Advanced Technologies, Systems, and Applications VIII: Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of ... (Lecture Notes in Networks and Systems, 644) [1st ed. 2023] 9783031430558, 9783031430565, 3031430557
This book presents proceedings of the 14th Days of Bosnian-Herzegovinian American Academy of Arts and Sciences held in T
123 89 80MB
English Pages 641 [631] Year 2023
Preface
Contents
Civil Engineering
Application of Microsimulation Models for Traffic Emission Assessment
1 Introduction
2 Previous Research
3 Research Methodology
4 Results and Discussion
5 Conclusion
References
Added Value in Implementation of Capital Projects by Monitoring, Evaluation and Learning (MEL)
1 Introduction
2 Research Methodology
3 Monitoring, Evaluation and Learning (MEL)
4 Project Representation and Organization for MEL Purpose
5 Elements of MEL Plan
6 Proposal for Possible Application of MEL for Different Type of Capital Projects
6.1 Construction of an Indoor Olympic Swimming Pool in Community X
6.2 Construction of the Electric Tram Line Between A and B in Community Y
6.3 Construction of the Community C’ Center for Healthy Aging
6.4 Improvement of Water Supply Service in Community D
7 Conclusion
References
Seismic Analysis of Buildings with a Soft Storey Using Pushover Analysis
1 Introduction
2 Elaborated Cases
3 Nonlinear Analysis
3.1 Modeling of Plastic Hinges
3.2 Modeling of the Structures (Models 1, 2, and 3)
4 Results
5 Conclusion
References
Methodology of Ranking of the Main Road Network in the Federation of Bosnia and Herzegovina from the Aspect of Demography and Economic Development as Part of the Climate Resilience Risk Assessment
1 Introduction
2 The Main Road Network in the Federation of Bosnia and Herzegovina
3 Methodology
3.1 Demography
3.2 Economic Development
3.3 Ranking of Main Road Network Sections
4 Conclusion
References
Methodology of Traffic Safety Management at Railway Crossings in Bosnia and Herzegovina
1 Introduction
2 Methodology for Management of Traffic Safety at the Railway Crossings
3 Definition of Criteria for Interventions at Railway Crossings
4 Case Study - Evaluation of Traffic Safety at Level Crossings on the Section of the “Sarajevo - Doboj” Railway
4.1 Creation of a Geospatial Database
4.2 Criteria for Evaluating Traffic Safety at LC
4.3 The Results of the Analysis and Ranking Level Crossing
5 Conclusions
References
Building Information Modeling Education at Bosnian and Herzegovina Universities
1 Introduction
2 Literature Review
3 Methodology
4 Analysis and Discussion
5 Conclusion
References
Spalling of Concrete
1 Introduction
2 Research Methodology
3 Types of Concrete Spalling
4 Causes of Concrete Spalling
4.1 Material Related Parameters
4.2 Structural/Mechanical Parameters
4.3 Heating Characteristic Parameters
5 How Does Spalling Affect Concrete Structures?
5.1 Reduction of Load Bearing Capacity of Concrete Members Affected by Spalling
6 Prevention of Concrete Spalling
7 Conclusion
References
Mathematical Model Identification of Measures for Improving the Energy Efficiency on Road Tunnels Facilities
1 Introduction
2 Tunnel Systems Components
2.1 Energetic Efficiency of Lighting System in Tunnels
2.2 Energetic Efficiency of Tunnel Ventilation System
2.3 Energetic Efficiency of Fire Alarm System
2.4 Energetic Efficiency of Video Surveillance System
2.5 Energetic Efficiency of Traffic Information System
2.6 Energetic Efficiency of Telephone Call System
2.7 Energetic Efficiency of Radio Communication System
2.8 Energetic Efficiency of Sound System
2.9 Energetic Efficiency of Hydrant System and Water Supply System
2.10 Energetic Efficiency of Power Supply System
2.11 Energetic Efficiency of Other Systems in Tunnels
3 Concept of Energy Inspection of Tunnel Systems
4 Mathematical Model of Identification of Potential Measures
5 Conclusion
References
II Geodesy and Geoinformatics
Use of UAV for Object Deformation Detection
1 Introduction
2 Methods and Data
3 Results, Analysis, and Discussion
3.1 Terrestrial Measurement
3.2 3D Model and Accuracy Assessment
3.3 Homogeneous Accuracy Test of Two Models
3.4 Detection of Deformations
4 Conclusion
References
An Evaluation of the Dams Crest Movement Influenced by Thermal Variations: A Machine Learning Approach
1 Introduction
2 Data and Methodological Framework
3 System Modeling
4 Results and Discussion
5 Conclusion
References
Geometric Enhancement of the Cadastre of Underground Utility Networks
1 Introduction
2 Materials and Methods
2.1 Underground Utility Map
2.2 TPS-Based Correction of the Scanned Map
3 Results and Discussion
4 Conclusion
References
Data Science and Geographic Information Systems
Interactive Narrative Design and Storytelling, Use Case with Geospatial Data
1 Introduction
2 Methodology
2.1 A Review of Existing Tools
2.2 Interactive Narrative Design
2.3 Visualization Patterns
3 USE CASE: Solution for Geospatial Storytelling
3.1 Architecture of Proposed Solution
3.2 Data Model
3.3 Process Model
3.4 Designing Interface
4 Discussion
5 Conclusion
References
Geoinformation Crowdsourcing and Possible Applications in Croatia
1 Introduction
2 Possibilities of Application of the System for Geoinformation Crowdsourcing
2.1 First, Second and Third VGI Script
2.2 Conducting a Geo-survey
3 Geo-survey Results
3.1 Discussion of the Conducted Geo-survey
4 Conclusion
References
Distribution of Medieval Necropolises and Tombstones in the Bosnia River Basin
1 Introduction
1.1 Geographical Characteristic of the Bosnia River Basin
1.2 Historical Development of Medieval Bosnia
1.3 General Characteristics of Medieval Bosnian Tombstones
2 Materials and Methods
3 Results and Discussion
4 Conclusion
References
Use of Augmented Reality to Present Archaeological Contents
1 Introduction
2 Augmented Reality Technology
3 Augmented Reality Cartographic Application for Archaeological Sites in Vinkovci
4 Testing and User Experience of the Application ArheVinkovci
5 Conclusions
References
Breast Cancer Classification Using Support Vector Machines (SVM)
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Dataset Description
3.2 Feature Extraction
4 Implementation
4.1 Data Preparation
4.2 Data Visualization
5 Machine Learning
5.1 Model Training
5.2 Support Vector Machine (SVM)
5.3 Implementation Details
5.4 Results and Discussion
6 Conclusions
References
Development of Soil Type Valuation Model in GIS for Sustainable Agricultural Production
1 Introduction
2 Data and Methodology
3 Results and Discussion
4 Conclusions
References
Geospatial Analysis of Residential Zones from the Aspect of Natural Limitations – A Case Study of the Urban Territory of Sarajevo
1 Introduction
2 Data Structure
3 Data Processing and Presentation
3.1 Hypsometric Scale of the Terrain
3.2 Aspect
3.3 Slope
3.4 Terrain Stability
3.5 Analysis of Residential Zones
3.6 Current Situation of Residential Buildings
3.7 Geospatial Analysis of Residential Buildings Spatial Distribution
4 Conclusions
References
Computer Science and Artificial Intelligence
Credit Card Fraud Payments Detection Using Machine Learning Classifiers on Imbalanced Data Set Optimized by Feature Selection
1 Introduction
2 Literature Review
2.1 Data Imbalance Issues in Credit Card Fraud Detection Studies
3 Methodology
3.1 Dataset
3.2 Synthetic Minority Oversampling Technique (SMOTE)
3.3 Feature Selection Methods
3.4 Machine Learning Methods for Classification
3.5 Proposed Method
4 Results
4.1 Performance Evaluation
4.2 Experiment Result
5 Discussion
6 Conclusion
References
Document-Based Sentiment Analysis on Financial Texts
1 Introduction
2 Background and Literature Review
2.1 Sentiment Analysis
2.2 Literature Review
2.3 Research Objectives
3 Research Methodology
3.1 Proposed Method
3.2 Dataset
3.3 Data Preparation
3.4 Text Summarization
3.5 Machine Learning Models
4 Results
4.1 Performance Evaluation
4.2 Experimental Results
5 Discussion
6 Conclusion
References
Emotion Detection Using Convolutional Neural Networks
1 Introduction
1.1 About Machine Learning
1.2 Deep Learning
1.3 Convolutional Neural Networks
1.4 Facial Expression Recognition
2 Problem Definition
3 Literature Review
4 Problem Solution
4.1 Choosing Development Environment
4.2 FER-2013 Image Dataset
4.3 Creating CNN Architecture
4.4 Saving Best Weights
4.5 Prevent Overfitting
4.6 Choosing Computing Device
4.7 Configuring Training and Validation Parameters
5 Results and Discussion
5.1 Training and Validation Results
5.2 Confusion Matrix and AI Metrics
5.3 Comparisons with Related Work
6 Conclusion
References
From Olympics to War – Pursuing Sarajevo Identity Using Sentiment Analysis
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Word Tokenization
3.4 Stemming and Stop Words Removal
3.5 Sentiment Analysis
4 Results
4.1 Text Preprocessing
4.2 Word Cloud Analysis
4.3 Word Frequency Analysis
4.4 Bigram analysis
4.5 Sentiment Analysis
5 Discussion and Conclusion
References
The Employment of a Machine Learning-Based Recommendation System to Maximize Netflix User Satisfaction
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Data
3.2 Developing the Recommendation System
3.3 Movie and TV Show Feature Classification
4 Experimental Results
4.1 Experimental Setup
4.2 Results
5 Conclusion and Discussion
5.1 Discussion of Results
5.2 Limitations and Possibilities for Extension
5.3 Conclusion
6 Future Work
References
A Recommendation System for Movies by Using Hadoop Mapreduce
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Data
3.2 Hadoop MapReduce
3.3 Item-to-Item Based Collaborative Filtering
3.4 Cosine Similarity
4 Experimental Setup
4.1 Computing Resources and Frameworks
4.2 Part 1
4.3 Part 2
4.4 Final Program
5 Discussion and Results
6 Conclusion
References
Business-Driven Analysis of Website Traffic: A Case Study
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Dataset Overview
3.2 Utilized Technologies
4 Results of Analysis
4.1 Exploratory Data Analysis
4.2 Analysis of US Visit Sessions
5 Discussion
6 Conclusion
References
Composable ERP – New Generation of Intelligent ERP
1 Introduction
2 ERP Evolution
3 The Composable ERP
4 Roadmap to Composable ERP Architecture
5 AI-Driven ERP
6 Conclusion
References
Building a Recommendation System for E-Commerce Using Machine Learning and Big Data Technologies
1 Introduction
2 Literature Review
3 Methods and Materials
3.1 Alternating Least Squares (ALS) Algorithm
3.2 Apache Spark
3.3 Proposed Methods
4 Results
5 Discussion
6 Conclusions
References
Implementation and Evaluation of a Deep Neural Network for Spam Detection: An Empirical Study of Accuracy and Efficiency
1 Introduction
1.1 Related Work
2 Neural Networks
2.1 Recurrent Neural Networks
3 Designing and Building a Neural Network
3.1 Designing the Network
3.2 Creating the Model
4 Results
5 Conclusion
References
Gradual Increase of Natural Disaster Occurrences in the Context of Climate Change
1 Introduction
2 Literature Review
3 Materials and Methods
3.1 Dataset Overview
3.2 Utilized Technologies
4 Results of Analysis
4.1 Exploratory Data Analysis
4.2 Analysis by Disaster Type
5 Discussion
6 Conclusion
References
V Information and Communication Technologies
A Comparative Performance Analysis of Various Antivirus Software
1 Introduction
2 Testing and Analysis of Antivirus Software
2.1 Analysis of the Quick and Full Scan
2.2 Analysis of the Installation Files Detecting
2.3 Analysis of the Antivirus Software Using Real Malicious Software
3 Conclusion
References
Enhancing the Accuracy of Finger-Based Heart Rate Estimation During At-Home Biofeedback Therapy with Smartphone
1 Introduction
2 Background
3 Calibration Procedure for Heart Rate Estimation
4 Experimental Setup and Results
4.1 Dataset
4.2 Results
5 Conclusion
References
Effects of Protection Cloud Accounting and Connection with the Frequency of Cyber Attacks
1 Introduction
2 Cloud Accounting
2.1 Advantages and Disadvantages of Cloud Accounting
2.2 Cloud Accounting Features
3 Cybersecurity in Cloud Accounting
3.1 Forms and Effects of Cybersecurity Incidents in Accounting
3.2 Effects of Cyber Security Deficiency in Accounting
4 The Aim and Hypothesis of Research
5 Materials and Methods of Research
6 Results of the Research
7 Conclusion
References
Applied Science in Mechanical Engineering Technology
Managing Risks of Renewable Energy Projects in Bosnia and Herzegovina Based ISO31000 and PMBOK
1 Introduction
1.1 Literature Review
2 Possible Risk Analysis of Renewable Energy Projects
2.1 Risks in Project Preparatory Phase
2.2 Risks in Construction Phase
2.3 Project Financing Risks
3 Project Risks and Limitations
4 Project Risk Management
4.1 Risk Management Planning
4.2 Risk Identification
5 Qualitative and Quantitative Risks Analysis
6 Case Study: Risk Analysis and Risk Management of 10 MW Solar Powerplant Project
7 Results
8 Conclusion
References
Exploring the Strength of Wood: Measuring Longitudinal Modulus of Elasticity in Structural Applications
1 Introduction
2 Materials and Test Methods
2.1 Materials
2.2 Performing the Experiment
2.3 Bending Testing Procedures
3 Results and Discussion
4 Conclusion
References
Contribution to the Diagnostics of the Internal Combustion Engine Timing Mechanism Chain
1 Introduction
2 Description of the Experimental Measurement Method
3 Analysis of the Measured Results
4 Conclusion
References
Parametric Optimization of MQCL-Assisted Turning Operation of X5CrNi18–10 Steel Using Definitive Screening Design and Multi-Criteria Decision-Making Approach
1 Introduction
2 Materials and Methods
3 Optimization Methodology
4 Multi-response Optimization of MQCL Parameters
5 Comparative Analysis of MCDM Methods
6 Conclusions
References
Influence of Different Cutting Speeds on CNC Milling on Surface Roughness of Objects Made from Steamed and Heat-Treated Beech Wood
1 Introduction
2 Tools
3 Materials
4 Surface Roughness
5 Experimental Set-Up and Adjustment of Cutting Speed
6 Measurement of Surface Roughness
7 Results
References
Thermal Modification of Wood
1 Introduction
2 Mathematical Model
2.1 Thermal Modification of Wood
2.2 Constitutive Relations
2.3 Mathematical Model
3 Numerical Model
3.1 Discretization
3.2 Problem Solving
4 Thermal Treatment of Wooden Planks
5 Conclusion
References
The Influence of Mill Loading on the Distribution of Pulverized Coal Particles
1 Introduction
1.1 Identification of Problems in Mill Operation
2 Numerical Model
2.1 Geometry Modelling and Discretization
2.2 Formulation of Physical and Mathematical Model
2.3 Simulation Conditions
2.4 Validation of Numerical Model
3 Results and Discussion
3.1 Impact of Mill Load on the Distribution of Pulverized Coal
3.2 Impact of Particle Size on the Distribution of Pulverized Coal
4 Conclusion
References
Influence of Coal Mixing Process on the Performance of the Steam Boiler
1 Introduction
1.1 System Under consideration – Boiler OB 650
2 Simulation Model
2.1 Validation of Model
3 Simulation and Experiment Results
3.1 Impact of Coal Homogenization Process on the Boiler Performance
3.2 Influence of Ash and Moisture Content in Coal on the Boiler Performance
3.3 Influence of Coal M1 Content in Mixture on the Boiler Performance
4 Conclusion
References
Exergy Analysis of a Solar Assisted Desiccant Cooling System
1 Introduction
2 Basics of Exergy Analysis of Heat-Driven Systems
3 Exergy Analysis Methodology
4 Results and Discussion
5 Conclusion
References
Investigation of the Concentration of Air Pollutants in the Vertical Profile in the Zenica Valley
1 Introduction
2 Research Methods
3 Discussion of Research Results
4 Conclusions
References
VII Advanced Electrical Power Systems
Data Mining Techniques Application for Electricity Consumption Analysis and Characterization
1 Introduction
2 Literature Review
3 Theoretical Background
3.1 Smart Metering
3.2 Data Mining and Machine Learning
4 Methodology
4.1 Clustering Analysis and Comparation of Electricity Consumption Datasets
4.2 Comparation of Machine Learning Models of Electricity Consumption Classification
5 Results and Discussion
6 Conclusions and Future Research Directions
References
University Campus as a Positive Energy District – A Case Study
1 Introduction
1.1 What is a Positive Energy District?
1.2 University Campus as PED
2 Case Study: Sjeverni Logor University Campus
2.1 Materials and Methods
2.2 Results and Discussion
3 Conclusion
References
State Estimation in Electric Power Systems Using Weighted Least Squares Method
1 Introduction
2 Theoretical Background
2.1 State Estimation in Electric Power Systems
2.2 Calibrating Electricity Consumption
2.3 Weighted Least Squares Method
3 Methodology
4 Results and Discussion
5 Conclusions
References
Adaptive Under Frequency Load Shedding Using Center-of-Inertia Frequency
1 Introduction
2 Applied Methodology
3 Illustrative Example
4 Conclusion
References
Predictive Maintenance of Induction Motor Using IoT Technology
1 Introduction
2 Predictive Maintenance of Induction Motor
3 Failures in Induction Motor
4 Predictive Maintenance of Induction Motor Using IoT Technology
5 Example of Predictive Maintenance of Induction Motor Using IoT Technology
6 Conclusion
References
Author Index
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Lecture Notes in Networks and Systems 644
Naida Ademović Jasmin Kevrić Zlatan Akšamija Editors
Advanced Technologies, Systems, and Applications VIII Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT 2023)
Lecture Notes in Networks and Systems
644
Series Editor Janusz Kacprzyk , Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Türkiye Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science. For proposals from Asia please contact Aninda Bose ([email protected]).
Naida Ademovi´c · Jasmin Kevri´c · Zlatan Akšamija Editors
Advanced Technologies, Systems, and Applications VIII Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT 2023)
Editors Naida Ademovi´c University of Sarajevo-Faculty of Civil Engineering Sarajevo, Bosnia and Herzegovina
Jasmin Kevri´c International Burch University, Francuske revolucije bb Ilidža, Bosnia and Herzegovina
Zlatan Akšamija University of Utah Salt Lake City, UT, USA
ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-031-43055-8 ISBN 978-3-031-43056-5 (eBook) https://doi.org/10.1007/978-3-031-43056-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.
Preface
The first chapter of the proceedings is devoted to the Civil Engineering Symposium. One of the biggest challenges today is traffic sustainability, its negative environmental impact, and safety issues. Several articles presented at the symposium deal with these topics. One article investigates the application of microsimulation tools for the assessment of harmful gas emissions from traffic at urban intersections. Traffic safety is dealt within a paper that gives a specific overview of railway traffic safety and proposes a methodology for evaluating safety at railway crossings. Deficiencies of the existing local regulations on this topic were noted, and their amendments were proposed. Also, within the framework of traffic topics, a paper was presented on the resistance of the main road network to climate change, with particular emphasis on extraordinary floods. The last article from the field of roads presents a mathematical model for improving the energy efficiency of road tunnel facilities. One of the frequent engineering problems is the stability of structures, which is covered in two articles emphasizing seismic analysis of buildings and concrete spalling. The symposium wrapped up papers on implementing capital projects by monitoring, evaluation, and learning (MEL) and Building Information Modeling education at Bosnian and Herzegovina universities. Eight peer-reviewed papers with high scientific and technical quality were presented. The symposium was by four chairs Naida Ademovi´c, Ammar Šari´c, Žanesa Ljevo, and Emina Hadži´c. The Symposium on Geodesy and Geoinformatics, included as the second chapter in this proceeding, witnessed the presentation of three peer-reviewed papers, each contributing valuable insights and advancements in the field. The presented papers showcased the latest research and innovative approaches in geodesy and geoinformatics, exploring diverse topics. The symposium fostered an environment conducive to networking and knowledge sharing, enabling researchers, professionals, and experts from various backgrounds to connect and establish fruitful collaborations. The discussions extended beyond the presented papers, encompassing broader trends, challenges, and opportunities in geodesy and geoinformatics. Overall, the Symposium on Geodesy and Geoinformatics provided a platform for researchers and practitioners to disseminate their research, exchange knowledge, and foster collaborations. The symposium was led by a distinguished panel of co-chairs, namely Dževad Krdžali´c, Džanina Omi´cevi´c, Esad Vrce, and Alma Tabakovi´c. The third chapter of the proceedings is dedicated to the Symposium on Data Science and Geographic Information Systems. This symposium aimed to bring together scientists, engineers, and students to present their latest research and development results in innovative and interdisciplinary applications of advanced technologies, and papers focused both on Data Science and Geographic Information Systems. The session also provided an interdisciplinary platform for researchers, practitioners, and educators to present and discuss their contributions both in fundamentals and applications. Six peer-reviewed papers were presented. The session was moderated by co-chairs Almir Karabegovi´c, Jasminka Hasi´c Telalovi´c, and Mirza Ponjavi´c.
vi
Preface
The Computer Science and Artificial Intelligence Symposium, covered within the fourth chapter of the proceedings, showcased the latest advancements and cuttingedge approaches in the realm of computer science and artificial intelligence. The discussions revolved around recommender systems, natural language processing, and AI applications. Esteemed programmers, students, and researchers gathered at this event to connect, network, and share their research ideas and findings. The symposium featured an array of eleven peer-reviewed papers, distinguished for their exceptional scientific and technical quality. Under the guidance of session co-chairs Jasmin Kevri´c, Adnan Dželihodži´c, and Dželila Mehanovi´c, the session proceeded smoothly and facilitated meaningful exchanges among the participants. The fifth chapter was dedicated to the Information and Communication Technologies Symposium dealing with innovative and interdisciplinary applications of information and communication technologies. Information and communication technologies represent a key segment of modern data systems. A significant number of user services are implemented in the cloud due to demanding algorithms, security issues, and required resources. Data transmission and processing are exposed to various attacks, which entails the constant development and testing of data protection systems and the applications used. The rapid development of information technology and the demands of the present digital era drive organizations to rethink their business models in order to address the problems posed by information security. Papers included in the InfComTec 2023 Symposium bring the application of the latest technologies in the area of protection of computer systems, analysis of the effects of protection in the case of cloud accounting, and applications of smartphones for finger-based heart rate estimation during at-home biofeedback therapy. This symposium includes various papers that present ICT in modern usage. The authors of the papers were students, professors, and researchers employed in various companies, which gave this symposium fresh and new ideas, resolutions, and concrete technologies to be used in the future. Three peer-reviewed papers are included in this symposium. The chair of the session was Aljo Mujˇci´c and Edin Mujˇci´c. Chapter six is dedicated to Applied Science in Mechanical Engineering Technology and Project Management. The main aim was to provide an academic platform to exchange experience for researchers in these fields. We would like to take this opportunity to express our thanks to the individuals and universities for their participation in the symposium, as well as to the members of academic staff, and reviewers. This year, research papers of doctoral candidates, professors, and their associates from mechanical engineering faculties and faculty of technical sciences of five universities in Bosnia and Herzegovina (Tuzla, Sarajevo, Zenica, Biha´c, and Ilidža) were presented, computer simulations, the application of new techniques for researching the quality of materials in the wood industry, new clean technologies in thermal power plants, the application of modern knowledge in the management of renewable energy projects, as well as research in the field of engines and motor vehicles. Ten peer-reviewed papers with scientific and technical quality were presented. The session was moderated by co-chairs Hajrudin Džafo and Adis Bubalo. The final chapter, the seventh chapter, is dedicated to topics in the field of Advanced Electrical Power Systems. It was an opportunity for researchers, engineers, and overachieving students to present their latest research and development results in all areas
Preface
vii
of power systems. During this symposium, current issues related to the energy transition, future power systems, and emerging technologies were discussed. The topics of the papers presented at the symposium include positive energy districts, power system stability, data mining applications in smart metering, electrical motor diagnostics, and energy transition. A very fruitful discussion was conducted following the presentation of the accepted papers. Representatives from leading universities in Bosnia and Herzegovina as well as industrial partners took part in this event. Five peer-reviewed papers with scientific and technical quality were presented. The Symposium on Advanced Electrical Power Systems was chaired by Mirza Šari´c.
Contents
Civil Engineering Application of Microsimulation Models for Traffic Emission Assessment . . . . . . Ammar Šari´c, Mirza Pozder, Suada Sulejmanovi´c, Sanjin Albinovi´c, and Žanesa Ljevo Added Value in Implementation of Capital Projects by Monitoring, Evaluation and Learning (MEL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanin Džidi´c, Ahmed El Sayed, and Adnan Novali´c Seismic Analysis of Buildings with a Soft Storey Using Pushover Analysis . . . . Naida Ademovi´c and Adnan Muratagi´c Methodology of Ranking of the Main Road Network in the Federation of Bosnia and Herzegovina from the Aspect of Demography and Economic Development as Part of the Climate Resilience Risk Assessment . . . . . . . . . . . . . Suada Sulejmanovi´c, Ammar Šari´c, Žanesa Ljevo, Emina Hadži´c, and Slobodanka Kljuˇcanin Methodology of Traffic Safety Management at Railway Crossings in Bosnia and Herzegovina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanjin Albinovi´c, Suada Sulejmanovi´c, Ammar Šari´c, Mirza Pozder, Žanesa Ljevo, and Kerim Bijedi´c Building Information Modeling Education at Bosnian and Herzegovina Universities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Žanesa Ljevo, Mirza Pozder, Suada Sulejmanovi´c, Ammar Šari´c, Sanjin Albinovi´c, and Naida Ademovi´c Spalling of Concrete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irfan Bidževi´c, Sanin Džidi´c, and Ahmed El Sayed Mathematical Model Identification of Measures for Improving the Energy Efficiency on Road Tunnels Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mirza Berkovi´c, Adnan Omerhodži´c, Ajdin Džananovi´c, and Samir Džaferovi´c
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Geodesy and Geoinformatics Use of UAV for Object Deformation Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Muamer Ðidelija and Esad Vrce An Evaluation of the Dams Crest Movement Influenced by Thermal Variations: A Machine Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Adis Hamzi´c Geometric Enhancement of the Cadastre of Underground Utility Networks . . . . 132 Nedim Tuno, Jusuf Topoljak, Admir Mulahusi´c, Muamer Ðidelija, Nedim Kulo, and Tomaž Ambrožiˇc Data Science and Geographic Information Systems Interactive Narrative Design and Storytelling, Use Case with Geospatial Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 ˇ Naida Congo and Almir Karabegovi´c Geoinformation Crowdsourcing and Possible Applications in Croatia . . . . . . . . . 157 Robert Župan, Fran Duboveˇcak, Stanislav Frangeš, and Ivana Racetin Distribution of Medieval Necropolises and Tombstones in the Bosnia River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Edin Hadžimustafi´c, Dževad Mešanovi´c, and Hamza Jašarevi´c Use of Augmented Reality to Present Archaeological Contents . . . . . . . . . . . . . . . 186 Vesna Poslonˇcec-Petri´c, Valentina Vukovi´c, Željko Baˇci´c, and Ivka Kljaji´c Breast Cancer Classification Using Support Vector Machines (SVM) . . . . . . . . . . 195 Jasminka Telalovi´c Hasi´c and Adna Salkovi´c Development of Soil Type Valuation Model in GIS for Sustainable Agricultural Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 ˇ Melisa Ljusa, Hamid Custovi´ c, Mirza Ponjavi´c, and Almir Karabegovi´c Geospatial Analysis of Residential Zones from the Aspect of Natural Limitations – A Case Study of the Urban Territory of Sarajevo . . . . . . . . . . . . . . . 213 Jasmin Taletovi´c and Nataša Pelja Tabori
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Computer Science and Artificial Intelligence Credit Card Fraud Payments Detection Using Machine Learning Classifiers on Imbalanced Data Set Optimized by Feature Selection . . . . . . . . . . . 233 Admel Husejinovi´c, Jasmin Kevri´c, Nermina Durmi´c, and Samed Juki´c Document-Based Sentiment Analysis on Financial Texts . . . . . . . . . . . . . . . . . . . . 251 Admel Husejinovi´c and Zerina Mašeti´c Emotion Detection Using Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . 263 Abdullah Bjelak and Ahmed Selimovi´c From Olympics to War – Pursuing Sarajevo Identity Using Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 Emina Zejnilovi´c, Erna Husuki´c, Zerina Mašeti´c, and Dželila Mehanovi´c The Employment of a Machine Learning-Based Recommendation System to Maximize Netflix User Satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300 Dinko Omeragi´c, Dino Keˇco, Samed Juki´c, and Be´cir Isakovi´c A Recommendation System for Movies by Using Hadoop Mapreduce . . . . . . . . . 329 Dinko Omeragi´c, Aldin Beriša, Dino Keˇco, Samed Juki´c, and Be´cir Isakovi´c Business-Driven Analysis of Website Traffic: A Case Study . . . . . . . . . . . . . . . . . 341 Aldin Kovaˇcevi´c, Amela Mudželet Vatreš, and Samed Juki´c Composable ERP – New Generation of Intelligent ERP . . . . . . . . . . . . . . . . . . . . . 359 ´ c, and Mirela Mabi´c Dražena Gašpar, Ivica Cori´ Building a Recommendation System for E-Commerce Using Machine Learning and Big Data Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 Naida Fati´c, Mirza Krupi´c, and Samed Juki´c Implementation and Evaluation of a Deep Neural Network for Spam Detection: An Empirical Study of Accuracy and Efficiency . . . . . . . . . . . . . . . . . . 388 ˇ Luka Varga, Caslav Livada, Alfonzo Baumgartner, and Robert Šojo Gradual Increase of Natural Disaster Occurrences in the Context of Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Amela Mudželet Vatreš, Aldin Kovaˇcevi´c, and Samed Juki´c
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Information and Communication Technologies A Comparative Performance Analysis of Various Antivirus Software . . . . . . . . . . 423 Una Drakuli´c and Edin Mujˇci´c Enhancing the Accuracy of Finger-Based Heart Rate Estimation During At-Home Biofeedback Therapy with Smartphone . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Alma Še´cerbegovi´c, Asmir Gogi´c, and Aljo Mujˇci´c Effects of Protection Cloud Accounting and Connection with the Frequency of Cyber Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 Valentina Stipi´c Vinšalek, Mile Viˇci´c, and Mohammad Al Awamrah Applied Science in Mechanical Engineering Technology Managing Risks of Renewable Energy Projects in Bosnia and Herzegovina Based ISO31000 and PMBOK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455 Hajrudin Džafo Exploring the Strength of Wood: Measuring Longitudinal Modulus of Elasticity in Structural Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 Irvina Šahinovi´c, Husein Roši´c, Leila Fathi, Damir Hodži´c, Aladin Crnki´c, and Redžo Hasanagi´c Contribution to the Diagnostics of the Internal Combustion Engine Timing Mechanism Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476 Jasmin Šehovi´c Parametric Optimization of MQCL-Assisted Turning Operation of X5CrNi18–10 Steel Using Definitive Screening Design and Multi-Criteria Decision-Making Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486 Adnan Mustafi´c Influence of Different Cutting Speeds on CNC Milling on Surface Roughness of Objects Made from Steamed and Heat-Treated Beech Wood . . . . . 501 Alen Ibriševi´c, Ibrahim Busuladži´c, Murˇco Obu´cina, and Seid Hajdarevi´c Thermal Modification of Wood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 Izet Horman, Aleksandra Kosti´c, Valentina Timoti´c, and Melisa Kustura The Influence of Mill Loading on the Distribution of Pulverized Coal Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 518 Izudin Deli´c, Amel Meši´c, Nedim Ganibegovi´c, and Midhat Osmi´c
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Influence of Coal Mixing Process on the Performance of the Steam Boiler . . . . . 533 Izudin Deli´c, Midhat Osmi´c, Nedim Ganibegovi´c, Almir Brˇcaninovi´c, and Amel Meši´c Exergy Analysis of a Solar Assisted Desiccant Cooling System . . . . . . . . . . . . . . 550 Haris Luli´c, Sadjit Metovi´c, and Almira Softi´c Investigation of the Concentration of Air Pollutants in the Vertical Profile in the Zenica Valley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562 Mirnes Durakovi´c, Husika Azrudin, and Sadjit Metovi´c Advanced Electrical Power Systems Data Mining Techniques Application for Electricity Consumption Analysis and Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 Hamza Turˇcalo, Lejla Mari´c, Adelina Muši´c, Erma Heldovac, Edin Kadri´c, Jasmin Azemovi´c, and Tarik Hubana University Campus as a Positive Energy District – A Case Study . . . . . . . . . . . . . 583 Emir Neziri´c, Damir Špago, Mirza Šari´c, Edin Šunje, and Mirsad Be´ca State Estimation in Electric Power Systems Using Weighted Least Squares Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 Amina Hondo, Elma Begi´c, and Tarik Hubana Adaptive Under Frequency Load Shedding Using Center-of-Inertia Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 606 Maja Dedovi´c Mufti´c, Samir Avdakovi´c, Ajdin Alihodži´c, Nedis Dautbaši´c, Adin Memi´c, and Adnan Mujezinovi´c Predictive Maintenance of Induction Motor Using IoT Technology . . . . . . . . . . . 616 ˇ Sanela Užiˇcanin, Nerdina Mehinovi´c, Edina Cerkezovi´ c, and Admir Užiˇcanin Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627
Civil Engineering
Application of Microsimulation Models for Traffic Emission Assessment Ammar Šari´c(B)
, Mirza Pozder , Suada Sulejmanovi´c , Sanjin Albinovi´c , and Žanesa Ljevo
Faculty of Civil Engineering, University of Sarajevo, Patriotske lige 30, 71 000 Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. Traffic-caused air pollution is one of the primary problems in urban areas. A reliable assessment of the emission level is necessary for a complete overview of the traffic network and for determining the optimal solution for ubiquitous traffic problems. Traffic microsimulations can be very useful for such decisions if their detailed calibration has been performed. The paper presents a technique for calibrating key parameters of microsimulation mod-els for reliable emission estimation. The calibration is based on analyzing the trajectories of individual vehicles obtained by a drone recording and ad-vanced processing of videos on the example of a signalized intersection in Sarajevo. After speed calibration, the Chi-Square Error (X2 ) decreased from 0.28 to 0.03 and from 0.71 to 0.06 after acceleration calibration. Also, the differences in individual speed ranges between observed and simulated values are less than 2%. Keywords: emission · traffic · microsimulation · Vissim · DataFromSky · modeling · intersection
1 Introduction Air pollution is one of humanity’s most significant problems because it directly affects people’s health and overall quality of life. Although there are numerous causes of pollution, road traffic has been one of the leading reasons for the high concentration of various harmful substances in the air for decades. A particular problem is represented by city intersections where vehicles change their movement dynamics due to frequent braking, stopping, and accelerating. Namely, vehicles with internal combustion engines still dominate the world and emit the highest concentration of harmful gases when changing speed and driving at high speeds. In this paper, the urban traffic conditions will be considered, so the problem of high speeds will not be analyzed. Data on the movement of individual vehicles were obtained based on the detailed processing of video recordings of a signalized intersection in Sarajevo, Bosnia and Herzegovina. These data were used to calibrate the microsimulation model of the intersection, after which CO (carbon monoxide), CO2 (carbon dioxide), NOx (nitrogen oxides), PM (particulate matter), and fuel consumption were estimated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 3–13, 2023. https://doi.org/10.1007/978-3-031-43056-5_1
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The main goal of this research is to show the application of microsimulation tools for analyzing intersections concerning the emission of harmful gases. Special attention is paid to calibrating the microsimulation model, which is mandatory to obtain reliable results. This methodology can be beneficial when deciding on the optimal type of intersection, where the decision will not be based only on average delay and level of service. In addition to analyzing individual locations, the methodology can be applied to the broader traffic network and to various traffic measures to reduce the emission of greenhouse gases (e.g., participation of zero-emission vehicles, greater use of public transport, etc.).
