for underground space planning (Figure 11). At the same time, geotechnical engineering problems which should be considered in planning and construction were proposed via the same technology. It is worth mentioning that relevant information in planning and geotechnical engineering can be formed an integrated expression through the above method, then a convenient 3D visualization platform can be provides for urban planners. 2.6. The Subway Third-party Monitoring

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At present, the subway third-party monitoring is of many aspects, and monitoring data are enormous. Using 3D information technology, this paper established the information model of monitoring points, which was an effective visual display of spatial distribution of subway monitoring objects and monitoring data management. Through the effective integration of GIS and 3D, it can not only rapid query, dynamic pre-alarm of the monitoring data and property information, but also can really guide the construction and effectively prevent accidents that may happen in subway construction. Figure 12 is a model of subway station monitoring information visualization.

Figure 12. 3D visualization of the third-party monitoring plan for a subway station in Beijing

3. Engineering Thinking The experience above explains that the 3D information technology shows powerful and prosperous vitality to geotechnical engineering which have special requirements in the systematicness, integration, visualization and the real-time performance. However, it is a ubiquitous problem how to refine the professional function to meet the demand of engineering and project management combining with the specific application trend. Now, this text plans to explain from several following respects.

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x

x

x

x

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x

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Three-dimensional informatization has laid a good foundation for 3D modeling and visualization of geotechnical and underground engineering, and it has achieved comprehensive dynamic management in a variety of original data, images, maps and written reports, while the data conversion is not fluent. In addition, it cannot optimally meet the professional needs of the production and research in geotechnical and underground engineering without specific aim. Focus on the Visual effect, 3D geological modeling technology merely plays a role in displaying the geological environment, while its potential powerful analysis and computer-aided calculation function has not yet been excavated. Therefore, 3D geotechnical engineering information system should develop from 3D visualization to analysis, which has a more far-reaching significance for improving the level of geotechnical engineering calculations. And through the front part of the engineering practice study, we believe in geotechnical engineering analysis should also need more assistance from 3D information technology; the research of this aspect should gradually to focus on practicability and solve problems in professional fields. The front part of the engineering practice study is a beneficial attempt in 3D information technology applying in geotechnical engineering field. With the continuous improvement of the analysis and calculation method, other more sophisticated analysis method can be embed to this system continually, and also could be considered more specific engineering problems, such as the design of pile foundation bearing capacity, foundation deformation and stability analysis, building settlement calculation and so on. At present, 3D geotechnical engineering information system's main function is to display the 3D visualization stratum. Implementation of the multi-field coupling expression, such as groundwater, pollutant etc, will be a qualitative leap for 3D information visualization. Many numerical analysis softwares of geotechnical engineering, such as FLAC3D, ANSYS, FEFOLW, have a common problem that pre-treatment is very complex, especially stratum modeling. Generally, the simplified model is adopted, but model calculation does not meet the requirements of complex stratum and affect the accuracy of the calculation results. 3D geotechnical engineering information technology can be used in its advantage of stratum modeling, and implementation of the software interface with FLAC3D can make the software obtain more complex stratum information and improve the quality of engineering analysis.

4. Conclusions In view of the reason mentioned above, the transformation from the basic theoretical research to the application practice has been preliminary accomplished by the application of 3D information technology in geotechnical engineering. However, it is so immature during the formative period of life-cycle that the application market should be exploited vigorously. As an engineering consultant, BGI is actively promoting the development of the industry informatization. After years of exploration starting from the exploration about the computer-aided geotechnical engineering, the

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realization that 3D information technology must bring a new revolution in geotechnical engineering inevitably, just as the impact on other industry revolutions by information technology, has been formed.

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References [1] Chris Hand. A survey of 3D interaction Techniques. Computer Graphics Forum. 1997, 16(5): 34~38. [2] Zlatanova Siyka. 3D GIS for Urban Development. ITC Dissertation. 2000. [3] Zhu Hehua. From digital earth to digital strata: new idea about the geotechnical engineering development. Geotechnical World, 1998,(12): 15-17 (in Chinese). [4] Bai Shiwei, Wang Xiaohai, Chen Jian, etc. Informatization and visualization of geotechnical engineering. Geotechnical Engineering World, 2001, 4(8): 16-17. (in Chinese) [5] He Manchao, Liu Bin, Xu Nengxiong. Numerical Simulation of 2D Transient Anisotropic Fluid in Porous Media Containing Horizontal Well. Journal of China University of Mining & Technology, 2003, 32(1): 38-43. (in Chinese) [6] Fang Haidong, Liu Yihuai, Shi Bin. 3D geoscience modeling and its engineering application. Hydrogeology and Engineering Geology, 2002,(3):52-55. (in Chinese) [7] Zhu Hehua, Zheng Guoping, Wu Jiangbin. Study on Ground Data Model Based on Drill Hole Information. Journal of TongJi University, 2003, 31(5): 535-539. (in Chinese) [8] Simon W. H. 3D Geosciences modeling: Computer technique for geological characterization. Hong Kong: Springer, 1994. [9] Arnaud de la Losa, Bermad Cervelle. 3D Topological modeling and visualization for 3D GIS. Computers & Graphics, 1999, 2:3, 469-478. [10] Kingston, R. et al. Web-Based Public Participation Geographical Information System: An Aid to Local Environmental Decision-Making. Computer, Environment and Urban Systems, 2000, Vol. 24: 109-125. [11] Wang Chunxiang, Bai Shiwei. Study on application of 3DSIS to geotechnical engineering. Rock and Soil Mechanics, 2003:24(4): 614-617. (in Chinese) [12] Zhang Peng, Zhang Zaiming, Yang Yuyou. Transfer Control and Visualization of Geotechnical Engineering Investigation Data. Geotechnical Engineering Technique, 2008, 22(2). (in Chinese) [13] Zhang Zaiming. Development and application of CAGE. Engineering Investigation, 1997, 4:1-5. (in Chinese) [14] Chen Lei 㸪 Zhang Fang. Study of 3D-GSIS geotechnical engineering information system. BGI Engineering Consultants LTD research report, 2008. (in Chinese) [15] Shen Xiaoke, Chen Lei. Geotechnical Engineering Investigation Information Practice and Reflection. Beijing Survey and Design, 2006, 1:14-20. (in Chinese) [16] HAN Xuan, LI Ning, Jamie R STANDING. An adaptability study of Gaussian equation applied to predicting ground settlements induced by tunneling in China. Rock and Soil Mechanics, 2007: 28(1). (in Chinese) [17] Han Xuan, Zhang Zaiming. The analysis and prediction of tunneling induced building movements㸬 BGI Engineering Consultants LTD research report, 2008. (in Chinese) [18] Zhang Zaiming, Du Xiuli, et al. The influence and Engineering Countermeasures of Beijing groundwater on the tunnel planning and construction. BGI Engineering Consultants LTD research report, 2009. (in Chinese)

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393

Research and Application of Artificial Ground Freezing Monitoring and Management System

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a

Xiangdong HUa, Chunlin ZHOUb, 1and Zhiyong ZHOUc Department of Geotechnical Engineering, Tongji University, Shanghai, P. R. China b China State Construction Engineering Corp., Beijing, P.R. China c Ningbo Rail Transit Group Co. Ltd., Ningbo, P.R. China

Abstract. Artificial ground freezing method (AGF) is used more and more popularly in underground engineering of soft soil area. However, because of the flaws of freezing theories and the lack of AGF real-time monitoring data, high risk exists in this method. Therefore, it is urgent to set up AGF information systems for freezing construction monitoring and analyzing. According to the special features of AGF and combined with temperature measurement technology, communications transmission technology and computer technology, a network information system for AGF method named GeoFreezer is studied and developed. This system could fulfill the request function of AGF management, including remote, real-time monitoring, visualization, analyzing, and so on. The main hardware of GeoFreezer is composed of advanced digital temperature sensor, “1wire bus” network, fiber technique, database server, personal computers and so on. Software environment includes Borland Delphi 7.0 as developing platform and Microsoft SQL Server 2000 as database. Client/Server network structure is used for data exchanging and back-up. For the requirement of AGF safety, remote and real-time monitoring is realized and the monitoring rate could be higher than 10 times per minutes. After acquiring data, it could be visualized in kinds of forms such as 2D chart and 3D model, based on which, temperature field, distributing character of frozen soil wall, and the formulas of frozen soil wall characteristics under non-zero freezing temperature are analyzed. The application of GeoFreezer in cross passage construction of Shanghai Yangtze River Tunnel project is specified in the end. The good monitoring performance, safety controlling, and decision-making service proves the practicability and advantage of this system. Keywords. Artificial ground freezing, temperature monitoring, visualization, telnet control, cross passage

Introduction Artificial Ground Freezing Method (AGF) is a special construction technique, which uses artificial freezing method to freeze underground water, change natural soil to frozen soil, enhance its strength and stability, and isolate underground water, so that construction could be executed in the protection of frozen soil curtain [1]. AGF has become one of the main methods to solve difficult problems for its advantages such as 1 Chun-Lin Zhou: China State Construction Engineering Corp, A12/F, CSCEC Mansion, No.15 Sanlihe Road, Beijing, P.R. China 100037, P.R. China. E-mail: [email protected]

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good water isolation, well controlling, high strength, little impact on the environment, and so on. With the advantage of preventing water inrush and distortion caused by digging and excavating without limitation of the supports range and depth, AGF become one of the most effective methods to solve the problems of ground stabilization in underground construction [2, 3]. The freezing procedure is susceptible to the influence of construction, and even small defect of frozen soil curtain would cause potential safety hazard, so AGF is also a high risk method [4]. The properties of frozen soil, including temperature field, thickness and shape, keep changing in the freezing and construction process. Accordingly, it is very important to master the operating status of freezing system and characters of frozen soil curtain to prove the safety of freezing engineering. In China, AGF monitoring technique was lagged behind of the requirement of construction in the following 2 aspects [5-8]: (1) Temperature data is acquired with thermocouple, galvanometer and multicircuit switch. 2 wires should be used for monitoring 1 point, which makes the installing and operating of monitoring system arduous and fallible. (2) It always takes a long time to acquire and deal with temperature data manually. Besides, data cannot be demonstrated with images and curves automatically and data sharing is also a problem. GeoFreezer as an artificial ground freezing method monitoring system is developed to solve these problems. It realizes automatic and telnet data acquiring and sharing with techniques including fiber, RS485 bus, database, internet, and so on. With convenient installation, automatic data acquiring, real time telnet monitoring and various ways of data exhibition, GeoFreezer fills the blank of this domain very well.

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1. Ground Freezing Monitoring and Management System Geofreezer system consists of hardware and software. Hardware includes equipments of temperature monitoring system such as computers, LTM-8000 series temperature collection modules, communication network and sensor system; software includes program and database. 1.1. Hardware Hardware includes computers, LTM-8000 series temperature collection modules, communication network and sensor system (Figure 1). 1.1.1. Computers Computers are the core part of GeoFreezer. There are 3 kinds of computers according to their function in the system—controlling computer, server and clients. Controlling computer works in field to acquire temperature data from sensors through temperature collection modules and send it to server; server works as a database on internet, saves data from controlling computer and publishes to clients; clients can view monitoring data with various ways and levels according to their authorities.

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Figure 1. Hardware scheme

1.1.2. LTM-8000 Series Temperature Collection Modules LTM-8000 series temperature monitoring modules are used to get temperature data from sensors in responds to the orders of controlling computer. Orders are sent with serial port communication, and the ports are isolated and protected in order to prove the real-time information interchanging with high speed stably [9].

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1.1.3. Communication Network Communication network includes RS485/fiber bus network and 1-wire bus network. RS485/fiber bus network is used to connect controlling computer and modules, which consists of optical terminal, fiber and RS485/fiber signal switch module. The area of freezing field may be very large, and the modules should be distributed to each main part of field, controlling computer would be far (10km sometimes) from modules consequently. The connection distance of traditional RS485 is 1200m in theory but only about 500m in practice. RS485/fiber bus network can expand communication distance to 50km, with which stable and fast connection of controlling computer and each module can be realized. 1-wire bus technique combine data and power wire together, and supply for all the sensor points with 1 wire [10]. This technique saves wires, makes sensors installation and maintenance more convenient, and enhances the reliability of system. 1.1.4. Sensor System Sensor system includes sensors and cables. Digital temperature sensors DS18B20 from DALLAS Company are used. This kind of sensor can get power from data wire directly without static power consumption. Considering about the complex circumstance and high interference in site, shielded cables are used to guarantee the efficiency and accuracy of data transmission [11].

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1.2. Software According to the technique characters of AGF and years of monitoring experience, GeoFreezer software is developed with Borland Delphi as development tool and Microsoft SQL Server as database in Windows XP system. 1.2.1. Database System Database system is created based on SQL Server 2000. There is a lot of attribution information and monitoring data need to be managed by database. Attribution information includes engineering information, boring type, boring information, freeze system information, signal type, signal group information, point information, sensor information, and so on. Monitoring data includes frozen soil temperature and brine temperature. The data corresponds to thirteen tables in the database. All the tables are shown in Table 1 according to their content and level. Table 1. Tables in database Engineering base information

Engineering information

ProjectInfo

Engineering parameter

ParameterInfo

System signal

LanceInfo

Freeze system

FreezeSystemInfo

FreezeSystem TypeInfo

Drill information

DrillInfo

DrillTypeInfo

Group information

GroupInfo

SignalTypeInfo

Monitoring position Sensor information Copyright © 2010. IOS Press, Incorporated. All rights reserved.

Monitoring type information

Layers

DetectPosInfo

Sensor

Monitoring data

SensorPosInfo SensorInfo

DetectedValueInfo

1.2.2. GeoFreezer Interface of GeoFreezer is shown as Figure 2. The most fundamental and important function of this system is monitoring real-time factors in freezing process, such as frozen soil temperature and brine temperature. They are useful for adjusting parameters of brine supplying system, preventing potential risks, and analyzing the influence of frozen soil melting process to underground structures. Besides, data management, data analysis and telnet monitoring are also important functions. With GeoFreezer all of functions mentioned above could be realized easily. The main functions are summarized as: (1) Database management of information includes engineering summary, serial numbers and properties of frozen system, kinds of boring hole, measuring points and sensors. (2) Multiple-projects management. (3) Automatic monitoring. (4) Data querying and displaying with forms of numbers, curves and figures. (5) Computing the frozen soil curtain thickness and average temperature with monitoring data.

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(6) Simulating and visualizing temperature field. (7) Generating report table automatically according to the need. (8) Data security is proved by saving it in telnet database, which also can be backup and restored easily. (9) System operation is controlled by account and password to prove system security.

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Figure 2. GeoFreezer interface of GeoFreezer

2. Project Application 2.1. Project Outline and Measuring Scheme The construction of the Yangtze River Tunnel was carried out by ɮP air pressed slurry shield machine with the digging distance of 7.5 km [12]. The whole tunnel is finished by one boring. 8 cross passages are set between east and west line in consideration of people escaping in fire and other accidents. For the cross passages are set under the bottom of river, surrounding soil is flow plastic and saturated with low autostability, as a result, the natural soil is not stable and high risk of drift sand and piping effect after excavation. According to the situation above, AGF is used to reinforce soil layer [13]. As a typical part in these projects, AGF in cross passage No.3 (CP3) is selected to introduce the application of GeoFreezer system. AGF for CP3 consists of up and down lines freezing systems. There are 2 freezing pipes rings, in which up line freezing system supplies 24 freezing holes and down line supplies 16 freezing holes. 7 temperature monitoring holes are set in the project, in which 4 for up line and 3 for down line (Figure 3). 105 monitoring points are set along

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the top, bottom and sides of the passage. Temperature collection modules are disposed in up line. Cables of down line connect modules through a special hole.

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Figure 3. Designing of freezing pipes and temperature monitoring holes

The distance between controlling computer and monitoring points in CP3 is about 6km. Optical fiber technique was used to solve the connecting problem. Normal monitoring frequency was 1 time per half hour, and 1 time per 10 minutes at the initial phase. For the well operation of communication network, temperature data was acquired fluently in the whole project. According to the situation of internet connection, system switched to internet/local mode automatically, uploaded data to server while connection well, otherwise saved it locally. 2.2. Application of GeoFreezer After boring and equipments installing, AGF for CP3 started in 2008.2.6 to freeze soil layers. With high monitoring frequency, the process of soil layer freezing was supervised carefully. After about 2 months, effective thickness and temperature of frozen soil were confirmed, excavation started in 2008.4.10, after that normal monitoring frequency was used. Excavation in frozen soil layer is fast and safe, concrete structure supports were finished in 2008.5.4, then hot brine of 70-90Υ was used for artificial thawing. The construction process may influence a lot to the temperature field of frozen soil curtain, so temperature field research is helpful to construction guidance. With GeoFreezer frozen soil temperature field could be computed in real-time and displayed with 2D and 3D images. With monitoring data of 2008.4.6, the processing is illuminated.

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In GeoFreezer, data could be acquired directly from database server or local database. The locations of each freezing pipe, temperature of each monitoring position could be exported automatically, but before computing some parameters need to be confirmed manually: space between freezing pipes need to be input, computing time and method should be chosen (Figure 4). The result is shown on the interface. The effective thickness is 2.7m, average temperature in the effective thickness of frozen soil curtain is -17.62Υ. According to computing result, 2D image of temperature field is displayed in Figure 5.a. In the assumption of temperature continuous changing, 3D image is simulated as Figure 5.b. It could be found that full closure was finished, which means the frozen columns expanding gradually and across each other, finally, frozen soil curtain was formed between the 2 pipe circles. The center of circle was not frozen, which could reduce excavating difficult.

Figure 4. Computing interface of GeoFreezer

a

b

Figure 5. Simulation of frozen soil in 2D(a) and 3D(b) image

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3. Conclusion AGF is widely used in many fields of underground construction. In order to enhance management level, constructing efficiency, security and quality, GeoFreezer is developed as a freezing monitoring and management system. After having described the hardware configuration and software developing processes, the application of this system in freezing engineering of the 8 cross passages in the Yangtze River Tunnel is specified. Based on this, the following conclusions are drawn: x GeoFreezer system can be adapted to freezing engineering site situation of high humidity, vibration and disturbing with proper operation, low failure rate, monitoring data acquiring stably in real-time. x The validity and effectiveness of GeoFreezer are proved by engineering application. The frozen soil condition can be described in various forms in the system, and this method can ensure the safety of the project. x Temperature fields can be computed and visualized with 2D and 3D images in this system. The validity is approved by comparing with actual situation.

References [1] [2] [3] [4] [5]

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[6] [7] [8] [9] [10] [11] [12] [13]

Jiajie Weng, Special constructional engineering of mine shaft and drift, China Coal Industry Publishing House, Beijing, 1991. Ma Wei, Wu Ziwang, Zhang Changqing, Strength and yield criteria of frozen soil, Journal of Glaciology and Geocryology 15(1993), 129-133. David C. E. Thermal Analysis, Construction, and Monitoring Methods for Frozen Ground, Virginia: American Society of Civil Engineers, 2004. LIU Rui-feng, ZHANG Qing-he, HU Xiang-dong, TAN Li-hua, Application of artificial horizontal ground freezing method to recovering collapse tunnel in Shanghai Metro, Journal of Anhui Institute of Architecture & Industry 16(2008), 21-25. HU Xiang-dong, MAO Lianggen, QIU Fan, Multifunctional Information Construction System for Artificial Ground Freezing Method, Proceedings of The 2nd National Symposium on Geotechnical Engineering (Part II). Beijing: Science Press, (2006), 186-191. Zhang Jianbo, Han Song. Simple Discussion of the Development and the Actuality of the Temperature Measuring, Metrlogy and Measurement Technique, 28(2001), 14-15. ZHAN G Xiaoguang, YUE Fengtian, ZHAN G Fengmin, et al., Research and development on measurement and test system of freezing temperature field in freezed mine shaft, Coal Science and Technology, 30(2002): 7-9. Yue Fengtian, Dai Yixie, Peng Lianhai, et al., Distributed Computer Testing System of Shaft Freezing Temperature Field, Journal of China University of Mining & Technology, 28(1999), 384-385. Li Min, Meng Chen. Digital Temperature Measurement Module LTM8003 and Its Application, International Electronic Elements, (2002), 50-52. HU Xiang-dong; LIU Rui-feng. Temperature monitoring system for freezing method based on “1-wire bus”, Chinese Journal of Underground Space and Engineering, 3(2007), 937-940. Dallas Semiconductor/Maxim. DS18B20 Programmable Resolution 1-Wire Digital Thermometer, http://www.dalsemi.com/, 2001. Z.H. Huang, X.D. Hu, J.Y. Wang, et al., Key techniques in cross passage construction of Shanghai Yangtze River Tunnel by artificial ground freezing method, The Shanghai Yangtze River Tunnel Theory, Design and Construction – Huang (ed), (2008), 205–210. Hu Xiangdong, Cheng Hua. Experiment report on frozen soil physico-mechanical properties of strata at the estuary of the Yangtze River for the Shanghai Yangtze River Tunnel Project, Shanghai: Department of Geotechnical Engineering, Tongji University, 2007.

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Research on Computer-Aided Decision-making Software of Advanced Geological Prediction in Tunnel a

Lubo Menga, 1, Tianbin Lia and Xing Fanga State Key Laboratory of Geohazard Prevention & Geoenvironment Protection, Chengdu University of Technology, Chengdu, PRC

Abstract. To enhance the speed and accuracy of the tunnel geological prediction based on the engineering geology, rock mechanics and geophysical exploration, non-linear intelligence in science and technology, such as fuzzy comprehensive evaluation, fuzzy neural network, etc. are adopted to solve some key technical issues, such as comprehensive forecast of unfavorable geological conditions. Ultimately, the computer-aided software of tunnel advanced geological prediction is developed. The prediction function of the software is divided into two phases: reconnaissance stage and construction stage. Among the items concerned, reconnaissance stage mainly focuses on inquiring the basic geological conditions about tunnels, and preliminarily predicting rock burst, large deformations and water inflow. Construction stage mainly focuses on prediction of basic geological conditions of tunnel, unfavorable geological conditions like fault, broken rock, cave and rich water as well as construction geological disasters, such as, rock burst, large deformation, collapse, water gushing and gas outburst. Through its application in several long tunnels, the software presents its advantages of timeliness and high prediction accuracy.