2 Previous Research Existing research on this topic includes the application of various microsimula-tion tools and models for the assessment of harmful gas emissions. This paper focused only on research that included microsimulation tools and possibly field research applied to different types of urban intersections. M˛adziel et al. [1] compared the two-lane and turbo roundabout emissions using the PTV Vissim and Versit+ pollutant dispersion model. They concluded that by applying the turbo roundabout, CO2 and PM10 emissions are reduced by 23% and NOx by 16%. Using the same methodology, Tenekeci [2] concluded that a two-lane rounda-bout emits less CO2 (up to 32%), NOx (up to 34%), and PM10 (up to 26%) com-pared to a one-lane roundabout. Fernandes et al. [3] used Vehicle specific power (VSP) methodology and field measurement to estimate emissions at different intersections. Their results showed that NOx, PM, CO2 , and CO emission for lower traffic volumes is higher on average by 12–20% on turbo roundabouts compared to two-lane roundabouts. For higher traffic loads, this difference is up to 29%. Giuffrè and Canale [4], using the PTV Vissim software and the EnViVer Versit+ model for emission estimation, concluded that the signalized intersection leads to the most significant air pollution compared to other classic types of city intersections (unsignalized, one-lane, and two-lane roundabout). Gastaldi et al. [5] used the microsimulation software Paramics and AIRE (Analysis of Instantaneous Road Emissions) to determine the emissions at signalized intersections and roundabouts. The obtained results showed that the emis-sion level is reduced by only 2–5% in the case of signalized intersections. By combining the microsimulation software Paramics and two pollutant emis-sion estimation tools, Motor Vehicle Emission Simulator (MOVES) and Compre-hensive Modal Emissions Model (CMEM), Chamberlin et al. [6] concluded that roundabouts lead to 7% higher CO emissions and 9% more NOx compared to signalized intersections.
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3 Research Methodology The research was conducted on the example of an existing four-leg semaforized intersection in Sarajevo, Bosnia and Herzegovina. Figure 1 shows the layout of the analyzed intersection. The main direction is represented by approaches 1–2, with the dominant through movement on all approaches and left on approach 2. Approach 5 is a bypass for right turns from direction 4. Data collection was carried out by drone recording for two days (Tuesday and Thursday) for two hours, in the morning (8:00–9:00 AM) and afternoon (4:30–5:30 PM), the busiest traffic periods. The highest recorded hourly volume is 2270 veh/h. Based on traffic data (number of vehicles and traffic flow structure) and geometric characteristics; a microsimulation model of this intersection was created in the PTV Vissim 2022 software (Fig. 2). The application of microsimulation models requires mandatory calibration. In practice, the most common calibration goals are travel time and the number of simulated vehicles. However, microsimulation models must reliably replicate individual vehicles’ trajectories for a reliable assessment of pollutant emissions. Vehicle trajectories contain essential information about speed and acceleration in short intervals, critical data for estimating emission levels. The videos were further used for processing using the DataFromSky tool to ob-tain accurate trajectories of all recorded vehicles. DataFromSky presents an ad-vanced AI algorithm for object recognition, classification, and determination of dynamic movement characteristics. The speed and acceleration of each vehicle were determined in every second of its movement, and these data were processed in detail to calibrate the microsimulation model.
Fig. 1. The layout of existing intersection.
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Fig. 2. Microsimulation model of analyzed intersection (PTV Vissim 2022).
Four functional dependencies determine the dynamic of vehicle movement in the PTV Vissim software: 1) desired speed distribution, 2) desired acceleration function, 3) maximum deceleration function, and 4) desired deceleration function. All four functions are calibrated based on the collected data. The calibration principle was adopted based on previous works, eg. [1] and [7]. The desired speed distribution is shown in Fig. 3 and was determined considering all vehicles moving at speeds greater than 40 km/h. For each acceleration and deceleration function, it is necessary to define three curves for the Maximum, Mean, and Minimum values. Recorded vehicle speeds are divided into intervals of 5 km/h so that the maximum, mean, and minimum acceleration values are determined for each speed class separately. The maximum deceleration function was determined using 5% of the highest deceleration values, while the remaining values were used to determine the desired deceleration (maximum curve - 5th percentile, mean curve - 52.5th percentile, minimum curve - 100th percentile). The maximum curve in desired acceleration function represents the 95th percentile of all values, while the mean and minimum curves are determined depending on the probability of reaching the desired speed and acceleration. Figures 4a, 4b, 4c, 4d shows the functional dependencies of speed and acceleration/deceleration.
Fig. 3. Cumulative probability function of desired speed.
Application of Microsimulation Models for Traffic Emission
Fig. 4a. Maximum deceleration function.
Fig. 4b. Desired deceleration function.
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Fig. 4c. Desired acceleration function.
Fig. 4d. Desired acceleration cumulative probability function for different speed class.
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4 Results and Discussion Chi-Square Error (X2 ) using the following equation was used to evaluate the calibration: 2 N fi,observed − fi,simulated 2 X = (1) fi,observed i=1
The results of the comparison of simulated and observed values are given in Figs. 5a, 5b. At the same time, Table 1 shows the Chi-Square Error (X2 ) values for the simulation results with the default values of the desired speed and acceleration function and with the application of calibrated functions. Table 1. Chi Square Error (X2 ) for default and calibrated values. Speed
Acceleration
Default
Simulation
Default
Simulation
0,2829
0,0355
0,7195
0,0644
Based on the obtained results, the calibrated model describes the dynamics of the vehicle’s movement with much greater accuracy than the default speed and acceleration values in the PTV Vissim software. Differences in speeds for all intervals do not exceed 2%. It can be noticed that the largest share of speeds is in the range of 0–5 km/h, which corresponds to the vehicle behavior at the signalized intersection, which causes the vehicle to stop by changing the cycle. Also, a similar trend can be observed for accelerations as the largest number of values is in the range −0,5 m/s2 to 0,5 m/s . The assessment of CO2 , NOx, CO, and particular matter (PM) emissions and fuel consumption was determined using Environmental Sensitive Traffic Management (ESTM) by Bosch, which is directly incorporated into the latest version of PTV Vissim. The Bosch ESTM model was developed based on many field measurements of pollution on vehicles of different types and ages. To estimate emissions, we used data on the traffic flow structure obtained based on the analysis of registered vehicles in the Canton of Sarajevo in 2021. The total share of diesel vehicles is 75%, and gasoline vehicles are 25%. In addition, data on the number of vehicles with engines of a certain EURO standard were also used. The intersection model was simulated five times, with each simulation lasting 4200 s with 600 s of the warm-up period. The results obtained by simulation and with the Bosch ESTM tool are presented in heat maps for each pollutant, fuel consumption, and speed distribution, which can be seen in Figs. 6, 7, 8, 9, 10 and 11. Based on the presented figures, it can be concluded that the highest pollution is on approach 2, which is also on a 4% uphill grade, contributing to this result. There is also a higher pollution level on approach 2, compared to the remaining approaches, due to a significant number of left turns. It can be noted that for the same reason, the concentration of pollutants is higher at the exit of approach 4.
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Fig. 5a. Observed vs. simulated speed values.
Fig. 5b. Observed vs. simulated acceleration/deceleration values.
Entry of approach 4, despite the higher traffic load, does not show a high pollutant concentration. Namely, the dominant movement on this approach is right, and the existing bypass (approach 5) significantly affects the smooth flow of traffic on this approach and, thus, lower emissions.
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Fig. 6. CO emission map
Fig. 7. CO2 emission map
Fig. 8. NOx emission map
Fig. 9. PM emission map
All emission maps show the same and consistent results. These results correspond to the speed distribution so that the areas with the lowest average speeds also represent zones with higher pollutant emissions. In addition, although the primary goal of this research was not a capacity analysis, based on microsimulation results it can be concluded that approaches with a lower level of service and long delays generate higher emissions of harmful gases.
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Fig. 10. Fuel consumption heat map
Fig. 11. Speed heat map
5 Conclusion The problem of air pollution from traffic is still a topical issue worldwide and will remain so for many years despite the appearance of zero-emission vehicles. The focus of this paper was the application of microsimulation tools for assessing emissions of harmful gases from traffic rather than specific pollution values. Through detailed data collection and processing, it is possible to correctly calibrate microsimulation models that will lead to a reliable emission assessment. The described procedure can be used to compare different traffic solutions from the aspect of environmental impact and not only from the aspect of average delay and level of service. Microsimulation tools need to be additionally confirmed by field measurement of pollution, which still implies a timeand financially de-manding process. Also, further research can include the analysis of different types of intersections, the impact of heavy goods vehicles and pedestrians, and different scenarios of using zero-emission vehicles and vehicles with a higher EURO standard engine.
References 1. M˛adziel, M., Campisi, T., Jaworski, A., Kuszewski, H., Wo´s, P.: Assessing vehicle emissions from a multi-lane to turbo roundabout conversion using a microsimulation tool. Energies 14(15), 4399 (2021). https://doi.org/10.3390/en14154399 2. Tenekec˙i, G.: Computation and assessment of environmental emissions resulting from traffic operations at roundabouts. Eur. J. Sci. Technol. Special Issue, 130–145 (2019). https://doi.org/ 10.31590/ejosat.637594 3. Fernandes, P., Pereira, S.R., Bandeira, J.M., Vasconcelos, L., Silva, A.B., Coelho, M.C.: Driving around turbo-roundabouts vs. conventional roundabouts: are there advantages regarding pollutant emissions? Int. J. Sustain. Transport. 10(9), 847–860 (2016). https://doi.org/10.1080/ 15568318.2016.1168497
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4. Giuffrè, T., Canale, A.: road intersections design and environmental performances. J. Multidiscipl. Eng. Sci. Technol. 3(11), 6050–6059 (2016) 5. Gastaldi, M., Meneguzzer, C., Rossi, R., Lucia, L.D., Gecchele, G.: Evaluation of air pollution impacts of a signal control to roundabout conversion using microsimulation. Transport. Res. Procedia 3, 1031–1040 (2014). https://doi.org/10.1016/j.trpro.2014.10.083 6. Chamberlin, R., Swanson, B., Talbot, E., Dumont, J., Pesci, S.: Analysis of MOVES and CMEM for Evaluating the Emissions Impact of an Intersection Control Change. Transportation Research Board, Washington, D.C. (2011) 7. Li, J., van Zuylen, H.J., Xu, X.: Driving type categorizing and microscopic simulation model calibration. Transport. Res. Record J. Transport. Res. Board 2491(1), 53–60 (2015). https:// doi.org/10.3141/2491-06
Added Value in Implementation of Capital Projects by Monitoring, Evaluation and Learning (MEL) Sanin Džidi´c1(B)
, Ahmed El Sayed2
, and Adnan Novali´c2
1 Faculty of Technical Sciences, Department of Civil Engineering, University of Biha´c, Biha´c,
Bosnia and Herzegovina [email protected] 2 Faculty of Engineering, Natural and Medical Sciences, Department of Civil Engineering, International BURCH University, Sarajevo, Bosnia and Herzegovina
Abstract. Many capital projects of various magnitudes are implemented every day funded by the community budgets. Funds for the implementation of these projects come from the budgets of these institutions, and they are collected by tax allocations from citizens. However, the criteria for the selection and implementation of capital projects are often unclear, including the project goals. The construction of the building or facility in some community should not be the goal by itself, but its higher societal benefit that the project will contribute to. What exactly is supposed to be achieved by the project implementation, how this particular project will improve the lives of citizens, and whether the project goals have been achieved in general? Very often, there is no sound and adequate data in this sense, apart from basic technical and financial data, but rather subjective opinions, created individual or groups’ perceptions that are not supported by solid and scientifically obtained data in relation to project goals. Monitoring, evaluation, and learning (MEL) can offer sound system and answers to above questions. Keywords: Capital Project · Project Management · Monitoring · Evaluation · Learning · MEL
1 Introduction The Project Management Institute defines that a project is “a temporary group activity designed to produce a unique product, service or result” and that project management is the “application of knowledge, skills, tools and techniques to project activities to meet the project requirements”, then humans have started working on projects since ancient history [1, 2]. “Project management processes are grouped into five categories known as Project Management Process Groups (or Process Groups): • Initiating Process Group. Those processes performed to define a new project or a new phase of an existing project by obtaining authorization to start the project or phase; © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 14–26, 2023. https://doi.org/10.1007/978-3-031-43056-5_2
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• Planning Process Group. Those processes required to establish the scope of the project, refine the objectives, and define the course of action required to attain the objectives that the project was undertaken to achieve; • Executing Process Group. Those processes performed to complete the work defined in the project management plan to satisfy the project specifications; • Monitoring and Controlling Process Group. Those processes required to track, review, and regulate the progress and performance of the project; identify any areas in which changes to the plan are required; and initiate the corresponding changes; and • Closing Process Group. Those processes performed to finalize all activities across all Process Groups to formally close the project or phase” [1]. Very often, in practice in the construction industry, it is observed the period of construction of the building or facility itself. In some cases, there is a slightly wider approach that includes the preparation of designs, permitting, the construction itself, and the completion of the technical acceptance of the building or facility. However, depending of managerial level, the project begins much earlier - in its initiation stage. Then the question arises as to why that particular project was selected, what are the criteria for selection? What are the goals of the project and what will it achieve? A capital project is a long-term, capital-intensive investment to build upon, add to, or improve a capital asset. Capital projects are defined by their large scale and large cost relative to other investments that involve less planning and resources. A capital project is an often-pricey, long-term project to expand, maintain, or improve upon a significant piece of property. Capital projects typically consist of the public sector building or maintaining infrastructure, such as roads, railways, and dams, and companies upgrading, expanding, or replacing their facilities and equipment [3]. Considering the situation in this sense in Bosnia and Herzegovina, a large number of capital projects of various magnitudes are implemented every day funded by the budgets of municipalities and higher levels of governments throughout the country. Funds for the implementation of these projects come from the budgets of these institutions, and they are collected by tax allocations from citizens. However, the criteria for the selection and implementation of capital projects are often unclear, including the project goals. What exactly is supposed to be achieved by the project implementation, how this particular project will improve the lives of citizens, and whether the project goals have been achieved in general? Very often there are no sound and adequate data in this sense, apart from basic technical and financial data, but rather subjective opinions, created perceptions that are not supported by solid and scientifically obtained data in relation to project goals. Is there a methodology that would provide clear data in order to adequately identify capital and other projects, to identify project goals that can be technical, financial, developmental, economic, health, social, etc., that can enable project monitoring during the implementation of such projects, but also whether the goals of the project achieved, to what extent and in the years after the actual implementation of the projects? It is not realistic to expect that the project will achieve its goals just by completing construction and becoming operational. The methodology of monitoring, evaluation and learning (MEL) can adequately respond to these requirements. However, it requires knowledge, effort, an organized approach and reliability.
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Monitoring, evaluation and learning help improve performance and results. The overall purpose of evaluation is measurement and assessment of performance meet outcomes and outputs also called results. Traditionally, monitoring and evaluation focusses on assessment of inputs and implementation process [3]. “Implementation of monitoring, evaluation and learning seeks to guarantee ultimate project success through the achievement of immediate project outcomes such as conformity to standards and the achievement of budget and schedule as well as long-term objectives such as fit for purpose (impact).” [4].
2 Research Methodology Initially, the analysis of situation with implementation of capital projects that includes construction of building or facility in Bosnia and Herzegovina was made based upon experiences with no traces of papers highlighting this subject. The research questions in this paper are: 1. Are there any solid and organized approach and system of evidence for decision making in selection of implementation of capital projects in Bosnia and Herzegovina? 2. Are there any solid and organized approach in setting the goals for implementation of capital projects in Bosnia and Herzegovina? and 3. Is there any technique or approach that can be applied and inform providing feedback to research questions 1 and 2. Based upon these findings, the basic concept of monitoring, evaluation and learning was adapted for the observed topic and was introduced based upon research questions. Then, four randomly selected capital projects were taken for qualitative research and analyzed for possibility to introduce monitoring, evaluation and learning principles and suggestions for performance indicators adapted for type of selected projects (but not limited to them) measuring and focusing their higher societal results of selected projects, so confirming applicability of MEL in response to research question 3.
3 Monitoring, Evaluation and Learning (MEL) “Monitoring and Evaluation (M&E) is a process whose main aim is help improve project performance and achieve expected or planned results. The objective of monitoring and evaluation is to improve current and future management of inputs, outputs, outcomes and impact in projects and programs being executed by assessing the progress, performance and results of projects and programs.” [4]. The dependance of project results is displayed in Fig. 1. Monitoring is a continuous effort that aims at providing management and stakeholders with indications of progress or lack of progress in implementation of project results [5]. Monitoring actually refers to the routine monitoring of project resources, activities and results, and analysis of the information to guide project implementation [7]. Evaluation is selective exercise whose objective is to systematically and objectively assess progress towards achievement of outcomes or results as planned. This involves assessment of scope and depth carried out at several points in time in response to changing needs for the purpose of knowledge and learning. [4] Evaluation refers to the periodic (mid-term, final) assessment and analysis of an on-going or completed project [7]. The
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Fig. 1. The Kellogg basic logic model of inputs to impact [6].
development of monitoring and evaluation mechanisms in project management through time in stages was presented in Table 1. In line with strategic phase from Table 1, since year 2000, the M&E concept evolved to MEL (monitoring, evaluation and learning). Learning is the process through which information generated from M&E is reflected upon and intentionally used to continuously improve a project’s ability to achieve results [7]. MEL is an integral part of project design, implementation and completion. MEL is done at all stages within the project cycle. The MEL cycle helps to position MEL in the life of project, as shown on the diagram at Fig. 2. However, the management of capital projects in communities in the current practice in Bosnia and Herzegovina generally lacks the long-term planning approach and setting of priorities, which is done often nontransparent and without clear vision. Essentially, it can be noticed that practice is addressing the symptoms. In this regard, communities should be able to implement their own monitoring, evaluation and learning tools to identify and manage capital projects, what usually is not the case [8]. There are other concepts of M&E developed for other purposes as well, like monitoring, evaluation, accountability, and learning (MEAL), where accountability, in particular, needs to reflect the situation, and the system used needs to be conflict-sensitive so that it does not aggravate grievances, tension or vulnerabilities – both directly or indirectly. It also needs to take into account the perspectives of local groups who may themselves be involved in or affected by conflict [9]. Another concept is monitoring, evaluation, research and learning (MERL), where research is defined as original investigation, undertaken in order to gain knowledge and understanding about issues critical for project and influencing priorities, through the use of qualitative and quantitative research methodologies [10].
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Table 1. Evolution of monitoring and evaluation mechanisms in project management through time [5]. Stage
Evolution of Monitoring and Evaluation Mechanisms
1 1860–1900
Period
Inception stage
First concerns regarding management as a science emerge while the mechanisms for monitoring and evaluation have not appeared yet
2 1900–1955
Empirical stage
First base concepts of the project management, being outlined. First definitions and approaches of the project concept, with regard to civil engineering or to military services. As monitoring and evaluation tools used the Gantt Diagram and simplified versions of it
3 1955–1970
Applied stage
Need for efficient activity generated as anachronism regarding the relation between theory and practice. The CPM and PERT methods are used as monitoring and evaluation mechanisms and was made the first reference to Logical Framework Approach, respectively to Logical Framework Matrix
4 1970–1990
Scientific stage
Theorization of project management as a science develop. There emerge magazines and publications with regard to project management, most of them being still current. Project management bodies emerge e.g. IPMA or PMI. The monitoring and evaluation mechanisms remain focused on Gantt Diagram, CMP and PERT methods and Logical Framework Matrix
5 1990–2000
Information stage Project management follows the world economy approach, detaching practically from the status of technical science and going quasi-definitively to the sphere of economics sciences with use of software. Flexible management structures, such as matrix organization emerge including management through projects or also new methods and techniques (Balanced Scorecard)
6 2000-present Strategic stage
Project management emerged as the key field of the organization strategy, capable of producing added value and competitive advantage. There developed companies that have as activity object the elaboration, development, implementation and project monitoring
4 Project Representation and Organization for MEL Purpose A results framework is both a planning and management tool that provides the basis for monitoring, evaluation and learning as displayed in Fig. 3. It provides a project-level framework for managers to monitor the achievement of results and to adjust relevant projects and activities when necessary. It gives the reader an instant idea of what a project
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Fig. 2. The MEL cycle [7].
is trying to achieve. Results Framework focuses specially on impact and the outcomes of the work done through the project or program [11]. The activities and tasks of the capital project implementation related to the actual construction, equipping, permitting, tendering and similar activities can be fit to the maximal output/outcome level associated by other activities necessary for project implementation, however results/impact level is reserved for higher societal project benefits that actual construction significantly contributes to. In other words, if project considers construction, without actual construction, most probably societal result/impact will not be achieved. However, actual construction does not guarantee that project will achieve societal result/impact if the construction activities are not followed by other necessary and carefully selected. This fact is especially important in the preparation phase of the project made by administrative units.
Fig. 3. Results Framework [12].
Another way of representing project for MEL purposes is using Log Frame as displayed in Fig. 4. “A log frame is a tool for improving the planning, implementation,
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management, monitoring and evaluation of projects. It is a way of structuring the main elements in a project that shows the logical linkages between them.” [11].
Fig. 4. LogFrame and causal links [12].
“Cause and effect “logic is usually based on hypothesis and may not have evidence to support the relationship between the cause and the effect [11].
5 Elements of MEL Plan The heart of any monitoring, evaluation and learning plan is a set of performance indicators. The performance indicators are tied for each of the results from results framework or logframe (outputs, outcomes, impacts), highlighting if the specified results are achieved. The performance indicator is a numerical measure of the degree to which the objective is being achieved. Performance indicators are usually seen as numerical measures of achievement [13]. Indicators are clues, signs or markers that measure one aspect of a project and show how close a project is to its desired path and outcomes. They are observable and measurable characteristics that can be used to show changes or progress a project is making toward achieving a specific change [11]. Performance indicator is a special characteristic or trait, or measurable quantity used to monitor change in a process. It is used to measure progress in a specific area against expected values or targets. The performance indicator serves to indicate “how” and “if” the change is taking place towards the given goal, but it does not give the answer “why” and “why the progress was not achieved”. Performance indicators are usually expressed in measurable quantities (numerical values, percentages, ratios, etc.) They need to fulfill specific characteristics like practicality, feasibility, costefficiency, to be sensitive to detect change in desired outcome if it occurs and unaffected
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by other changes, distinct and with possibility for data disaggregation [8]. For each performance indicator, it is necessary to document unit of measure, data source, method of data collection, frequency of data collection, responsibility for data collection and disaggregation per categories, if available. In its definition, the performance indicator needs to be neutral, not expressing desired direction. The desired direction of results should be defined by targets. However, baseline value should be established before the project implementation starts or at the very beginning of the project. A baseline data will establish situation for considered performance indicator how a was prior to the implementation of the project. It is so important to work from a good baseline as this is comparison of project results to show the change the project has made [7]. Based upon the baseline findings for each performance indicator, targets for each year of capital project implementation should be established by community in cooperation and expectation by stakeholders and citizens. So, the baseline is the value of a performance indicator before the implementation of project, while a target is the specific, planned level of result to be achieved within an explicit timeframe by project implementation. It is not possible to expect to reach cumulative target immediately after the construction of capital project, so it is recommended to collect data in years after project construction completion as well, depending on the type of capital project to find out if expectations of the capital project implementation are achieved and in which magnitude. The proposal for the performance indicator tracking table for documenting indicator definitions and necessary data including baseline and target values is presented in Table 2. Table 2. Performance Indicators Tracking Table. Indicator
Unit of Disaggregameasure tion
Target Target Y3 Y4 Period of construction
Target Y5
Cumulative target
Frequency
Data source
Baseline
Target Y1
Target Y2
Annually
Survey
To be determined (TBD)
TBD
TBD
TBD
TBD
TBD
TBD
Ministry records
TBD
TBD
TBD
TBD
TBD
TBD
TBD
Result/Impact: Text … Indicator Result/Impact
%
Age categories
Outcome 1: Text … Indicator Num1.1 ber Output 1.1: Text … Indicator 1.1.1 Indicator 1.1.2. Output 1.2: Text … Indicator 1.2.1. Indicator 1.2.2
Gender
Semi annually
Outcome 2: Text … Indicator 2.1 Output 2.1: Text … Indicator 2.1.1 Indicator 2.1.2
Monitoring, evaluation and learning (MEL) should not be interchanged with LM Approach in Project Management due to similarity with letters. Little I.M.D. and Mirrlees J.A. [14]. Have developed an approach for analysis of Social Cost-Benefit
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which is famously known as L-M approach. The core of this approach is that the social cost of using a resource in developing countries differs widely from the price paid for it. This approach propagates the use of shadow prices in order to find out the true value of a project to society [15]. However, L-M approach can be used in MEL framework, as technique to obtain data for specific performance indicators, if appropriate.
6 Proposal for Possible Application of MEL for Different Type of Capital Projects Capital projects should be structured according to the results framework and/or logframe, with a hierarchy of goals and objectives. The construction aspects themselves, related to the construction of the building itself, financial implications, procurement and installation of equipment and other accompanying aspects that complete the full functionality of the project should be classified at the outcome level. However, the goal of this paper is to point out the possibilities of tracking higher social benefits arising from the implementation of capital projects, both in the very initiation of the project and the needs of the community, and in achieving the goals of capital projects, which should be defined with the participatory involvement of stakeholders and residents. Potential indicators in this research were obtained based on analysis of the assumed development of the listed projects according to the possible result frameworks or logframes with respect of the actual socio-economic situation in Bosnia and Herzegovina, mode of governance operation and set up, as well as the expectations of citizens and various stakeholders. In this sense, the following considerations should be understood, while mostly the indicators at the project outcome level are relatively simple. 6.1 Construction of an Indoor Olympic Swimming Pool in Community X The pure construction of an indoor Olympic swimming pool is only one of the necessary phases for the full functionality of such project. There are also pool equipment purchasing and installation, organization of company that will run and maintain the pool, organization of sport and recreational swimming clubs, organization of swimming competitions etc. Depending on the community, and the goals that the community wants to achieve, these achievements should be linked to outcome/output levels of results, and the indicators for these levels of results are relatively simple. However, the essential question is what the community wants to achieve as a result at the level of the main result/impact and in what time period. There is also the question of what the current situation is in areas to be improved by constructing an indoor Olympic swimming pool. In this sense, there is a proposal of several indicators (but not limited to them only), which refer to the higher societal results that the community wants to achieve by building and implementing the project of construction of indoor Olympic swimming pool displayed in Table 3.
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Table 3. Possible performance indicators for measuring impact made by implementation of capital project for construction of indoor swimming pool in community X. Possible performance indicators for impact Percentage of primary school students that cannot swim in community X in calendar year Percentage of high school students that cannot swim in community X in calendar year Percentage of youth (7-18 years) with flat feet in community X in calendar year Percentage of youth (7-18 years) with spinal deformity in community X in calendar year Number of medalists in swimming on national level in community X in calendar year Number of medalists in swimming on international level in community X in calendar year Number of visitors to national/international swimming competitions in community X from country/abroad in calendar year
6.2 Construction of the Electric Tram Line Between A and B in Community Y The construction of an electric tram line between points A and B in community Y is certainly an expensive and complex project, both in terms of construction and in a broader sense, where exist need to purchase and install different equipment, trams, organize ticket offices, organize a new or reorganize an existing public transport company that will manage and run the tram line, contract power supply with power supply company, etc. However, the results can be significant for community Y. As in the previous case, the mentioned aspects of the opening of the tram line should be related to the outcome/output levels of results, while the next table contains possible indicators, but no limited to them only, that the community Y wants to improve by construction of the electrical tram line and are presented in Table 4. Table 4. Possible performance indicators for measuring impact made by implementation of capital project for construction of electric tram line between points A and B in community Y. Possible performance indicators for impact Percentage of citizens satisfied with public transportation in community Y in calendar year (disaggregated by age, category, gender) Number of citizens that regularly use tram line between points A and B in community Y in calendar year (disaggregated by age, category, gender) Average air quality index along the tram line between points A and B in community Y in calendar year (disaggregated by seasons) Number of businesses along the tram line between points A and B in community Y in calendar year (disaggregated by type of business) Number of employees that work along the tram line between points A and B in community Y in calendar year (disaggregated by age and gender)
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6.3 Construction of the Community C’ Center for Healthy Aging By building a center for healthy aging, community C wish to improve the social conditions of people in the third age of life in order to have a preventive effect on maintaining the highest possible level of their physical and psychological functionality. The center would organize various types of education, workshops, promotions and themed gatherings. In order to implement this project, it is necessary to construct a center, devise means of its functionality, connect with services of a health, social, cultural and sports character, as well as implement a campaign to involve elderly people in the center’s activities. In essence, the center itself does not exist without the construction of the building itself, but its value with an adequately designed and implemented entire project significantly exceeds the value of the construction itself. In other words, the construction of the center itself is a necessary but not a sufficient condition for the success of the project as a whole. In Table 5, possible indicators at the level of impact in community C are given (not limited to the only). Table 5. Possible performance indicators for measuring impact made by implementation of capital project for construction of the community C’ Center for healthy aging. Possible performance indicators for impact Percentage of elderly citizens (60+) in community C that feel improvement in their psychological satisfaction in calendar year (disaggregated by gender) Percentage of elderly citizens (60+) in community C that feel improvement in their physical health in calendar year (disaggregated by gender) Percentage of elderly citizens (60+) in community C that feel improvement of their social life in calendar year (disaggregated by gender) Number of elderly citizens (60+) in community C that regularly participate in center’s activities in calendar year (disaggregated by gender)
6.4 Improvement of Water Supply Service in Community D If consider a capital project by which community D wants to improve the water supply services for its citizens – typically such projects include various construction works from the reconstruction of the existing water supply network, the repair of faults and leaks to reduce losses in the existing network, to the construction of water tanks and the construction of new pipelines and house connections, as well as campaigns on rational water consumption and the consequences of illegal connections to the water supply system. Depending on the substance of such specific project, most of the results of these undertakings can be monitored through indicators at the outcome/output level, but the synergy of these different activities and tasks reaches higher societal goals at the impact level, which can be measured by proposed indicators listed in Table 6 (but not limited to them only).
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Table 6. Possible performance indicators for measuring impact made by implementation of capital project for improvement of water supply services in community D. Possible performance indicators for impact Percentage of homes that have continuous water supply 24/7 in community D in calendar year (disaggregated by settlements/villages of community) Percentage of citizens satisfied with quality of water supply that have continuous water supply 24/7 in community D in calendar year (disaggregated by settlements/villages of community and gender) Percentage of water loss in water supply network in community D in calendar year (disaggregated by settlements/villages of community) Rate of water bills collection in community D in calendar year
7 Conclusion In implementation of most capital projects, the construction of buildings or facilities is their integral or even main part, but not the only one. The pure construction of the capital project is not the ultimate goal for itself, but the full functionality and benefits of the project. If the capital project is adequately designed, systematized, and managed, it will bring results to the community of greater societal value. However, the construction sector mostly observes capital projects just as construction or its investment value, without entering into the higher societal benefits that the capital project brings with its full implementation. On the other hand, communities that implement capital projects very often limit the role of the construction sector in their contribution to a higher societal goal, limiting the construction sector to the mere construction aspects. However, only complete synergy, mutual cooperation and understanding of all stakeholders involved in the process of implementation of capital projects can lead to the achieving of expected results for the benefit of the community, both in the phase of participatory planning and results based decision making in project selection, as well as during implementation of capital projects. In this regard, the technique of monitoring, evaluation and learning (MEL) provides the sound basis, data and possibilities for the synergistic action of all involved parties towards the final goals that are set for the observed capital project, but also individual targets for all participants in the implementation process. MEL provides a platform for identifying the most necessary and important projects in a specific period of time for observed community through participatory development planning, the possibility and parameters for monitoring project implementation in all phases, possibilities and guidelines for information-based decision making in changing the course of project implementation if it proves necessary during the implementation itself, but also the basis for establishing the level of achievement of project goals during and after implementation. MEL is answer to all three set research questions, but also enables transfer of positive and negative experiences in the implementation of such projects and the lessons learned to the implementation of future capital projects in the same or other communities.