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Keywords. Prediction, geological disasters, tunnel, software, intelligent science technology

Introduction Tunnel construction is a very complex process, especially when making judgments of possibility of construction geology disasters, decision-makers have to face a great deal of complex information and data. How to effectively organize and analyze scientific data itself is an arduous and time-consuming and laborious work, which requires utilizing computer to develop tunnel advanced geological prediction system for decision makers to make decision. In the tunnel and underground engineering, there have been some research results about computer decision-making system, Williams (1994), Carr (2001), Huang (2005) studied the risk information databases of shield tunneling, construction risk management and controlling software; Wang (2002) developed a rock tunnel construction intelligent decision assistant system; Meng (2005) studied highway tunnel information-based construction and developed a computer decision making software system; Shi (2003) developed a information management 1 Corresponding Author: Lubo Meng, Chengdu University of Technology, Shilidian, Chengdu, Sichuan, 610059, China.˗ Email: [email protected].

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system of tunnel construction advanced prediction; Long (2006) established information management system for TSP. The software mentioned above focuses on the data management of advanced prediction, forecasting function is relatively weak, and unable to forecast unfavorable geology and construction geological disasters during the tunnel construction. Thus, the level of modern information technology in the tunnel geological prediction is low, relatively integrated tunnel advanced geological prediction software for technicians has not been formed.

1. System Design 1.1. Overall Design Advanced geological forecasting is divided into two phases: reconnaissance stage and construction stage. Among the items concerned, reconnaissance stage mainly focuses on inquiring the basic geological conditions about tunnels, and preliminary predicts rock burst, large deformations and water inflow. Construction stage focuses on predicting basic geological conditions, unfavorable geological condition, and construction geological disasters. Overall structure of software is shown in Figure 1. Tunnel Advanced Geological Prediction Software Tunnel Management

geological information

Data Management

geophysical information

Reconnaissance Prediction

query of basic geo-condition

prediction of disaster

Construction Prediction

prediction of basic geo-condition

prediction of disasters

Set

adverse geological body

Figure 1. Overall structure of software

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1.2. Software Function 1. Tunnel management It mainly manages the tunnel projects, such as building a new tunnel, opening a tunnel, removing a tunnel, modifying tunnel information, backing up tunnel data, recovering tunnel data. 2. Data management It mainly manages geological information during reconnaissance stage, information of geological survey and test of workface during the construction stage, information of ground stress and rock mechanical properties, monitoring information of tunnel wall displacement, advanced detection and geophysical information. 3. Advanced prediction during reconnaissance stage It mainly inquires the basic geological condition, and preliminary predicts rock burst, large deformations and water inflow. Among the items concerned: x Inquiries of basic geological condition includes: lithology, rock integrity, faults, groundwater, ground stress; x Prediction of construction geological disasters includes: rock burst, large deformation. Prediction methods include comprehensive geological analysis, strength stress ratio method. x Advanced prediction during construction stage

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It mainly predicts the basic geological conditions, unfavorable geological body, and construction geological disasters, and on this basis, comprehensive inquiries and prediction is made. Among the items concerned: x Prediction of basic geological conditions It includes prediction of lithology and rock strength, prediction of rock integrity, fault prediction, prediction of groundwater, high ground stress prediction. x Comprehensive forecasting of unfavorable geological body Unfavorable geological conditions, such as fault, broken rock, rich water conditions, cave, and so on, are comprehensively predicted by fuzzy neural network method on reconnaissance geological information, construction geological information, the detecting results of Tunnel Seismic Prediction (TSP), Ground Penetrating Radar (GPR), Transient Electromagnetic Method (TEM), and Bore Electrical Ahead Monitoring (BEAM). x Prediction of construction geological disaster It includes the prediction on the rock burst, large deformation, collapse, risk of water gushing, gas outburst. Prediction methods include fuzzy comprehensive evaluation method, stress strength ratio method, neural network prediction method, wall rock displacement monitoring method. x Query and Prediction Geological information of reconnaissance stage and construction stage, a variety of geophysical information and advanced drilling information, as well as a variety of basic geological conditions, unfavorable geological conditions, forecast results of construction geological disaster are uniformly inquired for certain pile NO., and to form a preliminary conclusions of comprehensive forecast. And the conclusions can be manually intervened, saving the results, and generate a word document. x Setting It sets system variables, parameters and toolbar, etc, such as allowable displacement of wall rock.

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2. Software Interface and Key Technique 2.1. Main Interface of Software Software name is “advanced geological prediction computer-aided decision-making system of tunnel”, according to the overall structure and main function of the software, menus, toolbars, and status bar of the main program interface is designed (Figure 2). Among the items concerned, the first toolbar is advanced prediction column of reconnaissance stage, the second toolbar is advanced prediction column of construction stage, bottom-left part of window shows the name of tunnel which has been connected.

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Figure2㸬Main program interface

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2.2. Comprehensive Forecasting of Unfavorable Geological Condition Centered on geology analysis, and combined with geophysical detecting, the software adopts fuzzy neural network method to predict caves, water-rich conditions, faults, wall rock integrity, soft rock and other unfavorable geological body. Indices of comprehensive prediction are divided into two types: geological indices and geophysical indicators indices. Among them, geological indices of reconnaissance stage are selected as: whether is karst region, water-rich conditions, the regional fault information, the integrity of rock, rock strength information; geological indices of construction stage are selected as: development characteristics of cave, groundwater conditions, fault precursor characters, joint and fracture characters, rock strength; indices of TSP method are selected as: Vp / Vs variation, Vp velocity variation, positive and negative amplitude of P wave, reflection interface features; index of GPR is selected as: comprehensive waveform characteristics of electromagnetic waves; index of TEM is selected as: apparent resistivity variation; indices of BEAM are selected as: percentage frequency effect (PFE) values, resistivity values. The software prediction flow chart is as Figure 3. First, membership function is used to fuzzily quantify the prediction indices, which will be inputted into neural network input layer. Secondly, typical samples are trained by BP neural network algorithm, using the trained BP network to predict, and calculating the maximum degree of membership. Finally, according to the principle of maximum membership grade, the output value is fuzzily restored, and comprehensive prediction results of unfavorable geological conditions are gained.

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Input stake No.

Inquire data

Input trained samples indexes

Fuzzily quantify by membership

Fuzzily quantify by membership

Train network by BP method

no Weight?

yes

yes import trained net structure

Calculate maximal membership

Re-train Train successful? no

Fuzzy reducing of prediction

Figure3ˊFlow chart of Fuzzy Neural Network Comprehensive Prediction

2.3. Comprehensive Prediction of Construction Geological Hazards Fuzzy comprehensive evaluation method is used to predict the construction geological hazards such as rock burst, large deformation, fault collapse, risk of water gushing, and so on. Taking the prediction of fault collapse for example, forecast flow chart is shown in Figure 4. Input stake No.

lithology mechanical properties

Inquire evaluation indexes of fault

compound features consolidation degree

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Establish the matrix

Calculate the weight of indexes

AHP rock structure type

Import trend of fault Calculate the maximal degree of membership

ground water ground stress

Predict risk level of fault collapse

Figure 4. Flow chart of fuzzy comprehensive prediction for fault collapse

First, the indices of fault collapse hazard assessment are given a mark through the analytic hierarchy process (AHP), using the geometric mean method or other methods to calculate the eigenvector corresponding to the maximal eigenvalue of matrix, and the weight of each index is obtained. Secondly, membership functions are used to fuzzily quantify each evaluation index, the matrix is established. Thirdly, the maximum degree of membership is calculated by evaluating the weights and matrix.

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Finally, according to the principle of maximum membership grade, the risk level of fault collapse is obtained.

3. Applications Taking the comprehensive prediction of unfavorable geological condition at K35+706~K35+676 of TongLuoShan tunnel for example. According to geological condition of the survey and design section, geological condition of tunnel workface, the detecting results of TSP, GPR, BEAM method, the value of comprehensive prediction indices of unfavorable geological are determined, as shown in Table 1.

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Table1.

Indices value at K35+706~K35+676

Name of evaluation index

Value of evaluation index

Rock integrity of Design Stage

Cracks are developed, very broken rock

Water-rich condition of Design Stage

Rich groundwater

Whether is karst region

True

Crack development level of workface

Developed

Water-rich condition of workface

Stock-like water gushing

Caves precursor characters of workface

No

Comprehensive features of electromagnetic wave tested by GPR

Noisy wave mode

Vp variation feature tested by TSP

Stable

Vp/Vs variation feature tested by TSP

Very little

PFE tested by BEAM(%)

-7

Resistivity tested by BEAM( : ˜ m )

20

Before predicting unfavorable geological body, the neural network is trained in this software, as shown in Figure 5. After completion of the sample training, the software will save the network weights. The prediction interface of software is shown in Figure 6, through fuzzy neural network comprehensive prediction, prediction results of rock integrity degree, water-rich conditions, karst caves and other unfavorable geological condition are as follows: the rock is relatively broken, with abundant groundwater, and contains cavities. After tunnel excavated, the rock was dolomitic limestone at K35+706~K35+676, developed three groups joint fracture. There happened gushing water at large area of the workface, and seeped water at the local position. The stability of wall rock was poor. It can clearly be seen that the wall rock integrity, groundwater, caves and other actual situation are in accord with the results of software prediction by fuzzy neural network.

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Figure5 Samples training of fuzzy neural network

Figure6ˊComprehensive prediction of unfavorable geological body at K35+706~K35+676

4. Conclusions The software of tunnel advanced geological prediction is developed. The prediction function of the software is divided into two phases: reconnaissance stage and construction stage. Among the items concerned, the function of reconnaissance stage includes: inquiring the basic geological conditions about tunnels, and preliminary predicting rock burst, large deformations and water inflow. The function of construction stage includes: the prediction of basic geological conditions, unfavorable geological body, construction geological disasters, and so on. After application in

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several long tunnels, it states clearly that this software has the advantage of timeliness and high accuracy of the prediction, enhances the efficiency and accuracy of prediction, and can support the important decision-making information for engineers and technicians when solving issues of geological disasters.

Acknowledgements This work was supported by National Natural Science Fund Project (40772176) and Sichuan Province Youth Technology Fund Subject (09ZQ026-083) and Open Fund Projects of State Key Laboratory of Geohazard Prevention & Geoenvironment Protection (GZ2007-06).

References

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[1] T.M. Williams, Using a risk register to integrate risk management in project definition. International Journal of project Management12 (1994),17 -22. [2] V.Carr, J.H.M. Tah, A fuzzy approach to construction project risk assessment and analysis: construction project risk management system. Advances in Engineering Software 32 (2001), 847-857. [3] H.W. Huang, M. Zeng, L. Chen, et al, Risk management software (TRM1.0) based on risk database for shield tunneling. Chinese Journal of Underground Space and Engineering 6 (2005),36-41. [4] S.H. Wang, F.S. Zhu, K. Zhang, et al, Research intelligent decision-making aided system for rock tunnel construction. Chinese Journal of Rock Mechanics and Engineering 4 (2002),590-594. [5] L.B. Meng, T.B. Li, Y.L. Li, et al, Study on the highway tunnel information construction and computer assistant decision-making system. Earth and Environment 33 (2005) (S1), 79-83. [6] Y.Q. Shi, L.J. Wang, M.Z. Bai, Geologic advanced forecast management information system. Site Investigation Science and Technology 6 (2003), 12-15. [7] H.B. Long, Y.G. Zhao, Y. Sun, et al, Management information system of tunnel TSP based on three-layer C/S architecture. Computer Engineering and Applications 3 (2006), 230-232.

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Research on Emergency Rescue Decision Support System of Geo-hazards under the Conditions of Extreme Snow and Ice Disasters Shimei WANGa, 1, HaiFeng HUANGa, Gang WANGb and Liang-Chao ZOUa Key Laboratory of the Ministry of Education for Geo-hazards in Three Gorges Reservoir Area, Yichang 443002, China b Liaoning Institute of Engineering Investigation, Jinzhou 121000, China

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a

Abstract. The emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters is a new challenge faced by government departments. The fast and effective emergency response to geo-hazards in the complicated meteorological environment demands the help of the IT means, in particular the decision support system. Based on the analysis of the characteristics of the emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters, this paper proposes the design thought of the decision support system for emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters. It analyzes the overall objective and functional requirements of the system, and designs the overall architecture, logical structure, integration mode and development environment of the system. Its final aim is to build a spatial decision support system based on a B/S (browser/server) mode and a WebGIS platform that offers the functions of allocating the information of geo-hazard points in the Web environment, performing the stability assessment, hazard assessment and risk decision-making. And the system will generate emergency rescue plans and schemes automatically that can assist leaders to decide and carry on the emergency rescue and rescue work. Keywords. Extreme snow and ice disasters, geo-hazards, emergency rescue, decision support system

Foreword In 2008, the southern China suffered a rare ice and snow disaster, which resulted into the blocked traffic and the interruption of power and communication service. It caused an extreme difficulty to the emergency rescue of geo-hazards under the conditions of extreme ice and snow disasters. To address the terrible losses caused by the ice and snow disaster as well as difficulties and problems encountered in the course of emergency rescue, the Ministry of Science and Technology of the People's Republic of China has established a special scientific & technological support program "Study on the Technology of Preventing and Controlling Geo-hazards under the Conditions of Extreme Snow and Ice Disasters" [1], and the "Study on the Decision Support System 1

Corresponding Author: Shimei Wang; E-mail: [email protected]

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for Emergency Rescue of Geo-hazards under the Conditions of Extreme Snow and Ice Disasters" is part of this program. By leveraging the modern risk decision theory, artificial intelligence theory, cost-benefit analysis and data warehouse and spatial data mining technology, this project aims to build the emergency rescue and fast decision method system for geo-hazards under the conditions of extreme ice and snow disasters. The project will study the architecture, extrapolation mode and decision-making model with respect to the integrated database and decision support system for the emergency rescue of typical geo-hazards under the conditions of extreme ice and snow disasters. It also will set up the decision support system for the emergency rescue of geo-hazards then provide technical support for the decision-making of emergency rescue of geo-hazards under the conditions of extreme ice and snow disasters. This paper mainly describes the design thought of the system and analyzes the construction objective and functional requirements of the system. It also designs the logical structure, integration mode and development environment of the system then introduces the major program modules of the system.

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1. Design Thought The decision support system (DSS) is a computer application system which can assist decision makers to perform semi-structured or unstructured decision under the human-computer interaction mode through data, models and knowledge. It provides decision makers with the environment of problem analysis, modeling and simulation of decision process and scheme, and calls different information resources and analysis tools to help them improve the decision level and quality. Under the conditions of extreme ice and snow disasters, the emergency rescue of geo-hazards involves not only the pure geo-hazards, but also ice and snow disasters and other secondary hazards caused by the ice and snow disasters, such as the damage to the traffic, electric power and communication facilities [1]. Therefore, the decision support system for the emergency rescue of geo-hazards under the conditions of extreme ice and snow disasters should be built on an integrated database and multi-field hazard forecast & assessment system involving multiple fields and multiple data types. It needs to study the particularity of the emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters. To this end, the design thoughts of the system are as follows: (1) Study the characteristics of the emergency rescue of geo-hazards when the traffic, electric power and communication facilities are damaged; analyze the data information involved in the emergency rescue of geo-hazards under this special condition, including meteorological information, ice and snow disaster information, geo-hazard information, and information on the damage to traffic, electric power and communication facilities; and construct a data framework combining a variety of all basic data, and develop an integrated database building model involving multiple fields and multiple data types; (2) Study the hazard evaluation system of the emergency rescue of geo-hazards under extreme ice and snow disasters involving multiple departments and multiple fields, and establish an integrated method base involving multiple fields and multiple knowledge types by integrating various method bases, including geo-hazard assessment, forecast and early warning, risk evaluation, risk decision and emergency rescue decision; and (3) Analyze the data information system and the forecast & assessment method system for the emergency rescue of geo-hazards under the conditions of extreme snow and ice disasters

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thoroughly, identify the functional requirements, basic framework and development environment, and on this basis, design the overall architecture of the decision support system, determine the expression method and development environment of the database and knowledge base as well as the reasoning pattern and development environment of the decision inference engine. It will study the integration interface technology of different functional modules which can realize the seamless connection of the entire decision support system from data information, knowledge method and reasoning to decision-making.

2. Overall Design 2.1. Overall Objective Its final aim is to build a spatial decision support system based on a B/S (browser/server) mode, a WebGIS platform and a decision support functions as core for the emergency rescue of geo-hazards under the conditions of extreme ice and snow disasters that offers the functions of allocating the information of geo-hazard points in the Web environment, performing the stability assessment, hazard assessment and risk decision-making. The system will generate emergency rescue plans and schemes automatically that can assist leaders to decide and carry on the emergency rescue and rescue work.

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2.2. Functional Requirements The basic procedures for the emergency rescue of geo-hazards under the conditions of extreme ice and snow disasters include the following: 1. Emergency field survey and emergency monitoring on the hazard body; 2. Stability analysis, hazard assessment and risk decision-making for the hazard body on the basis of analyzing the survey and monitoring results; and 3. Propose the emergency rescue scheme [3-5].The emergency survey includes the survey on the landform and topography, geological conditions, deformation behavior, inhabitant condition, building facilities, etc. of the hazard site. And it will use macroscopic geological judgment as the main analysis and assessment method. The emergency monitoring includes the monitoring scheme, monitoring content, monitoring instruments and monitoring arrangement. It will analyze the monitoring data by mathematical model. The assessment includes the assessment on the stability and hazard of the hazard site, and decision-making for resulting social risks. The emergency decision-making includes the selection of emergency scheme (such as removal, reinforcement, or monitoring, etc.) and the determination of the retreat route, detailed technical plan for reinforcement, or detailed schedule for monitoring. So the decision support system for the emergency rescue of geo-hazards under the conditions of extreme ice and snow disasters shall be provided with the following major functions: (1) Basic information inquiry. The system can provide the location, basic landform, traffic, meteorological condition and other basic information of the hazard site for the terminal user based on a GIS platform in the Web environment. (2) Emergency survey and emergency monitoring.

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According to the type and characteristics of the hazard body, the system can give the detailed proposals for the content, requirement and method of the emergency survey as well as the monitoring scheme, monitoring content, monitoring instruments and monitoring arrangement of the emergency monitoring. (3) Stability assessment and risk assessment. Based on the results of emergency survey and emergency monitoring, the system can perform the assessment on the stability of the hazard site as well as the risk assessment on its vulnerability and hazard as per a certain mathematical model and assessment method. (4) Decision-making for emergency rescue schemes. The system can propose the decision scheme in accordance with the information of the emergency survey and emergency monitoring, and the results of the stability assessment and risk assessment. (5) Update and maintenance. Every module of the system shall have the functions of update and maintenance. For example, the basic landform database, model base, method base and knowledge base can be updated in time to ensure the advancement and applicability of the system. User

Human-computer interaction system (General control system)

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Knowledge base Inference engine

Database

Method base

Result

Conclusion Figure 1. Overall Architecture and Workflow Diagram

2.3. Overall Architecture The decision support system for the emergency rescue of geo-hazards under the conditions of extreme ice and snow disasters includes five functional program modules:

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the general control system (human-computer interaction system), database, model base, knowledge base and inference engine. The general control system realizes the general calling of all submodules; all the bases are closely related, for example, the parameters in the model base can be extracted from the database and the calculation results of the model base can be added to the database. The mathematical model in the model base serves as a sort of process-type knowledge in the knowledge base, which can be called by the knowledge base. The inference engine matches the current information of the database with the rules in the knowledge base and educes a reasoning result. The reasoning result may be the final conclusion or intermediate result which can be added to the database to re-participate in the reasoning process as new data or used as a signal for starting the model to operate. The overall architecture and workflow of the system are shown as Figure 1. 2.4. Logical Structure The logical structure of the system includes the application system, application platform, data and hardware support system. The application system is composed of the user interface, system menu, etc. The application platform is composed of the professional model, calculation server, Web server, application server middleware, etc. The data support system is composed of the basic database system, model base and method base system. And the hardware support system is composed of basic hardware facilities such as computer network and communication system. The structural black diagram is shown as Figure 2.

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2.5. Human-Computer Interface In order to ensure effective communication between the system and user, a friendly human-computer interface must be built. In accordance with the functions of the system and the design principle of the user-friendly interface, the menu-type interface is adopted for the user’s demands. Based on complete system functions, a human-computer interface with visual, vivid and beautiful layout display and operational environment can be provided for the user by minimizing the complexity of the user’s subjective operation and making the operation formatted and simple. 2.6. Development Platform The development platform mainly includes a high-level language development platform, a GIS development platform and a large relational database platform. This system mainly adopts C# object-oriented high-level language for development, and the development platform is Visual Studio .NET. MapGIS is adopted to construct the GIS platform. The relational database platform is SQL Server SQL Server 2005. C# is a completely object-oriented high language, which is a special programming language for .NET Framework developed by the Microsoft. It has powerful .NET class library support, closely combines with Web, supports most Web standards such as HTML, XML, SOAP, etc. Visual Studio .NET is a complete set of development tools for building ASP Web applications, XML Web services, desktop applications, and mobile applications. Visual Basic .NET, Visual C++ .NET, Visual C# .NET, and Visual J# .NET all use the same integrated development environment (IDE), which allows them to share tools and facilitates in the creation of mixed-language solutions [6].