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References 1. Project Management Institute: A Guide to the project management body of knowledge (PMBOK® Guide) (5th Edition ed.). Newtown Square, Pennsylvania: Project Management Institute, Inc. (2013) 2. Seymour, T, Hussein. S.: The history of project management. Int. J. Manage. Inform. Syst. – Third Fourth 2014 18(4) (2014) 3. Baron, A.: Capital Project: Definition, Examples, and How Funding Works. Dotdash Meredith publishing (2022) 4. Callistus, T., Clinton, A.: The Role of monitoring and evaluation in construction project management. In: Karwowski, W., Ahram, T. (eds.) Intelligent Human Systems Integration, pp. 571–582. Springer International Publishing, Cham (2018). https://doi.org/10.1007/9783-319-73888-8_89 5. Kabeyi, M.J.B.: Evolution of project management, monitoring and evaluation, with historical events and projects that have shaped the development of project management as a profession. Int. J. Sci. Res. 8(12) (2019). ISSN: 2319-7064 6. W.K. Kellogg Foundation.: Logic model development guide. One East Michigan Avenue East Battle Creek, Michigan, pp. 49017–4012. USA. (2004) 7. NIDOS.: Monitoring, evaluation and learning MEL guide. Using MEL to strengthen your organizational effectiveness 8. Dzidic, S., Brackovic E.: Proposal of the monitoring and evaluation approach for community public infrastructure improvement projects. In: Conference Proceedings of the 6th International Scientific Conference on Project Management in the Baltic Countries- Project Management Development-Practice and Perspectives. Research Institute of the Project Management of the Faculty of Business, Management and Economics, University of Latvia in cooperation with the Professional Association of Project Managers. Riga, Latvia. April 27–28, 2017. ISSN 2256-0513. e-ISSN 2501–0263, pp. 71–81 (2017) 9. Walden, V.: A Quick Guide to Monitoring, Evaluation, Accountability, and Learning in Fragile Contexts. Oxfam (2013) 10. Plan International: Monitoring, evaluation, research and learning policy-a sub-policy of the program and influence quality policy (2023). https://plan-international.org/uploads/2022/01/ glo-merl_policy-final-io-eng-nov18.pdf 11. Center for Policy, Planning & Evaluation.: Step by Step Guide to Building a Results Framework. District of Columbia Department of Health (2011) 12. Doucette, A.M.: Monitoring and Evaluation – Frameworks and Fundamentals. The George Washington University, DC, USA, The Evaluator’s Institute (2015) 13. Dzidic, S., Kapetanovic O.: Application of the monitoring tools for university departments of architecture development and improvement projects. In: Conference Proceedings of the 6th International Scientific Conference on Project Management in the Baltic Countries- Project Management Development-Practice and Perspectives. Research Institute of the Project Management of the Faculty of Business, Management and Economics, University of Latvia in cooperation with the Professional Association of Project Managers. Riga, Latvia. April 27–28, 2017. ISSN 2256-0513. e-ISSN 2501-0263, pp. 82–90. (2017) 14. Little, I.M.D., Mirrlees, J.A.: Project Appraisal and Planning for Developing Countries. Heinemann Educational Books, London. UK (1974) 15. Baldwin, G.B.: A Layman’s Guide to Little/Mirrlees. International Monetary Fund, ISBN: 9781616353063 (1972)
Seismic Analysis of Buildings with a Soft Storey Using Pushover Analysis Naida Ademovi´c1(B)
and Adnan Muratagi´c2
1 University of Sarajevo-Faculty of Civil Engineering, Patriotske lige 30, 71 000 Sarajevo,
Bosnia and Herzegovina [email protected], [email protected] 2 HS Inženjering d.o.o., Džemala Bijedi´ca 279C, 71000 Sarajevo, Bosnia and Herzegovina
Abstract. The seismic performance of structures depends on numerous elements and one of the crucial ones is the construction system. The aftermath of earthquakes among other things shows all the engineering mistakes and lacks in conducted design and construction. Earthquakes that occurred in Turkey at the beginning of 2023 once again showed the high vulnerability of existing reinforced concrete buildings with soft and weak storeys. Many of these buildings were either heavily damaged or collapsed during the two devastating earthquakes. In this paper, three different models of three different structures with a soft storey together with a P-delta effect were elaborated. Cross-sections and properties of each structure are kept unchanged, while the soft storey was created only on the ground floor with the change in the height of the structures. Analysis was performed on structures having a different number of floors being representatives of low, medium, and tall reinforced concrete buildings. The performance of the structures with such an irregularity was checked with the implementation of the static pushover analysis. All the analyses were performed using the SAP 2000 software. The obtained results showed the weaknesses of the soft storey building during earthquake ground motion and the high vulnerability of these structures. It was been shown that the P-delta effect has a greater impact on high-rise structures and should not be neglected in their calculation. The importance of avoiding sudden changes in lateral stiffness and strength has been witnessed once again in the recent 2023 earthquakes in Turkey. Keywords: earthquake · soft storey · numerical modeling · inter-storey drift · plastic hinges · fiber plastic hinges · P-delta effect
1 Introduction An earthquake is considered one of the most unpredictable and destructive natural hazards, where damage to buildings as a direct consequence of the dynamic response of the structure to the ground movement is only one segment of physical damage [1]. Physical damage consists as well of damage to the transportation, infrastructure systems, and critical facilities. This physical damage produces a variety of social and economic consequences. French [2] identified four outputs as a result of an earthquake: shelter © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 27–43, 2023. https://doi.org/10.1007/978-3-031-43056-5_3
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and housing; direct and indirect economic losses, (3) health, including deaths and casualties, and (4) social disruption. Significant losses in human life, as well as damage to constructions, occur as a result of phenomena that are a consequence of earthquakes, such as fires, land subsidence, landslides, avalanches, floods, tsunamis, etc. The impact of earthquakes on multi-storey buildings is a broad scientific field that has occupied civil engineers all over the world for many years, especially in regions with a pronounced degree of seismic risk. The extreme devastation of the buildings as a result of earthquakes of high magnitude in Pazarcik and Elbistan, Mw = 7.9 and Mw = 7.6 once again revealed all the errors, mistakes, and wrongs that were conducted during the design, construction, and inspection process. As a result of such devastating earthquakes, it is estimated that 6,400 buildings across Turkey and Syria collapsed. According to the Disaster and Emergency Management Authority (AFAD) in Turkey 44,218 people died as a result of the earthquakes, while the death toll in Syria was 5,914 as of February 25, 2023. More than half a million people have been evacuated from the disaster area in Turkey alone. The number of structures that have collapsed or have been severely damaged rose to 173,000 buildings. When designing the load-bearing system of the structure, there are numerous highquality instructions and recommendations in modern seismic norms and the technical literature for the design and construction of seismically resistant structures. One of the elements which is pointed out and that should be avoided as much as possible is the construction of irregular structures. An example of irregular load-bearing structures is an irregular distribution of load-bearing capacity by height, i.e. different resistance of adjacent floors. Ground floors in buildings can be designed differently from other floors, with different heights and arrangement of load-bearing walls, or lack of them, due to the greater need for parking spaces in urban areas, office premises, or commercial needs. This type of irregular construction is a very common cause of severe damage and collapse of buildings during earthquakes. In this way, a soft or weak storey is created in such buildings. At the level of the soft storey which can be located at various heights of the structure (usually it is at the ground floor), there is a discontinuity in the rigidity due to the gap in the load-bearing elements [3]. At the soft storey level, if only columns are constructed, due to the inadequate shear resistance a soft storey will be formed which will lead to heavy damage and even to complete structural collapse like in the latest 2023 Turkey earthquakes (Fig. 1). Once these structural members are damaged the nature of earthquake shaking is not to move on and damage other members. Rather, the quake increases its energy input and continues to damage that same storey [4]. Frequently the whole structure above the soft storey remains virtually undamaged. As the columns in the soft storey do not have an adequate level of seismic resilience while supporting the vertical loads this kind of structure is condemned to failure. This kind of vertical irregularity is the most serious due to which numerous multi-storey buildings have been heavily damaged or collapsed after intense ground motion. As per [5] there is a difference between a soft storey and a weak storey. The former refers to lateral stiffness and the latter to lateral strength. In the first case, the lateral stiffness is less than 70 percent of that in the storey above or less than 80 percent of the average lateral stiffness of the three storeys above. This is often referred to as
Seismic Analysis of Buildings with a Soft Storey Using Pushover
a)
b)
c)
d)
29
Fig. 1. Devastation of a soft storey building in Turkey during the 2023 earthquake.
stiffness irregularity. The second case deals with discontinuity in capacity. The storey strength is less than 80 percent of that in the storey above. The storey strength is the total strength of all seismic-resisting elements sharing the storey shear for the direction under consideration [5]. One of the characteristics of building construction in Turkey is that the residential and commercial areas are located in the same building, consequently leading to the creation of a soft storey. Investigations after the Izmit and Duzce earthquakes both of which happened in 1999, only a few months apart, having a magnitude of Mw = 7.4 and Mw = 7.2, respectably, conducted by [6] discovered that 85–90% of all collapsed and damaged buildings had soft storeys. After their investigation, they proposed to call
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it a dangerous storey instead of a soft storey [6]. The collapse of the soft storey building in Turkey after the devastating 1999 earthquake is shown in Fig. 2 [7].
Fig. 2. Collapse of soft storey building in Turkey after the 1999 earthquakes [7].
2 Elaborated Cases In this paper reinforced concrete (RC) buildings having three, six, and twelve floors were analyzed. The buildings were designed according to Eurocode standards. These structures represent typical low, medium, and tall RC buildings. They are made as symmetrical RC frame constructions, which have 4 grids of 5 m each in the X and Y directions. Since floor height is an important parameter in irregular buildings, all floors are 3 m high, except for the ground floor which is 4.5 m high. The RC structures are
Fig. 3. 3D views of the analyzed structures.
Seismic Analysis of Buildings with a Soft Storey Using Pushover
31
used as residential-commercial buildings. The three-dimensional views of the modeled and analyzed structures in SAP 2000 [8] are shown in Fig. 3. The elaborated buildings are located in Sarajevo, with floor plan dimensions of 20 × 20 m. The soil on which the building is based belongs to soil category C according to Eurocode 8 [9]. The material from which the RC elements are made is concrete class C25/30 and steel for reinforcement B500A. The thickness of the slabs on all floors is 15 cm. The reference peak ground acceleration (PGA) for the return period of 475 years is 0.18 g according to the BAS National Annex [10]. Dimensions of the columns for a 3-storey building are 35 × 35 cm, for a 6-storey building 45 × 45 cm, and 60 × 60 cm for a 12-storey building. The dimensions of the beams are 25 × 35 cm, 25 × 40 cm, and 35 × 55 cm for, 3, 6, and 12-storey buildings respectively. Reinforcement details were done according to Eurocode standards. The total weight of a building with 3 floors is 1590 t, with six floors it is 2825 t, while for a building with 12 floors, it is 5300 t. The periods of vibration with associated participating masses are shown in Table 1. The first mode of vibration dominates the structure and the maximum inter-storey drift is limited to 2.0%. Table 1. The periods of vibration with associated participating masses. No. of storeys
Period of vibration [s]
Mass participation [%]
3
0.73
95
6
1.04
89
12
1.29
85
Considering that the height of the ground level is 4.5 m and differs from the other floors, which are 3 m, such a difference in the height of the floors is sufficient to cause differences in the stiffness of the floors, and therefore leads to the appearance of a soft storey. Table 2 shows the stiffnesses of all floors of the analyzed 3-storey building. Based on Table 2 and Fig. 4, it can be concluded that irregularity such as the soft storey appeared on the ground floor of the building. The stiffness of the ground level is less than 70% of the stiffness of the 1st level. To be more precise, the stiffness of the ground level is less by about 40% compared to the stiffness of the adjacent storey. Table 2. Floor stiffnesses for a 3-storey building. No. Storey
Height [m]
X direction [kN/m]
Stiffness ratio
3 (2nd level) 2 (1st level)
10.5
162,398.483
1
7.5
159,783.956
0.98
4.5
94,312.027
0.59
1 (ground level)
32
N. Ademovi´c and A. Muratagi´c
Fig. 4. 3D views of the analyzed structures
3 Nonlinear Analysis To determine the seismic behavior of the building, a non-linear static pushover method is applied. The static incremental pushover method is a series of nonlinear incremental static analyzes that are performed to determine the lateral deformation and damage state of structural elements while keeping the gravity load constant. Since the gradual pushover method can reliably and quickly determine the nonlinear response due to the simplicity and low numerical complexity of the method itself, scientists often use it instead of elastic static or dynamic methods [11–17]. Nonlinear dynamic analyisis of a reinforced concrete frame structure was presented in [17] and as well the ground motion records were scalled conducting Incremental dynamic analysis. A compareion of Equivalent Lateral Force (ELF) method, Modal Response Spectrum (MRS) analysis, and Linear Response History (LRH) analysis was conducted by [18] for a sixth-story reinforced concrete building. 3.1 Modeling of Plastic Hinges Material nonlinearity and behavior after yield strength can be modeled using discrete or fiber-plastic hinges. Each plastic hinge is modeled as a discrete point. Plastic hinges can be assigned to a structural element at any location along that element. For non-linear analysis of the structure, models with plastic moment hinges concentrated at the ends of the beam elements can be used, which means that each plastic hinge is modeled in discrete locations. All plastic deformations, whether displacements or rotations occur only in the plastic hinge. It is necessary to point out that in recent times fiber-plastic hinges are also used, which can be used to capture the spread of inelastic deformations along the beam element. Models of distributed plasticity along the element record better and more realistic results, however, the calculation is much more complex and demanding compared to discrete hinges.
Seismic Analysis of Buildings with a Soft Storey Using Pushover
33
The idealized curve (relationship between force and deformation) of the plastic hinge is shown in Fig. 5. It is defined in the software packages SAP 2000 [8] and it defines the plastic hinges of structural elements, and for its full definition, it is necessary to define the marked points. Point A refers to the no-load condition. When the strength of the joint is achieved, the yielding of the element occurs. After point B, the force in the hinge changes with respect to the deformation. When the displacement value reaches point C, the plastic hinge reaches the failure region. Finally, the plastic hinge completely loses its strength, and the moment of structural failure occurs after reaching points D and E [7] as presented in Fig. 5. There are three stages marked between points B and C. Immediate occupancy (IO) is very close to point B (start of the material yielding). In this stage, the damage is light and the structure retains most of its original strength and stiffness. There may be minor cracking on the structural members. The second stage is defined as life safety (LS) located in the middle of points B and C. At this stage, the structure experienced significant damage and it may have lost a big portion of its strength and stiffness. The last stage is marked as collapse prevention (CP). At this stage, the structure has experienced a drastic level of damage and diminutive strength and stiffness remain. The building is unstable and is near collapse [16, 19].
Fig. 5. Idealized diagram - the relationship between force and deformation [8].
The properties of plastic joints in the SAP2000 software package are defined according to FEMA 356 regulations [20]. The behavior of the plastic hinges depends on the cross-section, material characteristics, degree of reinforcement, and value of normal and transverse forces. Plastic hinges can be either automatically defined or user-defined. For a user-defined plastic hinge, it is necessary to estimate the rotation at the yield point, the rotation in the plastic region as well as the ultimate (limit) rotation (θy , θp , θu ). When automatic settings are used, the program combines its built-in criterion with the defined cross-sectional properties for each element to form the final plastic hinge characteristics. The main advantage of this procedure is that it takes less time to define the behavior because it is not necessary to define each joint individually [21]. Three models for the three different building heights were created and analyzed. The nonlinear behavior of the structure was defined using the lumped and distributed plasticity models. The distributed plasticity (DP) model is achieved with fiber plastic hinges. These plastic hinges simulate the propagation of plasticity along the element
34
N. Ademovi´c and A. Muratagi´c
length and across the section, unlike discrete plastic hinges where plastic behavior occurs only at one point. In the fiber model, the cross-section is divided into several types of fibers that represent different materials with their characteristics. The nonlinear behavior of the fiber plastic hinges is obtained by integrating the stress-dilation (σ-ε) constitutive law of each individual fiber into which the cross-section is divided. The number of fibers for a reinforced concrete section can be different, depending on the shape of the crosssection and the layout of the reinforcement. The reinforced concrete cross-section is divided into three areas unconfined concrete fibers, confined concrete fibers, and steel fibers. Using this method structural members are discretized into many line segments (fibers), meaning each segment of the cross-section is additionally subdivided into several finite elements [22]. Each fiber is linked with a uniaxial stress-strain relationship, and then the sectional behavior is attained by the integration imposing the Navier-Bernoulli hypothesis [23–25]. The location of the hinges must be chosen to be representative of where the largest moments occur so that the hinges are activated appropriately and the structure behaves as expected. The behavior of the structure should be as realistic as possible to actual behavior. For the first model, a standard input for a rectangular section was used, while for the other two, the cross-sections were created using the section designer. Plastic hinges (M3) are assigned to beams that are placed at the appropriate positions (0.05 and 0.95 of the relative length of the beam), and the columns are associated with interaction plastic hinges (normal force and bending moments: P-M2-M3), using the same material law. The moment–rotation relations and the acceptance criteria for the performance levels of the hinges were obtained from FEMA 356 guidelines [20]. Table 3 shows the three models used in this paper and their main characteristics. Table 3. Main characteristics of the models. Model
Cross-section
Hinge type
Plasticity model type
1
Frame sections
Automatic
Concentrated
2
Section designer
User defines
Concentrated
3
Section designer
Fiber
Distributed
In addition to material nonlinearity, geometric nonlinearity was also taken into account in the calculation. The P-Delta effect, known as geometric nonlinearity, is a nonlinear effect on a structure when the geometry of the structure changes due to loading. The P-Delta effect involves large lateral forces applied to small displacements making it significant in tall buildings. When these lateral forces are combined with axial loading, they can cause these axial forces to act eccentrically. The deformations caused by the P-Delta effect can be significant and cause second-order effects [8]. There are two types of P-Delta effects. The P-δ effect is related to the local deformation with respect to the finite element between the nodes. P-δ occurs on slender columns and at extremely large displacement values. The P- effect is more critical for nonlinear modeling and analysis, as it takes into account the entire structure, and thus gravity loading will have a more significant impact under lateral loading. If both irregularity and the P-delta effect are
Seismic Analysis of Buildings with a Soft Storey Using Pushover
35
taken into account according to [26] more structural elements will have plastic hinges, which will reduce the rigidity of the structural elements, and consequently, degradation of the overall rigidity will be noted. It has been reported that for tall structures variations in the stiffness reached up to 60%, and the overall stiffness of the structure was highly reduced [26] once the P-Delta effect was taken into account. Steel and RC buildings of various heights were investigated by [27] indicating that the crucial factor for the incorporation of the P-Delta effect is the height of the building. 3.2 Modeling of the Structures (Models 1, 2, and 3) Regarding the material characteristics of the model, it should be noted that only one material can be selected for the cross-sections in Model 1. In this case, concrete was chosen as the material of the model, while the reinforcement was neglected. In the models, Mander’s [28] model of unconfined (Model 1) and confined concrete (Models 2 and 3) was chosen (Fig. 6a). The stress-strain curve in this model computes the compressive strength and ultimate strain values as a function of the confinement (transverse reinforcing) steel [8]. In the program package SAP 2000 there are two types of parametric stress-dilation (σ-ε) diagrams for reinforcing steel, simplified (simple) and Park’s model. The two models are identical except in the hardening regime, where the simple model uses a parabolic shape and the Park model uses an empirical shape. The simplified stressdilation model of reinforcing steel has four different regions, namely the elastic region, the ideal plastic region, the hardening region, and the softening region [29]. In models 2 and 3, a simplified (simple) steel model was chosen (Fig. 6b).
a)
b)
Fig. 6. a) Unconfined and confined concrete Mander’s model; b) simple model for reinforcement.
The formulation of cross-sections for the three models is presented in Fig. 7 (a,b, and c).
36
N. Ademovi´c and A. Muratagi´c
a)
b)
c)
Fig. 7. a) Model 1; b) Model 2; c) Model 3.
For user-defined plastic hinges, the moment-rotation ratio (M-θ) was first determined. The moment-rotation ratio (M-θ) at specific points was calculated using FEMA 356 regulations [20]. According to the instructions from the FEMA 356 regulations, the moment and the associated rotation were determined at five points A, B, C, D, and E (Fig. 5). For each cross-section, it is necessary to calculate the moment of the first crack, the moment of the limit bearing capacity and the value of rotation at those points [30]. Based on the calculated values, the moment-rotation (M-θ) curve for M3 of the plastic hinge for beams and the moment-rotation (M-θ) curve P-M2-M3 for columns plastic hinge was obtained (Fig. 8 a and b).
a)
b)
Fig. 8. a) Moment-rotation (M-θ) curve for M3 of the plastic hinge of the beam; b) Momentrotation (M-θ) curve P-M2-M3 of the column plastic hinge [30].
Unlike the first two models, model 3 uses fiber-plastic hinges. Fiber plastic hinges are used to define the plastic behavior caused by the interaction of normal force and bending moments (Fiber P-M2-M3) along the linear element. The cross-section is discretized into a series of representative axial fibers extending along the plastic hinge. These plastic hinges are elastic-plastic and consist of a group of material points. Each material point represents a cross-sectional part of the same material. The moment-rotation curve (Mθ) of the plastic hinge is not predetermined but is calculated during the analysis from
Seismic Analysis of Buildings with a Soft Storey Using Pushover
37
the stress-dilation curve (σ-ε) of the material points. Depending on the material of the associated surface of the material point, each fiber has its own stress-strain relationship. By integrating the stress-dilation (σ-ε) curve along the cross-section and multiplying it by the length of the plastic joint, the normal force-deformation relations and biaxial moment-rotation relations are obtained. The cross-section of the beam is divided into a total of 134 fibers, of which 6 are reinforcing steel fibers, 64 fibers of unconfined concrete, and 64 fibers of confined concrete. The cross-section of the column (Fig. 7c) is divided into a total of 138 fibers, of which 10 fibers of reinforcing steel, 64 fibers of unconfined concrete, and 64 fibers of confined concrete.
4 Results Pushover analysis was carried out on buildings having 3, 6, and 12-storeys for the three different models. The capacity curves for the various analysis is presented in Fig. 9. From the results shown in Fig. 9, it can be observed that the models using discrete hinge locations have the same initial stiffness (for the same storey building), except for the model which uses fiber plastic hinges, which have a lower initial stiffness, and the transition to the nonlinear phase is less pronounced. This is a consequence of the application of a different material behavior model and the propagation of nonlinear deformations along the element, in contrast to the model with concentrated plastic hinges where the occurrence of nonlinear deformations is possible only at the ends of the elements. Similar behavior was observed [31].
Fig. 9. Capacity curve for the analyzed buildings implemented in the three models.
It is visible that all models have a linear response at the beginning and similar or almost the same stiffness, with a noticeable difference for the fiber model in all three cases. After a displacement of 25 mm for the 3-storey building the stiffness decreases, this is best seen in models 1 and 2 where discrete plastic hinges were used. This can be
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N. Ademovi´c and A. Muratagi´c
explained by the linear-elastic behavior of concentrated plastic hinges before the material starts to yield. Models 1 and 2 for a 3-storey structure, after reaching the displacement in the value of 35 mm and the lateral load of 1600 kN, exhibit a non-realistic behavior. In model 3 with fiber plastic hinges, a less pronounced plasticity region is observed. Similar behavior of the 6 and 12-storey buildings is observed (Fig. 9), with a noticeable difference in stiffness and strength. For a 6-storey building, the stiffness of the model decreases after a displacement of 40 mm, and for a 12-storey building, this occurs once a displacement of 65 mm is reached. Softening branch is evident in the case of models which have used fiber hinges. The maximum values of the loads for all the models are presented in Table 4. In this case, model 1 was taken as the reference model. It can be seen that the largest difference in the peak load is obtained in the case of the 12-storey building. Table 4 compares the peak loads of all models. Table 4. Peak loads of 3 models. Model
Number of storeys
Cross-section
Peak load [kN]
Difference [%]
Frame sections
1809
+9.2
2
Section designer
1945
+17.4
3
Section designer
1657
–
1
3
Frame sections
4029
+21.5
2
Section designer
3956
+19.3
3
Section designer
3316
–
1
6
Frame sections
9637
+26.6
2
Section designer
9476
+24.4
3
Section designer
7615
–
1
12
The formation of the plastic hinges in the 3, 6, and 12-storey buildings is shown in Fig. 10(a,b, and c). For a 3-storey building, the first plastic hinges formed at the bottom of the edge columns (first image). The second image shows the step in which a large number of plastic hinges were formed at the top and bottom of the columns located at the level of the soft storey. Unlike the first image, in this step, the appearance of plastic hinges can also be observed at the ends of the beams. The last step of the pushover analysis is shown in the third image, where it is noticeable that plastic hinges in the soft storey reached the stage of collapse. This shows that the limited capacity of the structure has been reached, and it is evident that the structure is no longer able to absorb the forces from the earthquake (Fig. 10a). For a building of 6 and 12 storeys, similar behavior is noticeable in the first steps, where only the formation of plastic joints occurs at the bottom of the edge columns located on the ground floor, i.e. at the location of the soft storey. Unlike a 3-storey building where, where all plastic hinges were concentrated only at the level of the soft storey (Fig. 10a), in the case of a taller structure such as 6- and 12-storey buildings (Fig. 10b and c), there is an expansion of the plastic hinges
Seismic Analysis of Buildings with a Soft Storey Using Pushover
a)
39
3 storey building
b) 6 storey buildings
c) 12-storey building Fig. 10. Appearance of plastic hinges in pushover analysis for a building: a) 3 storey-building; b) 6 storey-building; c) 12 storey-building.
40
N. Ademovi´c and A. Muratagi´c
along the height of the structure which can be seen at the top and bottom of the columns and the ends of the beams. In the last steps of pushover analysis for 6- and 12-storey buildings, plastic hinges reach the collapse stage on the soft storey, as is the case for a 3-storey building. The structures are no longer safe and are unable to absorb earthquake forces. Due to the irregularity of the building, such as the presence of a soft storey, and additionally, a P-Delta effect taken into account, there is an unfavorable formation of plastic hinges. Plastic hinges are formed on the soft storey and at the top and bottom of all columns of that floor. This ultimately leads to the instability of the entire system. The most favorable formation of plastic hinges is at the ends of the beams and at the bottom of the columns, which corresponds to the recommended design of the frame according to the principle of strong columns - weak beams, which is not the case in these models. The inter-storey drift represents the displacement of one floor in relation to the displacement of the floor above or below normalized by the height of the floor. This parameter is considered an important indicator of structural behavior in performance-based seismic analyzes used to assess structural damage, this may be since the displacement of the floors beyond certain levels can cause damage to structural elements. As shown in Fig. 11, the inter-storey displacement distribution reached its maximum value at the soft storey level. There is a sudden change in the slope of the inter-storey displacement due to the irregularity of the construction at the location of the soft storey. This can be the reason for the structure collapse due to earthquakes where the columns, in the case of a soft storey, are exposed to large deformations and the formation of plastic hinges at the top and bottom of the vertical elements. A maximum inter-storey drift in the amount of 0.0064% was obtained at the height of 4.5 m where the soft storey is located for the 12-storey buildings.
Fig. 11. Inter-storey drift of a 12-storey building during 9 characteristic steps.
Seismic Analysis of Buildings with a Soft Storey Using Pushover
41
5 Conclusion The results of the elaborated structures once again confirmed all the issues which arise if a structure is constructed with a soft storey. Additionally, as the height of the building increases it is inevitable to take into account the P-delta effects as the stiffness of the structure reduces significantly. The analyzed structures represent typical low, medium, and tall RC buildings. Eurocodes were implemented for the design and reinforcement detailing, while FEMA 356 was used for defining the plastic hinges which were used in the pushover analysis. It should be mentioned that it is quite time-consuming to create user-defined hinges, whereas usage of the Section Designer enables the modeling process to be much faster and leads to good results when hinges are applied in an automatic fashion. The most time-consuming is required for the creation of the fiber hinges. Models with discrete plastic hinges showed almost the same initial stiffness while the lower stiffness was observed for the models which used fiber plastic hinges. This could be commented to different material behavior models and the propagation of nonlinear deformations along the element. The difference in the peak load values was the smallest for the 3-storey building, while the largest difference was obtained for the 12storey building. It is interesting to note that for medium and high-rise building a larger difference was noted for model 2, which was not the case for low-rise buildings. In low-rise buildings, the concentration of the plastic hinges is located in the location of the soft storey while for the buildings with a larger number of storeys propagation of the plastic hinges to the higher floors is observed as well. The inter-storey drift as an important indicator of structural behavior was calculated in order to assess potential structural damage. As a result of earthquakes of stronger intensities that cause large losses, there is a need for continuous analysis and elaboration of the existing RC buildings with different irregularities. The earthquake in Turkey once again reopened a well-known problem with the RC building with soft storeys.
References 1. Pavi´c, G., Hadzima-Nyarko, M., Bulaji´c, B.: A contribution to a UHS-based seismic risk assessment in croatia—a case study for the city of Osijek. Sustainability 12(5) 1796, 24 (2020). doi:https://doi.org/10.3390/su12051796 2. French, S. P.: Connecting physical damage to social and economic impacts. In: 17th U.S.Japan-New Zealand Workshop on the Improvement of Structural Engineering and Resilience, pp. 4-2–1–4-2–8. Queenstown, New Zealand, November 12–14 (2018) 3. Ghalimath, A.G., Hatti, M.A.: Analytical review of soft storey. Int. Res. J. Eng. Technol. 2(6), 2395–56 (2015) 4. Charleson, D.: Seismic Design for Architects, 1st edn. Elsevier Ltd., Oxford (2008) 5. International Building Code Commentary–Volume I, 1st edn. ICC (2003) 6. Do˘gan, M., Kiraç, N., Gönen, H.: Soft storey behaviour in an earthquake and samples of Izmit-Duzce. In: ECAS 2002 Uluslarararası Yapı ve Deprem Mühendisli˘gi Sempozyumu, 14 Ekim 2002, Orta Do˘gu Teknik Üniversitesi, Ankara, Türkiye, pp. 42–49 (2002). http://www. bupim.com/yayinlar/bupim-pdf/ECAS66.pdf
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Methodology of Ranking of the Main Road Network in the Federation of Bosnia and Herzegovina from the Aspect of Demography and Economic Development as Part of the Climate Resilience Risk Assessment Suada Sulejmanovi´c1(B) , Ammar Šari´c1 , Žanesa Ljevo1 , Emina Hadži´c1 , and Slobodanka Kljuˇcanin2 1 Faculty of Civil Engineering, University of Sarajevo, Patriotske lige 30, 71000 Sarajevo,
Bosnia and Herzegovina [email protected] 2 Technical Faculty, University of Biha´c, Irfana Ljubijankica bb, 77000 Bihac, Bosnia and Herzegovina
Abstract. The core activities of road managers, among others, are preparing plans and programs for the development, reconstruction, renovation, construction, and maintenance of roads and road structures. Climate change and its increasing impact on road infrastructure require additional measures and activities to mitigate the impact of climate change and strengthen the resistance of the road network. Regarding climate change, it is very important to consider all possible impacts when ranking the priority intervention on the road network. Thus, in addition to influences such as natural flood disasters, droughts, fires, landslides, rockfalls, snows, etc., it is essential to consider the aspects of the proximity of critical infrastructure facilities, the size of traffic, socio-economic impacts, and demographics. This paper shows the methodology of ranking the main road network in the Federation of Bosnia and Herzegovina (FBiH) from the demography and economic development aspect as part of the climate resilience risk assessment. Keywords: climate change · risk assessment · road network · demography · socio-economic impacts
1 Introduction Climate change and environmental degradation are resources of structural changes that affect economic activity and, thus, the financial system. Extreme events caused by climate change and the failure to mitigate and adapt to climate change are recognized as some of the highest risks with permanent consequences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 44–52, 2023. https://doi.org/10.1007/978-3-031-43056-5_4
Methodology of Ranking of the Main Road Network in the Federation
45
Climate change adaptation refers to actions that reduce the negative impact of climate change while taking advantage of potential new opportunities. It involves adjusting policies and activities because of observed or expected changes in climate. Adaptation can be reactive, occurring in response to climate impacts, or anticipatory, occurring before the impacts of climate change are observed. In most circumstances, anticipatory adaptations will lower long-term costs and be more effective than reactive adaptations. As the UN has highlighted in the Fifth Assessment Report (AR5) on Climate Change [1] and the Sendai Framework for Disaster Risk Reduction [2], all countries must work on adapting to climate change and increasing resilience. The infrastructure sector is often highlighted as a vital sector. Functional infrastructure is the physical backbone of any society. Without it, it is inevitable to face serious problems. The risks associated with climate change in Bosnia and Herzegovina were highlighted in the 2014 floods that caused more than 20 deaths, left 90,000 people homeless, and caused millions of dollars in damage. These floods damaged bridges, roads, and houses, hampered electricity distribution, and destroyed dams and flood protection infrastructure, emphasizing the need to introduce climate risks into investments in short-term and long-term infrastructure buildings. Although most institutions have established one or more sustainability policies, most still need tools for assessing the influence of climate and environmental risks within their activity. Thus, only a few institutions fully involve climate and environmental risks in their risk management and risk assessment by defining the risk assessment and scenario analysis and including the necessary mitigation measures [3]. However, the increasing involvement of institutions in joint initiatives for developing appropriate methodologies and obtaining the necessary data is noticed. Adaptation to climate change involves actions to help reduce vulnerability to the effects of climate change. Concerning the project roads, adaptation involves activities that ensure the infrastructure can better withstand the physical impacts of climate change. Furthermore, the adaptation measures must be systematic, and their sustainability must be provided by integrating them early into the planning processes, staff training, and budgets. Road network vulnerability assessments provide a means to achieving a comprehensive approach to effectively managing risks by: • • • • • •
identifying the threats posed by geophysical and climate-related events assessing the risk of transport network failures assessing potential damage to network components (e.g., bridge and road links) assessing potential impacts on communities and economies; identifying potential measures to enhance the resilience of transport networks, and undertaking a cost-benefit analysis of potential measures to inform the prioritization of investments [4].