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MapGIS Building Platform is a new generation of Web-based development platform oriented by distributed service components, and the system based on network-controlled workflow model realizes flexible adjustment and customization of business. It has visualization workflow development environment, and the corresponding business process can be designed only by "dragging" the corresponding event element, which complies with the human-oriented characteristic of workflow. Interaction interface Integrated information service system

Emergency survey

Risk assessment

Decision-making for emergency rescue

Emergency monitoring

Application tier System maintenance and help

Application development and operating platform Professional model middleware

WebGIS service middleware

Intermediate tier

Data support system Spatial database

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Attribute database

Data support tier

Hardware support system Computer network

Communication equipment

Other equipment

Hardware support tier

Figure 2. Logical Structure Framework Diagram

2.7. Integration Mode This system adopts a B/S (browser/server) mode. The B/S structure is the browser and server structure, which is a change or improvement of C/S structure with the Internet technology springing up. Under this structure, the user's work interface is realized by the WWW browser. Only minor business logic is realized at the browser, and the major business logic is realized at the server, thus forming a 3-tier structure. That greatly

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simplifies the load of the client computer, reduces the cost and workload of system maintenance and upgrade, and lowers the user's total cost (TCO) [7].The information system is divided into application tier, middleware, and data service by functions in the B/S mode, which are configured in different or the same hardware platforms. The system structure is shown as Figure 3. Application tier

Middleware Http request

Client Web browser Standard

Web server GIS components Professional model

Data service ODBC database connection

Attribute database Spatial database

Figure 3. B/S Integration Mode Diagram

3. Conclusion The development for the decision support system for the emergency rescue of geo-hazards under the conditions of extreme ice and snow disasters is an enormous system engineering involving a great many contents and technologies, such as relevant professional knowledge for decision-making; relevant technologies in the construction of the database system; relevant technologies in the construction of the knowledge base, method base and model base; knowledge expression mode and reasoning mode; system development environment; high-level language programming technology; MapGIS secondary development technology; system integration technology; etc. Due to limited space, this paper only introduces the main contents of the design thought and overall design architecture of the system. The more detailed contents will be introduced in relevant study reports and papers.

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References [1] Feasibility demonstration on key project "Study on the technology of preventing and controlling geo-hazards under the conditions of extreme snow and ice disasters", Express Water Resources & Hydropower Information, 2008, (4): 3. [2] CHEN Zheng-hong, LI Rui-qin, LI Lan, Analysis on the main properties of the low temperature, raining and snowing, icing disaster and its effect in Hubei in early 2008, Resources and Environment in the Yangtze Basin, 2008, 17(4): 639- 644. [3] 11th Five-year Plan for prevention & control of national geological hazards [EB/OL], Ministry of Land and Resources of the People’s Republic of China, 2007. [4] HUANG Xue-yong, Discussion on control works of geological hazards, Science & Technology Information, 2008, (17): 116. [5] Basic essentials in prevention & control of geological hazards [EB/OL], Ministry of Land and Resources of the People’s Republic of China. [6] GAO Jie, Study on spatial decision support system of comprehensive disaster prevention plan for water supply engineering, Qingdao: Ocean University of China, 2008.

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Visual C++ Based Risk Assessment System for Ground Environment Damage Induced by Subway Tunneling Bo LIUa,b1, Li HUANGa, Yan Li a and Bo Lu a School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing, 100083, P.R. China b State Key Laboratory of Geomechanics and Deep Underground Engineering, Beijing 100083, P.R. China

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a

Abstract. In all environmental damages induced by subway tunneling excavation, the prediction and control of the ground surface settlement are the important problems to have to be solved. Therefore, combined with geographic information system (GIS), a new Subway Tunneling-induced ground-Environment-damage Assessment and control Design system, named STEAD, is developed and introduced. Subway Tunneling-induced ground-Environment-damage Risk assessment subsystem (STEAD-RISK) is a part of STEAD. This paper presents the development and application of STEAD-RISK. The developed subsystem employs stochastic medium theory and Peck prediction method, and is written in the Visual C++ program. The subsystem has the function of inputting monitoring data or predicting data by analyzing of computer to Access database, then exchanging data by Access database. STEAD-RISK includes three function menus for risk assessment: a) controlling of the surface deformation monitoring, is analyzed respectively from shield method, shallow-buried tunneling method and open cutting and shaft sinking method; b) controlling of the building foundation deformation, is divided into overall inclination of multi-storey and high building, partial incline of bricking-up bearing structure’s foundation and adjacent isolated subsidence of frame structure's foundation; c) influence subarea and safety classification in tunnel and foundation pit of the project. The developed subsystem permits users to identify potential risk areas by combining with graphics and text. STEAD-RISK has successfully applied to many construction site of subway, such as Guangzhou subway line No.2 and No.3, Beijing subway line No.4 and No.10. The applications demonstrated that STEAD-RISK can be used as an efficient tool in the perspective of tunneling-induced ground-environment-damage risk management for tunneling projects in urban areas. Finally, its practical significance t is also discussed. Keywords. STEAD-RISK; development; application; ground settlement; building; risk assessment

Introduction The rapid growth in urban development has resulted in an increased demand for the construction of tunnels for public transportation system and underground utilities. 1 Corresponding Author: Bo Liu, School of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing, 100083, P.R. China; E-mail: [email protected].

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Because of space limitation, the subway tunnels are constructed beneath the buildings and infrastructures. During the excavation of subway tunneling, different ground conditions and complexity of the project itself result in the changes in the state of stress in the ground mass, finally inevitably causes ground surface subsidence, foundation collapse, adjacent buildings and utilities damage and so on. Reports on spectacular tunnel collapses during the 1990s focused the publics’ attention on the inherent risk associated with underground construction works. In all environmental damages induced by subway tunneling excavation, the prediction and control of the ground surface settlement are the important problems to have to be solved. A computing concept is employed to take advantage of the revolutionary internet technology in tunneling excavation induce damage risk assessment. At present, attempts related to develop and implement computer to establish risk assessment systems is inadequate for subway tunneling works. In order to develop a better and efficient platform that can be used within the framework of subway tunneling risk assessment, many researches and attempts have been made by the author. Combined with geographic information system (GIS), a new subway tunneling induced ground environment damage assessment and control design system, named STEAD, was developed and introduced in 2004. STEAD is a 3-D assessment and control design system, employs stochastic medium theory, Peck prediction method and FLAC3D analysis prediction method. Based on Windows XP platform and programmed with Delphi and Visual C++, the system was applied in Beijing subway line No.10, Guangzhou subway successfully. Subway tunneling induced ground environment damage risk assessment subsystem (STEAD-RISK) is a part of STEAD. This paper presents the details of development of STEAD-RISK and its application in subway tunneling project.

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1. Programming for STEAD-RISK Two independent but fully interfaced basic modules are programmed in STEAD-RISK, basic operation module and assessment module. The developed subsystem provides users the risk classification combining graphics with text. 1.1. Basic Operation Module Basic operation module includes two function menus ‘File’ and ‘Display’. There are a number of submenus available in ‘File’, such as ‘Reading point data’, ‘Deleting point data’, ‘Reading ground object layer’, ‘Deleting ground object layer’ and ‘Exit’. ‘Display’ button is to adjust the method of display, divided into ‘Fill grid’, ‘Display grid’, ‘Display control point’, ‘Display datum’, ‘Display side’, ‘Display two-dimensional structures’, ‘Display three-dimensional structures’, ‘Display All’, ‘Display profile’, ‘Resume’ and ‘Setting’.

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PROGRAMMING FOR STEAD-RISK

BASIC OPERATION MODULE

ASSESSMENT MODULE

File Reading/Deleting point data

Surface deformation monitoring control Shield method

Reading/Deleting ground object layer

Shallow-buried tunneling method

Exit

Display

Open cutting and shaft sinking method

Building foundation deformation control

Influence subarea and safety classification

Overall inclination of multi-storey and high building Partial incline of bricking-up bearing structure's foundation

Tunnel Foundation pit

Adjacent plinth subsidence of frame structure's foundation

Display /Fill grid Display control point/ datum/side Display 2-D/3-D structures Display All/profile/ Resume Setting

Figure 1. Programming for STEAD-RISK

1.2. Assessment Module

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STEAD-RISK includes three function menus for risk assessment: a) controlling of the surface deformation monitoring, is analyzed respectively from shield method, shallow-buried tunneling method and open cutting and shaft sinking method; b) controlling of the building foundation deformation, is classified into overall inclination of multi-storey and high building, partial incline of bricking-up bearing structure's foundation and adjacent isolated subsidence of frame structure's foundation; c) influence subarea and safety classification in tunnel and foundation pit of the project. Figure 1 illustrates programming for the system.

2. Development of STEAD-RISK 2.1. System Overview STEAD-RISK is a risk assessment and control design system, which employs stochastic medium theory, Peck prediction method and FLAC3D analysis prediction method. In view of tunneling risk assessment, it is of prime interest to identify areas being at risk for a given tunnel design so that necessary modification to the original design can be made to meet preset requirements. It is therefore essential for any tunneling risk assessment system of this kind to be equipped with modules that can compute the quantities related to the impact. The main advantage of STEAD-RISK is its ability to identify potential risk areas and make a complete subway tunneling induced environment damage risk analysis.

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Figure 2. Access database used in subsystem

419

Figure 3. ‘Building’ datasheet in Access database

Monitoring data manual inputted and predicting data which STEAD have analyzed and computer inputted are two main sources for data exchange. Obtained data which is recorded in the datasheet and stored in the database can be read, updated and deleted. STEAD-RISK has proven to be a structure of inputting data to Access database, then exchanging data by Access database. Figure 2 illustrates the Access database used in the subsystem and Figure 3 takes ‘Building’ datasheet in Access database for example. 2.2. Surface Deformation Monitoring Control 2.2.1. Shield Method First step toward carrying out risk analyzing of controlling point in the shield tunnel excavation is to select related datasheet of monitoring point in the Access database. STEAD-RISK obtains the coordinates of ‘Maximum settlement point’ and ‘Settlement’ after clicking ‘Read data’, then calculates and displays ‘Maximum displacement rate’ of the control point. On analysis interface, computer acquires the assess results automatically based on ‘Double Control Method’ principle.

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2.2.2. Shallow-buried Tunneling Method The users need to select ‘Monitoring scope’ which is divided into ‘Interval tunnel’ and ‘Station’ in advance of reading data. STEAD-RISK can successfully predict the relationships between the obtained data and target output values. 2.2.3. Open Cutting and Shaft Sinking Method Besides the procedures described earlier, after clicking parameter button of ‘Foundation pit grade’ and ‘Excavation depth’, the deformation can be predicted. 2.3. Building Foundation Deformation Control 2.3.1. Overall Inclination of Multi-storey and High Building Maximum settlement and overall maximum inclined value are calculated at predefined ID and height (can be manually inputted if couldn’t acquire) of a particular building. Detailed computation results can also be displayed in a separate window by double-clicking the building of interest. The assessment module gives color according to building’s security grade in 3D mode, similar to employs yellow line to express the ‘two points causing the maximum inclination’ in 2D mode.

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2.3.2. Partial Incline of Bricking-up Bearing Structure's Foundation Foundation soil is divided into ‘Medium/Low-compressed soil’ and ‘High-compressed soil’. Assessment points collected with mouse or manual make up of a quadrilateral, which is marked in white, while, two points causing the maximum inclination are marked with yellow line. Maximum settlement, maximum inclined value and assess results are given in the subsystem. 2.3.3. Adjacent Isolated Subsidence of Frame Structure's Foundation Risk analysis of this section is similar to partial incline of bricking-up bearing structure's foundation. 2.4. Influence Subarea and Safety Classification Both in tunnel and in foundation pit are evaluated for possible damage in this section. In addition to schematic diagram and its explanation, there are parameters (including embedment depth of the bottom and tunnel radius in tunnel, excavation depth and excavation width in foundation pit) and color rendering that must take into account for influence subarea.

3. A Case of Application in Beijing Subway Line 10 Project 3.1. Site Overview and Monitoring Point Layout 0.00(39.72)m

-0.90 m

-0.90 m

-1.91 m -4.71 m

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-5.945 m

C

Shield tunnel

B

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Shield tunnel 6850mm

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Figure 4. Relation section plan

STEAD-RISK was implemented in a case of Beijing Subway Line 10 project started at Sanyuanqiao station and ended at Liangmahe station, which consisted of the construction of two adjacent metro shield tunnels beneath a densely populated area with a number of buildings and utility lines exist (especially beneath the South Street

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No.8 building). The South Street No.8 building was a raft slab structure with 12 floors on the ground and 2 underground with poor overall stiffness, embedment depth of raft foundation was about 5.0m. The horizontal space between the measurement section of left tunnel and the right tunnel was only 1.7m, and the horizontal space between the right tunnel and the South Street No.8 building foundation was only 7.15m. The right tunnel’s length paralleled with the South Street No.8 building was 100m. Figure 4 shows the relation section plan of the shield tunnel and South Street No.8 building. The South Street No.8 building was arranged with seven subsidence sensors to carry out deformation and subsidence risk analyze and assessment.

Figure 5. Risk analysis to the controlling points of South Street

3.2. Surface Deformation Risk Assessment The results of the surface deformation assessment along the entire route are illustrated in Figure 5. The risk assessment results thus indicated that surface deformation was orange alert grade in this project.

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3.3. Building Foundation Deformation Risk Assessment

Figure 6. Building foundation deformation of No.8 building in 3D mode

Presented in Figure 6 and Figure 7 were the results obtained of ‘building foundation deformation’ of No. 8 building in 3D/2D mode. The building foundation was appropriate in limiting the deformation as well as building damage to acceptable levels and therefore, no adjustment on the preliminary design was needed.

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Figure 7. Building foundation deformation of No.8 building in 2D mode

3.4. Influence Subarea and Safety Classification Risk Assessment Influence caused by excavation of two adjacent metro shield tunnels to South Street and No.8 building was showed in Figure 8.

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Figure 8. Tunnel influence subarea and risk assessment of South Street and No.8 building

4. Conclusions This paper presented the development and application of an Visual C++ based risk assessment system for ground environment damage induced by subway tunneling (STEAD-RISK). STEAD-RISK allows users to carry out efficient subway tunneling induced damage assessment for a proposed tunneling and permits users to identify areas potentially being at risk by performing computationally intensive tunneling induced damage risk assessment which takes full advantages of analytical models and the FLAC3D numerical modeling. Three function menus of assessment model are programmed to analyze relevant problem. STEAD-RISK was applied to a case of Beijing Subway Line 10 project. With the help of STEAD-RISK, the risk during construction was evaluated. As demonstrated in this paper, information technologies can be used as an efficient means of making tunneling induced damage assessment. Similarly, STEAD-RISK can be used as an efficient tool in the perspective of tunneling induced ground environment damage risk management for tunneling projects in urban areas. The practical significance can not be ignored.

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Acknowledgments The authors sincerely thank the following agents for their financial supports: National Natural Science Foundation of China (50974126, 50674095), Program for New Century Excellent Talents in University (Grant No. NCET-08-0835), Key Research Project of Chinese Ministry of Education (No.109034), Beijing Excellent Talents Program (20071D1600700414).s

References

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[1] B. Liu, Y. Han. A FLAC3D-based subway tunneling induced ground settlement prediction system developed in China, Proceedings of the 4th International. FLAC Symposium on Numerical Modeling in Geomechanics. Editor: Hart & Varona, Madrid, ISBN0-9767577-02, 2006 : 55-61 [2] Chungsik Yooa, Jae-Hoon Kim, A web-based tunneling induced building damage assessment system, Tunneling and Underground Space Technology, 2003(3) [3] Chungsik Yooa, Young-Woo Jeon, Byoung-Suk Choi, IT-based tunneling risk management system (IT-TURISK)-Development and implementation, Tunneling and Underground Space Technology, 2005(5) [4] Marte Gutierrez, Doug Bowman, Joseph Dove, Matthew Mauldon, Erik West man, An IT-based system for planning, designing and constructing tunnels in rocks, Tunneling and Underground Space Technology, 2006 [5] Peck R B. Deep excavation and tunneling in soft ground. New York: Published by A SCE, 1984. [6] SELBY A R. Tunneling in soils2ground movements, and damage to buildings, U K.Geotechnical and Geological Engineering, 1999, 17(3) : 351-371. [7] Oettl G, Stark R F, Hofstetter G. A comparison of elastic plastic soil models for 2D FE analyses of tunneling. Computers and Geotechnics, 1998, 23(1): 19-38.

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The Research and Application on Visual Information System of Safety Monitoring during Tunnelling Based on GIS Zhi LINa, 1, Yuan-Hai LI a , Xing-Ping LI a and Changjiang YANG b Chongqing Communications Research and Design Institute㸪Chongqing 400067㸪 China b School of Architecture and Civil Engineering㸪China University of Mining and Technology㸪State Key Laboratory for Geomechanics & Deep Underground Engineering㸪Xuzhou Jiangsu 221008㸪China

a

Abstract. In order to solve the problem of low efficiency in managing the monitoring information during tunnel construction, a new graphic information system with the capability of performance prediction was proposed. The system is designed to reach the target of rapid data calculation, quick analysis and high efficiency feedback by integrating the safety-related information, such as monitoring data, geological conditions and construction sequences. The core part of the system is an electronic construction monitoring map, which can be used to a basis for construction data management, daily construction monitoring services, query of the monitoring data and prediction of tunnel performance. It is envisioned that the system will influence design pattern and monitoring technology of tunnel engineering in the future.

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Key words. Highway tunnel, construction monitoring, GIS, visualization

Introduction In the construction of tunnel, geological conditions of the region where the tunnel locates are often very complicated. Therefore, how to ensure the safety of tunnel and the surrounding environment is one of the primary concerns in tunnel construction. The safety of tunnel construction depends not only on the reasonable design and reasonable construction, but also on safety monitoring throughout the tunnel engineering. Safety monitoring for construction can provide feedback information during construction which can be used to change the design dynamically. It can also provide the valuable engineering information for the design and construction of similar engineering in future. However, no matter how comprehensive and accurate the monitoring information is, if it can’t be used in the analysis and interpretation feedback, no influence is made on the safety evaluation in engineering and environment which it should be. Currently, there are some disadvantages in the management of tunnel monitoring information. 1

Corresponding Author. Zhi LIN, Chongqing Communications Research and Design Institute㸪 Chongqing 400067㸪China Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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By using unique ability of the GIS for spatial information, management and visualized analysis face specific engineering application. With the rapid growth of China's tunnel engineering, research and development on safety monitoring visual information system in highway tunnel construction with practical value, can not only improve the technical level of tunnel informatization construction, but also have large market for promotion and application, which also benefits a lot for social and economic.

1. The overall design of system 1.1. The Overall objectives of System Through the visual information system of safety monitoring during tunnel constructing, we can clearly describe the spatial relationship between monitoring and the surrounding environment. Because of the links between a lot of related information and graphic symbols, it will greatly improve the ability of monitoring information processing, rapid interpretation and effectively evaluation. Meanwhile, by using the GIS technology to establish tunnel 3d stratigraphic model, we can make the monitoring information more visualized. So that we can accurate characterization and describe the appearance of stratigraphic model. By cutting tunnel 3d stratigraphic model, we can browse and search the monitoring information of the position of tunnel profile conveniently and intuitively. On the basis of advance geological forecast and construction monitoring measurement technique, the monitoring information analysis of tunnel damages and the analysis of the inversion, we can make the optimization design for supporting parameters of the tunnel. So we can ensure the safety of tunnel design and construction. According to the main goal and function requirement of the system, preliminary design of the system framework is shown in Figure 1.

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1.2. The research of The Visualization key technology 1.2.1. Database management The system's database is able to deal with multi-sources data through classification and depots management, and combine with electronic map which based on project information and monitoring data. The data is managed scientifically and effectively, which can also be searched and feed information back. This system is managed by "database editors". We choose a relational database Microsoft Access for database construction and management, and use develop database’s applications ADO interface which is provide by Delphi to access database. It can be able to use conveniently and to control costs. 1.2.2. Database classification According to the different types and characters of engineering information, the system database mainly includes two broad categories--basic information repository and dynamic information repository. The basic database includes three databases named respectively engineering, engineering graphics and standards normative standard

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database, which is primarily used to manage some of the most basic data and normative standards on engineering. The dynamic information database includes three databases named construction monitoring, construction condition and 3D graphics database, which is primarily used to manage the changing information along with the engineering construction. We use the later one as the main basis for us to guide the construction and dynamic design.

… e s a b a t a d

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Figure 1.framework of the visual information system on safety monitoring during tunneling construction

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1.2.3. Structure design of data table Various types of data is to be stored in the form of data tables in this system. Therefore, According to the content which various data elements contained, we should do the data table structure design first. x Structural design of construction monitoring data table. It is achieved by two types of data tables, in which each item contains the basic information table of measurement points and for each measuring point corresponding to the measuring point monitoring data table. Therefore, we should do the structural design of two types of data tables separately for surface subsidence, ground turkey displacement, groundwater levels, steel stresses, earth pressure and so on. x Structural design of engineering information data table. The engineering

x

information data table mainly records the information including the progress of excavation, design information, construction information, building information, geological information, etc. Various types of engineering information is managed respectively through the engineering information data table. Structural design of image information data table

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During the construction process engineers will capture various images to record the project scene, which are recorded in the image information data tables to manage. x Structural design of other information data table The rest important data tables in this database include monitoring and warning numerical management data table, monitoring sectional drawing management data table and so on. 1.2.4. The establishment of database and data table The establishment of database and data table is achieved by the database management system, independent of the database software, making it easy to set up the database and data tables which can meet the system’s requirements 1.2.5. The connection between spatial database (electronic map) and attribute database In this system, it uses internal connection type to connect spatial database and attribute database, general relational database (this system used Microsoft Access) to manage the attribute data and specialized GIS software to manage the spatial data. Users can both operate two separate databases and access two databases in some way. The connection between attribute database and spatial database is achieved by the keyword section in the data tables.