There are several ways to look at the vulnerability of the transportation system, including from the perspectives of people, cars, traffic, infrastructure, and the environment. The term “vulnerability” can be used to describe both the potential for an incident to reduce the use of the transportation system and the physical susceptibility of road users [5]. So, it is important to include all these perspectives in the road climate risk assessment.
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Climate risk assessments of road sections are based on climate change and hazard data. The risks are evaluated according to their estimated frequency and impact. The risk assessment includes a variety of hazards influenced by climate change, such as landslides caused by precipitation, floods, rock falls, wildfire, and snow-covered roads. Also, extreme weather events impact the local environment and livelihoods of local people dramatically. Thus, besides the above-listed hazards, it is essential to include the demography and economic development aspects in the climate resilience risk assessment. So, when determining the priority of an intervention on the road network concerning climate change, it is crucial to consider all potential effects. The proximity of vital infrastructure facilities, the volume of traffic, socioeconomic impacts, and demographics must all be considered together with natural disaster effects like floods, fires, landslides, rockfalls, and so forth. This paper deals with the part of the risk assessment of the main road network relating to demographics and economic development.
2 The Main Road Network in the Federation of Bosnia and Herzegovina The main road network in FB&H spans 2,290.97 km of the main roads. The road network managed by PC “Ceste FBiH” is divided into 511 sections. These sections were used for the risk and criticality assessment results and for preparing a list of interventions. Some of the sections are very short locating in junction areas. Out of the total length of the highway network, about 2.22 km, is macadam. The following table shows the list and length of main roads in FBiH by cantons (Table 1). Table 1. List and length of main roads in FBiH by cantons. Cantons
Length of basic direction
Junctions, ramps, The total length and sections in of existing the main roads opposite direction
[km]
[km]
[%]
[%]
[km]
[%]
01
Una-Sana
414.28
18.5
2.483
5.0
416.76
18.2
02
Posavina
65.01
2.9
0.440
0.9
65.45
2.9
03
Tuzla
216.82
9.7
10.783
21.8
227.61
9.9
04
Zenica-Doboj
149.67
6.7
6.644
13.4
156.32
6.8
05
Bosnia-Podrinje
71.56
3.2
0.387
0.8
71.95
3.1
06
Central Bosnia
258.72
11.5
2.084
4.2
260.81
11.4
07
Herzegovina-Neretva
403.97
18.0
7.444
15.0
411.41
18.0
08
West Herzegovina
170.55
7.6
1.274
2.6
171.82
7.5
(continued)
Methodology of Ranking of the Main Road Network in the Federation
47
Table 1. (continued) Cantons
Length of basic direction
Junctions, ramps, The total length and sections in of existing the main roads opposite direction
[km]
[km]
[%]
[%]
[km]
[%]
09
Sarajevo
136.71
6.1
13.981
28.2
150.69
6.6
10
Canton 10
354.14
15.8
4.021
8.1
358.16
15.6
TOTAL:
2,241.43
49.541
2,290.97
Source: PC “Ceste” FB&H. https://jpdcfbh.ba/bs/aktivnosti/mreza-magistralnih-cesta/37
Fig. 1. Map of main road network managed by PC “Ceste FBiH”.
Figure 1 shows the main road network in the Federation of Bosnia and Herzegovina.
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The core activities of the PC “Ceste FBiH”, among others, are preparing plans and programs for the development, reconstruction, renovation, construction, and maintenance of roads and road structures. Also, activities related to the preparation of studies and projects of rehabilitation, construction, reconstruction, and maintenance of roads and road structures, road protection and maintenance, road safety improvement activities, and taking necessary measures to preserve and protect the environment, as well as efforts to mitigate the impact of climate change and strengthen the resistance of the road network to climate change.
3 Methodology The purpose of establishing Spatial Data Infrastructure (SDI) is to streamline spatial data collection and standardize them to be networked and used well. Linking different spatial data types and their interoperability enables users to conduct complex searches, analyze, and connect to spaces. Unfortunately, there is still no appropriate database model for natural and other disasters in Bosnia and Herzegovina [6]. Thus, the first step is collecting data and developing geospatial data sets. The collected data sets are as follows: 1. 2. 3. 4.
Administrative boundaries,1 Main road network,2 Social-economic data, and3 Demography (see footnote 3).
Based on population data and indicators of socio-economic development by local self-government units in FBiH, an assessment of the criticality of sections of the main road network of the FBiH. The evaluation was carried out using GIS tools. 3.1 Demography Demography data were used to assess the criticality of the strategic importance of each road section in terms of the most significant impact on people and their livelihood. After collecting demographic data for each self-governing unit (SGU) using GIS tools, each section of the road is assigned the corresponding number of points. The population ranged from 1,639 citizens for the local self-government unit Trnovo to 122,390 for the local unit Novi grad Sarajevo. This range is divided into five categories, and for each population range, award points from two to ten (Table 2). 3.2 Economic Development Economic development was initially interpreted in the context of total production growth, i.e., production per capita. However, the experience of the 1950s and 1960s showed that 1 Federal Administration for Geodetic and Property Affairs, https://www.fgu.com.ba/en/. 2 PC “Ceste FBiH”, https://jpdcfbh.ba/. 3 Federal Bureau of Statistics, https://fzs.ba.
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Table 2. Categorization of the SGU according to population 2021. in FB&H. Population range
Awarded points
100,000
10
many developing countries achieved their economic growth targets without a consequent change in living standards. An increase in the production of goods and services and an increase in the level of income in a country does not imply an improvement in the living standards of people since the gross social product is a minimal indicator of economic development that does not include non-economic indicators such as time for rest, access to health services, education, environmental quality, freedom or social justice. Economic development aims to ensure that the maximum number of people enjoy the fruits of development. The growth of the population’s well-being is primarily reflected in social indicators of development such as life expectancy, literacy rate, and access to basic amenities, such as drinking water, etc. The need to include a more significant number of indicators for measuring socioeconomic development led to the development of many different composite indexes. The new methodology for calculating the development index at the level of local SGU is prescribed by “The Regulation on the Calculation of the Development Index of the FBiH” [7]. Prof. Halilbasic, from the Economic Institute in Sarajevo, developed the methodology. According to this methodology, the following indicators are used to compile the index of development of the local self-government units in the Federation of BiH (F BiH): • • • • •
average income per capita (X1), average employment rate (X2), population movement (X3), old population in total population (X4), degree of education of the workforce (X5). The development index (CDI) is calculated using the formula: IC = 0,25 · XC1 +0,20 · XC2 +0,20 · XC3 +0,15 · XC4 +0,12 · XC5
(1)
c = 1, 2, …, m X Ci , (i = 1,2 ,…, 5) are the normalized values of the indicator Xi for the unit c. xi∗
=
xi −xi,min xi,max −xi,min xi,FBiH −xi,min xi,max −xi,min
=
xi − xi,min xi,FBiH − xi,min
(2)
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Based on the development index, the local SGU in FBiH were categorized into five groups with their awarded points, as shown in Table 3. The development index ranged from 0.30 for the local SGU Dobretici to 2.34 for the local unit Centar Sarajevo. Table 3. Categorization of the SGU according to the development index 2021. in FBiH. Development Index range
Awarded points
1.25
10
3.3 Ranking of Main Road Network Sections The joint influence of demography and the development index of local SGU was obtained by adding the assigned points. Using the QGIS tool, the vector layers of the main road network and local SGU were overlapped to determine the risk level of each section. It should be noted that some road sections cross the borders and belong to two local units. In that case, it is necessary to calculate the weighted average of the assigned points for the relevant road section. The calculated impact is divided into five risk levels (Table 4). As a result, a risk map of the F BiH’s main road network was obtained from the joint impact of demography and economic development (Fig. 2). Table 4. The joint influence of demography and the development index of local SGU.
Risk Category Very High High Moderate Low Very low
Awarded points 16-20 12-16 8-12 4-8 0-4
As shown in Fig. 2, the road network sections at the Very-High and High risk levels are about the biggest cities in FBiH, such as Sarajevo (capital city), Tuzla, Zenica, Mo-star, and Bihac. Figure 3 shows the riskiest road sections around the major economic centers in FBiH. As we can see, the risk level varies from 14 points for the city of Bihac to over 18 points for the capital city of Sarajevo. Around the central city areas, we can see yellow-colored
Methodology of Ranking of the Main Road Network in the Federation
51
Fig. 2. Risk map of the joint impact of demography and economic development on the main road network FBiH.
Fig. 3. The main road network FBiH risk map around the main cities.
roads, which means there is a moderate level of risk in the broader area of the mentioned cities. The low level of risk mainly refers to the southwestern part of FBiH.
4 Conclusion Environmental deterioration and climate change are causes of structural changes that impact every state’s financial system and economic activity. Extreme weather events caused by climate change and the lack of adaptation are acknowledged as some of the greatest threats with long-term repercussions. This paper demonstrates how to rank the main road network of FBiH, considering demographic and economic factors in climate resilience risk assessment.
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As one of the fundamental problems, the lack of unique geospatial databases was observed as a prerequisite for an adequate assessment of climate change risk on the road network. Also, one of the essential questions is which indicators to consider in the socioeconomic development of some areas. This paper provides an answer to that question as well. As mentioned below, the need to include a more significant number of indicators for measuring socio-economic development led to the creation of the composite index. The “Regulation on the Calculation of the Development Index of the FBiH” [6] by Professor Halilbasic is recommended for assessing the impact of socio-economic development on the climate risk assessment of the FBiH main roads network. As a result, a risk map of the F BiH’s main road network was obtained from the joint impact of demography and economic development. The methodology would be helpful to companies that manage the road network. This result is needed to be taken into account together with all other climate impacts to assess the risk level and range the priority on the road network interventions within climate resistance adaptation measures. In the next step, data on social impacts should be included to assess the criticality of the strategic importance of each road section in terms of the most significant impact on people and their livelihood, with particular attention to vulnerable population groups. Difficulties that can be expected are the need for geospatial data for this type of analysis.
References 1. Pachauri, R.K., Meyer, L., Brinkman, S., Kesteren, L.V., Leprince-Ringuet, N., Boxmeer, F.V.: Fifth Assessment Report, AR5 Synthesis Report: Climate Change 2014, Intergovernmental Panel on Climate Change, Geneva (2015). https://www.ipcc.ch/assessment-report/ar5/. Accessed 21 Jan 2023 2. Sendai Framework for Disaster Risk Reduction 2015–2030, United Nations, Geneva (2015). https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030. Accessed 21 Jan 2023 3. Vodiˇc o klimatskim i okolišnim rizicima, European Central Bank (2020). https://www.bankin gsupervision.europa.eu/ecb/pub/pdf/ssm.202011finalguideonclimate-relatedandenvironment alrisks~58213f6564.hr.pdf. Accessed 21 Jan 2023 4. Dzebo, S., Saric, A., Reeves, S., Ljevo, Z., Hadzic, E.: Flood impact and risk assessment on the road infrastructure in federation of Bosnia and Herzegovina. In: Avdakovi´c, S., Mujˇci´c, A., Mujezinovi´c, A., Uzunovi´c, T., Voli´c, I. (eds.) Advanced Technologies, Systems, and Applications IV -Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT 2019), LNNS, vol. 83, pp. 276–289. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24986-1_22 5. World Bank Group, Supporting Road Network Vulnerability Assessments in Pacific Island Countries (2018). https://www.gfdrr.org/sites/default/files/publication/ACP-EU%20NDRR% 20-%20transport%20knowledge%20note%20-%20supporting%20road%20network%20vuln erability%20assessments%20in%20PICspdf.pdf. Accessed 15 March 2023 6. Kljucanin, S., Rezo, M., Dzebo, S., Hadzic, E.: Spatial Data Infrastructure in Natural Disaster Management. Technical Journal 15(4), 455–461 (2021) 7. Uredba o izradi indeksa razvijenosti Federacije Bosne i Hercegovine, Službene novine FBIH, broj 17/19, (2019). https://propisi.ks.gov.ba/sites/propisi.ks.gov.ba/files/uredba_o_i zradi_indeksa_razvijenosti_u_fbih_sl_novine.pdf. Accessed 23 Jan 2023
Methodology of Traffic Safety Management at Railway Crossings in Bosnia and Herzegovina Sanjin Albinovi´c1(B)
, Suada Sulejmanovi´c1 , Ammar Šari´c1 Žanesa Ljevo1 , and Kerim Bijedi´c2
, Mirza Pozder1
,
1 Faculty of Civil Engineering, University of Sarajevo, Patriotske lige 30, 71 000 Sarajevo,
Bosnia and Herzegovina [email protected] 2 Design and QC, Džemala Bijedi´ca 25-D, 71 000 Sarajevo, Bosnia and Herzegovina
Abstract. The most important task of the traffic infrastructure manager is to ensure traffic safety for all participants in the traffic. The railway crossings at the same level as the road (in the following text, “Level Crossings” – LC, “Railway Crossings” - RC) generally represent locations with more traffic safety risks and the possibility of traffic accidents. Considering the significant differences in masses of road and rail vehicles, and the large brake lengths needed to stop trains, the consequences of such accidents are generally serious, with significant material damage, injuries, and fatalities. For the above reasons, when planning and designing new roads or railways, and during the maintenance and reconstruction of existing ones, it is necessary to pay great attention to these places and take adequate measures to prevent accidents. In Bosnia and Herzegovina, there is no unique methodology for assessing traffic safety at a level crossing (LC) and making decisions about measures to improve traffic safety at an LC. Since Bosnia and Herzegovina is at the top of the European countries regarding the number of accidents per train kilometers on LC, it is evident that the approach to solving these traffic safety problems must be completely changed. This paper will present a methodology for assessing traffic safety at LC using modern geospatial databases and multi-criteria analysis when making decisions about measures to increase traffic safety at existing and new crossings. Keywords: Level crossings · Traffic safety · Geospatial databases
1 Introduction Railway infrastructure managers constantly receive requests to construct new level crossings or reconstruct existing ones from local self-governments or individuals. The main reasons for these requests are the need to increase traffic safety at existing level crossings or because the development of a specific area has led to the need to build a railway crossing that will enable the smooth communication of the local population. In the first case, based on road and rail traffic volume data, it is necessary to check whether the appropriate type of railway crossing (LC, overpass, or underpass) and the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 53–63, 2023. https://doi.org/10.1007/978-3-031-43056-5_5
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appropriate traffic safety measures for the LC (active or passive protection), have been applied. Also, the chosen type of railway crossing must be designed, built, and maintained in accordance with current regulations. These conditions must also be satisfied in the case of the construction of the new railway crossings. Another essential requirement is to satisfy all users’ needs by choosing the appropriate location of the new crossing. This implies the least possible time losses due to longer journeys to crossings and staying at crossings while ensuring all requirements regarding safety for users. However, a significant factor in choosing the location of a new LC is the provision of requirements related to the smooth and efficient development of railway traffic, and it concerns the mutual distance between LC. According to the current legislation (Regulation 1) in the Federation of Bosnia and Herzegovina (FBIH) [1], building new level crossings on main lines is not allowed, which can be a big problem in developing some urban regions. Therefore, it is necessary to approach the solution to this problem very cautiously. It is essential to consider all possible scenarios (facts, criteria) and make the correct decision based on scientific methods (e.g., multi-criteria evaluation). One of the possible solutions that need to be considered is the replacement of existing level crossings that do not meet the necessary criteria with newly designed ones that meet all the required standards. When considering all possible solutions, it is necessary to pay attention to the applied geometric elements of the railway and road, that is, whether these elements were selected or built in accordance with the recommendations given in the current regulations [1–3]. In this regard, the problem is also a certain inconsistency of current regulations in the territory of Bosnia and Herzegovina (BIH). Several legal regulations treat the problem of crossing the railways and the roads with different conditions that need to be met regarding traffic safety. In the Federation of Bosnia and Herzegovina territory, the Rulebook on level crossings (Rulebook 1.) [1] is applied. At the same time, at the state of Bosnia and Herzegovina level, “Rulebook 322” was adopted - (Rulebook 2.) [2], which does not yet have practical application. The main goal of this paper is to create a unique methodology for managing railway crossings from the aspect of traffic safety in two basic steps: level-crossing risk assessment (evaluation of traffic safety) for existing LC and choice of appropriate measures (interventions) to improve the current state (traffic safety) of LC.
2 Methodology for Management of Traffic Safety at the Railway Crossings Several factors can lead to an increased risk of traffic accidents at railway crossings, which are usually classified as factors: engineering infrastructure (roads and railways characteristics), environment, traffic, and the human factor. The mutual connection of these factors is also significant. For example, any change in traffic conditions (increase in traffic volume or the number of freight vehicles) or spatial (construction of buildings or settlements near RC) may lead to the need to change existing engineering solutions (reconstruction of existing RC or construction of new ones).
Methodology of Traffic Safety Management at Railway Crossings
55
So, many parameters/data (traffic volume, location, geometric elements, data on accidents, etc.) must be considered when analyzing existing LC and determining criteria for constructing the new railway crossings. Very often, traffic infrastructure managers have a problem with the indiscriminate way of collecting and processing this data (which usually does not exist in digital form or is in an inadequate form), so it takes a lot of time to even get to that data. Given that making correct decisions about interventions (reconstruction, removal, or construction of a new LC) on the railway network depends to the greatest extent on the existence and quality (usability) of this data, it is necessary to form a unique and comprehensive geospatial database that will serve as a basis for decision making. In general, the Spatial Data Infrastructure (SDI) establishment implies creating an environment enabling a wide range of users to access, use and securely share spatial data. By implementing SDI, users save resources, time, and effort around collecting new datasets by avoiding duplication of data production and maintenance costs and having the ability to integrate their own data with other datasets. Using SDI, an efficient railway infrastructure management model can be developed [5]. The proposal of the structure and method of creating the database is given in the framework of the case study (Sect. 4). In addition to forming a geospatial database, it is essential to determine the key criteria (and their weights) for choosing an adequate level crossings solution. In the end, the choice of the optimal solution is made, which includes the selection of the optimal location and type of level crossing that is the most favorable from the aspect of traffic safety and requires minimal investment. Figure 1 shows the methodology algorithm for improving the management of traffic safety at railways crossings.
MANAGEMENT OF RAILWAY CROSSINGS
Planning of interventions (works) at railway crossings Existing level crossings (maintenance, reconstruction)
New railway crossings (construction)
Data collecting (geometry, traffic volume, location - the mutual distance between crossings, area population,...)
CREATING A GEOSPATIAL DATABASE Defining criteria (and their weights) for interventions
Traffic
Spatial
Construction
Safety
CHOOSING OF OPTIMAL SOLUTION - MCA
Location
Type of crossing Level crossing (LC) Underpass/Overpass
Type of safety systems Active Passive
Fig. 1. Methodology for evaluation of traffic safety at a level crossing in BIH.
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3 Definition of Criteria for Interventions at Railway Crossings After data collection and evaluation of railway crossings from traffic safety based on influential factors (and/or recorded data on accidents), in case of unfavorable results (increased risk of accidents), it is necessary to provide specific measures (interventions) to improve traffic safety. There are various measures to increase traffic safety [6, 7], but ensuring the applied actions under valid laws and regulations is indispensable. According to Rulebook 1 [1], construction of new LC on main railways in the FBIH is not allowed, except when the abolition of existing RC reduces their number. According to Rulebook 2 [2], the construction of new crossings is allowed, subject to the carrying out of certain conditions. In both Rulebooks, the minimum distance (locations) between two neighboring level crossings (DBLC) depended on the railway category (Main or Other) and road category (Main, Regional, Local), the posted speed at the railway section (Speed), traffic volumes at the railway (Number of Trains - NoT) and roads (Annual Average Daily Traffic- AADT) and local conditions. The criteria for DBLC according to Rulebooks 1. and 2. are shown in the Table 1. Table 1. The criteria for DBLC according to Rulebooks 1. and 2. Railway category
Main [1]
Speed (km/h)
>100
Other [1] ≤100
AADT(veh/day) Length(m)
>2500
>2000
>1000
Extraordinary1
>2000
>1500
>700
Other [2] >100
70–100
≤70
2500
2500
2500
>1500
>1000
>700
The railway crossing must be made with an under/overpass if the road is of the highest rank (motorway) or for large traffic volumes (>7000 veh/day), or if there is frequent railway traffic (120 trains at a single track, or 240 trains at a double-track railway), or the length of the accumulation of road vehicles is such that it endangers the emptying of the road crossing [2]. Therefore, the choice of type of crossing is most influenced by the traffic criterion, which includes the traffic volume and the category (rank) of roads and railways, which are also crucial for choosing the traffic safety systems (TSS) for LC (Fig. 2). One of the types of active protection must be applied for all crossings where the minimum required visibility between the road and the railway (visibility triangle) cannot be provided. In addition to the mentioned types of traffic safety systems (Table 2) on the railways in Bosnia and Herzegovina, there are LC with special measures (manual security) and LC for pedestrians and cyclists with fences or other devices. Regardless of the applied type of traffic safety systems at the crossing, basic construction and traffic conditions must be carried out by regulations [1, 2], and [4]. The types of TSS for LC applied at railways in B&H are shown in the Table 2. 1 If the construction of connecting roads is complex due to unfavorable terrain conditions.
Methodology of Traffic Safety Management at Railway Crossings
57
Table 2. Type of traffic safety systems for level crossings. TSS type
Type of traffic safety systems
Railway category
Roads category
Pasive
traffic signs on the road and visibility triangles
M,O
L,O
Active 3
light traffic signs and traffic signs on the road
M,O
R,L,O
Active 2
automatic half-barriers and warning light
M,O
M,R,L,O
Active 1
with automatic barriers and traffic signs
M,O
M,R,L,O
In addition to the already mentioned traffic conditions, it is also necessary to provide the crossing area with the installation of adequate traffic signals (Fig. 2), where they must be minimally equipped with “st. Andrew’s Cross” and “Stop” signs.
Fig. 2. Traffic signs for railway crossings.
At crossings with active protection, it is necessary to place additional traffic sign with the type of protection applied. From the construction aspect, the following conditions must be carried out: 1. The carriageway width in the area of the crossing must be at least 5.5 m at the existing level crossing or at least 6 m at the new one. 2. The road surface must be level with the upper edges of the rails for a length of at least 1m on both sides of the track, measured from the railway track’s or the end track’s axis when the road crosses two or more railway tracks. 3. The roads in the area of the crossing must not have a longitudinal slope greater than 3%. 4. The crossing angle between the railway and the road in the crossing area must be 90°. Exceptionally, it can be lower, but not lower than 60°, with the consent of the competent authorities. 5. The distance between the road crossing and the railway crossing must be greater than 25 m; that is, a shorter distance is possible only with the consent of the competent ministry. In addition to the mentioned basic construction and traffic requirements, at crossings with active protection, it is necessary to carry out some additional requirements related to the functionality of electrical devices and installed equipment and traffic signs. At crossings with passive protection, it is crucial to ensure visibility between the road and the railway through the visibility triangle.
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Increasing traffic safety can, in some cases, be achieved by applying low-cost measures (improvement of traffic signaling and visibility, replacement of part of the infrastructure and equipment, etc.) without using measures that require more significant investments (change of type of traffic safety systems complete reconstruction of crossings), or the most expensive (change of location and construction of new crossings or underpass/overpass.) which are usually necessary in the case of a significant increase in traffic volume. Therefore, it is essential to objectively analyze all the influencing parameters for choosing the appropriate type of railway crossing and the applied traffic safety systems, and other components (infrastructure, location, etc.) that affect traffic safety. The methodology for choosing the optimal railway crossing solution according to all the stated requirements/conditions by the valid regulations (rulebooks) is shown in the following figure (Fig. 3).
STEP 1. Choosing of crossing method and level crossing traffic safety system TRAFFFIC CRITERIA (AADT, road/rail category) – Table 1.
AADT >7000 veh/day if NoT > 120(ST) or 240(DT) I class - road category (highway)
Railway crossing with overpass or underpass
if not Table 2. With: Main road – active 1,2 Regional road – active 2,3 Local and other - passive
LEVEL RAILWAY CROSSING
Type of traffic safety systems
Active 1. LC with automatic barriers and traffic signs 2. LC with automatic half-barriers and warning light 3. LC with light traffic signs and traffic signs on the road
Passive Traffic signs on the road and visibility triangles Traffic signs
STEP 2. Choosing (checking) the level crossing location – DBLC TRAFFFIC CRITERIA (AADT, road/rail category) – Table 1.
Type of roads
Main roads Other roads
Distance (m) (Table 1.)
Speed (km/h)
STEP 3. Checking the condition of the infrastructure at the LC TRAFFFIC & CONSTRUCTION CRITERIA
Basic condition (all type of safety systems)
Special condition (for active)
Special condition (for pasive)
Fig. 3. Methodology for choosing the optimal railway crossing solution.
Methodology of Traffic Safety Management at Railway Crossings
59
4 Case Study - Evaluation of Traffic Safety at Level Crossings on the Section of the “Sarajevo - Doboj” Railway 4.1 Creation of a Geospatial Database The QGIS software was used to create the geospatial database. The first step was modeling the “Rajlovac-Doboj” railway section, defined as a “shape” file. In the second step, a total of 35 level crossings were drawn on the considered section, in the form of “shape” files of points (in yellow) and triangles of visibility as “shape” files of polygons, for both rulebooks (Fig. 4). The following parameters were entered into the created attribute tables: - Coordinates (X i Y) - Location - Traffic safety system - Type of crossing - Train speed - Section - Road width - Crossing angle (Left/Right) - Road rank - Railway rank - AADT - DBLC - Road crossing distance - Number of accidents - Number of killed persons
Doboj
Zenica
Rulebooks 1. Rulebooks 2.
Sarajevo Visibility triangles
Fig. 4. Railway section with attribute table and visibility triangles constructed in QGIS.
Some data were obtained by measurements in the software (lengths, angles, and distances) or at the location of LC (road width, road slope…), other data were obtained from publications of public companies (crossing insurance, type of crossing, traffic signals etc.), while some of the necessary data (AADT, number of accidents in a longer time interval, longitudinal slope of road over a length of 20 m) were unavailable.
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4.2 Criteria for Evaluating Traffic Safety at LC The defined criteria and their weights are determined based on the previous experience of experts in this field, but they can undoubtedly be subject to discussion. However, this work focuses on the methodology of managing road crossings, and no in-depth analysis of the criteria weights was undertaken. They are shown in Fig. 5. Criteria (weight) Traffic AADT Signs Road cath. Railway c.
6 6 3 3
Spatial DBLC
Σ
18
Safety Accidents
7
7
5.5 abs(3%) 1000
>700
0
2
20
Condition 5.
Fig. 5. Criteria for evaluating traffic safety at LC.
4.3 The Results of the Analysis and Ranking Level Crossing The data analysis shows that there are LC on the section in question that does not meet the minimum basic conditions given by the existing regulations: 1. The minimum road width for existing LC is at least 5.5 m. The available data showed that 21 road crossings (52.5%) do not carry out this condition. 2. The road surface must be level with the upper edges of the rails for a length of at least 1m on both sides of the track – satisfied. 3. The roads in the area of the crossing must not have a longitudinal slope greater than 3% - no available data.
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4. A minimum crossing angle of 60° - 7 level crossings (20%) do not satisfy this condition. 5. The distance between the road crossing and the railway crossing must be greater than 25 m - 19 level crossings (47,5%) do not satisfy this condition. A minimum distance (locations) between two neighboring level crossings (DBLC) - 16 level crossings (46%) do not meet this requirement according to Rulebook 2 (2.000 m/1000 m – Table 1), and 6 level crossings (46%) do not meet this requirement according to Rulebook 1 (1500 m/700 m – Table 1). Another problem that is not adequately solved is the existence of objects in the visibility triangle zone, and reducing these zones, represents a great danger to traffic safety according to Rulebook 2. The building boundaries of the railway are clearly defined in the amount of 20 m from the railway axis on both sides. However, at a large number (14) of railway-road crossings on the considered section of railway, there are objects in the visibility triangle (Fig. 6). This is because of the large area of the visibility field that needs to be ensured, according to Rulebook 2. According to Rulebook 1, the requirements regarding the visibility triangle have been satisfied.
Fig. 6. Example of objects in the visibility triangle.
Figure 7 shows that a certain number of LC does not satisfy more conditions defined by the rulebooks. And in the last step it has been done ranking of LC from the aspect of set criteria and their weights (Fig. 8). The level crossing with the highest total points has the highest priority for solving the problem.
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No. 03 04 05 07 10 11 12 13 14 15 16 17 18
TSS P P A P A A A P P P P P A
Road width 8.5 8.0 8.3 5.2 16.0 7.8 16.2 7.8 5.2 5.2 5.2 18.6 7.8
Crossing angle Road Road rank crossing Right Left 127 90 L 9 85 90 L 18 90 L 11 98 90 L 81 52 90 R 13 91 90 L 29 35 70 R 6 93 90 L 47 97 90 L 23 85 90 L 15 102 80 L 7 158 65 R 120 100 80 O 25
Location
Train speed
Number of
Stationage
Accidents Injuries Killed Kisikana Reljevo Semizovac Gavrica Alica Han Podlugovi II Podlugovi I Ljubnići Fazlića Ljeöevo Kokoruö Banjer Visoko
248.40 246.84 241.67 238.95 234.29 233.65 232.41 231.72 230.92 229.44 228.89 227.38 226.31
2 1 1 3
50 70 50 70 50 50 50 50 50 50 50 70 70
1 1
2 2 1 2
1
3
1
DBLC (m) 1558 838 6760 2836 1426 1243 697 793 1483 546 1512 1073 3056
Road slope Left -7.0 -6.0 -3.0 10.0 0.0 3.0 -1.5 -6.0 -6.0 -6.0 -6.0 3.0 -3.0
Right -5.0 -10.0 -2.5 -5.0 2.5 3.0 -2.3 -6.0 -8.0 8.0 -8.0 3.5 -5.0
Fig. 7. A part of the table with the analysis results.
Rank
No.
1 2 3 4 5 6 7 8 9 10
15 12 10 05 04 23 27 13 14 19
1 1 1 1 1 0 0 0 0 0 0
Traffic 2 3 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 0 1 0
signs Road AADT Signs 4 5 6 cath. 0 0 0 2 3 1 1 1 0 4 1 2 1 1 0 4 1 2 1 1 0 2 1 1 1 1 1 2 3 1 0 1 1 2 2 1 1 1 0 2 2 1 1 1 1 2 3 1 1 1 1 2 3 1 1 1 1 2 2 1
Railway cath. 3 3 3 3 3 3 3 3 3 3
DBLC 7 7 0 0 7 0 0 0 0 0
Condition Accidents Σ 1 2 3 4 5 5 5 5 0 5 45 79 0 5 0 0 5 45 68 0 5 0 0 5 45 61 0 5 0 0 5 35 50 0 5 5 0 5 20 49 5 5 5 0 5 20 46 0 5 5 0 5 20 41 0 5 5 0 0 20 37 5 5 5 0 5 10 37 5 5 5 0 5 10 36
Fig. 8. LC ranking from the safety criteria.
5 Conclusions Regular maintenance of the railway infrastructure is one of the essential prerequisites for the regular and safe flow of traffic according to the projected maximum speeds of the railways. We have witnessed many accidents at railroad crossings, both with material damage and fatalities. There are two main reasons for this: inadequately solved and maintained railway crossings and inadequate behavior of human factors. The paper presents the methodology of railway infrastructure maintenance as well as safety assessment at level crossings. The main drawback to the successful management of road infrastructure is the absence of a national infrastructure of spatial data that enables efficient aggregation, management, and maintenance of spatial data. Another problem is the lack of a unique methodology that would be used as a strategic instrument in managing railway infrastructure. In the paper, an example of the proposed methodology is given through a case study on the section of the Sarajevo -Doboj railway. The analysis showed that no one LC where traffic accidents occurred (based on the ranking of interventions) did not satisfy all the conditions defined by the Rulebook. To satisfy some of the above (missing) conditions, there is often no significant investment (for example, signaling), which may be realized quickly.
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Regardless of everything, it can never be unreservedly claimed that fulfilling these conditions will ultimately prevent the occurrence of traffic accidents. Numerous factors influence the risk of an accident. To determine the importance of each of them (implemented through the weight of criteria in the proposal of methodology), it is necessary to perform a “depth” analysis of accidents and survey of the road users and determine which are critical to the factors and increase the risk of an accident occurring. There are many examples of good practices in the world from this area [6]. When devesloping a case study, amount of data for road infrastructure in the LC area was lacking, and this also significantly influenced the quality of the ranking process. A significant problem is a non-coordinate legal regulation in this area at the level of the state of BIH and the coordination of road and rail infrastructure managers to provide LC with the necessary equipment and exchange information about infrastructure. Multi-criteria evaluation was chosen due to simplified implementation and potential integration with GIS software. The proposed methodology would significantly improve and expedite the decision-making process on interventions at level crossings to improve traffic safety. The recommendation is to examine the possibility of applying other safety assessment methodologies at level crossings and compare the results in subsequent research [8, 9].