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1.2.6. The control of database based on ADO The system’s development and design mainly use the program development language Delphi, component-based GIS software SuperMap Objects and database software Access to compile together. The database control is achieved by database access component of ADO components and GIS software in the database of program development design language. In the process of writing system program㸪database operations need to be executed including the establish a connection to database, the implementation of the database commands and queries, search the data in the database, the implementation of the SQL command and close the connection to the database and so on. 1.3. The Construction monitoring electronic map Construction monitoring electronic map is similar to an urban transport travel map which is constructed by using GIS development tools software and graphic-based information database. Besides, it integrates a large number of engineering information and construction monitoring information, which can reflect the spatial relationships between tunnel engineering and the surrounding (structure) building, underground pipelines intuitively. And it also include various of monitoring gauge point, which can do visual two-way query, statistics and analysis in the way of "property - graphical", dynamically updated and maintained, and serve to the tunnel engineering information construction directly. Construction monitoring electronic maps is a visualization platform for information exchanging between the user and the system. The production process of construction monitoring electronic map is shown in Figure 2.

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Import

The base map of construction monitoring electronic map

Remove the redundant field

The sort of spatial objects

Link to the database

Construction monitoring electronic map

Figure 2. The production process of construction monitoring electronic map

The features of construction monitoring electronic map include: Construction monitoring electronic map is generally based on GIS technology, which not only has the basic functions of GIS systems, but also has a general CAD functions; x It connected with the database closely through the connection between spatial database and attribute database, which can integrate a large number of engineering environment information and construction monitoring information on the graph. x On the construction monitoring electronic map, we can query these attribute information corresponding to the object through the spatial objects in the graphics. The result with problems received in the database can be reflected in the graph intuitively at the same time, which is the visual two-way query named "Property - graphical"; x Through the form of thematic map, we can see the statistics and distribution of the engineering attribute information on the graph intuitively. It makes the information we concern, the level of development and the difference of development of the distribution of attribute data striking at a glance

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x

1.4. The utility function of system 1.4.1. The management of graphic data Mainly attribute data based on the text, the basic graphics, images, and 3D stratum and orthographic drawings, the basic functions of entry, modification, query, statistics, extraction, and maintenance to the graphic data can be achieved through the development programming of the database management functions. Because most computers have installed the Microsoft Office, which usually contains the database

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software Access, considering using conveniently and cost-saving, we decided to use Access to establish and manage the database (graphics database will be managed by new developing special function modules). 1.4.2. The functions of daily monitoring service Throughout the monitoring of the construction, there are mounting daily workloads of data processing and chart production. Because of its repeatability, it can be achieved programmed and automation with computer, which greatly improves the efficiency of work, and monitoring technicians will be freed from the simple but heavy data processing work, such as the calculation and reporting, graphics generation and output, etc of various monitoring projects. In the design of system, taking into account the surface displacement, vault subsidence and convergence, a lot of monitoring projects’ data has the same characteristics of a continuous cumulative. In order to make the program more universal, and also facilitate the maintenance much easier, we can do "pre-processing" to some monitoring project data, which is achieved by data compiling pre-processing module. In addition, using the "construction monitoring electronic map" as a platform on information visualization query and analysis is an important characteristics function of the system. 1.4.3. The predict stability of surrounding rock and early warning

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On the basis of the algorithm research, in order to have deeper understanding and further comprehensive analysis of the predict result, and to achieve its function by programming, the key is to modify data easier and dynamic update data conveniently, consider how to connect the predict results to "construction monitoring electronic map " which is mentioned previously, reflect the relationship between the system and the specific location of the project, the stratigraphic geology, the surrounding environment on the way of visual intuitively. At the same time, you can set alarm according to the setting value of predict warning.

2. The practical application of engineering 2.1. The Overview of the project The west development inter-provincial road corridors Wu (Long) Water (River) Baiyun highway tunnel is a highway tunnel of dual-hole and four-lane, whose design speed is 80km per hour, clear width is 10.5m (single hole), clear height is 5m, and length is about 7.1km. It is a relatively long highway tunnel in the Chongqing’s highway tunnel under construction currently Baiyun tunnel passes through middle and low mountains where there are geology construction issues of dissolution and erosion.etc. The upgrade is large and unstable at the entrance to the tunnel, and the compressive strength of rock is usually high where the tunnel crosses the mountain. Most of them are hard rocks or extremely hard rocks, which are shallowly embedded at the entrance to the tunnel, broken by the impact of weathering and corrosion. Although the intensity is High at the Karst breccia, it is easy to occur karst water surging and there would be large areas of collapse on the roof of the tunnel. In a word, the geological conditions of tunnel surrounding rock are complex

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and changeable, and there are many engineering safety problems caused by variety adverse geological. 2.2. The main contents of the monitoring

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The measured items of Baiyun tunnel will be divided into two parts of must test item and optional test item. The must test item includes surface subsidence observations, dome subsidence measurements, the measurement of convergence around and description of the geological rock tunnel heading face. The must test item carries out as a routine measurement of work that make ensure the stability of the surrounding rock during construction process and the safety of construction. The optional test item includes the stress measurements inside the shotcrete, the stress measurements inside secondary lining, the measurement of bracing internal load, etc. The optional test item plays a role of measurement to special lots, dangerous lot and the representative lots. In order to inquire the steady state of the surrounding rock and support effectively, after making sure the measurement items, the corresponding measurement plans should be developed according to the surrounding rock conditions and the characters of engineering of the tunnel.

Figure 3. Baiyun tunnel monitoring electronic map (plane)

2.3. The management of monitoring information All of the Baiyun tunnel construction safety monitoring data is stored in Wushui monitoring and measurement database by using the database management system. Each individual information data table of measurement point records the monitoring point information of surface subsidence, dome subsidence, tunnel convergence, etc. Each record in individual information data table of measurement point corresponds to information table of one construction monitoring points respectively.

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2.4. Tunnel construction electronic monitoring map Tunnel project plan in the format of AutoCAD can be used to achieve the production of construction electronic map by data importing functions and editorial changes of the system. The display is shown in figure 3. In addition, according to the geological profile of the tunnel, the left hand lane are shown in figure 4 and figure 5.

Figure 4. The left lane of the Baiyun tunnel construction monitoring Map (section global)





Figure 5. The left lane of the Baiyun tunnel construction monitoring Map

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(ZK43 +975 ~ ZK44 +570 partial profiles, the blue text in the figure is the comment)

After arranging the logo of the tunnel monitoring section and improving the information of monitoring section logo, according to the mileage where the tunnel monitoring section are in the tunnel figure of AutoCAD format, we can query and browse the construction monitoring information of the monitoring sections whose figures can be corresponding shown by double-clicking the monitoring section logo in the window of "Monitoring Section". The system can show the curves of vault settlement deformation and the curves deformation around the convergence of the monitoring section corresponds to the mileage, and then the corresponding analysis can be carried out. 2.5. The effect of the application The geological conditions of WuShui highway Bayun tunnel is very Complex, the tunnels are extra-long tunnels, the data of safety monitoring construction is mass and the monitoring tasks are very arduous. But by using this system, we integrate large number of information in a unified graphical according to the platform of construction monitoring electronic map, and improve the efficiency of the construction monitoring work. The result of preliminary application shows that visual information system of

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safety monitoring during tunneling based on GIS can improve the technological level of the construction monitoring and feeding information back in a large extent, and it can also provide effective technical services to safety construction of the tunnel at the same time.

3. Conclusion

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As the current highway tunnel construction safety monitoring information management has problems of inefficiency, this system proposes to create a new type of graphical information platform by using the GIS technology, which can be used to unified and integrated mass information of monitoring, surrounding environment, other construction security-related and so on. And it can achieve the fast calculation, analysis and feeding the monitoring information back being combined with the further development of the predictive analysis method. It is able to provide the important basis of guiding the construction and dynamic design, and we initially achieve some of the basic features of visual information system of highway tunnel safety monitoring.

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Study and implementation of urban rail transit construction engineering security and risk management information system a

Yi-qi LIAO a,1, Huai JIN a,b, Pei-yin Lv a,b and Jun-wei LI a,b Beijing Urban Construction Design & Research Head Institute , Beijing 100037 , China b Beijing Agiletech Engineering Consultants Co., Ltd, Beijing 100037, China

Abstract. The large-scale construction period of metro engineering is coming in China. Fifty-five metro lines are planned in twenty cities. The whole length reaches to fifty kilometers and the investments exceed five-hundred billion RMB. But there are many problems existed in management now, including bad information transmission, poor data sharing, incomplete early-warning system, weak interoperability ability and serious leak of precious data source. And this makes the subway construction in high-risk state leading to frequent accidents. In order to reduce the subway construction risk, a risk evaluation and management program is developed in Guangzhou Metro between 2006 and 2010, which is the leading problem in the field. This paper introduces the present situation of the risk management program named “three evaluations and one management”, which developed in Guangzhou Metro construction, and the relevant information system. Meanwhile, the system architecture, core function and implementing effect of information system in Guangzhou Metro line 6 are described.

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Keywords. Risk management, three evaluations and one management, information system, Guangzhou Metro.

Introduction With the success of bidding for the 2008 Olympics in Beijing, the 2010 World Fair in Shanghai and the 2010 Asian Games in Guangzhou, urban development is facing opportunities. The urban infrastructure construction, particularly rail transit construction, is developed unprecedented. Until 2014, Fifty-five metro lines are planned in twenty cities. The whole length reaches to fifty kilometers and the investments exceed five-hundred billion RMB. The scale of urban rail transit construction is rare in China and even in world history. At present, the subway projects in domestic cities are confronted with many problems, such as large scale, tight schedule, and inadequacy of investigation, design and construction teams, as well as experts scattered, which lead to high-risk state in constructions. Since the 1990s, because of improper construction measures and unsoundness in construction management, heavy accidents occur frequently in 1

Corresponding Author. Yi-qi LIAO, Beijing Urban Construction Design & Research Head Institute ,Beijing 100037 , China Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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domestic subway projects, such as, water flush accident of Line 4 in Shanghai, Haizhu Square Station’s pit landslide in Guangzhou, and Suzhoujie southeast entrance’s landslide of Line 10 in Beijing subway. There are omens before the engineering accidents, which are reflected in the monitoring data and the inspection information (such as water seepage and cracks). However, subway constructions are in high-risk state for a long time because of the low degree of subway constructions’ Informatization, the scarcity of expert resources, the delay of information transit, and the incomplete early-warning system. Also, the monitoring data and the risk symptom of work surface can not be analyzed and feedback timely. Therefore, analyzing the unusual situation in construction timely, alarming quickly, making decision and activating the emergency plan accordingly are significant to reduce risk and the probability of the occurrence of accidents. As the state government attaches great importance to the security of urban rail transit construction [2-3], and the development of technology including network communications, wireless transmission and the web database, it is time to use information means for risk management in the urban rail transit construction, the establishment of which can improve the transmission and the processing of critical construction information, and achieve the engineering information’s accumulation, sharing and centralization, thereby reduce construction risks, and enhance the risk management of urban rail transit construction.

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1. Three Evaluations and One Management Security risk management is based on the risk occurrence probability and risk control technique. On the foundation of risk identification, risk assessment and risk evaluation, security risk management sector take the most economical method, optimize and combine various risk management techniques and control risk effectively to achieve the best scientific management [4-8]. Guangzhou subway projects are processing in large-scale, with more than 200-kilometer lines and about 120 construction sites in the same period and tight schedule, which are difficult to manage. Most of the construction sites are located in the old city region, where there are many buildings and have complicated surroundings. Also, there are many construction sites near or beneath the Pearl River and the tunnel beneath through the Pearl River for 9 times. The complicated geological conditions affect the engineering security greatly [9]. Subway construction has massive sites, high construction risk, limited expert resources, and low degree of informatization. These increase the difficulty for owners and experts to realize security status of construction sites. Therefore, subway construction needs urgently to introduce new security risk management mechanism to strengthen security risk management of construction projects. According to the security management system and the project status of Guangzhou Subway construction, Guangzhou Metro Corporation finds the starting point of risk management from the overall and stage constructions. It can be summarized as ‘three evaluations and one management’ and the details are as follows. (1) Risk evaluation of security management system in Guangzhou Metro construction (2) Risk evaluation of civil engineering construction in Guangzhou Metro

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(3) Risk evaluation of ‘new technique, new construction method, new equipment, new material’ in Guangzhou Metro construction (4) Risk management of critical construction sites in Guangzhou Metro Accompany with ‘three evaluations and one management’, an information system is established to support the security risk management. The project of ‘three evaluations’ is completed at present. And the risk management throughout the construction will be completed until 2010 [1,10,11].

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2. Security Risk Management Information System Many problems exist in recent subway construction management. Analysis and feedback of the information is poor, the construction early-warning mechanism is backward, lots of valuable engineering data lose in different stages, and a regional library of the construction technique can not be established to improve the design, construction, and the management level in new lines. To ensure quality and security, reduce risk and improve management in subway construction management, it is urgent to establish an information system with data sharing and collaborative work as a security risk management carrier. Security Risk Management Information System is established to support the ‘three evaluations and one management’ project in Guangzhou Metro, thereby achieving the following objectives: (1) Ensure the data analysis and feedback timely, accurate, and comprehensive (2) Transmit information quickly, and share information highly (3) Early warn automatic, and notify timely (4) make decisions timely and resolve risk incidents (5) Record processing of handling with warning incidents, so that can be traced back (6) Facilitate to classify accumulated information to avoid data loss, (7) Get a regional and industry-specific technique Library Security Risk Management Information System is a tool to support risk management in the subway construction. It uses geographic information technologies (GIS), network communication, databases, wireless transmission and model analysis to integrate the information of the construction, the supervision, the third-party supervision, the design and the consultant, and then the system carry out risk evaluation, supervision, early warning and handling of the risk incidents timely. It is a collaborative working system that includes security risk management, construction data collection, and multi-cooperating work functions together. Through the information system, the construction foundational data, the monitoring data by constructor and third-party supervision, the inspection reports, security analysis and evaluation, early-warning and GIS data can be manage centralized. And these can all be response in sequence in emergency. The early-warning and handling capability is enhanced through the system, which makes the incident treatment from evidence-taken after incident to early warning and control before incident, so as to promote the innovation in security management of subway construction.

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2.1. System Architecture System architecture is shown in Fig.1. The system is composed of three layers, data storage and maintenance layer, business layer and presentation layer. The databases consist of the document database, GIS spatial data database, and the monitoring data and risk management database. And it involves classification of a large number of foundational files, GIS data as well as monitoring data, which are relatively independent, so as to expand and maintain the system conveniently. The business layer describes the System’s architecture and the core functions, which is in accordance with the workflow of Guangzhou subway construction risk management project. The workflow consists of data upload, data analysis, security evaluation, early warning issued, and warning processing steps. The presentation layer displays the participation departments of the information system, including the Guangzhou Metro Corporation, Security Supervision Division, Security Risk Management Group of Owner, Security Risk Consultant, owner delegate, the constructor, the designer, the supervision engineer, and the third-party monitor engineer. 2.2. Core Functions Guangzhou Metro security risk management system consists of three subsystems. The monitoring data and inspection information submitting subsystem (C/S) is responsible for the on-site data collection and report generation. The Information Center Data Processing subsystem (C/S) makes the project informatization and manages the user’s privilege. The Risk Management Collaborative Working subsystem (B/S) provides a platform of data query, security evaluation and early warning handling for engineering participations. Risk Management Collaborative Working subsystem’ core functions are as follows:

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Monitoring Data Analysis Monitoring data can reflect the foremost insecurity risk. The use of system for automatic monitoring data analysis, automatic early-warning and automatic reminders, helps to grasp the security status of construction sites. The system reflects the history and current situation by time curves and profile curves of monitoring data, and the automatic early-warning in accordance with control index. What’s more, the system can do comparative analysis between construction monitoring data and third-party monitoring data on the same monitoring point, thus mutual authentication to ensure validity and accuracy. Construction Status and Inspection Information The construction status and inspection information are important data to analyze and control the security risk of construction sites. Making use of the construction status, inspection information and the monitoring data, the current security status of construction sites can be determined. The system visually expresses the construction status and inspection information by picture and text description according to different construction methods. Security Evaluation

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Based on the monitoring data, the construction status and the inspection information, consulting experts determine the security status of the risk region in construction sites. The GIS system displays the results of security evaluation real-time through green, yellow, orange and red lights on basis of the early-warning levels. And the SMS message is sent to relevant persons automatically as a timely reminder.

Figure 1. Architecture of risk management information system

Security Early-warning The security risk early-warning system has three early-warning levels, including automatic early-warning of monitoring points, risk region of construction sites and construction sites of the metro net. Through the three-level early warning mechanism, the security status throughout the construction can be comprehensive monitored. And also, the SMS messages are sent to relative person accompanying with different levels of early-warning. Collaborative Treatment of Early-warning Incident The relative person receive the early-warning SMS message, login in the system to get the early-warning incident, and then issue opinions on how to handle them according to his duty. When the early-warning incident is canceled, the historical process records on how to handle the incident are generated automatically, which could not be amended so as to meet the purpose of tracing the incidents handling process.

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Geographic Information System (GIS) The system adopts the GIS technology to achieve functions of real-time displaying different early-warning levels and visually inquiry of the monitoring data, the surrounding environment objects and the geological profiles. Engineering Documents Management Engineering documents are important materials for risk management and risk determination during the whole life of construction. The format of engineering documents is mostly CAD file and office document, which is massive and large. So how to decrease the file size, reasonably classify and flexibly distribute available privilege is the key problem. The system solves the problem of classification and privilege distribution by using IFC standard, which is the international standard for data exchange in AEC. And using DWF and PDF format greatly reduces the CAD and office file, which makes quickly inquiry online be realized. Regional Construction Technology Library After the completion of construction, the system will automatically classify and archives the data accumulated in the construction process. And the regional construction technology library will be formed, which can avoid data loss in different stages and accumulate precious data for design and constructions of new lines and for facility management.

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2.3. Data Flow The data flow of Security Risk Management Information System is show in Fig.2. Monitoring data, the information of construction process and on-site inspection and engineering documents are sent to the Information Center Database through client systems connected to Internet. Then the data are analyzed by risk consultant experts to identify early warning levels, and the early-warning results are published through Internet and wireless communications equipment. Meanwhile the process and the results of incidents are recorded automatically. System also offers query, report output, data predication and services management through Internet. 2.4. Topological Structure The network topology relationship of Security Risk Management Information System means the network connection mode of the data-provided layer, the information centre and the application layer. On-site operation layers sent information of monitoring data, reports and documents through Internet to information center, where information are stored, analyzed and managed, and which responses the operation affaires request sent by different users. The topological structures are shown in Fig. 3.

3. Project Cases At present, the Security Risk Management Information System has put into trial use in

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the four construction sites of line 6 of Guangzhou Metro. The locations are East Lake Station as well as the stop line in front of it, Yuexiu South Station, Haizhu Square Station, and the section between Datansha and Huangsha. The system reaches the target of managing the monitoring data, inspection information and engineering files, expert evaluation as well as issue early warning and of line 6. GIS spatial data GIS system

preliminary analysis

Monitoring data Inspection Information Construction process

Information Center DB

Internet Check data

`

Data analysis Input data

Client system

Risk evaluation Early warning

Project documents

Upload data Internet Monitoring data report

Output report

Inspection Information report

Report system

Early-warning history record

Forecast system

Collaborative processing Y

Risk management report Forecast curve

Select forecast model

Conclusion Output

Inquire document

handling incident Record

Receiving incident Privilege Management

Document system

Incident handling system

Expert suggestion N

Earlywarning system

Expert consultation Monitor data early-warning

Early-warning determine

Risk region earlywarning Site early-warning

System manage

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Figure 2. Data flow of system

As show in Fig.4, System using GIS geographic information system makes management of early warning of the whole net sites come true in Guangzhou Metro. As show in Fig. 5, the region of stop line in front of Station, and the foundation pit of Station and construction shaft are all be monitored and through the monitoring data, risk can be predicted by the System. Meanwhile, through the System, engineering files can be submitted, security status can be reported and inquired, and the security can be evaluated by experts. The application of information system standardizes the data-sending of sites, and accelerates the data transmission, data analysis and data feedback. Also the System can early warn through mobile messages. In conclusion, the System provides a platform of joint security risk management and collaborative work for the owners and the participations.

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Figure 3. Topological structure of system

Figure 4. Construction sites early-warning of Guangzhou Metro.

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Figure 5. Risk management of East Lake Station and the Stop line in front of it of Line 6 in Guangzhou Metro.