References 1. Pravilnik o putnim prijelazima-„Službene novine Federacije BiH“(42/06) (2006) 2. Pravilnik o naˇcinu ukrštanja željezniˇcke pruge i puta –Pravilnik 322 („Službeni glasnik BiH “Br. 40/13) (2013) 3. Pravilnik o naˇcinu ukrštanja željezniˇcke pruge i puta (322) - (Službeni glasnik Republike Srpske, broj 89/21) (2021) 4. Zakon o sigurnosti željezniˇckog prometa – “Službeni list BiH”(33/95) (1995) 5. Kljucanin, S., Rezo, M., Dzebo, S., Hadzic, E.: Spatial data infrastructure in natural disaster management. Techn. J. 15(4), 455–461 (2021) 6. International union of railways - Union Internationale des Chemins de fer (UIC): UIC Safety Platform Guidance: “Best practice for level crossing risk assessment” (2022) 7. Behzad, D., Seyed, M.E.: A review of methods for highway-railway crossings safety management process. Int. Electron. J. Math. Educ. 12(3), 561–568 (2017) 8. Ishak, Z., Yue, W.L., Somenahalli, S.V.C. The methodology development of railway level crossing safety systems – South Australia case study, WIT Transactions on The Built Environment, vol. 103, Computers in Railways XI, pp. 629–637 (2008) 9. Blagojevi´c, A., Kasalica, S., Stevi´c, Ž, Triˇckovi´c, G., Pavelki´c, V.: Evaluation of safety degree at railway crossings in order to achieve sustainable traffic management: a novel integrated fuzzy MCDM model. Sustainability 13(2), 832 (2021). https://doi.org/10.3390/su13020832
Building Information Modeling Education at Bosnian and Herzegovina Universities Žanesa Ljevo(B)
, Mirza Pozder , Suada Sulejmanovi´c , Ammar Šari´c , Sanjin Albinovi´c , and Naida Ademovi´c
Faculty of Civil Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. Since the seventies, Building Information Modeling has been known as a process in construction projects. Building Information Modeling courses in construction management education are present at universities around the world and in the countries of Southeast Europe. This paper presented the research results on education on Building Information Modeling at the Faculties of Civil Engineering and Faculties of Architecture at different Universities in Bosnia and Herzegovina. The research has shown that the implementation of Building Information Modeling education is present in only one university. Keywords: Building Information Modeling · Education · Faculty of Civil Engineering · Faculty of Architecture · University of Bosnia and Herzegovina
1 Introduction Building Information Modeling (BIM) courses are present at universities worldwide. Some universities incorporate BIM topics/contents into conventional courses or as standalone BIM courses [1–3]. The essential BIM standards were published back in 2007. It was the year when thoughtful thinking and discussions about BIM education took place, in practice, at universities, and various domains of the Architecture, Engineering, and Construction (AEC) industry. The academic implementation of BIM focuses on problem-solving, project-based learning, and or multidisciplinary approaches [4–7]. The need to include BIM in university teaching was identified by Ghosh et al. [8] in their research at Arizona State University back in 2013. Graduated engineers would understand the BIM concept and acquire BIM skills that would enable them to accept the use of BIM within the AEC industry successfully. Department of Architectural Engineering at Penn State University, Pennsylvania, since 2014, offers many courses with BIM elements integrated into the teaching material [9]. A comprehensive study was conducted in order to get an overview of BIM implementation at various universities throughout the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 64–74, 2023. https://doi.org/10.1007/978-3-031-43056-5_6
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At two Croatian universities, the master’s graduate students learned about BIM. They use Revit, Navisworks, Civil 3D, ArchiCAD, Gala, and Microsoft Project as BIM applications. Master’s degree students participated in the survey in 2016. From the University of Zagreb, Faculty of Civil Engineering (30 students), and University J. J. Strossmayer of Osijek, Faculty of Civil Engineering (14 students). The results are compared with the BiH survey results [2]. The twenty-four Australian universities in 2020 provide BIM education content in courses, short courses, and subjects. The teaching concept is different from On-campus lectures (classes, face-to-face), Workshops, Practical to Group activities (tutorials, seminars, presentations), or Private study (individual work). Over seventy BIM-related subjects are related to 3D modeling and/or BIM documentation. The students use BIM tools like Autodesk Revit Architecture, ARCHICAD, Autodesk Recap, Autodesk BIM 360, Microsoft Project, Autodesk Navisworks, Tekla, and Bentley [9, 10]. In Pakistan, over forty universities have BIM courses, and most universities teach BIM at an undergraduate level in AEC programs. The students of these universities use Autodesk Revit Architecture, Autodesk Navisworks, Structure, and TEKLA Structures as BIM software [3]. The Revit, AutoCAD, TEKLA Structures, Civil 3D, SOFiSTiK, MagiCAD, DDSCAD, and Navisworks are softwares that have been used in the study programs of the Faculty of Civil Engineering and Architecture at the biggest technological universities in Lithuania [11]. The primary barrier to introduce BIM into the curriculum is the fact that professors must be trained to teach BIM content. Further, the professors are unwilling to define new subject areas, lack of standardization of BIM, lack of money, and lack of professional development opportunities. The barriers to BIM implementation in Marocco faculties are the lack of skilled professionals for students’ education, lack of BIM materials, lack of faculty’s resources and time to develop a new course, and ambiguity about BIM [4, 12, 13]. This research aimed to compare and discuss BIM educational approaches in the education of AC faculties in Bosnia and Herzegovina (BIH) and compare it with research results from Croatia.
2 Literature Review One of the most detailed and comprehensive reports on the representation (at universities) of BIM education is NATSPEC [14] BIM education global report (from 2014, last update v 9.0 2022.). The countries covered in this report are Australia, Canada, the USA, Finland, the Czech Republic, Norway, the UK, South Africa, China, Hong Kong, Singapore, Japan, the Netherlands, and New Zealand. The literature review obtained information about which universities in each country had BIM education (BIM word in the name or description of the course). The information was categorized according to the content that could be learned by students and whether BIM education is part of a course or a master’s degree (see Table 1) [2, 11, 13–15]. According to the research by Adamu and Thorpe [5] there are six key considerations why universities should introduce the topic of BIM into their curriculums. The reasons are: plan, phase, and prioritize; create an ecosystem of BIM technologies; identify
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Table 1. The number of universities from different countries, their focus, and the scope of BIM education [2, 11, 13–15]. Country
No. of University
Focus on
Part of the course, Course, master or PhD
Lithuania
Most - Architecture, civil BIM model, BIM Part of the course, engineering, Building deployment, and strategy Course, master Services Engineering, Electrical Power Engineering, and Automation and Control study
Pakistan
41.4% of universities construction management
skills, tools - BIM
Part of the course, Course
Croatia
2
3D with schedule and cost - 5D BIM model
Part of the course, Course
Australia
24
3D, 4D, and 5D BIM models and produce technical clash detection reports
Part of the course, Course, BIM-specific Graduate diploma
Canada
94
BIM model Student Connect platform
Part of the course, Course, master
Czech Republic
5
3D CAD, BIM
Part of the course, Course, master
Finland
All universities Architecture and Construction
open BIM, modeling Part of the course, (BIM): visualization, Course renovation, maintenance of buildings, lifecycle thinking and energy efficiency
France
All universities Architecture and Construction
digital skills
Part of the course, Course
Germany
All universities - civil engineering
focused on the use of specific BIM software
Part of the course, Course, master (continued)
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Table 1. (continued) Country
No. of University
Focus on
Part of the course, Course, master or PhD
China
All universities building and civil engineering
3D BIM, why and how to use information modeling and management environments
Part of the course, Course, master
Norway
All universities building and civil engineering
BIM
Part of the course, Course, master, and one-year programs in BIM-specialisation
Sweden
5
BIM-related knowledge Course, master, and turning from a technical doctoral study focus on modeling, information transfer, and visualization to be complemented with management-related assignments with collaboration, requirements management and organizational strategies with BIM
learning outcomes and industry needs; get teaching and administrative support; develop student-centered learning methods and form university coalitions for multi-disciplinary learning.
3 Methodology There is no official data nor a study that has been conducted at public universities (faculties) in Bosnia and Herzegovina to show the representation of BIM education at these institutions. During this research, curriculums published on the official websites of the Faculty of Architecture, Civil Engineering and Geodesy (University of Banja Luka), Faculty of Civil Engineering, Architecture and Geodesy (University of Mostar), Faculty of Civil Engineering (University Džemal Bijedi´c Mostar), University of Sarajevo-Faculty of Architecture (University of Sarajevo) and University of Sarajevo-Faculty of Civil Engineering (University of Sarajevo) were elaborated, to see if they have BIM education in their curriculums for the academic 2021/22. After that, a Questionnaire Design - a survey with questions was conducted, that enabled the comparison and discussion of BIM educational approaches in the education of civil and architectural engineers in
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BIH and Croatia. In 2019, a survey was conducted among the master’s students of the University of Sarajevo-Faculty of Civil Engineering. The survey in the link form (using the web application docs.google.com) was sent to university professors (in their final year’s master’s studies) in 2021, and they distributed it to the students. At the start of academic 2022/2023, again the websites of the faculties listed above were checked for updated information regarding BIM courses and their incorporation into curricula.
4 Analysis and Discussion First, a survey among the master’s students who took the Project Management course (BIM was a part of it) at the University of Sarajevo-Faculty of Civil Engineering in 2019. via doc. Google forms (as was done earlier on the Croatian civil engineering faculty before) was conducted. Thirty-nine correctly answered surveys were obtained; almost 95% of the students had heard and knew what BIM meant. A large percentage, amounting to 91.9% wanted to use BIM in their future practice and thought BIM education was necessary to be a part of the curriculum (as a separate course). University of Sarajevo-Faculty of Civil Engineering has a course called “BIM in Civil Engineering” in its curriculum, which has been introduced for the academic year 2021/2022 for the Master’s study of Civil Engineering. The other faculties did not have any course with the word “BIM” in the title of their curriculums. Lectures about BIM in the Faculty of Civil Engineering, Architecture, and Geodesy, University of Mostar were noted. In the 2022/2023 curricula, the faculty had courses called “Introduction to Integrated Design – BIM” for bachelor’s study and “Integrated Design – BIM” for master’s study. Our survey resulted in 112 answers, but three of them answered that they had never heard about BIM, and these were excluded from the analyses. The percentage of male responders was 37.6%, while 62.4% were female. Figure 1 shows that 21.1% of respondents were from the University of Sarajevo-Faculty of Civil Engineering (abbreviation in the continuation of the text is UN 4.1) and 41.3% from the University of SarajevoFaculty of Architecture (UN 4), 9.2% of respondents were from the Faculty of Civil Engineering, University Džemal Bijedi´c Mostar (UN 3), 18.3% from the Faculty of Civil Engineering, Architecture and Geodesy, University of Mostar (UN 2) and 10.1% from the Faculty of Architecture, Civil Engineering and Geodesy University of Banja Luka (the label UN 1). In BIH Architecture and Construction (AC) universities, the most significant barrier for integration of the BIM courses into their programs is the need for or lack of trained BIM faculty teachers. All the responders agreed that BIM technology allows progress in education and understanding of construction management (100% answered yes). When asked whether they think that the application of BIM technology allows progress in education and understanding construction management, all of the responders answered yes (Fig. 2). Respondents shared the same opinion in Croatia [2]. In the following question, we asked respondents about the application of software tools currently available that can improve construction management and BIM. Over 95% of respondents thought that the application of software tools currently available could improve construction management (Fig. 3). Respondents shared the same opinion in Croatia [2].
Building Information Modeling Education at Bosnian and Herzegovina Faculty of Civil Engineering (University of Sarajevo) - UN 4.1
69
23
Faculty of Architecture (University of Sarajevo) UN 4
45
Faculty of Civil Engineering (University Džemal Bijedić Mostar) - UN 3
10
Faculty of Civil Engineering, Architecture and Geodesy (University of Mostar) - UN 2
20
Faculty of Architecture, Civil Engineering and Geodesy (University of Banja Luka) - UN 1
11 0
5
10
15
20
25
30
35
40
45
Fig. 1. Number of respondents.
Fig. 2. Answers to questions “Do you think the application of BIM technology allows progress in education and understanding construction management?”.
Interesting answers were given to the question of whether CAD and Microsoft tools are sufficient for realistic and proper planning. A confirmative response was given by 40% to 90% of all the respondents. It is significantly worrying that 60% of respondents from UN 3 think that CAD and Microsoft tools (Word, Excel, and Microsoft Project) are sufficient for proper and realistic planning (Fig. 4). The responders from the University of Osijek, Faculty of Civil Engineering and Architecture answered yes (35.71%) while this percentage was much smaller at the University of Zagreb, Faculty of Civil Engineering and amounted to only 6.67% [2]. All respondents from UN 3 and UN 4.1 thought that integrating drawings, costs, and time plans are necessary, like their colleagues from the universities in Croatia. Just 9%
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Fig. 3. Answers to questions “Do you think that application of currently available software tools can improve construction management and BIM?”.
Fig. 4. Answers to questions “Do you think CAD and Microsoft tools (Word, Excel, Microsoft Project) are sufficient for proper and realistic planning?”.
of students at UN 1 thought that the construction industry did not require the integration of drawings, costs, and time plans (Fig. 5). All respondents from UN 1 and UN 4.1 (100%) and more than 95% of all responders from UN 2 and UN 4.1 thought that better communication between different professions during the design and construction process with the use of software tools is important (Fig. 6). It is very interesting and praiseworthy that almost all students in all BIH universities want to use BIM in their future practice (Fig. 7). These results should be a wake-up call for every AC faculty in BIH to include BIM in their curricula.
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Fig. 5. Answers to questions “Do you think the construction industry requires the integration of drawings, costs, and time plans?”
Fig. 6. Answers to questions “Is there a need for better communication of different professions (with the application of software tools) during the design and construction?”.
Regarding the compatibility of the BIM software tools the given answers were in the range of 73% to 100%. The positive answer was given by 73% (UN 1), 84% (UN 4), 91% (UN 4.1), 95% (UN 2), and 100% (UN 3) (Fig. 8). Most respondents thought that BIM softwares were compatible with each other, like the students in Croatia [2]. The survey elaborated here was conducted from December 2021 to February 2022. During this time, only students from the University of Sarajevo-Faculty of Civil Engineering had a course about BIM called BIM in Civil Engineering.
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Fig. 7. Answers to questions “Would you like to use BIM in your future practice?”.
Fig. 8. Answers to questions “Do you think that the BIM software tools are compatible with each other?”.
5 Conclusion Implementing BIM in the realization of construction projects is a basic prerequisite for digitizing the industrial branch in which AE engineers operate. In this paper, through survey analysis, it was shown that students from AE faculties in BIH know what BIM means. They know that the application of BIM technology allows progress in education and understanding of construction management. They know that it is necessary for better communication between different professions during design and construction using software tools. They want to use BIM in their future work. Still, they need the opportunity to acquire knowledge, skills, and competencies through BIM courses at the faculties.
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The current education practice in BIM at AC faculties in BIH thought courses is not at a satisfactory level. It has emerged that the status of BIM in BIH universities is at its very early stage. Educational institutions have to adapt to the new circumstances and train future engineers of all profiles (construction, architects, mechanical, electrical engineers, etc.) to implement the acquired knowledge. The lack of BIM in university courses can be justified by the fact that BIM is not required to be used in BIH architectural and construction industires. So it can be considered that there is no need to “confuse” students with advanced technology like BIM. The course “BIM in Civil Engineering” aims to acquaint students with the basic principles of BIM in civil engineering and the general application of BIM in the AEC industry. They will acquire knowledge through knowledge of BIM principles. Students will have the skills to be able to create a BIM model using modern software packages, and they will have the competence to solve engineering problems from the BIM domain. From the literature review, it was noted that there are a lot of barriers for the integration of BIM courses into universities. Some of them are lack of money to purchase computer hardware and BIM software, lack of BIM materials, lack of experience among lecturers, lack of collaboration among the industrial players and the lecturers, no BIM regulation, etc. It is planned to make a study that will be covering the barriers to BIM implementation in higher education. Legal implications of adopting and implementing BIM are not present in the BIH AEC industry. There are no BIM regulations in BIH nor legal regulations on implementing BIM technology. Countries that have implemented BIM nationally as part of their regulation have developed national BIM standards.
References 1. Petronijevi´c, M., et al.: BIM at the faculty of civil engineering –university of Belgrade. In: 14th International Scientific Conference, iNDiS 2018, pp. 125–129. iNDiS, Novi Sad (2018) 2. Kolari´c, S., et al.: Assessing educational approaches to building information modelling (BIM) at construction management master studies in Croatia. Tehniˇcki vjesnik 24(4), 1255–1262 (2017) 3. Abbas, A., et al.: Integration of BIM in construction management education: an overview of Pakistani Engineering universities, In: Chong, O. et al. (eds.) International Conference on Sustainable Design, Engineering and Construction, Procedia Engineering 145, pp. 151–157. Elsevier, Tempe, Arizona, USA (2016) 4. Udomdech, P., et al.: An alternative project-based learning model for building information modelling-using teams. In: Proceedings of the 34th Annual ARCOM Conference (ARCOM), vol. 34, pp. 57–66. Association of Researchers in Construction Management, Belfast, UK (2018) 5. Adamu, Z.A., Thorpe, T.: How universities are teaching BIM: a review and case study from the UK. J. Inform. Technol. Construct. 21(8), 119–139 (2016) 6. Abdalla, S.B.: Re-exploring the potentiality of BIM: incorporating BIM into the multidisciplinary educational setup. In: Proceedings of the Joint 8th IFEE20173rd TSDIC, pp. 1–13. IFEE, Sharjah, United Arab Emirates (2017) 7. Sanchez-Lite, A., et al.: Comparative study of the use of BIM in teaching engineering projects. IEEE Access 8, 220046–220057 (2020)
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8. Ghosh, A., Parrish, K., Chasey, A.: From BIM to collaboration: a proposed integrated construction curriculum. In: ASEE Annual Conference and Exposition, Conference Proceedings, pp. 23.618.1–23.618.9. ASEE, Atlanta, GA, United States (2013) 9. Solnosky, R., Parfitt, M.: A curriculum approach to deploying BIM in architectural engineering. In: Proceedings of the AEI Birth Life Integr. BuildingAEI Conference, pp. 651–662. American Society of Civil Engineers (ASCE), Milwaukee, United States (2015) 10. Shuchi, S., et al.: BIM education at Australian universities: 2020 insights. Deakin University, Geelong, Australia (2021) 11. Šadauskien˙e, J., Pupeikis, D.: Review of BIM implementation in higher education. J. Sustain. Arch. Civil Eng. 22(1), 99–109 (2018) 12. Shibani, A., et al.: Investigating the Barriers of Building Information Modelling (BIM) implementation in the higher education in Morocco. In: 10th Annual International IEOM Conference, vol. 0, pp. 471–480. IEOM Society, United Arab Emirates (2020) 13. Casasayas, O., et al.: Integrating BIM in higher education programs: Barriers and remedial solutions in Australia. J. Archit. Eng. 27(1), 1–10 (2021) 14. International Construction Information Society: BIM education global 2022 update report (v 9.0), NATSPEC, Sydney, Australia (2022). https://www.icis.org/publications/papers/. Accessed 15 Jan 2023 15. International Construction Information Society: BIM education global 2021 update report (v 8.0), NATSPEC, Sydney, Australia (2021). https://www.icis.org/publications/papers/. Accessed 10 Jan 2023
Spalling of Concrete Irfan Bidževi´c1
, Sanin Džidi´c2(B)
, and Ahmed El Sayed1
1 Faculty of Engineering, Natural and Medical Sciences, Department of Civil Engineering,
International BURCH University, Sarajevo, Bosnia and Herzegovina 2 Faculty of Technical Sciences, Department of Civil Engineering, University of Bihac, Bihac,
Bosnia and Herzegovina [email protected]
Abstract. Various actions can affect the structural integrity of reinforced concrete structures such as earthquakes, fires, foundation settlement, etc. However, RC structures can be affected and jeopardized by factors and actions of a much smaller magnitude such as reinforcement exposure or cross-section reduction. A very wellknown fact is that cracks in concrete members can allow water to enter the member and cause reinforcement corrosion, which disturbs the rheological properties of the rebar. This can govern both the load bearing capacity and the serviceability of the structure significantly. RC structures can be compromised even at a molecular level. Composition of the concrete is very important. Aggregate origin, pH value of cement, water-to-cement ratio are just a few of the parameters that govern the quality of concrete and various unfavorable phenomena such as the alkali-silica reaction, corrosion, and even phenomena that occur during incidental actions such as fire. One of those phenomena is the spalling of concrete. Spalling of concrete is governed by all of the aforementioned factors. As much as the identification of the occurrence and cause of this phenomena is important, the prevention to minimize spalling of concrete is a inevitable aspect part in this research paper. Keywords: Concrete · Spalling · Fire · Alkali-silica reaction
1 Introduction The word “spall” is defined as a small fragment or chip, especially of stone. “Spalling” as a verb represents breaking off in form of chips, scales, or flake [1]. When it comes to concrete, spalling is a very common phenomenon that has various causes such as chemical reactions in concrete, fire exposure or increased heat, and improper installation of concrete. With that said, spalling in this aspect can be approached as the breakaway or flaking of a concrete surface, or in severe cases instantaneous loss of cross sections (explosive spalling) that can, in most cases, include the top layers of reinforcing steel [2]. This phenomenon significantly contributes to reducing cross-section, exposing rebar to corrosion, analogously the loss of load-bearing capacity, and even reduced serviceability. Spalling is known to occur in any type of concrete structure from high-rise buildings to tunnels and roads. A special aspect of spalling also needs to be addressed is the one where aggregates can rapidly and explosively burst out of the concrete at very high © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 75–91, 2023. https://doi.org/10.1007/978-3-031-43056-5_7
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velocities, hence the name “explosive spalling“. Concrete spalling can also occur in the forms of aggregate spalling, corner spalling, and surface spalling [3]. In summary, spalling has been defined by Khoury as “the violent or non-violent breaking off of layers or pieces of concrete from the surface of a structural element, when it is exposed to high and rapidly rising temperatures as experienced in fires” [4]. Historically, spalling has first been recorded in 1854 (Barret) and through years of experience and empirical knowledge types of concrete spalling have been characterized first time in 1911 [3]. Later on, the main governing factors of concrete spalling were updated in 1935 [5].
2 Research Methodology Spalling of concrete is a common occurrence in concrete structures. It can affect the serviceability, longevity and even the load bearing capacity of a structure at some point. The aim of this paper is to recognize and categorize the factors causing and governing the spalling of concrete which leads to studying the effects of concrete spalling. In addition, the prevention of concrete spalling is discussed, as a no less important part. In this paper qualitative overview research design based on secondary qualitative data was used since spalling of concrete is a relatively unexplored topic. The data was collected from previously written research and papers that have mentioned and described the spalling of concrete, as well as reviewing and breaking down the theories explaining the spalling of concrete, along with some papers that were focused on the data obtained from specific experiments, conducted by the same research. The questions raised are: what are the types of concrete spalling? What does cause the concrete spalling? What are the effects of concrete spalling, and is it possible to prevent the spalling of concrete?
3 Types of Concrete Spalling The first serious investigation and research of concrete spalling was done in 1916 by Gary [6]. At the time shorter buildings were tested (one or two-storey buildings) and the tests included beams, walls, columns, and stairs. It was shown that among other variables, aggregate types and concrete compositions governed the tests and spalling of concrete as well. Accordingly, spalling was categorized into the following types: Aggregate spalling - Spalling on individual aggregate grains – except for concrete containing basalt aggregates. The spalling was due to the mineralogical nature of the surcharges, in particular attributed to weathered feldspars. Surface spalling - Shell-shaped, explosive spalling on the component surfaces of the order of 100 cm2 up to several square meters, especially in the case of walls subjected to pressure and columns, where the reinforcement has been partially exposed. The water vapor stresses in relatively moist concrete explain this type of spalling. Corner spalling – Spalling on the corners of a structural element that exposes corner reinforcement. This type of spalling gave out a clue for further and deeper investigation of the cause of spalling since it was caused by water vapor stresses and temperature stresses as a result of rapid two-sided heating.
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Explosive spalling – It occurred that pieces of walls of up to 1 m2 in size were blown off. Parts of the wall were even thrown more than 12 m away from the test house. Interestingly, the load bearing capacity was contained. At the time the cause for this type of spalling could not be explained, only could it be characterized as very dangerous. In the dissertation “Zur Frage der Abplatzungen an Betonbauteilen aus Normalbeton bei Brandbeanspruchung” of Meyer-Ottens it is stated that: “In addition, structural loosening of concrete was observed on all components, but this was not observed directly related to explosive fracturing.” [3]. Later attempts by Endel [7] “for changes in the length and structure of the concrete aggregates gave reason to think that the spalling could be mainly caused by a sudden change in the volume of the quartzite aggregates. Quartz shows unequal thermal expansion in the crystal axes and changes its volume abruptly above 500 °C (transition point at ~ 575 °C).“ [3] (Fig. 1).
Fig. 1. Explosive spalling empirical envelope for normal-strength concrete [8, 9].
Progressive gradual spalling – This type of spalling is also called “sloughing-off”. It is caused by internal cracking and chemical degradation at the meso-level. The process begins at a too-high temperature. Small pieces of concrete fall out non-violently without producing any sound. Sloughing off type of spalling is mostly characteristic for columns. When the column gets heated enough from all sides a significant amount of pore pressure builds up on the heated sidewalls, thus exerting moisture deeper into the column. The thermal gradient and external loading cause compression of the heated surface, and that is when the entire heated surface possibly explosively spalls, followed by a booming sound. As a result of this phenomena, other failure modes including strength loss, shear, and buckling advance, and the column’s cross-section efficiency is significantly decreased [10]. Post-cooling spalling (thermal-chemical spalling) – post-cooling spalling is possible to occur after the fire has been extinguished. It was observed in concrete composed of aggregates that are of calcareous origin. When moisture returns to the surface after cooling it causes the rehydration of calcium-oxide [CaO to Ca(OH)2 ] which may expand above 44% [10]. This results in excessive internal cracking on the meso-level and total concrete strength loss [11]. In summary, with water available to rehydrate the CaO, expansion will go on, caused by Ca(OH)2 , causing pieces of concrete to fall off (Fig. 2).
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Fig. 2. Temperature range of thermal-chemical spalling (post-cooling spalling) [10].
When mentioning thermal-chemical spalling, it is worth adding that delayed ettringite formation (DEF) may contribute to alkali-silica reaction (ASR) in terms of inducing expansion and cracking. Delayed ettringite formation is actually a form of sulphate attack in concrete which occurs in Portland cement. Ettringite, in cement chemists’ notation is C6 AS3 H32 – hydrous calcium sulphoaluminate [(CaO)6 (Al2 O3 )(SiO2 ) x 32H2 0]. Besides its’ contribution to cracking and expansion, DEF causes loss of serviceability in worst case scenarios. The thermal and chemical aspect of this phenomenon is that, as mentioned, it is considered as a sulphate attack and occurs at temperatures from 65 °C and above. To prevent the DEF, it is recommended to restrict concrete hydration temperatures below 65 °C (avoid mass concreting or large-volume pours during high ambient temperatures since the exothermic hydration reaction contributes to DEF). If the temperature restrictions are not feasible, DEF can be circumvented by incorporating certain mineral additions (e.g. fly ash or ground-granulated blast-furnace slag). Another way to prevent DEF is to include thermal modelling in the design phase. As mentioned, concrete curing is an exothermal hydration reaction, so one more option in DEF prevention is reduction of water exposure (waterproofing). Reparation or remediation of DEF-caused effects is very perplexing, so the best option is to include monitoring of deterioration progress [12]. Surface spalling, explosive spalling and surface spalling set-off during the first 20– 30 min into a fire and are governed by the heating rate, while corner spalling occurs after 30–60 min of fire and is governed by the maximum temperature [9] (Table 1). The other research [10] provides links to the typical spalling phenomena with its related mechanisms in Table 2.
4 Causes of Concrete Spalling Concrete spalling may be caused by a wide variety of factors. A lot of research, case studies, and forensic examinations have been conducted. The takeaway is that, even though spalling of concrete may not necessarily reduce the load-bearing capacity of a certain structural member, it may very easily jeopardize the integrity of a structure and its serviceability by exposing the steel reinforcement to corrosion and allowing water, be it in a gaseous or liquid form, to enter further into the element by pores and voids [10].
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Table 1. Characteristics of the different forms of spalling [9]. Spalling
Time of Nature occurrence (min)
Sound
Influence
Main influences
Popping
Superficial
H, A, S, D, W
Aggregate 7–30
Splitting
Corner
Non-violent None
Can be serious T, A, Ft , R Can be serious H, W, P, Ft
30–90
Surface
7–30
Violent
Cracking
Explosive
7–30
Violent
Loud bang Serious
H, A, S, Fs , G, L, O, P, Q, R, S, W, Z
A = aggregate thermal expansion, D = aggregate thermal diffusivity, Fs = shear strength of concrete, Ft = tensile strength of concrete, G = age of concrete, H = heating rate, L = loading/restraint, O = heating profile, P = permeability, Q = section type, R = reinforcement, S = aggregate size, T = maximum temperature, W = moisture content, Z = section size
Table 2. Description of typical spalling phenomena with its related mechanisms [10]. Type Compartment Evaporation of moisture causes excessive pore pressure Thermal gradient results in compression Aggregate and cement paste induce internal cracking during the heat expansion Different deformations in steel and concrete caused by heat result in cracking Chemical transitions cause strength loss
Violent
Sloughing-off
Corner
Explosive
✔
✔
✔
✔
✔
✔
Post-cooling
✔ ✔
✔
✔
Hasenjäger [13] discussed the main factors that cause spalling are thermal stress and water pressure. The Hasenjäger pointed out the key factors as: • • • •
Rapid heating rate Tensile strength being exceeded Rapid volumetric changes in aggregates Formation of pressure from water vapor and gases Other causes of spalling may be:
• • • •
Corrosion of embedded reinforcing steel Fire exposure (free water converts to steam) Freeze and thaw cycling Expansive effects of Alkali-Silica Reaction
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Inadequate depth of cover over reinforcement Low-quality concrete cover Improperly constructed joints Bond failure in two-course construction (difference in shrinkage between topping and base courses) Improper water content Improper compaction of concrete Improper installment (no supervision, no vibration) Various on-site actions by personnel installing concrete and reinforcement such as dislodging rebars while walking over them, carelessness while pouring concrete over rebars, etc. [14] (Fig. 3).
Fig. 3. Some of the mechanisms that cause spalling [10].
When discussing explosive concrete spalling, experience has indicated that there are several parameters that make explosive spalling more likely to occur, being of structural or material nature. Klingsch [15] has collected data and divided the factors into three main categories: • Structural or mechanical parameters • Material related parameters • Heating characteristics. 4.1 Material Related Parameters Several material-related characteristics with a significant impact on spalling are identified by study on concrete spalling at high temperatures. Table 3 presents a short overview on material-related governing parameters depending on the concrete mix design or the choice of materials used in the concrete mix [15].
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Table 3. Governing material related parameters with an influence on spalling [15]. Material related parameter
Increasing risk of spalling Influence of spalling
Silica fume
Very high
Silica fume lowers the permeability and increases the possibility of explosive spalling due to the reduced release of high vapor pressure
Limestone filler
High
Lowers permeability, similar behavior compared to silica fume
Permeability
High
Low permeability and insufficient temperature-dependent increase in permeability increases the risk of spalling due to insufficient release of pore pressure
Porous/lightweight aggregates Variable
High porosity and permeability enable the release of high pore pressure and decreases the risk of spalling. The higher moisture content of lightweight aggregates promotes the risk of spalling
Quartzite aggregates
High
Can increase the risk of spalling due to a change in the quartzite phase at T = 573 °C
Carbonate aggregates
Lowering risk
Remains stable even at very high temperature, has a very low thermal expansion
Aggregate size
Moderate
Larger aggregates increase the risk of explosive spalling due to poor surface to mass ratio
Internal cracks
Variable
Two opposing effects. Small cracks might promote the release of high pressure and reduce the risk of spalling. However, parallel cracking close to the heated surface (i.e. due to the loads) might increase the risk to the spalling
Compressive strength
High
High strength grade usually increases risk of explosive spalling, mainly due to the lower w/c ratio and permeability (continued)
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Material related parameter
Increasing risk of spalling Influence of spalling
Moisture content
Very high
Higher moisture content (mainly free water) significantly increases the risk of explosive spalling, since more vapor pressure must be released depending on the permeability of concrete. Critical moisture content is difficult to obtain, in particularly for HPC
Cement content
High
High cement content increases the total amount of water added to the concrete, even with low w/c ratios
Concrete age
Variable
Young concrete has a high amount of free water, which increases the risk of spalling. This effect decreases with HPC and UHPC due to low permeability
4.2 Structural/Mechanical Parameters The following table summarizes the main structural/mechanical parameters governing spalling. However, it is barely distinguishable between pure structural/mechanical parameters pure material-related parameters leading to spalling because a few of the parameters can be put in both categories. Several material-related parameters impact compressive strength of concrete, which shows how structural/mechanical parameters and material-related parameters are mutually dependent [15] (Table 4). 4.3 Heating Characteristic Parameters Explosive spalling is strongly dependent on the heating rate, as well as the temperature gradients. The chances of the occurrence of explosive spalling proportionately increase with the increase of the heating rate and temperature gradients. The following table (Table 5) by Klingsch [15] summarizes how the heating characteristics parameters increase the risk of spalling and what influence do they have on spalling.
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Table 4. Governing structural/mechanical related parameters with an influence on spalling [15]. Structural/mechanical parameter Increasing risk of spalling Influence of spalling Tensile strength
Lowering risk
A high tensile strength is considered as lowering the risk of explosive spalling, since it offers a higher resistance: - against spalling due to a higher pore pressure; - of high thermal gradients, stresses and expansion; - of corner spalling or thermal stresses from two sides
Applied load
High
The risk of spalling increases with applied higher load levels. Preload as low as 5% of the cold strength increases the risk of spalling. It remains unknown if a low preload minimizes the risk of spalling, since small cracks used for the release of vapor are pressed together
Hindered thermal expansion
High
Fixed end as boundary conditions, eccentric load or bending increases risk
Cross section geometry
High
Round cross section, rounded corners, sufficient reinforcement cover and spacing and modified tie design lowers the likelihood of spalling or increased the remaining load bearing capacity of concrete members after spalling
5 How Does Spalling Affect Concrete Structures? The consequence of spalling, as well as the amount, can be insignificant as the surface pitting of concrete. However, it can seriously affect the fire resistance of the structural element since spalling contributes to the “reveal” of reinforcement below the concrete cover, or even tendons in prestressed concrete, to potentially rapid temperature rises, therefore jeopardizing the load-bearing capacity and decreasing the area of the cross section. All forms of spalling reduce the period of fire resistance [16]. Also, concrete spalling may result in fire resistance failure due to loss of load-bearing capacity and integrity. Application of concrete often governs the potential consequences of spalling, as [11] gives an example where, despite being a minor type of surface degradation, aggregate spalling causes substantial issues with concrete pavements used for military aircraft.