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References [1] Beijing Urban Construction Design & Research Head Institute. Risk evaluation report of civil engineering in Guangzhou Metro. Guangzhou, 2006. [2] General Office of State Council. Notification of improve Management in urban rail transit construction engineering, No.81, 2003.10. [3] Ministry of Construction, Ministry of Public Security, Ministry of State Security, State Development Innovating Commission, State Bureau of Quality Supervision, Ministry of Railways, State Environmental Protection Administration, Office of Legislative Affairs. Issues of improve security management of subway construction, 2003, No.177, 2003. [4] H. Ma, S. Sun, Modern Management encyclopedia, Beijing: China Development Press, 1991. [5] IUGS, Working Group on Landslide, Committee on Risk Assessment. Quantitative risk assessment for slope and landslides-the state of the art, In: D. M. Cruden and R. Fell (eds.), Landslide Risk Assessment, Rotterdam: A. A. Balkema, 1997. [6] Eskesen Soren Degn, Tengborg Per, Kampmann Jorgen, Trine Holst Veicherts. Guidelines for tunneling risk management: International Tunneling Association. Working Group No. 2, Tunneling and Underground Space Technology, May, 2004 [7] Z. Guo, Risk Analysis and Design, Beijing: China Machine Press, 1986. [8] Y. Ru, Project Risk Management, Beijing: Tsinghua University Press, 1998. [9] G. Liu, Research and Practice for Safety and Risk Management in Guangzhou Metro. URBAN RAPID RA IL TRANSIT, 20(2007), 21-24. [10] Risk evaluation report of security management system in Guangzhou metro. Beijing Urban Construction Design & Research Head Institute Guangzhou, 2006. [11] Risk evaluation report of using “new technique, new construction method, new equipment, new material” in civil engineering in Guangzhou metro, Beijing Urban Construction Design & Research Head Institute, Guangzhou, 2006.

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Novel Computational Techniques and Numerical Methods

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A Parallel Factorization Algorithm for Stiffness Matrix Based on Threadpool Juntao CHEN a,1, Ming XIAOb and Yuting ZHANGb State Key Laboratory of Water Resources and Hydropower Engineering Science, Hubei Wuhan 430072, P R China b Key Laboratory of Rock Mechanics in Hydraulic Structural Engineering (Wuhan University), Ministry of Education, Wuhan 430072, P R China

a

Abstract. Stiffness matrix factorization is a major step in solving finite element problems or in pretreatment of iteration (e.g. ICCG). According to the two essential conditions of parallel computing, which are parallel algorithm and multithreading, the parallel algorithm for stiffness matrix factorization is presented based on multicore processor environment. Firstly, the Cholesky's LDLT method is translated in order to make it applicable to parallel computing. Afterwards, threapool is employed. It is able to generate multithreading which can be used repeatedly. Then the algorithm is optimized considering load-balancing of each thread. Thus the proposed algorithm can overcome the limitations of OpenMP when it is applied in nested loop. Therefore, high efficiency of parallel factorization of stiffness matrix is achieved. Numerical tests are carried out on different processor platforms. The comparison results indicate that the proposed algorithm has high efficiency of parallel computing and a low demand of platform. Moreover, this algorithm has explicit concept and minor programming difficulty. It is particularly applicable to solving the problems caused by the limitations of OpenMP. Thus it is of considerable practical values.

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Keywords. Stiffness matrix, parallel calculation, threadpool, LDLT factorization

Introduction With the rapid development of computer technology, the multi-core processors have been widely adopted. Thus the multi-core processor-based parallel computing technique also develops rapidly and becomes daily popularized [1-4]. Currently, the most common adopted parallel libraries are MPI [5] and OpenMP [6]. MPI employs message passing model and is usually used in cluster. As the issues of communication overhead as well as network delay must be taken into account, MPI is only applicable to coarse-grained parallel computing and not capable of parallel computing based on multi-processors. OpenMP employs shared variable model and is particularly applicable to fine-grained parallel computing. However, when OpenMP is used in loop (especially nested loop), there are certain limitations. For instance, the logical dependence of data in different cycle should be irrelative. Otherwise, the logical dependence should be decomposed or converted to independent relation.

1

Corresponding Author. Juntao CHEN, State Key Laboratory of Water Resources and Hydropower Engineering Science, Hubei Wuhan 430072, P R China Information Technology in Geo-Engineering : Proceedings of the 1st International Conference (ICITG) Shanghai, edited by D. G. Toll,

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The factorization of stiffness matrix is a crucial step in FEM analysis, or in pretreatment of iteration. ILU (incomplete LU) factorization is an effective method to construct preconditioners, but it is not easy to be deserialized. Most factorization algorithms are based on distributed-memory computers which are communicated by MPI[7-8]. However, the efficiency is not very high due to communication delay. If a highly efficient parallel factorization can be realized on shared-memory computers with multi-core processors, the computing time can be reduced and its practical value is remarkable. A parallelizable block ILU factorization preconditioners for a blocktridiagonal matrix was proposed by Yun [9], but its calculation process is so complicated that the algorithm is difficult to carry out. The parallel algorithm and multithreading are two necessary conditions for the realization of parallel computing on multi-core processors. Based on the two necessary conditions, this paper firstly analyzes the features of LDLT factorization algorithm for symmetric positive definite matrixes. By adopting the parallelizing procedures, the LDLT factorization parallel algorithm for stiffness matrixes is obtained. Afterwards, the issue of the application of parallel library OpenMP to the above proposed algorithm is discussed. The parallel factorization algorithm based on threapool is put forward. The presented algorithm is further optimized by considering load-balancing. Finally, numerical experimental analysis is conducted to test the efficiency of the proposed parallel algorithm.

1. Parallelization of factorization algorithm for finite element stiffness matrix The finite element stiffness matrix is usually positive definite matrix. It can be factorized by LDLT factorization algorithm. The algorithm is of high precision. Therefore, it is the most effective method to factorize the positive definite matrix.

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1.1. LDLT factorization algorithm Let A

LDLT , in which

§ a11 a12 ¨ a22 ¨a A ¨ 21   ¨ ¨a © n1 an 2

 a1n · ¸  a2 n ¸ L  ¸ ¸  ann ¸¹ ,

· §1 ¸ ¨ ¸ D=diag(d1,d2,…,dn) ¨ l21 1 ¨   ¸ ¸ ¨ ¸ ¨l © n1 ln 2  1 ¹ ,

The common procedure of factorization is to factorize the matrix line by line. The common algorithm is illustrated as follows (hereinafter referred to as Procedure 1): Procedure 1: do i=1 to n do j=1 to i-1 j 1

tij

aij  ¦ tik l jk

㸦1㸧

t ij d j

㸦2㸧

k 1

lij

enddo i 1

di

aii  ¦ tik lik

㸦3㸧

k 1

enddo

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1.2. LDLT parallel factorization algorithm It can be seen from Procedure 1 that, there is evident data correlation among elements tij , lij , di ,which are locating at the same line. Therefore, the computation can be only implemented according to their sequence. To realize parallel computing, it is necessary to eliminate data correlation. Thus, the computation sequence of Procedure 1 is altered as implementing computation according to line and column simultaneously. The modified algorithm is illustrated as follows (hereinafter referred to as Procedure 2): Procedure 2: do i=1 to n // external loop /* Domain 1*/ do i=1 to i-1 lij t ij d j

(4)

enddo /* Domain 2*/ i 1

di

(5)

aii  ¦ tik lik k 1

do j=i+1 to n i 1

t ji

a ji  ¦ tik l jk

(6)

k 1

enddo enddo

It can be seen from Procedure 2 that, when computing line elements (hereinafter

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referred to as line computation) before diagonal elements aii in domain 1, the line elements are interdependent and do not influence each other. Equally speaking, the line elements before diagonal elements aii are data independent. Therefore, parallel computation can be conducted in the line computation. When computing diagonal elements d i and the column elements (hereinafter referred to as column computation) d beneath diagonal elements i in domain 2, the column computation is also interdependent. Therefore, parallel computation can be conducted in the column computation as well. As a result, parallel computation can be both realized in domain 1 and domain 2. It is thus able to obtain the fine-grained parallel factorization algorithm. The variation regarding to the stiffness matrix before and after the computation of line i and column j is illustrated by Fig. 1 and Fig. 2. § d1 ¨ d2 ¨ l21 ¨   ¨ t t ¨ i1 i2 ¨t ¨ i 1,1 ti 1, 2 ¨   ¨¨ tn 2 © t n1

 

aii

 ai 1,i  

ai 1,i 1 



an,i 1

ani

· ¸ ¸ ¸ ¸ ¸ ¸ ¸ ¸  ¸ ann ¸¹

Figure 1. Matrix before factorization of row i and column i

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J. Chen et al. / A Parallel Factorization Algorithm for Stiffness Matrix Based on Threadpool § d1 ¨ d2 ¨ l21 ¨   ¨ li 2 ¨ li1 ¨t ¨ i1,1 ti 1, 2 ¨   ¨¨ tn 2 © t n1

 

di

 ti1,i    t ni

ai1,i 1  an,i1

· ¸ ¸ ¸ ¸ ¸ ¸ ¸ ¸  ¸ ann ¸¹

Figure 2. Matrix after factorization of row i and column i

2. Realization of parallel factorization of finite element stiffness matrix

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2.1. Realization of Multithreading It can be discovered from Procedure 2 that nested loop is involved in the factorization of stiffness matrix. Moreover, the parallel domain is inside the external loop. The parallel principle of OpenMP is to establish multiple threads. If OpenMP is employed inside the loop, then within each cycle OpenMP needs to repeatedly establish and destroy thread. As the establishment and destruction of thread involve switch operation between user mode and kernel mode, allocation and revocation of memory, the computation expense is considerable. Therefore, the application of OpenMP to Procedure 2 requires repeated establishment and destruction of thread. The consumed time is no less than or even exceeds the time when OpenMP is not employed. Based on the above findings, it is noticed that if a thread can be temporarily stored after it is used; it can be used again in subsequent steps. The total computation expense is therefore reduced. Threadpool in this case is just able to realize this assumption [10]. The threadpool can be perceived as the container of threads. An initial thread is established after receiving the establishing request. The established thread is the same with other threads. The uniqueness is the established thread will be returned to threadpool and in suspend state rather than destroyed after it is used. When the suspended thread is requested by applications again, it will be activated to prosecute requested work. Therefore, the cost regarding establishing new thread is avoided and the burden of the system is lowered considerably. It is thus enhancing the efficiency of the applications.

2.2. Realization of threadpool In this paper, the threadpool is realized by employing ThreadPool static class provided by .net framework of Microsoft [11]. This static class represents a threadpool which is maintained by the operation system. When it is used, users do not need to establish the thread. It only requires converting the jobs into functions and transmitting the function addresses as parameters to the static method QueueUserWorkItem(). The thread can be stored at threadpool. The delivering method is to use WaitCallback as delegate object. The establishment, management, operation and destruction of the thread are all automatically completed by the operation system. Therefore, the consideration of complicated and detailed issues within the above steps is all avoided, which make threadpool more safe, simple and efficient.

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As the synchronization of each thread should be considered, an object ManunalResetEvent is needed. This object is functioned as a signal lamp. The other threads can be informed by its signal. When all threads in threadpool finish their jobs, the signal of ManualResetEvent is set to be positive. The main thread is therefore informed to proceed.

2.3. Optimization of thread number It can be seen from equation (4) that only division operation is involved in line computation and the parallel granularity size is very small. The number of threads is usually the same with the number of cores in the processor. In this way, high efficiency can be obtained. From equations (5) and (6), it is discovered that large amounts of multiplication and division are involved during the computation of diagonal and column elements. The condition of load imbalance exists among different threads. To fully utilize the resources of the processor, number of threads can be increased to a proper extent. However, if the number of threads is too large, the switch among different threads will consume much time and lower the efficiency instead. The number of threads is generally determined empirically as follows: t= c*2+2 㸦7㸧 In which t is the number of threads when performing line computation, c is the cores of processor. When the number of threads is determined, line and column computation can be separated into blocks in terms of the number of threads. Parallel computing can then be conducted within each block.

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3. Algorithm test To test the efficiency of the presented fine-grained finite element stiffness factorization algorithm based on threadpool, Procedure 1, Procedure 2 and threadpool-based parallel algorithm are tested on Intel Core 2 Duo E8400 3.0GHz dual-core processor and Intel Core i5 750 2.66G quad-core processor, respectively. Two finite element stiffness matrixes are used, which are stored by variable bandwidth storage method with auxiliary addressing array (see Table 1). The order of the latter is approximately 10 times of the order of the former. The test results are shown in Table 2 and Table 3. Table 1. Two compute samples elements 6446 43860

sample 1 sample 2

nodes 7746 46546

orders 18358 133690

bandwidth 3.932*107 5.238*108

Table 2. Runtime on E8400 CPU (Unit: second) algorithm 1 sample 1 sample 2

algorithm 2

55.3 930

59.0 1037

parallel algorithm 33.5 600

speed-up Single-core ratio efficiency 1.76 88.1% 1.73 86.4%

Table 3. Runtime on i5 750 CPU (Unit: second) algorithm 1 sample 1 sample 2

56.5 945

algorithm 2 60.8 1057

parallel algorithm 17.4 313

speed-up ratio 3.49 3.38

Single-core efficiency 87.4% 84.5%

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Following findings are derived by comparing the results of Tab.2 and Tab. 3. x

When the parallel algorithm is implemented, as each thread is allocated to different cores of CPU, the speed of parallel algorithm is remarkably higher than serial algorithm. Even higher speed can be achieved if the algorithm is prosecuted on processors with more cores.

x

When the order of stiffness matrix increases, the efficiency of parallel algorithm is lowered to a small extent. This is due to the employment of auxiliary addressing array. As a result, the addressing becomes complicated and brings about additional computation expense. With the increase of order of stiffness matrix, the addressing workload accounts for more computation expense. Therefore, the efficiency of a single core is lowered to a small extent.

x

When the parallel algorithm is applied to quad-core CPU, as the proportion of inconsistent blocks increases, the load imbalance of threads increases as well. Therefore, with the increase of cores, the efficiency of a single core will be lowered but the decreasing extent is not considerable. If better strategy regarding load-balancing is employed, the efficiency of proposed algorithm will be further enhanced.

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4. Conclusions x

The efficient and stable parallel factorization algorithm for stiffness matrix stored by variable bandwidth storage method based on threadpool is presented. The presented algorithm avoids the expense of communication and considerably reduces the processing expense of establishment and destruction of threads. Therefore it acquires higher acceleration ratio and computation efficiency.

x

The parallel factorization method presented by this paper belongs to finegrained parallel computation. Its theory is simple and concise with low programming difficulty. The proposed idea not only applies to positive definite matrix, but also is feasible for other algorithms, especially for the algorithms in which the application of OpenMP is restricted, such as nested loops.

x

The computer cluster, computer parallelization and vector machine are characterized by their high hardware demands. Compared to the above solutions, the solution presented in this paper has a comparatively lower demand of hardware platform. The proposed high efficient parallel algorithm can be implemented on common multi-core CPU. Therefore it is of great practical value.

Acknowledgments This study was supported by the National Natural Science Foundation of China (No.90715042), the National Natural Science Fund for Distinguished Young Scholars

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of China (No.50725931) and the National Key Technology R&D Program (No.2008BAB29B01). These supports are greatly acknowledged and appreciated.

References

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[1] Heejo Lee, Jong Kim, Sung Je Hong, Sunggu Lee. Task scheduling using a block dependency DAG for block-oriented sparse Cholesky factorization. Parallel Computing, 29(2003), 135–159. [2] Zhang Jianfei, Jiang Hongdao. Preprocessing of FEM parallel computation. Chinese Journal of Computational Mechanics, 20(2003), 500-503. [3] LI Hai-jiang, YANG Gang, YI Nan-gai. Object-oriented serial/ Parallel finite element analysis system. Chinese Journal of Computational Mechanics, 23(2006), 500-503. [4] D. Zheng, T. Y. P. Chang. Parallel cholesky method on MIMD with shared memory. Computers & Structure, 56(1995), 25-38. [5] William Gropp, Ewing Lusk, Anthony. Using MPI - 2nd Edition: Portable Parallel Programming with the Message Passing Interface , The MIT Press, 1999. [6] Barbara Chapman, Gabriele Jost, Ruud van der Pas. Using OpenMP Portable Shared Memory Parallel Programming, The MIT Press, 2008. [7] FAN Yan-hong, LV Quan-yi, NIE Yu-feng. Improved parallel algorithm for solving block-tridiagonal linear equations. Computer Engineering and Applications, 45(2009), 60-63. [8] Cui Xi-ning, Lv Quan-yi. A parallel algorithm for band linear systems. Applied Mathematics and Computation, 181(2006), 40-47. [9] J. H. Yun, Parallel Performance of Block ILU Preconditioners for a Block-tridiagonal Matrix. The Journal of Supercom putting, 24(2003), 69-89. [10] Stephen R. G. Fraser. Pro Visual C++/CLI and the .NET 2.0 Platform. Apress, 2006. [11] Jeffrey Richter. Applied Microsoft .Net Framework Programming. Microsoft Press, 2002.

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An Accelerated Meshfree Analysis of Three Dimensional Soil Slope Failure under Finite Deformation a

Dongding WANGa,1 , Zhuoya LIa, Ling LIb and Youcai WUc Department of Civil Engineering, Xiamen University, Xiamen, Fujian 361005, China b Guangdong Research Institute of Water Resources and Hydropower, Guangzhou, Guangdong 510610, China c Karagozian & Case, 2550 N Hollywood Way, Suite 500, Burbank, CA 91505, USA

Abstract. A three dimensional updated Lagrangian Galerkin meshfree formulation with improved computational efficiency is presented to analyze the failure of soil slopes under finite deformation. This nonlinear meshfree formulation is featured by the Lagrangian stabilized conforming nodal integration method where the low cost nature of nodal integration approach is kept and at the same time the spatial stability is achieved. The initiation and propagation of failure in the soil slope is modeled by the coupled constitutive equations of isotropic damage and pressure dependent plasticity. The effectiveness of the present method is demonstrated by a three dimensional slope failure example. Keywords. Meshfree method, soil slope, failure, finite deformation, stabilized conforming nodal integration

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Introduction The analysis and prediction of soil slope failure has been a major concern for civil engineering practice. However the complexity of soil failure problems, i.e., the coupled large deformation plasticity and damage behaviors, makes the analytical study often not realistic. Thus various computational methods have been developed for numerical study of slope failure and among which the finite element method is the most widely used approach. Despite of the great success the finite element method has achieved in practical application it does face severe issues related the topological mesh connectivity, such as the large deformation problems and moving boundary problems where remeshing is commonly required. Up to date the finite element re-meshing is still a very challenge problem and has not been well resolved especially for three dimensional modeling. Consequently a new generation of computational methods, collectively called meshfree methods (Li and Liu, 2004), has received significant attention since 1990s and has gained rapid development and growing applications. Among the versatile meshfree methods, the Galerkin meshfree method with moving least square (MLS) or reproducing kennel (RK) approximation is the most popular one because of its sound 1 Corresponding Author: Dongding WANG, Department of Civil Engineering, Xiamen University, Xiamen, Fujian 361005, China; E-mail: [email protected].

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stability and accuracy (Li and Liu, 2004). It turns out that the MLS and RK approximations are equivalent when monomial basis functions are used. Nonetheless the low computational efficiency due to the high Gaussian quadrature rule for Galerkin weak form integration becomes a bottleneck for the application Galerkin meshfree method to engineering problems. Different methods have been introduced to improve the computational efficiency. One very unique and promising approach is the stabilized conforming nodal integration (SCNI) method originated by Chen et al (2001, 2002). Later on this method has been systematically developed for structural problems like beams, plates and shells (Wang and Chen, 2006; Wang and Wu, 2008). This method doest not involve any artificial and problem dependent parameters and has been shown to be highly efficient, accurate and robust. A two dimensional large deformation dynamic meshfree method for the failure analysis of geomaterials has been developed by Wang (2006). In this paper as a continuous effort a three dimensional Galerkin meshfree formulation is proposed for soil slope failure analysis based upon the SCNI approach.

1. Meshfree Approximations of Deformation Field Consider a soil slope which occupies a domain Ω0 with boundary Γ 0 at the initial configuration, after a given deformation mapping x = φ( X, t ) with x , X , t being the spatial and material coordinates and the time, respectively. The problem domain and boundary become Ω and Γ at the current configuration. In a Lagrangian MLS/RK approximation, the initial problem domain is discretized by a set of points X I , I = 1, 2, ⋅⋅⋅, NP , therefore the MLS/RK approximation of the displacement ui , i = 1, 2,3 , can be expressed by: uih ( X, t ) = ∑ I =1 Ψ I ( X)d Ii (t ) Copyright © 2010. IOS Press, Incorporated. All rights reserved.