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Table 5. The governing parameters depending on the heating characteristics with an influence on spalling [15]. Heating characteristic parameter Increasing risk of spalling Influence of spalling High heating rate
Very high
Higher heating rates usually lead to explosive spalling with HPC mixes
Temperature gradient
High
Closely related to the heating rate. Higher temperature gradients (T > 1.0 K/mm) promote the risk of explosive spalling due to thermal stresses
Absolute temperature
Moderate
Explosive spalling might occur with temperatures as low as T = 300–350 °C. Very high temperatures T > 1000 °C increase the risk of post cooling spalling
Exposure on multiple surface
High
Heat exposure on more than one side increases the risk of corner or explosive spalling due to higher temperature gradients and thermal stresses
Fig. 4. Calculated effect of spalling on fire resistance of concrete columns [11].
Figure 4 compares three constitutive models for the structural response of a 200 x 200 mm column [17]. After 15 min, there is a correlation between the amount of spalling and the fire resistance time [11].
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Table 6. Important test experiences on spalling on ceilings and roofs [3]. Design type
Coffered ceiling
Ribbed
Roof or slab cross section
Thickness
Moisture content
cm
cm
%
3.6
≥4
4-5
Concrete class
Loss of spac e min
Load capacity failure min
Compressive
17
-
Surface Destructive
Compressive
18
18
Surface Destructive
Compressive
21
21
26
26
46
-
-
-
Spalling type
Spalling zone
B 600
Surface Destructive
≥4
B 600
5
4.3
B 300
6
4.3
Folded
B 450
Statical system
Surface Destructive
Compressive and Tension
7
~4
Carrier frame
6.6
4.4
B 450
Surface
≥3
≥ B 225
Tension Compressive
-
≥6
Surface Surface
-
Slab
-
-
Slab with sheeting
≥7
≥3
≥ B 225
Surface
Tension
-
-
Compressive
Figure 5 shows the effect of spalling on the fire resistance of concrete columns. The results point out that regardless of the size of the column, the occurrence of spalling is critical in determining fire resistance [11].
Fig. 5. Effect of spalling on fire resistance of concrete columns [11].
Meyers-Ottens [3] gathered data obtained by examination of damaged structures in WW II and experiments. In that data it was concluded that although spalling was reported extensively, the spalling itself was not found to be as dangerous as the (diminished) loadbearing capacity of the individual components in the buildings examined was more or
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I. Bidževi´c et al. Table 7. Important fire hazards of spalling on ceilings and roofs [3]. Design type
Roof or slab cross section
Thick ness
cm
cm
RC slab
20
RC slab with canopy
20
Prestressed concrete
68-114
Prestressed concrete double T-section
Carrier frame
Moisture content1) %
≥3
In the building ≥3 At cantilever ≥5
≥5
Concrete class
Statical system
Spalling type
Spalling depth
Load capacity failure
cm
B 300
B 300
B 450
4
≥4
B 450
5
≥4
B 6000
Surface spalling, both tension and compression, particularly strong in places with densely laid reinforcement Surface spalling, especially in pressure zones and on the canopy Strong surface spalling in the compression zone Destructive surface spalling which led to the loss of room closure in the panel area Surface, destructive
≤8
-
≤5
18
≤ 25
Selfweight was still carried
≤4
The load bearing capacity of the webs was retained
≥ 2.5
-
1) Based on existing experience
less intact. “Destructive spalling” with subsequent structural collapse is possible but only rarely occurred at that time. Meyers-Ottens [3] also mentions that a combination of implementing concrete of higher strength in slender and delicate members and the gradual increase of fires with time, has brought spalling forward as a more reoccurring phenomenon. It can then be said that spalling in fire tests always primarily occurred when the concrete had a high moisture content. Destructive spalling mostly occurred with dimensions < about 6 to 8 cm. In natural fires, less favorable experiences were sometimes made, see Table 6. Spalling up to a depth of 25 cm and component failure was also detected. 5.1 Reduction of Load Bearing Capacity of Concrete Members Affected by Spalling Loss of section or the concrete cover’s ability to safeguard the steel reinforcement are two possible outcomes of spalling. This section concerns all the possible mechanisms and modes of both structural failure and loss of structural integrity induced by fire spalling (Tables 6 and 7).
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Table 8. Evaluation of preventive measures for the spalling of concrete [11]. Method
Effectiveness
Comments
Polypropylene fibers
Extremely effective even in high-strength concrete, but less so in ultra-high-performance concrete
May not stop ultra-high strength concrete from spalling. If concrete gets heated while under load or if stronger concrete is needed, more fibers are required. Neither lowers temperatures nor prevents strength loss
Air-entraining agent
Very effective
Can reduce strength. Effect is questionable if concrete is saturated with water
Additional thermal protection
Very effective
Cost goes up, but fire resistance goes up as well. Adhesion to the surface is the main possible issue. Decreases heating rate and maximum temperatures, hence reducing spalling and loss of compressive strength
Moisture content control
Vapor pressure reduced
For most buildings, normal moisture content is typically higher than the “no spalling” standard
Compressive stress control
Explosive pressure reduced
Not cost-effective as section sizes grow
Choice of aggregate
Use small-size aggregate with little expansion whenever possible
Additional fire resistance is feasible when using lightweight concrete with low moisture, however in high moisture environments, violent spalling is encouraged
Supplementary reinforcement
Spalling damage reduced
Not usable in narrow-spaced and small sections
Choice of section shape
The thicker the cross-section, Useful in I-beams and the less damage rib-sections
A concrete structural member affected by spalling, may have a drastically reduced cross-sectional area and be rendered incapable of withstanding compressive stress. Nowadays, with modern methods of design by implementing Eurocodes and taking fire resistance of structures into consideration, this effect can be mitigated [11].
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The load bearing capacity of a member can be jeopardized by exposing steel reinforcement to various actions such as fire, moisture, carbonaceous attacks, etc., considering that the protective cover of concrete is destroyed by spalling. High temperatures already excessively reduce the yield strength of steel, so spalling may effectuate reinforcement to reach its yield point in a much quicker manner, which in fact leads to flexural failure of the member. It is not rare to see this form of spalling in practice. As stated by [11] “The likelihood of flexural failure is increased by the fact that spalling and crumbling of concrete from around the reinforcement may cause loss of bond, and loadbearing failure could result from the loss of composite action”. Columns are a weaker point in structure than continuous floor, that is why the outcome of spalling on structural stability depends on the form of structure. In severe cases of spalling, and depending of a structural member which has been affected by spalling, the separating function can be reduced or even brought to a zero since spalling can cause holes to appear in thin slabs and panels. This allows fire to breach further into other fire compartments and affect other structural members, which definitely jeopardizes the integrity of the structure. Thin slabs are particularly susceptible to this type of failure [11].
6 Prevention of Concrete Spalling Khoury, G. A. & Anderberg [11] referred to BS 8110 in their review and quoted the following: “It may be possible to show that a particular form of construction has given the required performance in a fire resistance test without any measures. to avoid spalling. Alternatively, the designer may be able to demonstrate by fire engineering principles that the particular performance can be provided, even with spalling of concrete cover to the main tensile reinforcement” [18]. In the aforementioned review it is also brought out from the standard that “in any method of determining resistance to fire where loss of cover may affect a structural member, measures must be taken to prevent its occurrence.” The elements that affect concrete spalling have been discussed above, and it appears that there are nine main steps that may be taken to stop spalling or lessen its effects. Both actions may be taken at once or separately (Table 8). Khoury and Anderberg conclude in their review that the most effective methods of preventing explosive spalling are implementing propylene fibers into the concrete, adding air-entraining agents and providing thermal barriers. A lower moisture content (5% by volume or 2–3% by weight) can lessen the likelihood of explosive spalling during the first 30 min of a fire attack. The management of compressive stress and quick shape changes can also help prevent spalling [11]. If the heat exposure lasted for 60 min or longer, non-explosive spalling may take place. Additional reinforcement in the concrete slab might reduce its impact. Most standards call for additional reinforcing to control fractures in areas where the outer bars’ cover exceeds 40 mm. However, narrow profiles, like ribbed floors, make it challenging to add reinforcement. Spalling does not imply structural failure, according to fire evidence from actual structures. The continuity of the building and superior detailing are crucial for enhancing the fire performance (such as anchoring the bars which could be affected
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by heat). Spalling, however, does have an impact on the element’s overall fire resistance, therefore appropriate precautions should be taken [11]. Spalling of concrete can be prevented in the design phase as well. Khoury also stated that three methods can be used in order to assess fire resistance, which are: Fire testing, Performance-based methods (which are flexible), prescriptive methods (which are rigid). Performance-based methods can be divided into three categories depending on their complexity and sophistication, and those are: limit state analysis (simplified calculations), thermomechanical finite element analysis, and comprehensive thermohydromechanical finite element analysis [9]. A specific approach is needed for high strength concrete (HSC) classes, because spalling is more likely to occur with those classes since HSC is more sensitive to fire [19]. It is recommended in order to reduce and eliminate the effect of spalling in HSC, content of silica must be maximum at 6 percent with respect to cement weight for concrete classes from C55/67 to C80/95. In concrete classes from C80/95 to C90/105, spalling can happen in any situation when concrete is directly exposed to fire [20].
7 Conclusion There is not just one single cause of spalling of concrete. As discussed in this paper, there is a wide variety of factors that both cause and govern spalling. Besides improper concrete curing, installation, and caution during the concrete casting and formwork disassembly, some of the key factors are increased heat or fire exposure, corrosion or ASR, tensile strength being exceeded, pressure from water vapor and gases, and rapid volumetric changes in aggregate. Concerning the governing factors of concrete spalling, there are three main categories, and those are material related parameters, structural or mechanical parameters, and heating characteristics. It is difficult to draw a line between some of the material-related parameters and mechanical parameters, since some of those can be categorized in both categories i.e. compressive strength of concrete. Regarding material-related parameters silica fume, moisture content, and cement content govern the highest risk of spalling. Carbonate aggregates lower the risk of spalling. When it comes to mechanical and structural properties, the only lowering-risk parameter is tensile strength. On the other hand, applied load, hindered thermal expansion, and crosssection geometry govern a high risk of spalling. Heating characteristics proportionately increase the risk of spalling since as the heating rate and temperature gradients rise, spalling is more likely to occur. Throughout history, various types of spalling have been identified. With thorough research and examination followed by decades of experience, concrete spalling types can be categorized as aggregate spalling, surface spalling, corner spalling, explosive spalling, progressive gradual spalling, and post-cooling spalling, also known as thermal-chemical spalling. Progressive gradual spalling, also known as sloughing-off is strongly related to chemical degradation and chemical processes occurring in cement paste at the meso-level and micro-level. It has been discovered that delayed ettringite formation plays a huge role in ASR, therefore in the thermal-chemical type of spalling by inducing expansion and cracking. One of the major causes of concrete spalling is the alkali-silica reaction (ASR), colloquially called “concrete cancer“. ASR is mainly caused by concrete that has a
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high pH value. The reaction produces a gelous matter which is composed of Na, K, Ca, and Si, and forms around and within the aggregate. Water from the surrounding cement paste and eventually external water (atmospheric) that enters the concrete makes the gel expand. The swelling causes cracking and spalling of the concrete. This can be mitigated mostly by introducing pH-lowering constituents into the cement mixture such as SCMs (supplementary cementing materials) such as fly ash, silica fume, and slag. SCMs can also prevent sulfate attacks. Aggregate size plays another role in ASR. It has been observed that the lesser the particle size, the smaller the expansion, especially at younger ages. Sufficient moisture, reactive mineral aggregates, and high concentrations of alkali hydroxides contribute to the ASR the most. If fire is the cause of spalling it is most likely to take on the explosive pattern. Besides the explosive pattern, fire spalling is also known to cause post-cooling spalling and it is the main promoter of thermo-chemical spalling. By far, it is explained by a couple of theories which are based mostly on moisture migration and pressure buildup. Besides the cross-section reduction of a structural element that is caused by explosive spalling, weakened rheological properties of rebar steel, and its reduced protection from the further influence of fire, fire spalling jeopardizes a concrete structure by making it less fire resistant overall. Accordingly, the moisture content, cement content, compressive strength, restrained thermal expansion silica flume content and aggregate size are the most important governing factors.
References 1. Mish, F.C.: Merriam-Webster’s collegiate dictionary. In: Mish, F.C. (ed.) Merriam-Webster, Inc, Springfield, Merriam-Webster, Inc. Mass (2011) 2. Shah, S., Akashah, F., Shafigh, P.: Performance of high strength concrete subjected to elevated temperatures: a review. Fire Technol. 55, 1571–1597 (2019). https://doi.org/10.1007/s10694018-0791-2 3. Meyers - Ottens, K.: Zur Frage der Abplatzungen an Betonbauteilen aus Normalbeton bei Brandbeanspruchung. PhD diss. Institut für Baustoffe, Massivbau und Brandschutz (IBMB), Braunschweig (1972). https://doi.org/10.24355/dbbs.084-201503301029-0 4. FIB: Fire design of concrete structures - materials, structures and modelling. State-of-art report. fib Bulletin No. 38, Fédération internationale du béton (2007). ISBN: 978-2-88394078-9. https://doi.org/10.35789/fib.BULL.0038 5. Hasenjäger, S.: Über das verhalten des betons und eisenbetons im feuer und die ausbildung von dehnungsfugen im eisenbetonbau. PhD diss. Braunschweig Technischen Hochschule (1935) 6. Gary, M.: Brandproben an Eisenbetonbauten. Deutscher Ausschuß für. Deutcher Ausschlutss für Eisenbeton, Berlin, pp. 1911–1916 7. Endell, R.: Versucje über Längen un Gefügeänderung von Betonzuschlagstoffen und Tementmörteln unter Einwirkung von Temperaturen bis 1200°C. Deutscher Ausschuß für Eisenbeton, Berlin (1929) 8. Zhukov, V.V.: Explosive failure of concrete during a fire (in Russian) – Translation. No. DT 2124. Borehamwood, Joint Fire Research Organisation, United Kingdom (1975) 9. Khoury, A.G.: Effect of fire on concrete and concrete structures. Prog. Struct. Mat. Eng. 2(4), 429–447 (2000). https://doi.org/10.1002/pse.51 10. Amran, M., Shan-Shan, H., Onaizi, A.M., Murali, G., Hakim, A.S.: Fire spalling behavior of high-strength concrete: a critical review. Constr. Build. Mater. (2022)
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11. Khoury, G.A., Anderberg, Y.: Concrete Spalling Review. Swedish National Road Administration (2000) 12. Ingham, J.: Briefing: Delayed ettringite formation in concrete structures. In: Proceedings of the Institution of Civil Engineers, Forensic Engineering, vol. 165, pp. 59–62 (2012) 13. Hasenjäger, S.: Über das verhalten des betons und eisenbetons im feuer und die ausbildung von dehnungsfugen im eisenbetonbau, PhD diss. Braunschweig Technischen Hochschule (1935) 14. Duckett, M.: Concrete Spalling. The Experts Robson Forensic, 354 North Prince Street, Lancaster, PA 17603, USA (2018) 15. Klingsch, E.W.: Explosive spalling of concrete in fire. Institut für Baustatik und Konstruktion der ETH Zürich, Zürich (2014).https://doi.org/10.3929/ethz-a-010243000 16. British Stanadard Institution BSI: BS 476. Fire Tests on Building Materials and Structures. British Standards Institution, London, UK (1987) 17. Mustapha, K.N.: Modelling the effects of spalling on the failure modes of concrete columns in fire. PhD Thesis. Aston University, Birmingham, UK (1994) 18. British Stanadard Institution BSI: BS 8110: Part 2:1985, Structural use of concrete. Code of practice for special circumstances. Section four, Fire resistance, London, UK (1985) 19. Dzidic, S.: Otpornost betonskih konstrukcija na pozar. International BURCH University Sarajevo. Bosnia and Herzegovina, ISBN 978-9958-834-47-9, COBISS.BH-ID 22444550 (2015) 20. EN 1992-1-2:2004, Eurocode 2: Design of concrete structures, Part 1-2: General rules- Structural fire design. European Committee for Standardization (CEN), B-1050 Brussels, Belgium (2004)
Mathematical Model Identification of Measures for Improving the Energy Efficiency on Road Tunnels Facilities Mirza Berkovi´c(B)
, Adnan Omerhodži´c , Ajdin Džananovi´c , and Samir Džaferovi´c
Faculty of Traffic and Communications, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. The system of tunnel facilities undoubtedly represents a key part of the road infrastructure and subsystem of high energy consumption. Rational use of energy through increasing the energy efficiency of tunnel facilities and environmental protection are becoming a primary activity of road infrastructure managers. Tunnel buildings, as potentially the largest consumers of electricity with a significant energy balance and infrastructure facilities dispersed over a wider area, lead in the consumption of electricity in the road traffic system (lighting, mechanical installations, tunnel ventilation, etc.). For this reason, the aim of the work is to propose an original model for identifying potential measures to improve energy efficiency and energy saving of complex road structures. The results of previous research have identified several potentially significant areas for achieving energy savings, but have not yet offered an integrated mathematical model for quick and prioritized identification of the primary measures. By applying scientific methods, we proved the thesis that it is possible to establish such a generic model. Keywords: Model · Energy efficiency · Tunnel facility
1 Introduction The ventilation system is the biggest consumer of electricity during the exploitation of the road tunnel, and this necessitates the search for efficient structures and control algorithms that will minimize the consumption of electricity in normal operation while preserving the prescribed air quality in the tunnels with all accompanying elements. In general, the task of energy efficiency consists in comparing the current project documentation of tunnels and other facilities with international standards and the best solutions and identifying areas where there is an opportunity to reduce fuel and energy consumption, as well as the introduction of renewable energy sources while improving safety standards, lighting, power supply, and management. Based on the characteristics of the tunnel facilities and equipment on certain sections, the energy inspection steps are often specific. Different tunnels on certain sections and the necessary tunnel equipment form the main subject of energy efficiency research. In general, the methodological approach © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 92–102, 2023. https://doi.org/10.1007/978-3-031-43056-5_8
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to the implementation of the energy audit of the tunnel facility consists of three specific tasks: initial data collection (intensive communication, planning a visit to the tunnel location, etc.), implementation of the visit to the tunnel location and overall analysis of the collected data and the creation of a report. Also, the paper presents additional necessary sub-steps of the process of energy inspection and identification of measures. The main goal of the work is to create an applicable mathematical model for identifying priority measures for improving the energy efficiency of tunnel road subsystems. The scope of the research is related to basic energy consumption in the various tunnels planned on the highway network of corridor Vc (section through Bosnia and Herzegovina).
2 Tunnel Systems Components Tunnels are infrastructure facilities of various lengths and constructions that include various technical systems: tunnel fire alarm and fire protection systems, tunnel TV systems, tunnel radio communication systems, lighting systems, ventilation systems and LV power supply systems, and tunnel substations. During the design and construction of these facilities, it is necessary to comply with national and international regulations, standards, and recommendations for tunnel equipment, such as [1–3]: 1. Regulations on technical standards for electricity initial installations taken from SFRY (fig. List SFRY 4/74, 13/78, 53/88), 2. Austrian national guidelines for equipping VS tunnels, 3. Directive of the European Parliament on safety in tunnels in the trans-European road network 2004/54/EC, 4. Standard on the functionality of fire-resistant systems DIN EN 4102-12, 5. A set of instructions for the design, procurement, installation, and maintenance of elements, facilities, or parts of facilities on the highway (Public enterprise of Highways of the Federation of Bosnia and Herzegovina, 2013), 6. German national guidelines for equipping and managing RABT road tunnels. Also, the presence of tunnel equipment components and their basic division depends on the length of the tunnel and the road category. Figure 1 shows the presence of different tunnels on the Vc corridor [1, 4]. Depending on the lengths and adopted guidelines, different types of equipment can be identified in tunnel facilities (Table 1) [5, 6]. 2.1 Energetic Efficiency of Lighting System in Tunnels The lighting system in tunnels (LST) usually consists of internal zones and zones that adjust to the intensity of natural, external lighting [6]. Lighting zones can be in different modes of operation, which can be automatically and manually changed using a distributor, based on the signal of the intensity of external lighting. The remote monitoring and control system must provide operators in the control center with a choice between manual and automatic modes of operation, should the need arise. In the control center, it is necessary to enable: monitoring of the current lighting operating mode, fault signaling by distribution cabinets, and within them by fault groups, remote selection of the
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Fig. 1. Distribution of tunnels according to lengths (meters) on LOT 1 and 2 of corridor Vc [1].
lighting operating mode, etc. Regulation of energy consumption can be carried out in two important ways: by controlling the illuminance in the free space and by controlling the brightness in the free space. From a photometric point of view, the brightness control is more convenient in the approach zone. A camera measuring L20 is placed at a point that is distant from the entrance to the tunnel facility by a length equal to the stopping distance of the vehicle. Based on brightness changes in the access zone, the camera should activate or deactivate parts of the tunnel lighting installation, which is of course more economical [7] and energy efficient. 2.2 Energetic Efficiency of Tunnel Ventilation System The tunnel ventilation system (TVS) includes fan groups with dividers and devices for measuring the atmosphere in the tunnel (CO concentration, visibility, etc.). The mode of operation (manual/automatic) of the ventilation system is determined within the remote monitoring and control system. The automatic control of the tunnel ventilation system has two modes: normal and fire mode. The system must work fully automatically, but the possibility of continuous manual intervention under all working conditions must
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Table 1. Different types of tunnel equipment. The length of the tunnel (meters)
0–500
500–1000
1000–3000
Portal transformer station systems
+
+
+
Tunnel substation systems
–
–
+
UPS systems
+
+
+
Transformer station power supply systems
+
+
+
Tunnel lighting and LV power supply systems
+
+
+
Ventilation systems
–
+
+
Fire alarm systems with sensor cable
–
+
+
Automatic fire alarm systems
+
+
+
Automatic incident detection systems
+
+
+
Radio communication systems in the tunnel
–
+
+
Sound systems in the tunnel
–
–
+
Telephone call systems
+
+
+
Remote tunnel guidance systems
+
+
+
Traffic information system
+
+
+
Hydrant network pipe heating systems
Depending on the hydrotechnical project
also be ensured. The data that must be measured and recorded when managing and optimizing the ventilation system are CO concentration, visibility (in and outside the tunnel), longitudinal airflow speed, airflow direction, number of vehicles in the tunnel (for each tube), vehicle speed (for each tube), congestion detection, vibration on the fan, etc. From the aspect of energy efficiency, the management of the operation of the ventilation system requires an integrative approach and the synthesis of numerous multidisciplinary knowledge from several professions. Manual management of the ventilation system is more economically solution but carries a significant risk of liability. Tunnel management must be defined as a systemic process to achieve and maintain an acceptable level of safety. It is important to emphasize that the applied longitudinal ventilation can destroy the smoke stratification on the downwind side of the focus. 2.3 Energetic Efficiency of Fire Alarm System The automatic fire alarm system (FAS) consists of: a fire alarm control panel with a control-indication panel, sensor cable, controller for optical signal processing and its conversion into an electrical signal, automatic fire detectors, manual fire detectors, control input-output modules, local monitoring system, associated cable installation. At the tunnel level, fire alarm signals are used to control ventilation, lighting and are forwarded to the traffic system to achieve control of variable traffic signals. Considering the critical role of people’s safety, the remote-control algorithms related to the fire alarm system should be implemented within the tunnel equipment, and in this way, maximum safety
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and reliability of work will be achieved even with strict energy efficiency. Longitudinal ventilation using jet fans very effectively manages the spread of smoke for fires up to 100 MW. That is, it is only suitable for one-way tunnels. A potential foam flood system can be combined with a (mandatory) hydrant system into one installation, which can contribute to the cost efficiency and energy efficiency of the overall system. 2.4 Energetic Efficiency of Video Surveillance System The video surveillance system (VSS) consists of video surveillance cameras (mobile cameras with a motor zoom lens and fixed cameras that are additionally connected to devices for virtual vehicle detection), equipment and cables for transmitting video signals to the control center, equipment in the control center (software system, monitors and video recording system), etc. It is necessary to pay attention to the standard ETSI TS 103 199, “Environmental engineering of equipment” Criteria for the design of video systems and data centers for video recording should be based on international standards (TIA 942), adjustability, modularity, and energy efficiency. 2.5 Energetic Efficiency of Traffic Information System Traffic information system (TIS) includes the following functions: variable traffic signals, road traffic stations, and road traffic substations, measuring devices - meteorological stations, for measuring weather conditions on the section, measuring devices - traffic counters for measuring the density and speed of the traffic flow, i.e., traffic counting, PIS system equipment and the necessary equipment for the functional integration of all subsystems. When building these systems, it is necessary to pay attention to the application of the ecological GHG Protocol Product Standard for the ICT sector. 2.6 Energetic Efficiency of Telephone Call System Communication between road users and organizations and services for providing assistance or providing the information is achieved through the telephone call system. The telephone call system (TCS) is planned for all tunnels and consists of TPS stations installed in front of the entrance and exit portals of the tunnel and SOS stations in the tunnel SOS niches at a distance of 130–150 m from each other, as well as on the route by placing call posts on both sides of the highway at a distance of 2 km and a distance of 3 m from the edge of the road, i.e., the stop lane. In addition to the central equipment of the TPS system, the equipment of the traffic information system and equipment for monitoring and management in the control room are located in specially prepared rooms. 2.7 Energetic Efficiency of Radio Communication System The system of radio communication connections (RCS) should: enable voice communications, and quickly establish voice communications in emergency cases to save human lives and material goods. According to European directives and guidelines, the radio
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system is installed in tunnels longer than 500 m, and consists of: a central radio frequency station, central station of the mobile network, tunnel amplifier substations VHF, tunnel amplifier substations VHF/FM, tunnel amplifier substations of the mobile network, antenna system, handheld and mobile radio stations, radiating cable, connecting coaxial cables and fiber optic cable. Within this area, it is necessary to harmonize elements with the ETSI standard for ICT technologies of radio connections (ETSI provides certain technical standards for ICT in the area of measurement methods for the energy efficiency of radio access). 2.8 Energetic Efficiency of Sound System The system has been established so that all parts of the tunnel can be sounded loudly enough, while the clarity of the speech is such that users who have not turned off the vehicle can hear the operator’s notice or instruction. It is extremely important to foresee a good arrangement of speakers, to minimize the impact of noise caused by fan operation and traffic density. Sound systems (SO) should comply with Directive 2004/54/EC of the European Parliament, as well as guidelines for designing equipment in tunnels longer than 1000 m. 2.9 Energetic Efficiency of Hydrant System and Water Supply System The hydrant system (HS) is a set of pipelines, devices, and equipment that bring water from a safe source to protected areas and buildings where a fire can occur. The hydrant network control system should provide monitoring and management of access to the hydrant network, using switches on the doors of the hydrant niche that signals the opening of the niche door. With this system, it is necessary to pay attention to the minimum safety requirements of EU Directive 2004/54/EC, NFPA 502 standards, and PIARC recommendations for hydrant systems in tunnels [8]. 2.10 Energetic Efficiency of Power Supply System The power supply system (PSS) energy includes transformer stations and uninterruptible power supply devices. The control center should have available insight into the entire power supply system, energy, which enables monitoring and optimization of electricity consumption energy as well as quick fault location. The remote-control system of the tunnel monitors the state of the electricity supply, and in the event of a loss of the main power supply, takes measures for the safe flow of traffic following the project solutions of individual subsystems. According to the European Directives, the tunnel power supply system should be economical. It is recommended that tunnels longer than 900 m or a system of several tunnels be powered by two independent sources, each of which can power the entire tunnel system. While for tunnels shorter than 600 m, one source is quite sufficient.
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2.11 Energetic Efficiency of Other Systems in Tunnels Other systems (OS) that do not consume a lot of energy are the system for monitoring SOS niches and the system for monitoring the doors of pedestrian passages and passages for vehicles.
3 Concept of Energy Inspection of Tunnel Systems The proposed model includes a preliminary and detailed energy review of the system of tunnel facilities. A key part of the energy inspection of tunnel facilities is the identification of measures for changes in the operation of facilities and equipment or changes in the behavior of users, as well as recommendations for the implementation of interventions and the implementation of measures that improve the energy efficiency of facilities and equipment without jeopardizing or with the improvement of working conditions, comfort of stay, service process or quality of service in the facility. The primary goal of the energy review is to determine the energy properties of new or existing buildings and to provide recommendations for increasing energy efficiency. The energy review must contain the data required for energy certification. A detailed energy review is the first step in all programs of rational energy management of buildings and equipment. The review of the energy efficiency of buildings and equipment implies a detailed analysis of technical and energy characteristics and the analysis of all technical systems in tunnel buildings that consume energy and water to determine the efficiency/inefficiency of energy and water consumption and make conclusions and recommendations for improving the energy efficiency of buildings and equipment. The primary goal of the energy inspection of tunnel facilities and equipment is the collection and processing of detailed data on all technical systems in the facility and the determination of energy properties. The energy review of the tunnel facility could include [9, 10]: 1. Analysis of the technical specifications of the energy properties of the ventilation system; 2. Analysis of the technical specifications of the energy properties of the domestic hot water system; 3. Analysis of the technical specifications of the energy properties of the electricity consumption system - the system of electrical installations, lighting, and other subsystems of electricity consumption; 4. Analysis of the technical system management specifications of the facility; 5. Analysis of the possibility of using renewable energy sources and efficient systems. The energy audit of tunnel facilities and equipment, along with the identification of the possibility of applying energy efficiency improvement measures, must also include all the necessary information for conducting the energy certification procedure [11]. The energy efficiency of all tunnel processes can be defined as the ratio of the used output energy (work and usable energy) and the input energy used in the process (energy loss) [12, 13]. The energy efficiency of the tunnel system, i.e. the degree of useful effect (energy efficiency) is equal to the ratio of final energy to input energy: DETFS = FE / EITS
(1)
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Where are: DETFS - degree of efficiency of the tunnel facility system FE - finite energy EITS - energy input to tunnel subsystems
4 Mathematical Model of Identification of Potential Measures The mathematical model for identifying potential measures to improve the energy efficiency of the tunnel facility system must include a detailed audit of all components of the facility’s energy consumption, formalized: EEMTF = MTVS + MLST + MFAS + MVSS + MTIS + MTCS + MRCS + MSOP + MHS + MPSS + MSP + MOS + AD
(2)
Where are: TVS - tunnel ventilation subsystem LST - lighting subsystem in tunnels FAS - fire protection (alarm) subsystem VSS - video surveillance system TIS - traffic - the information subsystem TCS - telephone call subsystem RCS - radio communication connections subsystem SOP - sound subsystem HS - hydrant subsystem PSS - power supply system SP - sensor subsystem OS - other systems (for example the doors of pedestrian crossings) AD - additional measures that are within the scope of activities Within individual subsystems, it is necessary to further identify adapted measures to improve energy efficiency. For example, the lighting subsystem contains subcomponents: GTLn + TSLn + TELn + TMLn (3) MLST = Where are: GTL - general tunnel lighting TSL - tunnel safety lighting TEL - tunnel evacuation lighting TML - tunnel marking lighting or traffic information subsystem with subcomponents: MTIS = VTSn + TRFLn + SILn + TCn + AIDn Where are: VTS - variable traffic signaling TRFL - traffic lights and flashing lights SIL - signs with internal lighting
(4)
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TC - traffic counters AID - automatic incident detection camera The mathematical model proposes summarizing a comprehensive energy efficiency management system that includes detailed equipment on all major machines and processes, data loggers, software analysis, as well as further organizational policies. In addition to detailed analysis and sub-measurement, the system includes an overview of alternative energy sources, with all data in real time, with the possibility of intervention. E.g., mathematical modeling of the energy efficiency of the ventilation system and the dynamics of air mass flow inside the tunnel includes both ventilation equipment and traffic conditions. Electrical niches used for installation of equipment for power supply and management of ventilation and lighting equipment of the tunnel must also pass an energy audit, as well as SOS niches used for installation of equipment for power supply and accommodation (CPS devices, SOS cabinets and sound cabinets) [14].
5 Conclusion This model defines points of potential improvement, such as ways of using energy and systems and places where large energy losses are present, in order to determine measures for rational use of energy and increasing energy efficiency. The management of road infrastructure should be dedicated to an energy efficient model so that we can identify measures. All data from the billing system and measuring equipment should be available for calculating energy consumption and procurement trends. It is necessary to establish and define energy consumption records of all subsystems and prepare a list of equipment for energy certification. Also, the preparation of the subsystems list will enable the monitoring of energy costs, and reduce energy costs with appropriate measures. The final decision on the use of energy-efficient systems in all tunnel buildings must be made based on all parameters, and not only financial ones since the decision is significantly influenced by the rate of return on the investment. It is necessary to take a detailed look at the trend of electricity prices, which are very high today. The introduction of energy-efficient lighting is a positive step towards the implementation of new technologies and the implementation of energy efficiency measures. To be able to see the whole picture of the energy efficiency improvement project, it is necessary to have an integral mathematical model, which would be able to quantify in detail the effects of the introduction on all facilities and equipment. There are several ways to improve the energy efficiency of the tunnel ventilation system. E.g., improvement of installation factors, optimization of the system, improvement of the traffic monitoring system in the access zones to the tunnel and inside the facility, and the control ventilation system. These measures can be described as improving the ventilation control system and taking precautions to shut down the system when not needed. It is necessary to emphasize that the result of the work of the mathematical model can provide reliable information on the control energy consumption and the given limits, a reduced number of fan connections and disconnections, and thus an increased lifespan of both fans and switching equipment, savings in maintenance, increased safety, significant savings in electricity and an increase in energy efficiency. Bearing in mind the increasingly strict regulations on the content of pollutants in the exhaust gases of motor vehicles as well as
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the reduction of specific fuel consumption, it can be expected that the need for mechanical ventilation of tunnels in regular operation will decrease in the future and thus extending the period of use of natural ventilation. However, in unfavorable weather and traffic conditions, it will be necessary to include forced ventilation, which is feasible with a suitable management system. When it comes to the measure of renewable energy sources, it should be emphasized that these systems are still underutilized. The defined model for the identification of energy-saving measures and methodologies its application requires the decomposition of all consumers of tunnel systems.