NP

(1)

where d Ii is the nodal coefficients and Ψ I ( X) is the MLS/RK shape function: Ψ I ( X) = pT (0)M −1 ( X)p( X − X I ) ka ( X − X I )

(2)

with ka ( X − X I ) being the cubic spline kernel function centering at X I with a compact support a . M ( X) is the moment matrix and p( X − X I ) is the n-th order polynomial basis vector (Li and Liu, 2004). It can be shown that Ψ I ( X) satisfies the following nth order reproducing conditions:

∑

NP I =1

Ψ I ( X) X Ii1 X Ij2 X Ik3 = X 1i X 2j X 3k , 0 ≤ i + j + k ≤ n

(3)

Based upon Eq. (1), a smoothed material nodal gradient of the displacement field for SCNI can be constructed as follows (Chen at al, 2002; Wang, 2006):

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D. Wang et al. / An Accelerated Meshfree Analysis of Three Dimensional Soil Slope Failure

% Ψ ( X )d (t ) uih, J ( X K , t ) = VK−1 ∫ uih, J ( X, t )dV = ∑ I =1 ∇ XJ I K Ii NP

VK

(4)

where VK is the nodal representative domain of node K as shown in Fig. 1, and by using the divergence theorem one has the following smoothed material nodal gradient where the derivatives of shape function are not involved: % Ψ ( X ) = V −1 Ψ ( X) N ( X)dS ∇ XJ I K K ∫ I J

(5)

SK

Figure 1. Various choices of meshfree nodal representative domain

Hence the smoothed nodal deformation gradient is given by: NP % Ψ ( X )d (t ) F%i , J ( XK , t ) = δ i , J + ∑ I =1 ∇ XJ I K Ii

(6)

% Ψ ( X ) can be obtained as: With Eq. (6), the smoothed spatial nodal gradient ∇ I K xi

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% Ψ ( X ) = F% −1∇ % Ψ (X ) ∇ xi I K J ,i XJ I K

(7)

2. Discretized Equations of Motion A variational statement of the equations of motion for the soil body is:

∫ η ρ a dV + ∫ η Ω

i

i

Ω

i, j

σ ij dV − ∫ ηi bi dV − ∫ h ηi hi dS = 0 Ω

Γ

(8)

where ηi s the test function, ρ is the soil density, σ ij is the Cauchy stress, bi is the body force, hi is the surface traction on the natural boundary Γ h . Introducing a nodally integrated Bubnov Galerkin meshfree approximation using Eqns. (1) and (4) into Eq. (8) gives the following matrix equations: Ma = f ext − f int

with

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(9)

D. Wang et al. / An Accelerated Meshfree Analysis of Three Dimensional Soil Slope Failure

455

⎧M IJ = 13 ∑ NP ρ0 Ψ I ( X K )Ψ J ( X K )VK K =1 ⎪ NP NB ⎪ ext ⎨f I = ∑ K =1 Ψ I ( XK )b 0 ( X K )VK + ∑ L =1 Ψ I ( X L )h( X L ) S L ⎪ int NP T ⎪⎩f I = ∑ L =1 B% I ( X K )Σ( X K ) det(F% ( X K ))VK

(10)

⎧ ⎡∇ x Ψ I 0 ⎪%T ⎢ ∇yΨI ⎪B I = ⎢ 0 ⎨ ⎢ 0 0 ⎣ ⎪ ⎪ΣT = σ { xx σ yy σ zz ⎩

(11)

0 0

∇yΨI ∇xΨI

∇z ΨI 0

∇z ΨI

0

∇xΨI

σ xy

σ xz

σ yz }

0 ⎤ ⎥ ∇z ΨI ⎥ ∇ y Ψ I ⎥⎦

where ρ 0 and b 0 are the initial soil density and body force, 13 is the 3 by 3 identity matrix, X L and S L , L = 1, 2,..., NB , are the traction surface integration points and corresponding weights. The time integration of Eq. (9) is carried out by the classical central difference algorithm within the Newmark family of time integration methods at the time interval [tn , tn +1 ] and Δt = tn +1 − tn , thus the fully discretized equations are: ⎧Ma n +1 = f next+1 − f nint ⎪ 2 ⎨d n +1 = d n + v n Δt + a n (Δt ) / 2 ⎪ v = v + (a + a )Δt / 2 n n n +1 ⎩ n +1

(12)

The stress update is implemented through employing the Jaumann’s rate:

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σ ∇J = Cepd : σ, σ ∇J = σ& − ωσ + σω, ε& = (∇ xu& + u& ∇ x ) / 2, ω = (∇ x u& − u& ∇ x ) / 2 (13) where Cepd is the elasto-plastic-damage material response tensor (Simo and Ju, 1987). The damage and plasticity behaviors are governed by the isotropic damage model and the pressure dependent Drucker-Prager plasticity criterion: ⎧ζ = [e (e − e )] [e(e − e )], e = t ε& : ε& dt ⎪ c i c i ∫0 ⎨ ⎪⎩ f (σ ) = s : s − α Iσ 1 − β = 0, σ = (1 − ζ )σ

(14)

where s and Iσ 1 are the deviatoric part and the 1st invariant of the effective stress σ , ζ is the damage parameter. ei and ec are the initial and accumulated damage thresholds. α and β are material parameters.

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3. Numerical Demonstrations

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Consider the three dimensional soil slope problem whose geometric dimensions are shown in Fig. 2, the material properties employed in this study are: Young’s modulus 26MPa , Poisson ratio 0.35 , ρ0 = 1650kg / m3 , ei = 0.3 , ec = 5 , α = 0.1 , β = 0.2MPa . This problem has fixed boundary conditions at the bottom surface and a one-directional fixed boundary condition at the left, right and back surfaces, respectively. A prescribed vertical displacement of 1m is applied on the top of the slope with a speed of 10−2 m / sec . The problem domain is discretized by 8775 meshfree particles. A linear basis and a normalized kernel support of 1.5 are used for the kernel function of meshfree approximation. The progressive slope failure configurations are shown in Fig. 3, as demonstrates that the proposed method is very robust for modeling the complex soil damage process.

Figure 2. Problem statement and meshfree discretization

4. Conclusions An efficient and stable three dimensional Galerkin meshfree formulation with the stabilized conforming nodal integration was presented for failure analysis of soil slope. This formulation employs the rate formulation to account the finite deformation effect and the coupled strain-based isotropic damage and pressure-dependent plasticity model to model the plastic damage deformation. Three dimensional numerical results evince that the present method is fully capable of simulating the complicated failure process in the soil slope.

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457

Figure 3. Meshfree simulation of failure propagation in soil slope

Acknowledgements

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The financial support of this work by the National Natural Science Foundation of China (10972188, 10602049) and the Program for New Century Excellent Talents in University from China Education Ministry (NCET-09-0678) is gratefully acknowledged.

References [1] Li S, Liu WK, Meshfree particle methods, Springer-Verlag, Berlin, 2004. [2] Chen JS, Wu CT, Yoon S and You Y, A stabilized conforming nodal integration for Galerkin meshfree methods. International Journal for Numerical Methods in Engineering, 50 (2001), 435-466. [3] Chen JS, Yoon S and Wu CT, Nonlinear version of stabilized conforming nodal integration for Galerkin meshfree methods, International Journal for Numerical Methods in Engineering, 53 (2002), 2587-2615. [4] Wang D, Chen JS, A locking-free meshfree curved beam formulation with the stabilized conforming nodal integration, Computational Mechanics, 39 (2006), 83-90. [5] Wang D, Wu Y, An efficient Galerkin meshfree analysis of shear deformable cylindrical panels, Interaction and Multiscale Mechanics 1 (2008), 339-355. [6] Wang D, Large deformation dynamic meshfree simulation of damage and failure in geomaterials. Key Engineering Materials 324-325 (2006), 141-144. [7] Simo JC, Ju JW, Strain and stress-based continuum damage models-I formulation. International Journal of Solids and Structures, 23 (1987), 821-840.

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Meshless Natural Neighbor Method based on implicit integration algorithm for elastoplastic analysis a

Hehua ZHUa, Wenjun LIUa,b,1 , Yongchang CAIa and Yuanbin MIAOa,c Geotechnical Engineering Key Lab., Department of Geotechnical Engineering, Tongji University, Shanghai 200092, P.R.China b Wuxi Metro Development Co., Ltd., Wux i214043, P.R.China c Fujian Communication Planning and Design Institute, Fuzhou 3500043, P.R.China

Abstract. In this paper, an implicit stress integration algorithm based on the closest point return projection algorithms is used in a meshless natural neighbor method (MNNM). In its application to the associated Mises plasticity model, the implicit formulations show remarkable accuracy and stability due to the fact that the consistent linearization is adopted in computing incremental elastoplastic equations. Finally, two numerical examples using the implicit integration in MNNM for two dimensional elastoplastic problems are presented to demonstrate the efficiency and advantages of this method. Keywords. Natural neighbor, Laplace interpolation, Implicit integration algorithm, Consistent tangent modulus

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Introduction As a new and powerful numerical tool, meshless methods [1] have been more and more successfully used in various engineering fields where the conventional FEM are met with the difficulty of remeshing or mesh distortion. However, the normal meshless methods have some drawbacks in imposing the essential boundary conditions. The natural element method (NEM) introduced by Braun [2] uses Voronoi polygon and Delaunay triangulation to build its shape functions. Then Sukumar [3] applied the NEM to solve various problems in two-dimension linear elastostatics to examine its accuracy and robustness. Natural neighbor shape functions pocess several identical properties with the Lagrangian interpolations used in FEM, such as the Kronecker delta. Many of the nonlinear elastoplastic stress-strain problems in engineering require robust algorithms. It is difficult to analysis progressive failures and large deformation in soil structures without an effective iterative stress integration algorithm because the accuracy and stability are directly controlled by the integration scheme. Wilkins [4] began to study the classical radial return algorithm for the associated Mises flow rule. Krieg[5] extended the radial return method and summarized alternative formulations of elastic-

1

Corresponding Author. Civil Engineer, Tel.: +86 510 826 90 206, E-mail: [email protected].

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H. Zhu et al. / Meshless Natural Neighbor Method Based on Implicit Integration Algorithm

459

predictor/plastic-corrector method. Aravas [6] presented an unconditional new implicit algorithm that named the back ward Euler method. Lee [7] extended this back ward Euler method for the problems of three-dimension, plane stress and mix hardening. Based on the associative flow rules’ rate-independent plasticity with arbitrary hardening laws, Simo [8] proposed a closest point return projection method (CPP) for pressure-independent problems, viscoelasticity problems, viscoplasticity problems and finite strain problems. The present paper is based on the research by Zhu [9]. The implicit stress integration algorithm was used in the MNNM to analyze elastoplastic problems including large deformation problems. The arrangement of this paper is as follows: In part 1, an overview of the meshless natural neighbour method is presented. The implicit integration algorithm based the closes point return projection is described in Section 2. Section 3 demonstrates the effectiveness and stability of the proposed method by analyzing two elastoplastic problems. Conclusion is addressed in Section 4.

1. The meshless natural neighbour method(MNNM) 1.1. Search for natural neighbours

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For a given problem domain Ω with arbitrary geometry, a set of distributed nodes, N = {n1 , n2 , ⋅⋅⋅, nm } namely , are used to represent the internal domain and its boundary. Suppose that one sample point p is a random numerical integral point in the domain, then its dual of Voronoi diagram and Delaunay triangles is constructed in the paper3. By the empty circumcircle criterion, natural neighbour nodes about the point p will be determined. Because those natural neighbour points are uniquely defined if the nodal set N has been set up in Ω , Cai10,11 presented that adding square region can improve efficienty of searching natural neighbours by eliminating unqualified nodes.

1.2. Laplace interpolation Once the Delaunay triangles are constructed, Laplace functions corresponding to the point p can be defined. If aj(x) is the Laplace weight function, sj(x) and hj(x) are the length of corresponding Voronoi edge and Euclidean distance, then value of Laplace interpolation function at the node i is determined as φi ( x ) = α i ( x )

n

∑α j =1

j

( x ) 웍α j ( x ) = s j ( x ) h j ( x ) n

φi , j ( x) = (α i , j ( x) − φi ( x)∑ α k , j ( x)) k =1

n

∑α k =1

n

∑ φ ( x) = 1 , 0 ≤ φ ( x ) ≤ 1 㧘 φ ( x ) = δ i =1

i

i

i

j

ij

k

( x) ,

(1) n

u h ( x) = ∑ φi ui

(2)

i =1

㧘 x∈Ω

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(3)

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H. Zhu et al. / Meshless Natural Neighbor Method Based on Implicit Integration Algorithm

Where ui (i = 1, 2,L , n) and φi ( x) are vectors displacements and shape functions. As mentioned earlier, Laplace interpolation function possesses several useful properties identical to that of the Lagrangian interpolation functions used by the FEM. From Eq. (3), it can be seen that the Laplace interpolation passes through the nodal values in contrast to most meshless approximations, so these properties make the enforcement essential boundary conditions as easy as that in the conventional FEM. 2. Implicit integration algorithm Improving computation accuracy and stability for elastoplastic problems including large deformation, this paper adopts the implicit integration algorithm based on the CPP algorithm8. Through the algorithm, we can get better results in a few load steps for elastoplastic large deformation problems. Noticeably, the hardening rule contains one hardening parameter and one plastic modulus, and then the stress tensors follows Eq. (4) to Eq. (7), more complete proofs and details can be referred to Simo8.

(

σ n +1 = K E ( tr [ε ]) δ + 2GE ( ε e )n +1 − ( ε ep )

(4)

⎡ ∂Λ ∂n ⎤ dσ n +1 = D : dε n +1 − 2GE d ( Λn n +1 ) = ⎢ D − 2GE n n +1 ⊗ − 2GE Λ ⊗ n +1 ⎥ : dε n +1 ε ∂ ∂ε n +1 ⎦ n +1 ⎣

(5)

⎡ 1 ⎤ Dep = K E δ ⊗ δ + 2GEθ n +1 ⎢ I − δ ⊗ δ ⎥ − 2GEθ n +1n n +1 ⊗ n n +1 3 ⎣ ⎦

(6)

θ n +1 = 1 − Copyright © 2010. IOS Press, Incorporated. All rights reserved.

)=K

− Λ n n +1 )

2GE Λ ξ trial n +1

n +1

E

( tr [ε ]) δ + 2G ( ( ε ) E

e n +1

−1

⎡ K ′ ( ε n +1 ) + H ′ ( ε n +1 ) ⎤ , θ n +1 = ⎢1 + ⎥ − (1 − θ n +1 ) 3GE ⎣ ⎦

(7)

Where the four order unit tensor I designates 1 ⎡⎣δ ik δ jl + δ il δ jk ⎤⎦ ei ⊗ e j ⊗ ek ⊗ el . 2 3. Numerical examples The present MNNM is coded in C++, two cases are investigated in order to examine the implicit MNNM in solving two-dimension elastoplastic problems. 3.1. Thick-walled cylinder A plane strain thick-walled cylinder is considered in quasi-statically elastoplastic analysis with internal radius a=10mm and external radius b=20mm. As shown in Figure 1, it is subjected to internal pressure p=12kPa . Other parameters of the Thick-walled cylinder are that: Young’s modulus E=85570kPa, Poisson’s ratio v=0.3 and equivalent

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461

yielding stress σ s = 17.32 KPa . The following section will demonstrate the stress analysis of the cylinder via the presented implicit integral method.

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Figure 1. Thick-walled cylinder under pressue (mm)

Figure 2. Meshless nodes

Figure 3. Plastic yield zone of the MLNNIM under p=12kPa㧔14.1mm㧕

The elastic/plastic limit solution pe and pl are expressed in the paper12 as pe =

σs 3

(1 −

2σ s ⎛ b ⎞ a2 ) , pl = ln ⎜ ⎟ 2 b 3 ⎝a⎠

(8)

Then the elastic/plastic limit solution pe and pl are 7.5kPa and 13.8kPa for the present case. So under p=12kPa, a plastic zone occurs adjacent to the internal surface and the cylinder is divided into two zones: the elastic zone and the plastic zone. In the elastic zone, the analytical solution of displacement (radial displacement u ) and stress (radial stress σ r and hoop stress σ θ ) are

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H. Zhu et al. / Meshless Natural Neighbor Method Based on Implicit Integration Algorithm

u=

2 2 2 σ s rp b 2 σ s rp b2 1 + v σ s rp 2 2 − v r + b σ = − − , σ = (1 2 ) , ( 1) (1 + 2 ) { } θ r 2 2 2 2 r 3E b r 3b r 3b

(9)

In the plastic zone, the analytical solution of displacement (radial displacement u ) and stress (radial stress σ r and hoop stress σ θ ) are Eq. (10). Based on the consistent condition, the equilibrium equation for rp is written as Eq. (11). u=

p=

(1 + v)σ s rp2 ⎡⎣(1 − 2v ) rp2 + b 2 ⎤⎦ 2

3Eb r ⎛ rp ln ⎜ 3 ⎝a

2σ s

2 ⎞ σ s ⎛ rp 1 + − ⎜ ⎟ 2 3 ⎜⎝ b ⎠

, σr =

2σ s 3

ln

⎞ ⎟⎟ ⎠

Figure 4. Radial displacement and radius ( p = 12 KPa ) Copyright © 2010. IOS Press, Incorporated. All rights reserved.

2σ r r − p , σ θ = s (1 + ln ) − p a a 3

(10)

(11)

Figure 5. Radial stress and radius ( p = 12 KPa )

Utilizing the symmetry of the cylinder, the distribution of nodes by the MNNM are shown in Figure 2. Considerable accuracy is achieved when comparing with the results obtained by analytical solution and by triangular element in the FEM. Under the internal pressure p=12kPa, plastic yield zone of the MNNM is shown in Figure 3, and the equivalent radial of the elastoplastic interface is 14.1mm. From Eq. (11), the analytical solution is about 14.24mm, and the error is about 1%.

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H. Zhu et al. / Meshless Natural Neighbor Method Based on Implicit Integration Algorithm

Figure 6. Hoop stress and radius (P=12kPa)

463

Figure 7. Elastoplastic interface radius and pressure

Figure 4 gives the relationship of radial displacement and radius by analytical solution (A.S.), referenced solution (R.S.), and numerical solution (N.S.) of the MNNM. It shows that numerical results of the MNNM are in a very good agreement with these analytical results, and the largest error between these two methods is only 4%. Stresses are computed by the methods12. The relationships between stress (radial stress and hoop stress) and radius are shown in Figure 5 and Figure 6. Numerical results of the MNNM are revealed to match the analytical results with rather accuracy. Furthermore, when only the internal pressure P is changed, the elastoplastic interface radius and different internal pressure is shown in Figure 7. Numerical solution by the implicit MNNM is in good agreement with analytical solution, and the large error is 2.4%. This also proves further the accuracy of the proposed methods.

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3.2. Strip footing A strip footing settlement problem13 in Figure 8 is analyzed under plane strain conditions, assuming the Mises following criterion with the bilinear plastic strain hardening. By the MNNM, Updated Lagrangian (UL) formula of large deformation theory12 will be used to analyze the strip footing settlement. Material properties andgeometric values are: Young’s modulus E is 2.0e8KPa, Poisson’s ratio v is 0.25, equivalent initial yielding stress is 2.4e5KPa, and plastic tangent modulus is 5e6KPa.

Figure 8. Strip footing under plane strain (m)

Figure 9. Meshless nodes’ distribution

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Figure 10. Upper nodes' vertical displacement (1.0m) Figure 11. Upper nodes' vertical displacement (3.0m)

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The bottom surface is typically fixed on the rigid, and boundary surfaces have no movement along X direction but it has free movement along Y direction. During the entire deformation process, the rigid bar on which the strip footing is acting moves vertically uniformly down. Due to the symmetry, only half of the material is modeled. Numerical analysis difference of computing elastoplastic large deformation between classical explicit and implicit integration algorithm using the MNNM, when the displacement which is relative to the initial state is 1.0m and 3.0m. Before the strip footing is applied to Mises material (the body force has been neglected), 114 meshless nodes generated for the MNNM is shown in Figure 9. After 100 load steps, its referenced numerical solution (R.N.S.), the deformed shapes of the FEM and the MNNM are shown in paper13. The problem’s numerical solution of the implicit and explicit integration algorithm in the MNNM and its referenced numerical solution are given in Figure 10. It can be found that the implicit numerical results are better than those of explicit integration algorithm in the MNNM when other conditions are completely the same.

 (a)

 (b)

Figure 12. Upper nodes’ vertical displacement under footing settlement 1.0m (a. Implicit, b.Explicit)

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465

 (a)

(b)

Figure 13. Upper nodes’ vertical displacement under footing settlement 3.0m (a. Implicit, b.Explicit)

Numerical results are shown in Figure 12 by the implicit and explicit MNNM in different load steps. Then we can find that the implicit MNNM achieves better stability and accuracy. And good computing results can be obtained in a few load steps (for example in the 15 load steps), but the explicit MNNM might produce unstable computing results due to error accumulating for this large deformation problem. In the similar way, when strip footing settlement reaches 3.0m, numerical results are shown in Figure 11 and Figure 13. The same phenomenon and conclusion may be discovered. So it is clear that the computing results of the implicit MNNM are more stable and accurate than those of the explicit MNNM. These show the implicit MNNM is effective and advantageous in solving large elastoplastic deformation problems.

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4. Conclusion This paper proposed the implicit MNNM to analyze material elastoplastic problems. By investigating elastoplastic problem of thick-walled cylinder, it proves stability and accuracy of this method. To investigate its performance in large deformation problems, then it is used to analyze elastoplastic deformation problems. Numerical results of this method are compared with those of the classical explicit MNNM. It shows that the implicit MNNM can get more stable and accurate results. Generally it could get good result in a few load steps while the classical explicit MNNM might get unstable results in some degree. Two representative numerical examples are involved to demonstrate the validity and advantage of this new method.

Acknowledgments The authors gratefully acknowledge the financial support of the research project sponsored by NSFC(50579093), NSFC(40672184) and Key Project of Chinese Ministry of Education (107041).

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References

Copyright © 2010. IOS Press, Incorporated. All rights reserved.