References 1. Berkovi´c, M., Kosovac, A., Omerhodži´c, A.: Study of the identification of energy efficiency improvement measures on the highways of the Federation of Bosnia and Herzegovina. Faculty of Traffic and Communications University of Sarajevo (2014) 2. Lundin, J., Antonsson, L.: Road tunnel restrictions - Guidance and methods for categorizing road tunnels according to dangerous goods regulations (ADR). Saf. Sci. 116, 170–182 (2019) 3. Public company Motorway of the Federation of Bosnia and Herzegovina: Set of instructions for design, purchase, installation and maintenance of elements, objects or parts of objects on the highway (2012). https://www.jpautoceste.ba/images/set%20uputa.pdf. Accessed 17 Jan 2023 4. Public company Motorway of the Federation of Bosnia and Herzegovina: Highways on the Vc corridor in the Federation of Bosnia and Herzegovina, Report on project dynamics (2021). https://www.jpautoceste.ba/wp-content/uploads/2022/01/Q4-2021-Izvje% C5%A1taj-o-napretku-projekta-Izgradnja-autoceste-na-Koridoru-Vc.pdf. Accessed 25 Dec 2022 5. Lihi´c, H., Nišandži´c, R.: Electrotechnical systems on the highway - Coridor Vc (2015). http://www.ingkomora.me/ikcg_sajt/cms/public/image/uploads/Prezentacija_Elektr otehnicki_sistemi_na_autocesti_14112015.pdf. Accessed 13 Dec 2022 6. Božiˇcevi´c, J.: Ceste i cestovni objekti. Fakultet prometnih znanosti Zagreb, Zagreb (1974) 7. Nuhanovi´c, A., Mujaˇci´c, E., Radojˇci´c, R.: Analysis of the economic and development advantages of implementing the energy efficiency program in the local communities of BiH (2014). http://www.civilsocietylibrary.org/CSL/558/Analiza-ekonomske-i-razvojne-predno sti-provodjenja-programa-energetske-efikasnosti-u-lokalnim-zajednicama-BiH. Accessed 15 Jan 2023 8. International Performance Measurement and Verification Protocol - Concepts and Options for Determining Energy and Water Savings, vol. 1. http://www.eeperformance.org/uploads/ 8/6/5/0/8650231/ipmvp_volume_i__2012.pdf. Accessed 12 Jan 2023 9. Riess, I.: Improving the Energy Efficiency of Road Tunnels (2022). https://www.researchg ate.net/publication/358817296_Improving_the_Energy_Efficiency_of_Road_Tunnels#ful lTextFileContent. Accessed 11 Mar 2023 10. Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on energy efficiency, amending Directives 2009/125/EC and 2010/30/EU and repealing Directives 2004/8/EC and 2006/32/EC. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri= OJ:L:2012:315:0001:0056:en:PDF. Accessed 18 Dec 2022 11. Dzhusupova, R., Cobben, J.F.G., Kling, W.L.: Zero energy tunnel: renewable energy generation and reduction of energy consumption. In: 2012 IEEE International Universities Power Engineering Conference (UPEC), pp. 1–6. IEEE, Uxbridge (2012)
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12. Ministry of Spatial Planning, Federation of Bosnia and Herzegovina: Regulations on the conditions for persons performing energy certification of facilities, No 28/10, 59/11, 29/12 (2012). http://beep.ba/wp-content/themes/beep/pdf/Pravilnik_energetsko_certificiranje.pdf. Accessed 15 Jan 2023 13. Ministry of Spatial Planning, Federation of Bosnia and Herzegovina: Guidelines for conducting an energy audit for new and existing objects with a simple and complex technical system (2009). https://fmpu.gov.ba/wp-content/uploads/2021/05/smjernice_energijski_preg led.pdf. Accessed 17 Jan 2023 14. United Nations Development Programme: Guidelines for management and maintenance of thermo-energy systems in public facilities, Environmental Protection Fund. https://www.undp.org/sites/g/files/zskgke326/files/migration/ba/Smjernice-za-upravl janje-i-odrzavanje--termo-energetskih-sistema-u-javnim-objektima.pdf. Accessed 17 Jan 2023
Geodesy and Geoinformatics
Use of UAV for Object Deformation Detection Muamer Ðidelija(B)
and Esad Vrce
Faculty of Civil Engineering, University of Sarajevo, Patriotske lige 30, 71 000 Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. The popularization of small unmanned aerial vehicles on the market opened up the possibility of their wide application in Geodesy. This paper is focused on examining the possibility of using unmanned aerial vehicles when detecting deformation on an object. In addition, the minimum deformation that can be detected on the 3D model with an appropriate degree of reliability was empirically determined. An experimental field surveying of the stone subwall was carried out, after which, through data processing and deformation analysis, it was concluded that the minimum displacement value must be 15 mm to detect it on the 3D model. Keywords: Object deformations · Unmanned aerial vehicle · 3D model · Photogrammetry
1 Introduction Geodetic work in the context of monitoring construction facilities is present from the first stages of construction up to the monitoring of the geometric parameters of the facilities immediately after construction, and periodic monitoring of the objects later on. Traditional methods of monitoring and determining the geometric parameters of objects and their deformations generally involve the use of high-precision total stations from local geodetic networks, specially designed for geodetic observation [1–3]. The popularization of small unmanned aerial vehicles (UAV) has opened up a wide range of possibilities for their use in Geodesy, including deformation detection [4]. With the wellknown method of close-range photogrammetry and the use of appropriate software, it is possible to obtain detailed three-dimensional (3D) models of objects. The quality of those models depends on many factors, such as the quality of the camera and sensors on the UAV, the type of surface of the object, the number of control points, the number of photos and their overlay, etc. According to Yongquan et al. [4], a general categorization of UAV methods for deformation detection could be made into (a) visual detection of changes in the shape of an object or its parts and (b) detection of changes in positions (coordinates) of selected points on the object. Hallermann et al. [5] demonstrated the possibility of using a UAV during the visual inspection of bridges and gave the first steps in the development of a semi-automated structural damage detection method. Chen et al. [6] developed a UAV system whose photo analysis method can evaluate the degree of damage to buildings © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 105–114, 2023. https://doi.org/10.1007/978-3-031-43056-5_9
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after an earthquake. Weng et al. [7] presented a displacement detection method based on video captured by a digital camera mounted on a UAV. Catt et al. [8] installed two correlated digital cameras on a UAV and presented a system that can directly measure deformations on objects. By combining the method of photogrammetry and machine learning, Nex et al. [9] created an orthophoto and showed damage on objects. Tiwari et al. [10] presented a complex system for object deformation detection that includes the use of a terrestrial laser scanner (TLS), a global navigation satellite system (GNSS), a total station (TS), a multi-temporal interferometric synthetic radar (MT-InSAR) and a UAV. Similar systems were presented by Chen et al. [11] and Sangiorgio et al. [12]. In the context of using UAV for deformation detection, the question arises, “what is the lower limit of deformation on objects that can be detected on the model with adequate reliability?”. This paper is focused on examining the possibility of using 3D models for object deformation analysis, emphasizing detecting the minimum spatial deformation that can be detected. On that occasion, experimental surveying of the stone subwall was carried out in the area of the Faculty of Civil Engineering in Sarajevo. Two flights were made with the DJI Mavic Air drone, resulting in the creation of two 3D models. Between the first and second flight, manual movements were made to 10 selected points placed on the subwall, after which the possibility of reading the coordinates of those points on the models and analyzing the deformations was opened.
2 Methods and Data To test the possibility of detecting deformations on a 3D model obtained by photogrammetric surveying with a UAV, a stone subwall with a length of about 30 m and a height of about 2 m was chosen in the area of the Faculty of Civil Engineering in Sarajevo (see Fig. 1).
Fig. 1. Surveying location (43°.867661 N, 18°.413767 E). Source: Google Maps [13].
The research plan included the creation of two 3D models of the subwall, model I before displacement and model II - after displacement of certain points. Twelve ground control points (GCP), and 20 checkpoints (CHK) were set for the needs of model accuracy assessment. Due to the later analysis of deformations, a total of 10 deformation test points (DTP) were set. The three classes of points are visualized on the subwall in different ways (see Fig. 2). Figure 3 schematically shows the position of all points placed on the subwall.
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Fig. 2. Three classes of points set on the subwall.
Fig. 3. Points layout on the subwall.
Before surveying with a UAV, a geodetic survey base was established, consisting of two points stabilized near the subwall. From the points of the base, all the points placed on the subwall were observed in two giruses. On that occasion, a Trimble S7 total station was used, with an angular accuracy of 1” and distance accuracy of 2 mm + 2 ppm. The network is adjusted as a free network or network with a free datum. With free networks, the singularity of the system of normal equations appears, which is why they are solved by pseudo-inversion [14]. After surveying with a total station from both base points, surveying with a UAV was started. The DJI Mavic Air (see Fig. 4) and the corresponding Android application DJI GO 4 were used on that occasion. The most important parameter during the flight mission was the longitudinal and transverse overlap of the photos, and it amounted to 85% for both subwall models. The distance of the aircraft from the wall was on average 5 m. It should be noted that the camera was calibrated before surveying.
Fig. 4. DJI Mavic Air with basic specifications. Source: Mavic Air Specs [15].
After surveying the subwall with the original setting of DTP points, manual movements of them were performed. In this regard, the DTP points suffered displacements of 5 mm, 15 mm, 30 mm, 45 mm, and 60 mm in different directions. Five displacements were made along the subwall surface, that is, in the xH plane, and five radial displacements, that is, displacements in the yH plane, were made. The new point positions were
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given the nomenclature DTPP for practical reasons. Figure 5 illustrates the layout of the DTP points and the manual displacements performed.
Fig. 5. Schematic presentation of manual displacements (not in scale). Radial displacements are marked with filled dots.
It is important to note that the GCP and CHK points did not move before the second flight. After the appropriate movement of the DTP points, the drone was used again and the subwall was surveyed using the same method of close-range photogrammetry. This created the conditions for making both models and reading the coordinates of the DTP points before displacement (model I) and after displacement (model II), as well as deformation analysis. It is emphasized that before the deformation analysis, the presence of gross errors of terrestrial measurements and the accuracy homogeneity of the two models were checked (variance test), according to the recommendation from [14]. A summary of the research methodology is shown in Fig. 6.
Fig. 6. Research methodology diagram.
3 Results, Analysis, and Discussion 3.1 Terrestrial Measurement After all fieldwork was completed, data processing began. First of all, the coordinates of all surveyed points on the subwall were calculated in the DKSBIH coordinate system with their standard deviations. The total degree of freedom during adjustment was: f = 162, and the mean reference error was: s0 = 0.79. The adjustment report states that there are no gross errors in measurements. Average values of standard deviations and absolute error ellipses for all classes of points are shown in Table 1. Given that all classes of points have identical values of average standard deviations, it can be concluded that there is homogeneous accuracy. The total values of standard deviations and error ellipses indicate a high positional quality of the coordinates. Relatively high values of standard
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deviations of point heights of 9 mm are expected given that the heights were determined using the trigonometric leveling method, and that the lengths were measured without a reflector, i.e. using a total station laser - DR (Direct Reflex). Table 1. Average values of standard deviations and absolute error ellipses. Units are [mm]. s3D
a
b
θ [°]
3
9
4
2
82
3
9
3
1
86
3
9
3
1
88
9
3
2
88
9
3
1
86
Point class
sy
sx
sH
s2D
GCP
2
2
9
CHK
2
2
9
DTP
2
2
9
DTPP
2
2
9
3
Overall
2
2
9
3
3.2 3D Model and Accuracy Assessment The next step in the research process involved the creation of a 3D model of the subwall before displacement (I model) and after displacement (II model), and the assessment of the absolute and relative accuracy of both models according to the method presented in [16]. The basic parameters of the models are shown in Table 2, and Fig. 7 illustrates the number of photos that overlap on certain parts of the model. Figure 8 shows one detail from both models. Table 2. Basic parameters of 3D models. Parameter
Model I
Model II
No. of photographs
57
53
Spatial resolution
1.37 mm/pix
1.39 mm/pix
No. of points in the cloud
66070
67356
RMSEGCP
5.9 mm
9.7 mm
Fig. 7. Photos overlay and camera positions.
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Fig. 8. Detail from model I (left) and model II (right).
After the creation of both models, their accuracy was assessed. To assess absolute accuracy, a total of 20 checkpoints (CHK) were placed on the subwall, which were evenly distributed, and for the assessment of relative accuracy, 32 different lengths ranging from 3 cm to 30.157 m were recorded. Shorter lengths, of the order of a few centimeters, were not surveyed directly by the total station, but the characteristic lengths of the squares on the symbol of the GCP or CHK point and the like were taken in advance. It should be noted that identical points and lengths were used to assess the accuracy of both models to make the results consistent. Tables 3 and 4 show the main parameters of the assessment of the absolute and relative accuracy of both models. Note: I stand for model I and II stand for model II. Table 3. Absolute accuracy of models. Units are [mm]. dy
dx
dH
d 2D
d 3D
Model
I
II
I
II
I
II
I
II
I
II
Min
–3
–6
–6
–8
–4
–11
1
1
1
2
Max
3
7
8
8
3
9
9
9
9
11
Range
6
13
14
16
7
20
8
8
8
9
Average
1
0
–1
0
0
–2
4
5
4
8
RMSE
1
3
4
4
2
5
2
2
2
3
Where residuals are: d y = yTS – yUAV , d x = x TS – x UAV , d H = H TS – H UAV , d 2D = (d y 2 + d x 2 )1/2 , d 3D = (d y 2 + d x 2 + d H 2 )1/2 . Figure 9 illustrates the absolute accuracy of both models through the field obtained by IDW (Inverse Distance Weight) interpolation of residual values at checkpoints. Figure 10 shows the percentage distribution of 3D residuals in both models. The absolute accuracy of the first model of 2 mm and the second model of 3 mm, and the relative accuracy of 4 mm and 5 mm for the first and second models respectively,
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Table 4. Relative accuracy of models. Values represent residuals’ statistics computed by subtracting distances measured on a 3D model from appropriate distances measured by the total station on the field. Units are [mm]. Model I
Model II
Min
–9
–9
Max
9
10
Range
18
19
Average
–1
2
RMSE
4
5
Fig. 9. Absolute accuracy of model I (a) and model II (b).
Fig. 10. Percentage distribution of 3D residuals.
indicate the high quality of the obtained 3D models. This is the result of factors such as the relatively small distance of the UAV from the subwall during flight (5 m), a large number of GCP (12 of them), and a large number of overlapping photos (9 of them) on almost the entire surface of the subwall (see Fig. 7). The same factors also affected the high spatial resolution of the model (1.37 mm/pix). By analyzing the distribution of residuals in both models, it is obvious that the 3D residuals are approximately normally distributed (see Fig. 10). 3.3 Homogeneous Accuracy Test of Two Models Given that the measurement and creation of two models were carried out for deformation analysis, it was necessary to statistically examine whether the two models, or rather the
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two samples, have homogeneous accuracy. On that occasion, a residual variance test (F-test) was performed. F=
s12 s22
= 2.10
FC (0.05, f1 , f2 ) = 2.15
(1) (2)
where are: F empirical value of F-distribution, s1 2 model I variance, s2 2 model II variance, F c critical value of F-distribution, f 1 model I degree of freedom, f 2 model II degree of freedom. As the empirical value of the F-distribution F is less than the critical value F c , it can be stated that there is no statistical evidence that the two samples do not have homogeneous accuracy, which means that the null hypothesis of the F-test is accepted [17]. Statistically speaking, the residuals of the two models belong to the same population, that is, they have homogeneous accuracy, which enables their direct comparison during deformation detection. 3.4 Detection of Deformations The main part of this paper is the analysis of deformations and the examination of the possibility of detecting them based on two 3D models obtained by the photogrammetric method, as well as the determination of the minimum deformation that can be detected. In this regard, deformation test point (DTP) coordinate readings were taken on the model I (before displacements) and model II (after displacements), and the corresponding displacements were calculated. Movements at the GCP and CHK points were not detected because those points were not moved. Figure 11 shows the results. True and detected deformation are compared and visualized, and also angular differences in deformation directions are shown.
Fig. 11. Results.
By comparing the deformations detected on the model and the actual manual deformations (true deformations) made in the field, differences are noticeable. The biggest
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difference occurs with the smallest manual displacement of 5 mm in both observed planes. This indicates that such a small deformation cannot be detected with high reliability on the 3D model. Differences in other manual deformations are approximately within the confidence interval defined by the relative accuracy of the model (± 5 mm). The average difference in the direction of the detected deformation is 21°, which does not represent a large error considering the small linear values of the deformations. When all the results are presented and analyzed, an empirical conclusion is imposed that relies on the well-known geodetic rule “3”. Three times the value of the model spatial resolution (SR) is approximately equal to the relative accuracy of the model (RA), while three times the value of the relative accuracy of the model (RA) is approximately equal to the minimum deformation (MD) that can be detected with adequate confidence, which is again determined by the relative accuracy of the model. The mathematical notation of this empirical conclusion is as follows: RA ∼ = 3 × SR
(3)
MD ∼ = 3 × RA = 9 × SR
(4)
4 Conclusion Detecting deformation on objects is a geodetic work that traditionally involves the use of high-precision total stations. Measurements are performed in at least two epochs, which assume the state before and after the deformations. According to the same principle, this paper presented a method of detecting deformation on a stone subwall using the DJI Mavic Air drone, and two 3D models (before and after manual deformations) obtained by the method of close-range photogrammetry. The main task was to answer the question “what is the minimum deformation that can be detected on a 3D model with adequate reliability?”. To answer the question, a field experiment was conducted in the area of the Faculty of Civil Engineering in Sarajevo. A stone subwall with a length of about 30 m and a height of about 2 m was chosen as a test object. Fieldwork implied the establishment of a geodetic surveying base in such a way that two geodetic points were stabilized. From these points, all the points placed on the subwall were surveyed in two giruses: GCP, CHK, and DTP points. After that, the subwall was surveyed with a UAV for the first time (model I). Then, manual displacements were made at 10 deformation test points and the subwall was surveyed for a second time (model II). The two obtained models are equivalent to two epochs in classical deformation analysis. By processing the measurements and analyzing the results, a conclusion was reached that the UAV can be used when analyzing the deformations, provided that the spatial resolution of the model, as well as the absolute and relative accuracy of the 3D model, is high. The minimum value of displacement or deformation that can be reliably detected directly depends on the spatial resolution and the relative accuracy of the model. With the equipment used in this experiment, it was empirically concluded that the minimum detectable deformation is three times the relative accuracy of the model, while the confidence interval of the detected deformation is directly determined by the relative
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accuracy of the model. The minimum deformation that could be detected was 15 mm, with a confidence interval of ± 5 mm, which is also equal to the relative accuracy of the model.
References 1. Marjetiˇc, A.: TPS and TLS laser scanning for measuring the inclination of tall chimneys. Geodetski glasnik 49(52), 29–43 (2018) 2. Ðidelija, M.: Regresiona analiza u funkciji ispitivanja geometrijskih parametara objekata visokogradnje. Geodetski glasnik 50(53), 45–70 (2019) 3. Hamzi´c, A., Kamber Hamzi´c, D.: Ispitivanje vertikalnosti objekata sa kružnom bazom: komparacija metoda. Geodetski glasnik 52(55), 42–60 (2021) 4. Yongquan, G., Yu, X., Chen, M., Yu, C., Liu, Y., Zhang, G.: Monitoring dynamic deformation of building using unmanned aerial vehicle. Math. Probl. Eng. 2021, Article ID 2657689 (2021). https://doi.org/10.1155/2021/2657689 5. Hallermann, N., Morgenthal, G.: Visual inspection strategies for large bridges using unmanned aerial vehicles (UAV). In: Proceedings of the IABMAS, Shanghai, China (2014). https://doi. org/10.1201/B17063-96 6. Chen, J., Liu, H., Zheng, J.: Damage degree evaluation of earthquake area using UAV aerial image. Int. J. Aerosp. Eng. 2016, Article ID 2052603 (2016). https://doi.org/10.1155/2016/ 2052603 7. Weng, Y.F., Shan, J.Z., Lu, Z., Lu, X.L., Spencer, B.F.: Homography-based structural displacement measurement for large structures using unmanned aerial vehicles. Comput.-Aided Civil Infrastruct. Eng. 36(9), 1114–1128 (2021). https://doi.org/10.1111/mice.12645 8. Catt, S., Fick, B., Hoskins, M., Praski, J., Baqersad, J.: Development of a semi-autonomous drone for structural health monitoring of structures using Digital Image Correlation (DIC). In: Niezrecki, C., Baqersad, J. (eds.) Structural Health Monitoring, Photogrammetry & DIC, vol. 6, pp. 49–57 (2018). https://doi.org/10.1007/978-3-319-74476-6_7 9. Nex, F., Duarte, D., Steenbeek, A., Kerle, N.: Towards real-time building damage mapping with low-cost UAV solutions. Remote Sens. 11(3), 287 (2019). https://doi.org/10.3390/rs1 1030287 10. Tiwari, A., Narayan, A.B., Dikshit, O.: Multi-sensor Geodetic Approach to Deformation Monitoring. Scientific Assembly of the International Association of Geodesy, Beijing (2021) 11. Chen, M., Zhang, G., Yu, C., Xiao, P.: Research on the application of an information system in monitoring the dynamic deformation of a high-rise building. Math. Probl. Eng. 2020, Article ID 3714973 (2020). https://doi.org/10.1155/2020/3714973 12. Sangiorgio, V., Martiradonna, S., Uva, G., Fatiguso, F.: An information system for masonry building monitoring. In: IEEE International Conference on Service Operations and Logistics, and Informatics, Bari, Italy (2017). https://doi.org/10.1109/SOLI39614.2017 13. Google Maps. https://maps.google.com. Accessed 30 Oct 2022 14. Vrce, E.: Deformacijska analiza mikrotriangulacijske mreže. Geodetski glasnik 40(45), 14–27 (2012) 15. Mavic Air Specs. https://www.dji.com/mavic-air/info. Accessed 30 Oct 2022 16. Mulahusi´c, A., et al.: Quality evaluation of 3D heritage monument models derived from images obtained with different low-cost unmanned aerial vehicles. Geodetski List 76(99), 7–23 (2022) 17. Franki´c, K.: Metoda najmanjih kvadrata u geodeziji. Geodetski odsjek Gradevinskog fakulteta u Sarajevu. Sarajevo (2010)
An Evaluation of the Dams Crest Movement Influenced by Thermal Variations: A Machine Learning Approach Adis Hamzi´c(B) Faculty of Civil Engineering, University of Sarajevo, Patriotske lige 30, 71 000 Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. Structural health monitoring of the dam is a very important task that is conducted by geodetic and civil engineers. The methods, instruments, and models used by engineers to identify the current and future state of the object are different but have the same goal: to ensure that the object is operable and safe for use. This paper presents a model based on machine learning methods for evaluating and predicting the dam’s crest movement based on time series data of concrete temperature. The dam’s crest movements obtained by the geometric alignment method and the concrete temperature of the dam were used to fit a model for evaluation and prediction of the dam movements. Classical artificial neural networks (ANN) were used to determine the correlation between concrete temperature and the dam’s crest movement, while a nonlinear autoregressive (NAR) neural network was used for univariate time series prediction of the concrete temperature. The short-term predictions of the concrete temperature obtained by NAR networks were used to predict dam crest movements. The proposed model was compared with a statistical model based on autoregressive integrated moving average (ARIMA) models and multilinear regression (MLR). The results showed that the proposed model can give accurate short-term predictions of the dam movement while there are no significant differences in obtained results between the machine learning model and statistical model which was used for comparison. Both models had satisfactory mean absolute errors of the prediction but at the same time maximal errors were significant which should be addressed in future research. Keywords: Structural Health Monitoring · Large Dams · Thermal Variations · Time Series Prediction · Artificial Neural Networks · Nonlinear Autoregressive Neural Networks
1 Introduction International Commission on large dams (ICOLD) gives the following definition of a large dam: “A dam with a height of 15 m or greater from lowest foundation to crest or a dam between 5 m and 15 m impounding more than 3 million cubic meters” [1]. As for any other large structure, large dams should be subjected to regular monitoring. This © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 115–131, 2023. https://doi.org/10.1007/978-3-031-43056-5_10
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monitoring usually involves different methods and instruments which are commonly used by geodetic and civil engineers. These experts observe not only the object itself but also the surrounding area, additionally, dam movements are just one variable that is involved in process of determining the health of the dam but all factors which influence these movements are monitored and analyzed. Besides the current state of the dam, dam monitoring should give answers about the future safety of the object. To give an accurate prediction of the future dam movement it is necessary to identify all factors which influence dam behavior. Three main factors which influence dam movements are dam ageing, hydrostatic pressure, and thermal variations. Of course, other factors such as water temperature, frost, snow, pore pressure, etc. also influence dam movement but these factors are less significant compared to the abovementioned three factors. Earthquakes are also a significant factor that influences dam stability but due to their unpredictable nature and unknown direction of influence, it is hard to practically include them in a model of a dam behavior. There are numerous researchers and practitioners who publish hundreds of papers regarding dam monitoring each year. In the following text, an overview of used models for dam movement analysis and prediction is given based on the papers which were available to the author. The focus is on the used models for modeling and predicting dam behavior and not on the methods and instruments used for acquiring data. The most commonly used model for dam movement prediction of concrete dams is the hydrostatic-seasonal-time (HST) model which was proposed by Willm and Beaujoint in 1967 [2]. This multilinear regression (MLR) model assumes that dam movement is a linear combination of hydrostatic load, the reversible influence of the air temperature, and the evolution of the dam response over time. The assumption that the abovementioned factors are not correlated is the main weakness of this model. The HST model is still used today but it is often altered or used in combination with other statistical or machine learning methods. Chouinard and Roy [3] created a simulation model that was used to develop guidelines on the applicability of the HST model to different types of dams as a function of the frequency of readings, the length of measurement, and the uncertainty associated with measurements. These guidelines were then evaluated by comparing the predicted and actual performance of the model. The authors concluded that for dams that are mainly subjected to seasonal effects, 6 yearly measurements are enough to develop a quality model for the dam movement. Gamse and Oberguggenberger [4] successfully used the HST model for the movement modeling of a rock-fill embankment dam which shows the flexibility of the model since it was originally developed for concrete dams. The authors analyzed the correlation between the long-term coordinate time series of a geodetic point on the crest of the dam and factors which influence dam movement. In the research, the authors concluded that after the inclusion of significant coefficients of the hydrostatic load and long-term trend, the residual time series still expose underlying periodicities. To address the weaknesses of the HST model, a hydrostatic-thermal-time (HTT) model was introduced in [5]. The main idea of this approach is to replace the seasonal function from the HST model with recorded temperatures that better represent the thermal effect on dam behavior. The principal component analysis (PCA) was applied in two ways in this research: to choose thermometers for use in the HTT model and to represent thermal effects. The proposed HTT model had better accuracy compared to the
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HST model and additionally, it was easy to implement. Penot, Fabre, and Daumas [6] further address issues of the classical HST model and propose a thermal HST (HSTT) model. The HSTT model improves seasonal function by taking into account a delay effect on the difference between the seasonal values of the daily air temperatures. The model change led to a decrease of residual scatter in the majority of cases (approximately 50 dams used in the research) which further allowed better real-time detection of anomalies and a better understanding of the behavior of the dams. Tatin et al. [7] developed a hybrid physical-statistical model named HST-Grad to improve the HSTT model which does not explicitly account for the influence of the water temperature on the dam movement. HST-Grad model considers both the mean temperature and the temperature gradient for modeling dam movements. In this model, not only the air temperature is introduced but also the water temperature in order to estimate the upstream temperature. Compared to the original HSTT model, the HST-Grad model had 5 times smaller standard deviations of the residuals. An improvement of the previous model was presented in [8] where an arch dam is modeled by means of the finite element method to serve as a virtual case study to evaluate the proposed model. In this model water temperature profile is taken into account since the thermal gradient may be very different from the top to the bottom of the dam depending on the seasons. The model was compared with HSTT and HSTGrad models and the authors stated that the newly proposed model is more realistic, with only the cost of measurement of the water temperature profile. In HST models the residual time series can still expose some underlying periodicities. Hence, Gamse et al. [9] extended HST model by adding additional sinusoidal terms for the statistically most significant frequencies. In the study case on Alqueva concrete arch dam, the extended HST model showed improved results compared to the optimal classical HST model. Yang et al. [10] proposed a hybrid hydraulic-seasonal-time (HHST) model including the finite element method and particle swarm optimization (PSO) algorithm. The research was conducted on Jinping I project - the highest concrete dam in the world. In conclusion of the research, authors stated that the HHST model performed better than the HST model and the PSO algorithm shortens the computation time and avoids premature convergence. Since the dam behavior is known to be complex and nonlinear it was common sense to apply machine learning methods for modeling the dam movement. These methods are used independently and in combination with statistical methods. Belmokre, Mihoubi, and Santillán [11] developed a thermal model based on random forest (RF) regression that accounts for solar radiation, shading, night and evaporative cooling, convection with the air, and longwave radiation exchange. The proposed model was compared with other statistical models and an artificial neural network model. The results showed that the proposed thermal model provides good predictions of the dam movements and even improves the results of the other models. Liu et al. in [12] proposed models based on long short-term memory (LSTM) networks for the long-term deformation of arch dams. PCA and moving average (MA) method were combined with the LSTM network and the results were compared to traditional statistical and ANN models. Both proposed models performed better than classical HST models for dam movement prediction and the LSTM-MA model proved to be more suitable for engineering applications due to its convenience. Su et al. [13] proposed a model based on LSTM networks that can provide an accurate prediction of dam deformation response. In this approach, the RF
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model was used to assess s importance of impact factors and to screen input variables. Additionally, the density-based spatial clustering algorithm was used to reduce random errors in the measurements. Proposed model performed the best among all tested models which included: classical HST model, models based on support vector machines (SVM), RF model, LSTM model, and multilayer perceptron (MLP) model. Mata [14] compares the performance of the MLR and ANN models for modeling of the dam behavior under influence of the environmental loads. NN models showed flexibility and had better performance in months with extreme temperatures compared to MLR models with the same variables. Kang et al. [15] applied a radial basis function neural network (RBFN) to mine the temperature effect from the long-term time series measured daily air temperature which was then used to model dam behavior. The research showed that the proposed model by utilizing the long-term air temperature of the dam performs better compared to the classical model which used harmonic sinusoidal functions to simulate temperature effect. Authors conclude that the advantage of the proposed model is that it is data-driven and does not have to make previous assumptions about the physics of the phenomenon. Hamzi´c, Avdagi´c, and Beši´c [16] proposed a localized approach for dam movement prediction. Since influencing factors do not have the same effect on every part of the dam, two individual models are developed for every point strategically placed on the object: one model for the analysis and prediction in the direction of the X-axis and the other for the Y-axis. Influencing factors were predicted by ARIMA models and time series ANN, later those values were used in fitted MLR models and ANN models for prediction of the dam movement. The proposed approach is complex and time-consuming but it gives accurate assessment for every part of the object. A comprehensive study on models and methods for structural health monitoring of the dams is presented in [17] by Salazar et al. The authors provide theoretical background for the most commonly used models for dam stability and movement analysis but also highlight advantages and issues for those models. After studying more than 50 research papers from authors and study cases all around the world it is concluded that although dam movement models are improving daily, engineering judgement based on experience is critical for building the model, for interpreting the results and for decision making with regard to dam safety. In this research the data is obtained from measurements on the HPP Salakovac concrete gravity dam. The displacements of the dam crest were measured using the optical alignment method, and the temperature of the dam was measured by automatic sensors that were distributed in several locations in the dam body. Nonlinear autoregressive (NAR) neural networks were used to predict the time series of the concrete temperature measurements of the dam 30 days ahead, while classical ANNs were applied to establish the correlation between the temperature of the dam concrete in the three levels (foundation gallery, inspection gallery, and dam crest) and the displacement of the dam crest. Complete data processing was performed by using MATLAB and MS Excel program packages. Obtained results were compared to those presented in [18]. Study [18] used ARIMA models to predict the time series of concrete temperature, while MLR was used to investigate the correlation between dam crest movement and concrete temperature – the used model is a pure statistical model. In this research used data (concrete temperature and dam movement data) is exactly the same as in [18] but the model is
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based on classical ANNs and time series ANN – NAR network. Classical ANNs were used to investigate the correlation between dam movement and thermal variations while the NAR network was used to predict the concrete temperature of the dam.
2 Data and Methodological Framework This research was conducted by using real data from the concrete gravity dam Salakovac which was built on the Neretva River approximately 15 km north of the city of Mostar. Table 1 gives a technical specification of the Salakovac dam and the longitudinal crosssection of the dam is presented in Fig. 1. Table 1. Technical specifications of the Salakovac dam [18]. Feature
Value
Type
Concrete gravity dam
Construction height
70.00 m
Geodetic height
52.00 m
Dam crest length
230.50 m
Dam crest height above sea level
127.00 m
Maximal water level
124.70 m
Number of blocks
17
Length of the blocks
6.00 m–22.50 m
Fig. 1. Longitudinal cross-section of the Salakovac dam [18].
The data on the temperature of the dam’s concrete and dam crest displacements obtained by alignment measurements were used in this research. Sensors for concrete temperature measurements of the dam are installed in three vertical and three horizontal
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levels in blocks VI, VIII, and XIII (see Fig. 1). Sensors are installed in the dam walls at the level of the foundation gallery, the inspection gallery, and on the crest of the dam. In the horizontal sense, the sensors are arranged on the upstream side, downstream side, and in the middle of the dam. The sensors make constant measurements and the data is sent to a server that records hourly temperatures in the form of time series. A time series measurement of the daily mean concrete temperature in three levels (foundation gallery, inspection gallery, and dam crest) of the dam’s block VIII, and dam movement observed in the three observation points is presented in Fig. 2.