[1] T. Belytschko, Y.Y. Lu, L. Gu, Element-free Galerkin method. International Journal for Numerical Methods in Engineering 37(1994), 229-256. [2] J. Braun, M. Sambridge, A numerical method for solving partial differential equations on highly irregular evolving grids. Nature 376(1995), 655-660. [3] N. Sukumar, B. Moran, T. Belytschko, The natural element method in solid mechanics. International Journal for Numerical Methods in Engineering 43(1998), 839-887. [4] M.L. Wilkins, Calculations of elastic-plastic flow. In methods of computational physics. Adler B. et al., vol. 3, Academic press, New York, 1964. [5] R.D. Krieg, D.B. Krieg, Accuracies of numerical solution methods for elastic-perfectly plastic model. J. Pressure Vessel Technology, ASME 99(1977), 2-15. [6] N. Aravas, On the numerical integration of a class of pressure-dependent plasticity models. International Journal for Numerical Methods in Engineering 24(1987), 1395-1416. [7] J.H. Lee, Z.L. Zhang, On the numerical integration of a class of pressure-dependent plasticity models with mixed hardening. International Journal for Numerical Methods in Engineering 32(1991), 419-438. [8] J.C. Simo, T.J.R. Hughes, Computational inelasticity. New York, Springer, 1998. [9] H.H. Zhu, et al., A meshless local natural neighbor interpolation method for two-dimension incompressible large deformation analysis. Engineering analysis with boundary elements 31(2007), 856-862. [10] Y.C. Cai, H.H. Zhu, A meshless local natural neighbour interpolation method for stress analysis of solid. Engineering Analysis with Boundary Elements 28(2004), 607-613. [11] Y.C. Cai, H.H. Zhu, A local search algorithm for natural neighbours in the natural element method. Solids and Structures 42(2005), 6059-6070. [12] X.C. Wang, Finite element method. Tsinghua University Press, Beijing, 2003. (In Chinese) [13] Y.B. Miao,. Meshless natural neighbour method for large deformation analysis. PH.D. Thesis, Tongji University, 2005. (In Chinese)

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The Mean Stress as the Governing Parameter in the Implicit GPM Stress Integration of Modified Cam-Clay Model Mirjana VUKICEVIC a,1 and Dragan RAKIC b Faculty of Civil Engineering, Belgrade, Serbia b Faculty of Mechanical Engineering, Kragujevac, Serbia a

Abstract. An implicit computational governing parameter method (GPM) for stress integration of the modified Cam-Clay model is presented in the paper. In the previous studies, the increment of the mean plastic strain demp was adopted as the governing parameter for the modified Cam-Clay model. While in this paper, the effective mean stress sm’ is used as the governing parameter, which is much more convenient than demp. Such algorithm is simple, reliable, efficient and robust. This approach makes it possible to calculate the true tangent constitutive matrix in a simple way. Keywords. Implicit stress integration, modified Cam-Clay model, soil

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Introduction Stress integration represents one of the very important steps in the inelastic finite element analysis [1]. We present here an implicit computational algorithm for stress integration of the modified Cam-Clay model in different manner then in the previous papers [2], [3]. The algorithm represents an application of the governing parameter method (GPM) [1], [4] for implicit stress integration of inelastic constitutive relations. We here recapitulate a basic concept of GPM. In the incremental solution procedure, the known quantities are t V , tH , tH p , t E , t  ' tH and the unknows at end of time step Dt are t  'tV , t  'tH p , t  ' t E which we should calculate from constitutive relations ( V , H , H p , E are stesses, strains, inelastic strains and internal variables, respectively). The basic steps in the GPM concept are as folows: a) Express all unknown variables in terms of one (governing) parameter p t  't

V

t  't

H

V  tV , tH p , t E , t  'tH , p

t  't

t  't

H  tV , tH p , t E , t  'tH , p

1 Corresponding Author. Mirjana VUKICEVIC, Faculty of Civil Engineering, Belgrade, Serbia E-mail: [email protected]

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M. Vukicevic and D. Rakic / The Mean Stress as Governing Parameter t  't

t  't

E

E  tV , tH p , t E , t  'tH , p

(1)

b) Solve the governing equation t  't

f p

(2)

0

c) Calculate all the unknown variables substituting t+Dtp into (1). The algorithm provides solution for stresses under any boundary and loading conditions, high accuracy and efficiency. Efficiency is provided by possibility of calculation of the true tangent constitutive matrix t+DtC and by selection of the governing parameter in solving the governing equation. The approach outlined above enables calculation of the true tangent constitutive matrix. A number of iterations in solving the governing equation depend on the selection of the governing parameter. In the previous papers [2], [3] we adopted increment of the mean plastic strain Demp as the governing parameter. Here we present the effective mean stress sm as the more convenient governing parameter then Demp for the folowing reasons: 1) The effective mean stress has evident physical meaning; 2) At start of the iteration procedure, in the load step Dt in incremental inelastic finite element analysis, we can define precisely the interval of possible values of t+Dtsm. In consequence of that, the number of iterations is significantly reduced; 3) Derivation of the constitutive matrix t+DtC is simple.

1. Stress Integration Algorithm

The constitutive relations of the modified Cam Clay model [6], [7] are presented for general three dimensional stress state. The yield condition we define in sm-q plane

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where q= J 2d ( J 2d is second invariant of stress deviator). We now consider the relations for stress integration within the load (time) step Dt. First we define the basic stresses and strains using in the stress integration:

'H v

'H ii

'H ijc

'H ij  'H ij ˜ G ij

'V m

'H11  'H 22  'H 33 1 3

1 V ii 3

Hq

1 V 11  V 22  V 33 3

Sij

1 3

1 H ijc ˜ H ijc 2

V ij  V ij ˜ G ij

q

1 Sij ˜ Sij 2

The mathematical form of the yield condition in V m  q plane (Fig. 1) f V ij ; p0 V m2  V m ˜ p0 

q2 M12

0

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M. Vukicevic and D. Rakic / The Mean Stress as Governing Parameter

M1 M 3 (M is slope of critical state line in p-q plane from triaxial test), p0 is hardening parameter (diameter of yield surface) The elastic constitutive relations employed for the model:

'H ve

'V m 1 e Vm N

'Sij

'H ijce

˜

(4)

2G

According to the model, the hardening rule in incremental form

'H vp

O  N 'p 0 1 e

˜

(5)

p0

where are: O , N constants of the model, G shear modulus, e void ratio J2d

CSL t+ t

P

t

t

P

t

P0 /2

Figure 1. Modified Cam Clay model in Copyright © 2010. IOS Press, Incorporated. All rights reserved.

t+ t

P0

P0

V m  J 2d

m plane

On the other hand, we can obtain increments of the volumetric and deviatoric plastic strain using associated flow rule: wf wV m

'H vp

wf wSij

'H cijp 'O ˜

'H vp

'O ˜

'H ijcp

'O ˜

'O  2V m  p 0 Sij M12

(6a)

(6b)

where 'O is positive scalar The void ratio can be obtained explicitly from the volumetric strain by integration of differential relation:

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M. Vukicevic and D. Rakic / The Mean Stress as Governing Parameter

dH v



de 1 e

1  e0 ˜ exp  H v  1

e

e0 is initial e

(7)

The hardening parameter p0 is in relation to the mean stress sm from differential equation de

O  N

dp 0 dV  N ˜ m Ÿ e Vm p0

 O  N ln(p0 )  N ln(V m )

(8)

As we noted, the mean stress is adopted as the governing parameter. In the incremental solution procedure, GPM consists the following steps: The known quantities t V ij , tH ij , tH ijp , t p 0 , a)

t  't

H ij , t  't e

Elastic prediction

'V mE

'H v ˜ tV m N  'H v

t  't

VE

t

V m  'V mE

(9)

1 E E Sij ˜ Sij 2

(10)

1 e

t  't

SijE

t

t  't

S  2G ˜ 'H ijc

qE

Check the yield function qE2 d 0 solution is elastic, go to the next load M12 step. If not, plastic flow occurs, go to step b).

If f V m E ,SijE , t p 0 V mE  V mE t p 0 

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2

Plasticity calculations b) Calculate the trial value of the governing parameter section 3. c) Calculate t  't p0 t  't

p 0 i

C1



V mi

t  't



N O N

C1

t  't

V m(i) , as it is shown in

N § t e  t  't e · t t O N exp ¨ ¸ p 0 ˜ V m from Eq. (8) (11) © O N ¹

d) From equation (5): 'p 0 i O N ˜ 'H vp i 1  t  't e t  't p 0 i e)

From equation (6a):

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M. Vukicevic and D. Rakic / The Mean Stress as Governing Parameter

'H vp  i

'O i

2 t  'tV m i 

t  't

(13a)

p0 i

If 'H vp  d TOL and 2 t  'tV m   i

i

t  't

p0 d TOL , the stress state in t  ' t i

reaches the critical state and then

'q · § M12 ¨ 'H q  ¸ 2G ¹ © 2 t  't q

'O f)

t  't

q=M1 t  'tV m(i)

(13b)

From equations (4) and (6b): t  't t  't

'H ijc  tH ijcE 'O (i) 1

Sij i

M12



t  't

t  't

1 (i) (i) Sij ˜ Sij 'H ijcp i 2

q (i)

'O  i ˜

Sij i

M12

(14)

2G

g) Check the yield condition

f



t  't

i

Vm ,

t  't

i

q ,

t  't

p0

i



t  't

i

Vm 

t  't

i

Vm ˜

t  't

p0

i

 

t  't

qi

M12



2

d TOL (15)

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If not, go to step b) with new value of t  'tV m(i 1) , if yes, go to h). h) Calculate all quantities at end of time step

2. An Interval of Possible Values of Governing Parameter V m

The solving of the nonlinear governing equation in GPM stress integration is one of the basic steps. At the start of the process we shall need to determine the trial value of the governing parameter. If we know the interval of possible values of the parameter, we can reduce significantly a number of iterations in solving the governing equation. We will consider time step 't in e  ln V m plane (Fig. 2) where tV m , t p0 and e t  't are known quantities. Because e t  't is known before the start of the stress integration, we can calculate maximum possible value of t  'tV m (point on the normal

consolidation line (NCL) for e e t  't ). However, if we take into consideration the condition that 'O in flow rule is positive scalar, we can determine a more precise interval of possible values of t  'tV m according to modified Cam Clay model (see Fig. 3).

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e

NCL LNK t

t

e

m t

t

Po

t

e m

ln m

(max)

ln m

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Figure 2. Maximum value of

t  't

Vm

Figure 3. The intervals of possible value of

t  't

Vm

There are four potential cases:  The elastic wall for known state tV m , t p0 (AA1E) intersects e

e t  't between

NCL and CSL (critical state line). A interval of possible values of t  'tV m is A1C1. Only for this interval 'O ! 0 ( 'p 0 ! 0 and 2V m  p 0 ! 0 , see Eq. 12 and 13a)  The elastic wall for known state tV m , t p0 (BB1F) intersects e

CSL. A interval of possible values of

e t  't left of the

t  't

V m is B1C1 ( 'p 0 F @e >H @e >Q@e The procedure for developing the element flow matrix is similar to that followed when deriving the stiffness matrix in finite element analysis of deformation. Furthermore, the procedure for assembling the individual element flow matrices is also similar to that used in assembling the stiffness matrix in finite element analysis of deformation. Therefore the global flow equation is written as follows: >F @>H @ >Q @ , where [F] is the assembled flow matrix; [H] is the matrix of nodal groundwater head for the full mesh and [Q] is a flow matrix containing terms associated with the flow boundary conditions. To solve for the nodal groundwater heads, the global flow equation is inverted (e.g. using Gauss elimination procedure). Finally the flow rate within a given element is determined from that element’s flow equations.

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3. A case study on the use of the integrated FEA and load-transfer analysis A disused basement in South Wales (UK), back-filled with pulverized fuel ash (PFA), was 9 m deep and of rectangular plan dimensions 90 m by 600 m. It was intended to remove the backfill to allow the basement to be converted into underground space for machine installations for a power station. The problem was that there had always been high ground water table around the basement and so removal of the backfill could lead to uplift failure of the basement structure. Therefore it was planned to excavate the backfill in stages while lowering the water table sufficiently to avoid the risk of instability. Another problem was that the basement was close to the switch station building whose piled foundations could undergo settlement due to negative skin friction induced by ground water lowering. In this paper, focus is on the analysis of the negative skin friction and settlement of the piles. Figure 2 shows the geotechnical model used in this problem. Use is made of the symmetry line at the centerline of the basement. The depth of the water table within the backfill was measured to be 3.44 m OD (ordnance datum) while the bottom of the basement was at 0.78 m OD. It was envisaged to carry out the process of ground water lowering in two stages. In the first stage, the back-fill in the basement was to be excavated to 3.91 m OD. In the second stage, well pumping was to be carried out for a period of 12 weeks to lower the ground water level around the basement from 3.44 m OD to -3.00 m OD. Ultimately a deeper excavation was to be created by extending the bottom of the existing basement from 0.78 m OD to -4.0 m OD as shown in Fig. 2. A site investigation borehole sunk close to the switch station house established the ground water level to be at 4 m depth (5.34 m OD) and revealed the soil profile illustrated in Table 1, which lists the soil data abstracted from various test results.

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J.R. Omer / Modelling the Effects of Dewatering on Pile Settlement

Figure 2. Geotechnical model for the ground water lowering problem

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Table 1: Soil properties interpreted from site investigation results Layer No. Layer description

1 Red brown silty sandy GRAVEL

2 Firm brown sandy gravelly CLAY

J sat (kN/m3) N c u (kN/m2) Ic (deg) k h (m/s) E o c (kN/m2) c hv (m2/s)

19 36 33.5 3.7x10-7 85x103 3.15x10-3

19 10 22 1.5x10-8 7.7x103 1.16x10-5

3 Soft grey slightly sandy organic CLAY 15 6 15 1.5x10-8 5.25x103 7.88x10-6

4 Grey silty fine-medium SAND (Alluvium)

5 Brown silty sandy fine to coarse GRAVEL

21 21 35 8.5x10-6 30x103 2.55x10-2

20 32 40 9.7x10-7 85x103 8.25x10-3

6 Very weak to weak red brown MUDSTONE (zone II/III) 22 50 1000 ? ? ? ?

Key: J sat = saturated unit weight; N = SPT blow count; c u = un-drained strength; I = angle of internal friction; {k h |k v }= permeability coefficients (horizontal & vertical) direction; Ec o = soil stiffness in one-dimensional compression; c hv = consolidation coefficient for vertical compression with horizontal drainage flow. In FEA, all strata were treated as Mohr-Coulomb materials with assumed drained behavior while the basement wall and floor structures were modeled as elastic plates.

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The soil was modeled using 2649 numbers of 6-node triangular elements; the plates are being represented by 66 numbers of 3-node line elements. Also, 66 numbers of 3-node line elements were used to model the interface between the soil and the plates to allow soil-structure interaction analysis. The area beneath the switch station building was modeled with finer mesh to increase the accuracy of analysis. All calculation phases, representing the staged construction explained before, were defined as plastic. Part of the FEA results is shown in Figure 3 which is accompanied by a large Table listing the calculated ground settlement at various depths. The settlements along a typical pile shaft of the switch station building are transferred into a pre-formatted data file which is automatically read into the author’s pile analysis program. Half-width of basement

Piles for the switch station building

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Figure 3. FEA result (deformed mesh)

From the FEA results, the predicted drawdown curve is shown (Fig. 3). This accounts for most of the factors which are ignored when using the simplified method [1]. With the FEA results file inputted into the author’s pile analysis program, it was found that the relative pile-soil displacements (which reached 24.7 mm) due to ground water lowering were enough to induce the maximum drag down force of 337 kN on a typical pile; thus matching the positive skin friction. This load (additive to the known working load of the pile of 482 kN) would obviously cause additional settlement of the pile. From the t-z analysis in the program (see Fig.4), the settlement corresponding to the increased pile load was computed to be 14.3 mm. This was close to the value 9.8 mm monitored in the switch station building over 6 months period. 4. Conclusions The method for integrating FEA results of ground settlement with pile load transfer analysis was shown to be successful. The pile analysis program developed by the author allows the user to customize t-z and q-z curves by defining different parameters as appropriate to the pile site. Unlike other simplified analytical methods, the FE method takes into account ground water flow through multiple strata with different permeability values. It also considers flow in two dimensions which is more realistic than in the simple methods. To demonstrate the applicability of the method, a case record is analyzed for ground water lowering effects on piles. The predicted pile settlements caused by down drag forces of soil settlement were found to be reasonably consistent with the measured ones.

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535

Figure 4. Part of the t-z analysis user interface in the author’s program

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References [1] M. Preene, T. O. L. Roberts, W. Powrie and M. R. Dyer. Ground water control. CIRIA Report C515. Construction Industry research Information Association, London, 2000. [2] H. M. Coyle and L. C. Reese. Load transfer for axially loaded piles in clay. American Society of Civil Engineers Journal of Soil Mechanics and Foundations Division, 92, No. 2 (1966), 1-26. [3] J. R. Omer, R. Delpak and R. B. Robinson. A new computer program for pile capacity prediction using CPT data. Geotechnical and Geological Engineering, 24 (2006), 399-426. [4] S. Kim, S. Jeong, S. Cho and I. Park. Shear load transfer characteristics of drilled shafts in weathered rocks. American Society of Civil Engineers Journal of Geotechnical and Geoenvironmental Engineering, 125, No. 11 (1999), 999-1010. [5] W. G. K. Fleming. A new method for single pile settlement prediction and analysis. Geotechnique, 42, No. 3 (1992), 411-425.

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Slope Angle Influence on the Seismic Wave Amplification Effect in a Double-sided Slope a

Shiguo XIAOa, 1, Zhijian SONGa and Jianjing ZHANGa Department of Geotechnical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China; email: [email protected]

Abstract. Slope angle significantly influences the seismic dynamic amplification effect of slope body. This paper carried out elasto-plastic dynamic finite element analysis on a double-sided slope and studied the influence pattern of slope angles upon dynamic amplification effect of seismic wave. It was found that the maximum dynamic response of the slope top, including peak acceleration and amplification effect, increases with slope angle. In addition, the magnification ratios of the gentle slope side exceed those of the steep slope side under the slope top, especially at the bottom of the slope. Key words. Seismic wave; amplification effect; double-sided slope; slope angle

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Introduction Under the action of seismic load, different locations of the slope usually have different dynamic response features. Generally speaking, the amplification effects increase gradually from bottom to top at the same slope. The dynamic amplification effect of the slope body has direct influence upon the failure features of the slope. Therefore, it is important to understand thoroughly the slope body amplification effects under seismic conditions. Many factors influence the dynamic amplification effect of slope body, but previous analyses concentrated mainly on influences by elevation upon the amplification effect produced by seismic action upon the slope [1-4], or on seismic displacement of the slope [5-10], or sometimes on the seismic response spectrum of the site [11]. These researches have added some understanding of the seismic amplification effect of single-sided slope, but they did not discuss in depth the slope-angle influence upon dynamic amplification effect, especially for double-sided slopes common in mountainous areas. We will focus on the double-sided slope in our discussion.

1. Simulation of Seismic Load Figure 1. shows the acceleration time history recorded in eastern Wolong during the May 12 Wenchuan Earthquake in 2008. For convenience of explanation and to reduce the amount of calculation involved, we derive the acceleration time history curve with 1

Corresponding Author.

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537

PGA = 400 gal as shown in Figure 2 from Figure 1 by using an identical proportion. The acceleration time history curve shown in Figure 2 is taken as the input seismic load of the analysis model for horizontal-direction seismic dynamic analysis.

Figure 1. Acceleration Time History Records in East Wolong in Wenchuan Earthquake

Horizontial acceleration (gal)

500 400 300 200 100 0 -100 -200 -300 -400 0

10

20 30 Time (s)

40

50

Figure 2. Acceleration time history curve used in numerical model

2. Analysis of an Example

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2.1. Slope Body Model for Analysis To analyze dynamic response of the slope body under seismic action, we applied the elasto-plastic finite element analysis package “Plaxis” to a test model of a double-slope with both steep and gentle slope topographies as shown in Figure 3. The height of the double-sided slope model is 1810mm, and the width of its bottom surface is 3700mm. The shape of the slope is shown proportionally in Figure 3. The two-dimensional analysis adopted M-C strength failure criteria and maximum tensile stress failure criteria and applied the non-associative flow rule. In the numerical model of the slope, the left and right vertical side of the slope are respectively extended out one height of the slope so as to reduce the boundary effect as much as possible. Standard damping boundaries are applied on the left and right vertical side and bottom side of the numerical analysis model.

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S. Xiao et al. / Slope Angle Influence on Seismic Wave Amplification Effect in Double-Sided Slope

2#

300

unit: mm

2#

1810

538

2# 1#

3700

Figure 3. Test model of a double-sided slope

The model is principally of slightly weathered sandstone (model material serial number No. 1) and surface weathered rock body (model material serial number No. 2). The material parameters are given in Table 1. Table 1. Main parameters of materials of slope body model Material number

Unit weight

Cohesion

(kN/m3)

1㸡 2㸡

Tensile strength

Elastic modulus

Poisson’s

(kPa)

Internal friction angle (°)

(MPa)

ratio

22

15.6

37.9

75

0.25

8

21

8.3

28.8

10

0.35

3

(kPa)

2.2. Calculation Result and Analysis

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To analyze the influence of slope angle on seismic wave transmission, the right slope of the model is presumed to be fixed while the average left slope angles are taken as 40°, 45°, 50°, 55° and 60°respectively for the calculation and analysis of dynamic response features of various models. Observation points selected for numerical analysis and result analysis are shown in Figure 4, and for grid division of the model we have adopted the triangular element of 15 nodal points. Figures 5-9 demonstrate the horizontal acceleration time history calculations of representative observation points on the slope body with each of the various left slope angles.

(a) Average slope angle of 40°at the left side

(b) Average slope angle of 45°at the left side

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(c) Average slope angle of 50°at the left side

(d) Average slope angle of 55°at the left side

(e) Average slope angle of 60°at the left side

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Figure 4. Numerical models of the slope and observation points of different average slope angles of the left side

(a) Left side

(b) Right side (the unchanged side)

Figure 5. Calculation result of horizontal acceleration time history when average slope angle of the left side of the slope body is 40°

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S. Xiao et al. / Slope Angle Influence on Seismic Wave Amplification Effect in Double-Sided Slope

(a) Left side

(b) Right side (the unchanged side)

Figure 6. Calculation result of horizontal acceleration time history when average slope angle of the left side of the slope body is 45°

(a) Left side

(b) Right side (the unchanged side)

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Figure 7. Calculation result of horizontal acceleration time history when average slope angle of the left side of the slope body is 50°

(a) Left side

(b) Right side (the unchanged side)

Figure 8. Calculation result of horizontal acceleration time history when average slope angle of the left side of the slope body is 55°

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(a) Left side

541

(b) Right side (the unchanged side)

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Figure 9. Calculation result of horizontal acceleration time history when average slope angle of the left side of the slope body is 60°

Using the peak values of horizontal acceleration of each observation point given above, we obtain the results summarized in Figures 10-13 below. Figure 10 shows that the PGA of the observation point at the slope top increases with the increase of the slope angle of the left side, but with limited increment. This demonstrates that dynamic response of the slope top increases with the increase of slope angle. The slope top acceleration reaches a peak value when the slope angles at the left and right sides are both 50°. The magnification ratio reaches a value of 3.5, possibly caused as the natural frequency of the slope body approaches the fundamental frequency of the seismic wave. Figure 11 shows that the amplification ratio of each observation point is not less than 1 and to some extent there is a change of the ratio along with the increase of slope angle of the left side. In addition, the amplification ratios of the observation points located at the same height but at different slope face differ at different slope face dip angle of the left side. At the slope top, the amplification ratios of both the left and right slope face are identical. But at different locations under the slope top, the amplification ratio of the gentle slope side is slightly larger than that of the steep slope side, particularly at the bottom of the slope, where the difference is greatest. It is worth noting that the amplification ratio of point H at the right side slope foot remains relatively large under different left slope angles, larger even than that at waist points E and F. The visible increase in amplification ratio might be a topographic effect because the point is located at the bottom of the slope. The horizontal acceleration amplification value of ratio of the steep slope side (1.85–3.0) is greater than that of the gentle slope side (1.43–1.75) when the average slope angles of the left and right sides of the slope are not less than 45°. From Figs.12 and 13, under different left side slope angles, the amplification ratio of the slope body increases with the height of the slope. That is to say, the amplification effect of the upper part of the slope body is more prominent than that of the lower part, and in general, the amplification effects along the slope height of both slope faces are more apparent as the left side slope angle increases. The amplification effect of both of the slope faces becomes most prominent along the height of the slope when the left side slope angle is 50°.

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PGA at the top of the slope(m/s 2)

14 13 12 11 10 9 8 30

40

50

60

70

Slope angle of the left surface(deg) Figure 10. Curves representing changes of slope top PGA along with the left side slope angle

4 A

Amplification ratio

3.5

B

3

C

2.5

D

2

E

1.5

F

1

G

0.5

H

0 40

50

60

70

Slope angle of the left surface (deg) Figure 11. Curves representing changes of amplification ratio of each observation point along with the left side slope angle

Height from the bottom of the slope model (m)

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30

2.1 1.8

40deg

1.5

45deg

1.2

50deg

0.9

55deg

0.6

60deg

0.3 0 0

1

2

3

4

Amplification ratio Figure 12. Curves representing changes of amplification ratio of left side slope face along the height of the slope under different left side slope angles

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Height from the bottom of the slope model (m)

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2.1 1.8

40deg

1.5

45deg

1.2

50deg

0.9

55deg

0.6

60deg

0.3 0 0

1

2

3

4

Amplification ratio Figure 13. Curves representing changes of amplification ratio of right side slope face along the height of the slope under different left side slope angles

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3. Conclusions According to the above analysis, the slope angle of a slope influences the transmission of seismic waves with the following main feature patterns: (1) For two-sided slopes, the peak acceleration of the slope top increases with the increase of slope angle, but the acceleration increase is limited when the slope angle of only one side is subject to change. Under different slope angle, the amplification effect of the upper part of the slope body is more visible than that of the lower part. In general, with increasing slope angle, the amplification effect of faces of both sides of the slope becomes more prominent along the slope height direction. The natural frequency of the slope body is possibly made closer to the fundamental frequency of the seismic wave during the process, during which time, the dynamic response (including peak acceleration and amplification effect) of the slope top reaches a maximum. (2) When the slope angle of only one side of a double-sided slope is subjected to change, the amplification ratio of each observation point changes to some extent. Amplification ratios of observation points located at the same height but at opposite sides of the slope are different. At the slope top, the amplification ratios of both sides of the slope face are identical. However, at locations under the slope top, the amplification ratio of the gentle slope side is larger than that of the steep slope side along with the increase of slope angle. This is particularly noticeable at the bottom of the slope, where the amplification ratio of the gentle slope side is significantly larger than that of the steep slope side.

References [1] J. D. Bray and T. Travasarou, Pseudostatic Coefficient for Use in Simplified Seismic Slope Stability Evaluation. Journal of Geotechnical and Geoenvironmental Engineering, 135 (2009), 1336-1340. [2] C. Shi, J.W. Zhou, Q. Ren, and X.Q. Zhou, Ray theory sol ution of the elevation amplification effect on a single-free-face slope. Journal of Hohai University(Natural Sciences), 36 (2008), 238-241 [3] S.W. Qi, Two patterns of dynamic responses of single-free-surface slopes and their threshold height. Chinese Journal of Geophysics, 49 (2006), 518~523 [4] Y.L. He and S.Y. Lu, A method for calculating the seismic action in rock slope. Chinese Journal of Geotechnical Engineering, 20 (1998), 66-68

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[5] N. N. Ambraseys and J. M. Menu, Earthquake-induced ground displacements. Journal of Earthquake Engineering, 16 (1988), 985–1006. [6] J. D. Bray and T. Travasarou. Simplified procedure for estimating earthquake-induced deviatoric slope displacements. Journal of Geotechnical and Geoenvironmental Engineering, 133 (2007), 381–392. [7] S. L. Houston, W. N. Houston, and J. M. Padilla, Microcomputer-aided evaluation of earthquake-induced permanent slope deformations. Microcomputer of Civil Engineering, 2 (1987), 207–222. [8] S. L. Kramer and M. W. Smith, Modified Newmark model for seismic displacements of compliant slopes, Journal of Geotechnical and Geoenvironmental Engineering, 123 (1997), 635–644. [9] J. S. Lin and R. Whitman, Earthquake induced displacements of sliding blocks. Journal of Geotechnical. Engineering, 112 (1986), 44–59. [10] J. Wartman, J. D. Bray, and R. B. Seed, Inclined Plane Studies of the Newmark Sliding Block Procedure. Journal of Geotechnical and Geoenvironmental Engineering, 129 (2003), 673-684. [11] A. Rodriguez-Marek, J. D. Bray, and N. Abrahamson, An empirical geotechnical seismic site response procedure. Earthquake Spectra, 171 (2001), 65–87.

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Numerical Simulation Analysis Applied to Excavation of Deep-foundation above the Highway Tunnel in Downtown a

Shu XUa, 1 and Zhi LI b Shanghai Construction (Group) General Co. Technology Center b MIDAS Information Technology Co., Ltd.(shanghai)

Abstract. The construction of downtown traversing tunnel is very difficult. In this paper MIDAS GTS is used to analyze the influence of the underlying tunnel when the top foundation pit is excavated. And the construction stage control indicators base on the theoretic calculation are proposed to provide the theoretical for the technical measures as block excavation. Keywords. Three-dimensional model , Down Tunnel , Deformation control

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Introduction With the growing urban underground transport network improvement will inevitably encounter all kinds of construction problems as underground passage through each other. Such new construction near the existing tunnel structure are often referred to nearby works, and may adversely affect the existing tunnel. The question of how to build new underground space as does not affect old tunnel has become a growing concern by many experts and scholars. This article will focus on the construction of new underground passage above an existing tunnel, and the whole process of excavation is simulated and analyzed. Excavation pit above the exiting tunnel , will inevitably lead to bottom heave, thereby bringing additional internal force and deformation of the subjacent existing tunnel, and thus affect the security of the existing tunnel . Rational use of finite element simulation technology to evaluate the impact of security and stability on the exiting tunnel in the course of construction is very important. It could be better guide adjacent construction and reduce construction risk.

1. Project Overview The whole length of a new underground Fast Track in Urban core areas is 3720m. The section B of this underground passage has double layout, the upper from south to north, the lower from north to south, its width is 12m and height is 9.5m. As the section B

1

Corresponding Author: Xu Shu, Shanghai Construction (Group) General Co. Technology Center.

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needs span two Highway tunnel which diameter are both 11m, the construction is extremely difficult. According to design scheme, the underground passage cross the North tunnel into 75 degree, and the South tunnel nearly 90 degree. The average excavation depth above the North tunnel is approximately 10.76m, and the vertical distance between the floor of the new underground passage and the top of the North tunnel is about 5.4m. Above the South tunnel, the average excavation depth is 10.68m and the vertical distance is about 7.23m. The both pits adopt cast-in-place pile as the retaining structure, and with three vertical support, the first support is concrete, and the other two are steel supports. After the excavation unloading, the earth covering above the operation tunnels turns is very thin, it will cause the tunnel floating upward. So the effective control measures of construction must be adopted to restrict the tunnel deformation in safety range.

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Figure 1. Engineering plan.

Figure 2. Project profile.

Figure 3. Tunnel section.

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2. Finite Element Simulation of the Construction 2.1. Deformation Control Index of the Exiting Tunnel As the current national rules are not clear and definite the absolute deformation control standards of the large-diameter tunnel , the protection requirements of subway with 6m diameter are referenced in the process of research and analysis. x Absolute settlement and displacement levels of the structural ”PP x The relative curvature of the tunnel deformation ӊ1/2500 x Radius of curvature of the deformation curvature Ӌ15000m Through the preliminary analysis, the integrity and total stiffness of the large diameter highway tunnel are higher than subway tunnel. According to the monitoring results, the deformation trend of the operation tunnel is relatively stable. As effects by the whole subsidence of the tunnel are small, under the current technology base, the protection standards of the Subway tunnel can be referenced. 2.2. The Total Construction Process in Protected Areas

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In order to minimize the construction effect on the tunnel, the long and narrow pit must be carried into several parts, simultaneously, the excavation of the order as the pit above the tunnel must be constructed after the lateral pit have to be followed. According to this principle, the whole foundation in the protection zone will be divided into five blocks. From south to north, the order is 4A, 4B1, 4B2, 4B3, 4B4. And the most reasonable general construction process is 4A, 4B4 4B2 4B1, 4B3.

Figure 4. Construction process.

2.3. The Brief Introduction of the Finite Element Model Table 1. Soil parameter table ij(°)

Layer

Name of soil

Thickness(m)

Gravity(kN/m3)

Cohesion(kpa)

ձ

Fill

1.3

19

10

17

ղ0

Marshland soil

6.7

18

6

28.5

մ

Muddy clay

10

17.0

14

12.5

յ1

Silty clay

7.0

17.6

12

14.5

յ3

Silty clay and clay silt

20.5

18.0

11

18.5

յ4

Silty clay

2.4

19.3

36

16

շ1

Sandy Silt

5.1

19.85

5

28.5

շ2

Fine sand

7.3

19.9

2

40

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For simplified calculation, this paper using MIDAS software for 3-d numerical analysis on the north tunnel and above pit. The boundary of the soil Model :50×45×60m, Model elements include the soil, retaining structure, support and the tunnel lining. In the model, the soil is regarded as an ideal elastoplasticity and follow Drucker - Prager yield criterion, Cast-in-place pile, steel support, concrete support and concrete structure are considered as isotropic elastic material.

Figure 5. The computational model.

In order to verify the actual working condition, excavation process is divided into 9 times, especially, the last 4m thickness soil is divided into five blocks during the excavation. 2.4. Results of Simulation and Analysis of Working From the first step to the fourth step, excavate to the bottom of the lateral foundation pit . x

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x

x

x

In the first step, the central uplift maximum of the lateral foundation pit is 4.253mm, of the above foundation pit is 0.15mm, and the uplift maximum of the exiting tunnel is less than 0.1mm. In the second step, when the lateral pit is excavated to the bottom of the second support, the central uplift maximum of the lateral foundation pit is 12.87mm, of the above foundation pit is 1.47mm, and the uplift maximum of the exiting tunnel is 0.229mm. In the third step, when the lateral pit is excavated to the bottom of the third support, the central uplift maximum of the lateral foundation pit is 28.25mm, of the above foundation pit is 3.13mm, and the uplift maximum of the exiting tunnel is 1.065mm. In the forth step, when the lateral pit is excavated to the bottom, the central uplift maximum of the lateral foundation pit is 33.935mm, of the above foundation pit is 4.54mm, the uplift maximum of the exiting tunnel is 1.93mm.

Figure 6. The central uplift of the lateral pit in the forth step.

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Figure 7. The central uplift of the tunnel in the forth step.

From the fifth step to the seventh step, excavate to the bottom of the third support of the above foundation pit. x

x

x

In the fifth step, when the above pit is excavated, the central uplift maximum of the lateral foundation pit is 33.94mm, of the above foundation pit is 6.99mm, and the uplift maximum of the exiting tunnel is 2.01mm. In the sixth step, when the above pit is excavated to the bottom of the second support, the central uplift maximum of the lateral foundation pit is 33.97mm, of the above foundation pit is 11.82mm, and the uplift maximum of the exiting tunnel is 2.49mm. In the seventh step, when the above pit is excavated to the bottom of the third support, the central uplift maximum of the lateral foundation pit is 34.07mm, of the above foundation pit is 24.22mm, and the uplift maximum of the exiting tunnel is 6.67mm.

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Figure 8. The central uplift of the pit in the seventh step.

Figure 9. The central uplift of the tunnel in the seventh step.

In the eighth step , the last 4m thickness soil adopt block excavation. The central uplift maximum of the lateral foundation pit is 34.12mm, of the above foundation pit is 26.7mm, and the uplift maximum of the exiting tunnel is 8.5mm.

Figure 10. The central uplift of the pit in the eighth step.

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Figure 11. The central uplift of the tunnel in the eighth step.

In the ninth step , when the lateral pit is excavated to the bottom, the central uplift maximum of the lateral foundation pit is 34.18mm, of the above foundation pit is 27.14mm, and the uplift maximum of the exiting tunnel is 10.6762mm.

Figure 12. The central uplift of the pit in the last step.

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Figure 13. The central uplift of the tunnel in the last step.

The numerical simulation results show when the last layer of soil is excavated, the bottom of the pit uplift 27.14mm, the corresponding floating deformation of the tunnel reach the maximum 10.6762mm. This is the result of the block excavation, if the last layer of soil is excavated as a whole , the tunnel's deformation will reach 12.63mm, at this point, the block excavation has good effect on the deformation-control of the tunnel, accounting for 15% of the total.

Figure 14. The schematic of the block excavation.

At the same time form the related trend between rebound of the above pit and the tunnel itself uplifting, we can see the tunnel deformation is very small in the process of lateral excavation. This process shows that the excavation of the order as the pit above the tunnel must be constructed after the lateral pit is very useful. On the other hand, though the lateral and the above pit has the same dig deeper, but the final rebound is different(27.14 㸺 34.18mm), that shows reduce the excavation surface can control deformation more favorable.

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Figure 15. The deformation relation between the rebound of the pit and the uplift of the tunnel.

According to long-term monitoring data and engineering experience, the overall settlement or floating have small effect on the tunnel, while the differential settlement has greater impact. Below figure is the unit deformation of the central surface on the tunnel which normal to vertical axis of the tunnel. In this figure, the deformation curve is gently, the difference between the maximum and the minimum is 4.978mm, this deformation value is less than general limition which is 5mm.

Figure 16. The unit deformation of the central surface on the tunnel(normal to the vertical axis).

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The below figure shows the vertical deformation of the upper surface on the tunnel which parallel to vertical axis of the tunnel. In this graph, the maximum difference value is 7.943mm, this value satisfy the basic deformation-control requirements of the tunnel.

Figure 17. The unit deformation of the central surface on the tunnel (parallel to the vertical axis).

As the maximum value of the horizontal displacement of the exiting tunnel is only 1.577mm, it will not affect the structural safety of the tunnel.

3. Conclusions During the actual construction, the excavation steps and protective measures are optimized according to results of the finite element simulation. Especially, the excavation method of the last soil is agreed. Under this agreement, the last layer was divided into five blocks as "ᕤ"-shape, and every block's width must less than 4m. Through effective guidance of the theoretical calculation, and match with means such as soil improvement, the anti-uplift structure and timely ballast, the maximum uplift of the tunnel is only 5mm during excavation stage, safeguard the normal operation of the both tunnel.

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1

2

4

3ˈ5

Figure 18. The optimal excavation method.

References X. Jiang, The difficulties and innovation of the construction of the WaiTan tunnel, Shanghai Construction Science & Technology, 04 (2009), 29-33.

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[1]

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Parametric Study and Design Charts Based on Movement of Reinforced Earth Retaining Wall Y. M. Mowafy a , N .R. El-Sakhawy b, R. R.El-Sakhawy c, and O.A. El-Gaaly d a Prof., Al-Azher Univ., Cairo b Prof., Zagazig Univ., Zagazig, Egypt, c Prof. HBPRC, Cairo d Doctor, Educational Buildings, Cairo

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Abstract. The deformation resulting from wall movements is an important performance criterion for reinforced earth retaining walls. However, conventional wall design procedures are primarily stress–based and do not consider deformations explicitly. This may lead to imprecise hypothesis of the real forces in reinforcement, and conditions for soil shear failure. This paper represents numerical investigation on the compatibility behavior of reinforced earth during wall movement. The effect of reinforcement global stiffness, wall height, and soil internal angle of friction on the developed earth pressure coefficient and the maximum wall movement and the compatibility of these parameters are vital aspects in the investigation. The finite element program, ANSYS, was utilized for the analysis. The objective of this investigation is to activate parametric study in a scale adequate to get advanced design charts. The design charts were developed and their application was outlined. Keywords. Reinforced earth retaining wall, global stiffness of reinforcement, finite element analysis, coefficient of earth pressure at equilibrium, strain compatibility behavior, wall movement, mobilized force in reinforcement at equilibrium, parametric study, design charts.

Introduction Design of classical retaining wall is based on limit equilibrium. The soil is assumed homogenous, and isotropic. At failure, soil moves, as a rigid wedge, with a plane of failure depending on the soil internal angle of friction, I, [1]. Most of the available design procedures for reinforced soil walls utilize same concepts combined with”tiebacks.” That is the reinforcement protruding beyond the failure plane is modeled as a horizontal forces which increases the stability of the earth mass, [2 - 4]. The entire reinforced soil mass may, then, behave as a conventional rigid retaining wall, [5]. [6] proposed the composite material approach where the reinforced soil mass is considered as an equivalent homogenous material with a higher modulus. Few investigators have emphasized on the estimation of wall movement based on the elongation of reinforcement [e.g., 7]. Limit equilibrium design procedures imply that the reinforcement and soil will supply their design strength at the same instant. However, the strength depends on the

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Y.M. Mowafy et al. / Parametric Study and Design Charts

Tank boundary Displaced Facing 1000

Geogrid layer sand

242m

Required and available force

deformation characteristics of the materials. The design criteria for acceptable wall performance may turn to be the possible wall deformation, [8]. Comprehensive experimental investigation on the compatible behavior of reinforced earth during wall movement was investigated, [9]. Figure 1A shows the Experimental Set-up. The movement is essentially taken place in order to reach the state of equilibrium between the required forces propagated by soil and the available forces executed by reinforcement, as shown in Figure 1B. Wall movement may arise from deformation of the reinforced and the unreinforced zones of backfill, movement. The investigation depicted that reinforcement global stiffness, S r , as well as soil strength is having considerable effect on the developed earth pressure coefficient.

PEQ

HEQ Tensil

1800m

(A)

(B)

Figure 1. A. The Experimental Set-up, B Compatible curves for Soil and Reinforcement during wall movement showing the equilibrium point

S r is defined as:

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S r = J. bS h (h/n)

(1)

where: b = gross width of the reinforcing element, S h = center to center horizontal spacing between reinforcements, h/n = Average vertical spacing based on the number of layers (n) over wall height (h). The finite element program, ANSYS, was utilized for analyzing the experimental investigation. The finite element analysis predicted the experimental behavior reasonably well. This paper represents the numerical investigation through parametric study.

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1. Designed-Strength Parameters for Analysis of Reinforced Earth Retaining Wall Shear strain is required to mobilize the peak shearing resistance. Soil strength reduces, thereafter as the soil shears, towards the critical state. Then, the design internal angle of friction, I D , should be equal to the angle of friction at the critical state, I cr . Alternatively, equivalently, gave [10]: tan I D = I cr = tan I max /F s

(2)

To determine the design values for reinforcement strength, three degradation factors may be applied [10, 11]. These factors are f d , f env , and f m . The index strength, ID, obtained from the uniaxial test is divided by the degradation factors in order to get the design strength, P D , (kN/m). That is: P D = ID/f d . f env . f m

(3)

If the reinforcement material is particularly extensible or prone to significant creep deformation or if only small structural deformations are acceptable, then, serviceability strength of reinforcement, P s , should be checked out. The mobilized force, P m should not be greater than P s . If P D is less than P s , then the ultimate limit state calculation is likely to govern the design.

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2. Numerical Study The finite element program, ANSYS, was utilized for the analysis. Soil was modeled using Drucker-Prager formulation. It takes into account the nonlinear inelastic soil behavior, the effect of soil dilation on strain compatibility, and nonlinear interface behavior. The yield function depends on the state of stresses and on soil strength parameters; cohesion, C, and internal angle of friction, I. The plastic potential function, which determines the direction of plastic straining, is a function of the stress and, dilatancy angle, \. Janbu’s equation was used to update Young’s modulus of the soil, E s , at the beginning of each increment of wall displacement. Then: E s = N P a (V 3 /P a )K

(4)

In this study, the fill is taken as quartz sand. Table 1 presents its strength parameters for different densities, [12]. The table shows also the corresponded constants for determination of nonlinear elastic modulus for the elastic zone of the stress-strain behavior. Soil-reinforcement interface is treated with two parameters; index strength, ID and pullout stiffness, W r /G. It was found that the generated force in the reinforcement is not sensitive to pullout stiffness, when its value exceeds 2kN/m2/mm. The Finite element model is selected to be of plane strain condition. Figure 2 shows the finite element mesh used in the analysis. Finite element mesh with 92 soil elements and 114 nodal points has been chosen. It gives stable results.

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Table 1. Soil Strength Parameters Density