Fig. 2. Mean daily temperature in block VIII observed from 04.04.2014. to 15.07.2020., and dam movement data observed in points M 131, M 151, and M 171.
As seen in Fig. 2 the shape of the red curve is shifted in time compared to the blue curve, and additionally, the black curve is shifted compared to the blue and red curves. The crest of the dam is directly influenced by warm and cold air, hence concrete of the dam in this area has faster and more significant changes in temperature. The concrete of the dam slowly absorbs heat from the air which causes temperature delays in the central and lower parts of the dam – in the inspection gallery and foundation gallery. This causes that maximal temperature of the concrete in the inspection gallery to be delayed compared to the dam’s crest, and the temperature of the foundation gallery to be delayed compared to both dam’s crest and inspection gallery. Since the air temperature influences directly (solar radiation) and indirectly (through water temperature in the accumulation) the concrete temperature, minimal and maximal differences for all three curves is approximately 6 months. Due to this connection between temperature values in different parts of the dam, it is possible to predict the temperature in one part of the dam based on observations in another part of the dam. The connection between the concrete temperature of the dam and the dam’s crest horizontal movement is evident from the graphs presented in Fig. 2. Based on the graphed
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observation data of the three points (M 131, M 151, and M 171) it is evident that the maximal and the minimal values of the concrete temperature corresponds to minimal and maximal values of dam movements. Observations of only three observation points are presented in Fig. 2 to avoid graph lines overlapping since observation points that are close have similar dam displacement values. The displacements of the dam’s crest were obtained by geometric alignment method by using Pizzi high-precision optical collimator. The collimator allows the measurement of the relative displacement of several points along an optical alignment fixed by two points positioned on the stable ground (see Fig. 3).
Fig. 3. Geometric alignment scheme measurement for the arch dam (left) and gravity dam (right).
The instrument is positioned at the point known as the “station point” which is usually a concrete pillar specifically build for this purpose. Sometimes, a small object is built that consists this pillar hence when the instrument is once positioned on the pillar it does not have to be moved during the long-term observation period of the dam. The main purpose of this object is to protect the instrument from all negative influences (natural and human). An optical alignment is defined by the station point and fixed the target known as “reference point” which is also positioned on the stable ground. In the specific case of the Salakovac dam, the station point for alignment measurements is at the same time a geodetic pillar used for dam observation by geodetic methods while the reference point is a geodetic prism which is also observed during dam monitoring by precise tacheometry. The purpose of this dual function is to confirm the stability of the station point and reference point by geodetic measurements and not just assume they are built on stable ground. Additionally, by including these points in the geodetic network in the case that the station point or reference point is subjected to movements it is possible to recalculate observed displacements by alignment method as these points are on the original coordinates. For every point to be monitored on the dam body (observation points) there are fixed bases for the mobile target. The mobile target is equipped with a special trolley carrying the screen, which can move horizontally with respect to the base. The extent of the
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translation is read on special decimal nonius placed between the trolley and the base. By means of a special knob, it moves the carriage until the screen is centered on the optical alignment. The difference between the measure previously detected (usually the first measurement in the time series) and the last one detected represents the displacement of the observation point with respect to fixed alignment and therefore the movement of the structure [19] (Fig. 4).
Fig. 4. Optical collimator, fixed base for the moving target, and moving target [18].
A total of 74 series of measurements of the displacement of the dam’s crest were used in the research, of which 62 are series used for model fitting, and 12 series for model validation. A series of measurements that are used in the research were observed in the period from 04.04.2014. to 16.07.2019., and validation series in the period from 16.07.2019. to 15.07.2020. Measuring points M111 and M121 are not included in the research considering that they are located far from the sensors for measuring the temperature of the concrete which are installed in blocks VI, VIII, and XIII (see Fig. 1). In this research, for the prediction of the concrete temperature of the dam NAR networks were used, and for the prediction of the dam’s crest movement classical feed-forward backpropagation (FFBP) ANN, cascade-forward backpropagation (CFBP) ANN, and layer recurrent backpropagation (LRBP) ANN were used. ANNs mimic the human brain to solve complex tasks. ANNs are very popular among researchers because they are data-driven and can recognize patterns that are otherwise hidden. By using historical data NAR network enables the prediction of future values of a time series. NAR networks use a re-feeding mechanism, in which a predicted value serve as an input for new predictions at later points in time [20]. The network with this re-feeding mechanism is also called a closed-loop network because they continue to predict when external feedback is missing, by using internal feedback [21]. NAR model can be defined by the following equation [21]: (1) y(t) = f y(t − 1), y(t − 2), . . . , y t − ny In Eq. (1) ny denotes the number of previous observations used to predict the y value at the moment t. To predict dam crest movement three classical ANNs with BP algorithm were used. FF ANN consists of a series of layers. The first layer has a connection from the network input. Each subsequent layer has a connection from the previous layer. The final layer
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produces the network’s output. A variation on the FF ANN is the CF network, which has additional connections from the input to every layer, and from each layer to all following layers. While in the LR networks, there is a feedback loop with a single delay around each layer of the network except for the last layer. This allows the network to have an infinite dynamic response to time series input data [21]. BP algorithm has the role to test for errors and to optimize weights by working backward, i.e. from output nodes toward input nodes. From the total of 74 series of observations of the dam movement, 62 series were used for ANN fitting and 12 series were used to assess model performance on previously unseen data - to assess the generalization abilities of the proposed model for the dam movement prediction. The 62 series of data used for fitting ANN models were divided so 70% of data were used for training, 15% for testing, and 15% for validation. For ANN training, Levenberg- Marquardt’s backpropagation algorithm is used because of its known fast convergence ability. The training set is used for computing the gradient and updating the network weights and biases. The error on the validation set is monitored during the training process. The validation error normally decreases during the initial phase of training, as does the training set error. However, when the network begins to overfit the data, the error on the validation set typically begins to rise. When the validation error increases for a specified number of iterations the training is stopped, and the weights and biases at the minimum of the validation error are returned. The test set error is not used during training, but it is used to compare different models [21]. This method which uses validation error monitoring (stopping ANN training if the validation error is increasing) for improving the generalization of ANN is called “early stopping”. This technique is automatically provided for all of the supervised network creation functions in MATLAB software [21]. The main issue when using ANNs is to determine the number of hidden layers and the number of neurons in the layers. Heaton [22] states that ANN with one hidden layer can approximate any function which contains a continuous mapping from one finite space to another, while two hidden layers can represent an arbitrary decision boundary to arbitrary accuracy with rational activation functions and can approximate any smooth mapping to any accuracy. To determine the number of neurons in the hidden layers the following guidelines are given: • The number of hidden neurons should be in the range between the size of the input layer and the size of the output layer, • The number of hidden neurons should be 2/3 of the input layer size, plus the size of the output layer, and • The number of hidden neurons should be less than twice the input layer size. These guidelines were used to determine starting structure of the ANN and then it was experimented to find the optimal ANN structure for the specific task.
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3 System Modeling The proposed model for the dam’s crest movement due to the thermal variations observed in the dam’s concrete on multiple vertical levels is presented in Fig. 5. Observed values of the dam’s crest movement obtained by optical collimator measurements of the observation points and concrete temperature in three levels (foundation gallery, inspection gallery, and dam’s crest) were used for fitting of ANNs.
Fig. 5. A machine learning model for the dam movement prediction.
The classical ANNs were used to evaluate the correlation between dam movements and concrete temperature while NAR networks were used for time series prediction of concrete temperature in three levels of the dam for 30 days ahead. To determine the initial ANN structure the guidelines proposed in [22] were used. In all classical ANNs, only one hidden layer was used while the number of neurons was variable. When testing NAR network models number of hidden neurons and the number of delays were variable and only one hidden layer was used. All ANNs perform internal validation to assess networks performance and optimize ANNs architecture but in the research also external validation was used to assess the level of model generalization – to assess ANNs ability to predict the concrete temperature and dam movements on the unseen data. For the concrete temperature prediction, three NAR networks were used – one for every level of the dam. A total of six classical ANNs were used to the predict movements of observation points M131, M141, M151, M161, M171, and M181 (one model for every point). To model behavior of observation points multiple ANN models were tested: FFBP models with various architectures, CFBP models with various architectures, and LRBP models with various architectures but only one (the best of all tested) was used for prediction of dam movements. Observation points M111, and M121 were excluded from the research due to the large distance from the sensors for temperature measurements compared to other observed points. A total of 74 series of dam crest movement monitoring data were used in the research and that data was divided into two data sets – the model fitting data set consists of 62 series while 12 series were used to evaluate the performance of the model on previously unseen data. Model fitting data were obtained in the period from 04.04.2014. to
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17.06.2017. and model validation data was obtained from 16.07.2017. to 15.07.2020. Concrete temperature time series data consists of 2295 mean daily temperature measurements in the three levels of the dam. In the cases when a sensor lost signal or was malfunctioning that data was neglected and the mean daily temperature was calculated based on the operational sensors at that level. In rare cases when there was no signal from any sensors the temperature in that period was linearly interpolated based on available measurements. This was a rare occurrence and periods without any signal from sensors never exceeded more than a couple of hours hence linear interpolation was an adequate method for completing the data series. There were no other corrections to the data that was used in this research, exact values of the daily temperature of the dam’s concrete in three levels were used, and observed movements of the dam’s crest were used in the proposed model.
4 Results and Discussion To evaluate ANN performance for prediction of the concrete temperature and dam movement prediction three criteria were used: mean squared error (MSE) of ANN prediction, regression coefficient (R), and mean absolute error (MAE). In the case when multiple ANN structures showed very similar results regarding these criteria, the simpler structure was used for prediction, i.e. the structure with a smaller combined number of neurons and delays. NAR network structure with 1 hidden layer, 15 delays, and 6 neurons in the hidden layer showed the best results on the training data set for the dam’s crest temperature, while the structure with 1 hidden layer, 10 delays, and 9 neurons performed the best for concrete temperature in the inspection gallery of the dam. Due to the very low amplitude of temperature variations in the foundation gallery, any NAR model would give satisfactory results and even naive prediction would suffice, but to satisfy the requirements of the proposed model for dam movement prediction NAR structure with 1 hidden layer, 11 delays, and 7 neurons was used to predict the concrete temperature in the foundation gallery. The mean absolute error and maximal error of the prediction of concrete temperature by the NAR network and ARIMA models are presented in Table 2, and the results of the NAR network and ARIMA models concrete temperature prediction in the three levels of the dam are presented in Table 3. In Table 3, the following abbreviations were used: M. – measured, P. – predicted, FG – foundation gallery, IG – inspection gallery, and DC – dam crest. Bolded values in Table 3 represent the largest absolute errors of the prediction. Table 2. The results of the prediction of concrete temperature (unit: °C). Method
Foundation gallery MAE and max. Error
Inspection gallery MAE and max. Error
Dam crest MAE and max. Error
NAR network
0.05 (0.15)
0.13 (0.46)
0.87 (1.98)
ARIMA models
0.03 (0.07)
0.33 (0.85)
1.46 (3.54)
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Table 3. NAR networks and ARIMA models: concrete temperature prediction (Date format: dd/mm/yy). Predicted on
Predicted for
NAR M.-P.FG
NAR M.-P.IG
NAR M.-P.DC
ARIMA M.-P.FG
ARIMA M.-P.IG
ARIMA M.-P.DC
16.06.2019
16.07.2019
0.05
0.04
0.75
0.07
0.51
0.90
27.07.2019
26.08.2019
0.05
0.07
0.82
0.02
0.09
1.15
14.08.2019
13.09.2019
0.02
0.08
0.26
0.02
0.55
3.54
15.09.2019
15.10.2019
0.03
0.25
1.53
0.06
0.33
3.35
20.10.2019
19.11.2019
0.03
0.10
1.45
0.01
0.37
1.71
12.11.2019
12.12.2019
0.09
0.46
0.13
0.02
0.85
0.93
18.12.2019
17.01.2020
0.15
0.08
0.65
0.06
0.11
0.84
18.01.2020
17.02.2020
0.00
0.01
0.01
0.05
0.03
0.35
19.02.2020
20.03.2020
0.06
0.12
0.52
0.01
0.41
0.62
19.04.2020
19.05.2020
0.02
0.10
0.55
0.03
0.34
0.25
18.05.2020
17.06.2020
0.01
0.18
1.82
0.03
0.13
1.01
15.06.2020
15.07.2020
0.05
0.06
1.98
0.02
0.25
2.90
As it can be seen from Tables 2 and 3 NAR networks showed overall better results in predicting concrete temperature which is especially evident when comparing maximal errors of the prediction (0.46 °C compared to 0.85 °C in the inspection gallery and 1.98 °C compared to 3.54 °C at the dam’s crest). To establish a connection between the dam’s concrete temperature and dam movements a total of 62 measurement series were used. The concrete temperature in three levels was used as the input set in ANN training while dam movements for every point separately was target set. Regression coefficient R was used to evaluate the correlation between temperature and dam movements. The result of ANN fitting and MLR are presented in Tables 4 and 5. Table 4. Regression coefficients from ANN fitting. Point
M131
M141
M151
M161
M171
M181
Average R
R
0.71
0.87
0.87
0.86
0.92
0.84
0.85
ANN type
FFBP
CFBP
FFBP
FFBP
LRBP
LRBP
No. of neurons
7
9
5
3
8
7
Observed temperature and predicted temperature by NAR networks and ARIMA models in the three levels of the dam are visualized by using line graphs and presented in Fig. 6.
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Table 5. Regression coefficients from MLR [18]. Point
M131
M141
M151
M161
M171
M181
Average R
R (MLR)
0.59
0.81
0.77
0.80
0.88
0.79
0.77
Fig. 6. Observed temperature and predicted temperature by NAR networks and ARIMA models in the three levels of the dam.
Many trained ANN models showed very similar results and the differences were only on the 3rd or 4th decimal in the value of R, and differences were even smaller regarding MSE of ANN prediction. There was no clear winner when comparing the three used types of ANNs: FFBP, CFBP, and LRBP. The final step of the research was to use fitted ANNs to predict the dam’s crest movement influenced by thermal variations. A total of 12 measurement series were used to validate the proposed model for dam movement prediction. In Tables 6 and 7 bolded
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values represent the largest absolute errors of the prediction for every observation point and for the series. Table 6. Absolute errors (AE) of the dam crest movement prediction by using NAR networks and classical ANNs (unit: mm). Series No
M131
M141
M151
M161
M171
M181
1
1.31
2.78
3.42
1.77
1.26
1.47
Series MAE 2.00
2
2.28
1.95
0.53
0.36
1.72
0.10
1.16
3
1.27
3.66
1.44
0.34
1.49
0.45
1.44
4
4.02
6.16
4.16
0.65
2.42
1.34
3.12
5
2.43
3.91
1.35
2.21
1.10
0.09
1.85
6
3.37
2.77
0.44
0.01
0.84
0.53
1.33
7
0.21
0.50
0.15
1.24
1.79
0.66
0.76
8
0.23
0.10
1.47
0.57
1.01
0.70
0.68
9
3.49
1.69
3.10
2.21
1.70
0.92
2.18
10
1.68
0.90
0.10
0.74
1.11
0.14
0.78
11
0.37
0.57
0.58
1.00
0.47
0.45
0.57
12
2.81
0.40
1.13
0.75
1.72
0.17
1.16
Point MAE
1.96
2.11
1.49
0.99
1.39
0.58
(AE) = 102.23
Table 7. Absolute errors (AE) of the dam crest movement prediction by using ARIMA models and MLR (unit: mm) [18]. Series No
M131
M141
M151
M161
M171
M181
Series MAE
1
2.78
2.21
1.57
0.85
0.80
0.48
1.45
2
0.13
1.54
0.04
0.42
0.39
0.20
0.46
3
0.19
2.61
1.48
0.72
1.43
0.55
1.16
4
5.02
7.12
4.49
3.16
3.90
1.17
4.14
5
4.18
5.23
2.74
2.75
2.23
0.17
2.88
6
2.90
1.91
1.78
1.43
0.70
0.12
1.47
7
0.97
1.39
0.51
0.54
0.32
0.46
0.70
8
0.20
1.42
1.46
0.43
0.68
0.12
0.72
9
3.12
2.30
2.72
1.86
1.54
0.05
1.93
10
1.08
0.30
0.21
0.60
0.37
0.36
0.49
11
2.20
0.29
0.31
0.03
0.18
0.34
0.56
12
4.47
1.98
2.35
2.27
1.52
0.16
2.13
Point MAE
2.27
2.36
1.64
1.25
1.17
0.35
(AE) = 108.50
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As it can be seen from Tables 6 and 7, a machine learning approach, based on the NAR networks and classical ANNs, and a statistical approach, based on the ARIMA models and MLR, showed similar results regarding dam crest movement prediction. Observed and predicted values of the observation points movement are visualized by line graphs and presented in Fig. 7.
Fig. 7. Comparison in prediction results between machine learning model and statistical model.
The weaknesses of both models were the large maximal errors which were in some instances larger than 5 mm. By taking into account values of regression coefficients and the results of the concrete temperature prediction it was expected that ANNs significantly improve the results obtained by MLR but the final results were very similar.
5 Conclusion In this paper, a machine learning model based on nonlinear autoregressive neural networks and classical neural networks was proposed to predict the dam’s crest movement influenced by thermal variations. The concrete temperature of the dam was measured in
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the three levels (foundation gallery, inspection gallery, and dam crest) while the dam’s crest movement was observed by means of the optical collimator (geometrical alignment method). Univariate time series prediction of concrete temperature 30 days ahead was performed by NAR networks. Obtained results from NAR networks were used as inputs in fitted classical networks to predict future dam movements. For every observed point on the dam’s crest, an individual ANN was trained since different parts of the object do not act the same when influenced by the same external factors. The results obtained with the machine learning model presented in this paper were compared with those of a statistical model [18] where time series prediction was performed with ARIMA models and dam crest movement by MLR. Although, the machine learning model performed better when predicting concrete temperature, and ANNs showed a better fit compared to MLR (larger values of R), the final results were very similar. Overall, prediction results were satisfactory since only 2 validation series had MAE larger than 2 mm. The issue with both models (machine learning and statistical) are large maximal errors of dam movement prediction. This is something that needs to be addressed in future research since maximal errors can be regarded as a measure of the reliability of a model. Large prediction errors can produce false alarms for critical movement, and even worse not detect critical movements of the dam. The main goal of future research is to minimize maximal errors to an acceptable level. If it is not possible to achieve maximal errors smaller than 2 mm by using only concrete temperature than it is necessary to add more variables in the model. A hybrid model which combines statistical and machine learning methods will be used in the future as an attempt to use strengths of used models and at the same time to minimize weaknesses of the two abovementioned models.
References 1. ICOLD Homepage. https://www.icold-cigb.org/GB/dams/definition_of_a_large_dam.asp. Accessed 2 Feb 2023 2. Willm, G., Beaujoint, N.: Les methodes de surveillance des barrages au service de la production hydraulique d’electricit de france-problmes ancients et solutions nouvelles. In: 9th ICOLD Congres, Istanbul, pp. 529–550 (1967) 3. Chouinard, L., Roy, V.: Performance of statistical models for dam monitoring data. In: Joint International Conference on Computing and Decision Making in Civil and Building Engineering, Monteral, pp. 199–207 (2006) 4. Gamse, S., Oberguggenberger, M.: Assessment of long-term coordinate time series using hydrostatic-season-time model for rock-fill embankment dam. Struct. Control Health Monit. 24(1). Wiley Online Library (2017) 5. Mata, J., Tavares de Castro, A., Sá da Costa, J.: Constructing statistical models for arch dam deformation. Struct. Control Health Monit. 21(3), 423–437. Wiley Online Library (2014) 6. Penot, I., Fabre, J.P., Daumas, B.: Analyse et modélisation du comportement des ouvrages de génie civil par la prise en compte des températures de l’air: Méthode HST Thermique. 23ème congrès des grands barrages (CIGB ICOLD), Brazilia (2009) 7. Tatin, M., Briffaut, M., Dufour, F., Simon, A., Fabre, J.P.: Thermal displacements of concrete dams: accounting for water temperature in statistical models. Eng. Struct. 91, 26–39. Elsevier (2015)
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8. Tatin, M., Briffaut, M., Dufour, F., Simon, A., Fabre, J.P.: Statistical modelling of thermal displacements for concrete dams: influence of water temperature profile and dam thickness profile. Eng.. Struct. 165, 63–75. Elsevier (2018) 9. Gamse, S., Henriques, M.J., Oberguggenberger, M., Mata, J.T.: Analysis of periodicities in long-term displacement time series in concrete dams. Struct. Control Health Monit. 27(3). John Wiley & Sons (2020) 10. Yang, G., Gu, H., Chen, X., Zhao, K., Qiao, D., Chen, X.: Hybrid hydraulic-seasonal-time model for predicting the deformation behaviour of high concrete dams during the operational period. Struct. Control Health Monit. 28(3), e2685. John Wiley & Sons (2021) 11. Belmokre, A., Mihoubi, M.K., Santillán, D.: Analysis of dam behavior by statistical models: application of the random forest approach. KSCE J. Civil Eng. 23(11), 4800–4811. Springer (2019) 12. Liu, W., Pan, J., Ren, Y., Wu, Z., Wang, J.: Coupling prediction model for long-term displacements of arch dams based on long short-term memory network. Struct. Control Health Monit. 27(7). John Wiley & Sons (2020) 13. Su, Y., Weng, K., Lin, C., Chen, Z.: Dam deformation interpretation and prediction based on a long short-term memory model coupled with an attention mechanism. Appl. Sci. 11(14), 6625. MDPI (2021) 14. Mata, J.: Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng. Struct. 33(3), 903–910. Elsevier (2011) 15. Kang, F., Li, J., Zhao, S., Wang, Y.: Structural health monitoring of concrete dams using long-term air temperature for thermal effect simulation. Eng. Struct. 180, 642–653. Elsevier (2019) 16. Hamzic, A., Avdagic, Z., Besic, I.: Multistage cascade predictor of structural elements movement in the deformation analysis of large objects based on time series influencing factors. ISPRS Int. J. Geo-Inform. 9(1), 47. MDPI (2020) 17. Salazar, F., Morán, R., Toledo, M.Á., Oñate, E.: Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch. Comput. Methods Eng. 24, 1–21. Springer (2017) 18. Hamzic, A., Kamber Hamzic, D.: Dam movement modeling by using multiple linear regression and ARIMA models (in Bosnian). Geodetski glasnik 51, 49–64 (2020) 19. High precision optical collimator Homepage. https://pizzi-instruments.it/en/products/highprecision-optical-collimator. Accessed 2 Feb 2023 20. Pereira, F.H., et al.: Nonlinear autoregressive neural network models for prediction of transformer oil-dissolved gas concentrations. Energies 11(7), 1691. MDPI (2018) 21. Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network Toolbox User’s Guide. The MathWorks Inc. (2017) 22. Heaton, J.: Introduction to neural networks with Java. Heaton Research, Inc. (2008)
Geometric Enhancement of the Cadastre of Underground Utility Networks Nedim Tuno1(B)
, Jusuf Topoljak1 , Admir Mulahusi´c1 , Muamer Ðidelija1 Nedim Kulo1 , and Tomaž Ambrožiˇc2
,
1 Faculty of Civil Engineering, Department of Geodesy and Geoinformatics, University of
Sarajevo, Patriotske lige 30, Sarajevo 71 000, Bosnia and Herzegovina [email protected] 2 Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova 2, 1000 Ljubljana, Slovenia
Abstract. The need for accurate data on underground utilities is very high. Through adequate databases and the establishment of an appropriate geoinformation system, it is possible to respond with great certainty to the request of the end user for a particular area at any time: which lines exist in that location, where they are positioned, which are their characteristics, etc. In this way, the quality of the utility infrastructure management system is largely improved and it prevents the possibility of damage to existing underground lines when performing construction work. The purpose of such a cadastral system is to provide detailed information on the facilities of public utility infrastructure and therefore basic information on the occupancy of the space. However, there are problems with the positional quality of data on utility lines, especially in cases where the digital cadastre is being established by the digitization of existing paper maps. Therefore, the information about the location of the subsurface facilities provided by the cadastral offices is not reliable. This study gives insight into the non-homogeneity of the digital utility maps caused by the scanning irregularities, deformations of material on which maps are drawn, and the errors in the drafting of the maps, and proposes a method that would improve the map geometric quality. Keywords: underground utility · digital map · utility cadastre · geometric transformation · positional accuracy
1 Introduction Public utility infrastructure is a very important element of the development of each area, and this is especially true for complex urban spaces. Most interventions in the space are associated with utility networks (electricity, telecommunications, water supply, sewage, hot water supply, gas pipeline, etc.), and without them, development is practically impossible. The collected sets of data on public utility infrastructure are crucial in space management and planning at local levels [1, 2]. Data acquisition to make the cadastre of underground infrastructures and its geospatial databases is mainly performed by primary and secondary methods. In this way, the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 132–142, 2023. https://doi.org/10.1007/978-3-031-43056-5_11
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exact knowledge of the positions and routes of the pipelines and underground cables is ensured [3]. Primary methods are based on standard surveying procedures (reflector-less and measurements to prisms with total stations, GNSS RTK measurements, photogrammetry, airborne and terrestrial laser scanning) and provide the most accurate data [4–9]. They apply to utility networks while those are exposed in an open pit, e.g., during construction. It is also possible to use such technologies for the mapping of buried objects. In that case, information about the buried utility networks can be retrieved without any excavation but it is first necessary to detect, locate and identify the buried utilities. Subsurface geophysical technologies, such as Ground Penetrating Radar (GPR) or Electromagnetic Locators, can be used for this purpose [10]. Although primary methods provide the best information on subsurface utility infrastructure, it should be considered that their application is very expensive. For this reason, the underground utility database is often established by secondary methods i.e. by digitizing existing maps that contain information on underground utilities. This indirect method of underground utility data acquisition is cost- and time-effective. Approximately 10,000 km of utility lines were registered on paper maps by the end of the 1980s in Bosnia and Herzegovina [11, 12]. The Bosnian authority responsible for utility infrastructure started a process to create a digital version of the utility maps at the beginning of the 21st century. Although the first steps in the conversion of existing Bosnian-Herzegovinian analog utility maps into the digital form were made in the late 1990s until now this topic has not been scientifically researched. Most of the research on the geometric processing of cadastral and topographic maps showed that optimal methods can significantly improve the positional quality of geospatial data [14–17]. This study aimed to investigate the real accuracy of the map transformation and whether improvements in the planimetric accuracy of underground utility data can be reached through a transformation based on an appropriate set of common points. The transformation procedure that would contain information on the errors in the paper map’s primary plotting can contribute to the improvement of the positional accuracy of existing digital utility maps.
2 Materials and Methods 2.1 Underground Utility Map The official cadastre for underground infrastructures in Sarajevo, the capital city of Bosnia and Herzegovina, is kept in the form of the geospatial database at the Institute for Construction of Canton Sarajevo. The graphic segment of that database was established in the first decade of the twenty-first century when analog maps were digitized. The analog utility maps were produced by the Geodetic Institute of Sarajevo at a scale of 1:500, 1:1000, and 1:2500. To create these maps, the Geodetic Institute used the survey conducted with the ground-based methods (orthogonal and polar). Original cadastral maps were drawn in the Gauss-Krüger projection, i.e., the B&H state coordinate system, using analog drafting equipment. The research conducted in this study involved the geometric processing of the map sheet 1:500 from 1978, entitled SARAJEVO 226b, which is kept in the Archive of the
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Faculty of Civil Engineering, University of Sarajevo (Fig. 1). This map shows the positioning and identification of an ultra-dense network of buried pipes and cables beneath the historical and cultural center of Sarajevo city – Bašˇcaršija (Fig. 1).
Fig. 1. Clip of the scanned underground utility map sheet SARAJEVO 226b (Source: Faculty of Civil Engineering, Department of Geodesy and Geoinformatics, University of Sarajevo).
2.2 TPS-Based Correction of the Scanned Map The thin plate spline (TPS) transformation method [18] was used as a functional transformation model in this research. This model yielded the best results in the process of geometric correction of scanned Bosnian maps, in the studies conducted by [13–15, 19]. The TPS transformation function is composed of the global affine part and pure elastic part, which is, for n tie points, defined as follows [20]: f (x, y) = a + bx + cy +
n i=1
wi U (ri ),
(1)
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where: a, b and c – coefficients of the global affine transformation, wi – weights (TPS coefficient at point i) determined from the condition depicted by: f (x i , yi ) = vi , where vi (i = 1, …, n) are given target values, U(r i ) = ri2 logri2 – radial basis functions, ri = (xi , yi ) − (x, y) – distances from the observed point (x, y) to all n points i (xi , yi ). Since it is necessary to determine n + 3 unknown parameters from n equations f (xi , yi ) = vi , three additional orthogonal conditions are introduced [21]: n
wi =
i=1
n i=1
xi wi =
n
yi wi = 0.
(2)
i=1
Since the influence of individual tie points is localized and rapidly diminishes with distance, TPS is a locally sensitive transformational function – contrary to polynomial and other global functions. This implies that more local deformations can be eliminated [22].
3 Results and Discussion The transformation procedure was based on the state plane coordinate system map grid, as recommended by state regulations and practice [23]. Points defined by the grid intersections were used as tie (common) points because their coordinates in the source and target systems are known [19]. The 1:500 scale utility map sheet contains a reference grid whose tick marks are placed 50 m apart, except those in the western-most or easternmost part of the sheet, where ticks are 25 m from each other in the east–west direction. In total, 54 points of the coordinate grid are pricked in the map sheet [22]. The accuracy of geometric transformations largely depends on the number and arrangement (density) of common points. To examine how the density of tie points affects the horizontal accuracy of map transformation, 2 different densities of these points were selected: – Variant 1: 4 tie points (four points defined by corners of the map neatline). – Variant 2: 54 tie points (all coordinate grid points). Geodetic control points, set by traverses, were used for the purpose of analysis and evaluation of the geometric accuracy of map sheet transformation. During the original elaboration of the map, 103 geodetic points were plotted onto the map sheet SARAJEVO 226b. According to National Standard for Spatial Data Accuracy (NSSDA), the horizontal positional accuracy of TPS transformation was evaluated with original coordinates of geodetic points (yi , x i ) as reference values, and transformed coordinates (yi tr , x i tr ) as measured values [23]. Table 1 shows the quality of the utility map, evaluated on the basis of differences between transformed and reference coordinates along y and x axes, dyi = yi − yitr. , dxi = xi − xitr. , and remained positional distortions dyx = dy2 + dx2 .
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The root mean square errors (RMSE): n n 1 1 2 dy2 + dx = RMSEy2 + RMSEx2 , RMSEr = n n 1
1
were calculated based on two variants of transformation (Table 1). Table 1. Statistics of remaining residuals after performing two variants of TPS transformation. Statistical indicator
Variant 1 dx
dy
Variant 2 d xy
dx
dy
d xy
Minimum [m]
–0.16 –0.07 0.03 –0.11 –0.09 0.01
Average [m]
0.07
0.09
0.15 0.04
0.04
0.08
Maximum [m]
0.26
0.32
0.33 0.14
0.14
0.15
Range [m]
0.42
0.39
0.30 0.25
0.23
0.14
RMSE [m]
0.10
0.13
0.17 0.06
0.06
0.09
31.1
31.1
4.9
53.4
53.4
25.2
28.2
20.4
17.5 40.8
38.8
48.5
10–15 cm 29.1
19.4
30.1 5.8
7.8
24.3
15–20 cm 8.7
14.6
25.2 0.0
0.0
1.9
20–25 cm 1.0
11.7
13.6 0.0
0.0
0.0
>25 m
2.9
8.7
0.0
0.0
Distortion residuals distribution [%] 0–5 cm 5–10 cm
1.9
0.0
It is visible that the RMSE r value, calculated on the basis of 1st variant of transformation, doesn’t fulfill the requirement that the map’s graphical accuracy should be 0.2 mm · M or better, where M is the scale denominator of the analog cadastral map. The obtained value of the root mean square error reflects the errors from the analog map and scanning irregularities that are not removed by transformations. A significant number of control points (almost half of them), revealed positional errors larger than 0.15 m, which is unsatisfactory. Remained large distortions decreased the reliability and quality of the content of the digitized map. The RMSE r value for the 2nd variant of transformation amounts to 0.18 mm · M and meets the requirement for the graphical positional accuracy of map detail points. Distortion residuals exceeding 20 cm do not appear in any control point, which indicates that the transformation based on all coordinate grid ticks is very accurate and homogeneous. In this variant of transformation, the values of RMSE r and maximum positional deviation are 2 times smaller than those obtained by the first variant. Unlike the first transformation variant, in which 52.5% of positional deviations had a value less than 0.15 m (0.3 mm · M), in the second variant almost all deviations (98%) were less than 0.15 m. Statistics in Table 1 clearly indicate that an increase in the number of common points reflects favorably on the positional accuracy of the transformed contents of the
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map. This numeric evaluation of the results is confirmed by the conducted visual checks. The visualization of the planimetric accuracy through displacement (residual) vectors demonstrates the fact that the systematic and gross errors, from the transformation based on four points placed at the neatline corners, remain in the transformed map (Fig. 2). It is clear that there are two different patterns of locally homogenous vector orientation. Vectors in the eastern part of the map have the same east–west propagation, while in the rest of the map, they are concentrated in a north-south direction. Vectors with the largest magnitude are located in the southern part of the map. Figure 3 reveals randomly distributed vector propagation, while the magnitudes of vectors are significantly smaller compared to those resulting from the 1st transformation variant. Histograms of the remaining coordinate distortions were created; as well as the curve of a normal distribution which corresponds to empirical data (Fig. 4 and Fig. 5). Figure 4 clearly shows that there is a significant shift in value dx (arithmetic mean of dx) that is caused by the large systematic errors, as well as gross errors. On the other hand, the normal distribution curve corresponding to the 2nd transformation variant indicates that the systematic errors are considerably eliminated from the coordinate differences (Fig. 5).
Fig. 2. Effects of TPS transformation based on the use of 4 points in the map grid.
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Fig. 3. Effects of TPS transformation based on the use of 54 points in the map grid.
Fig. 4. Histograms and distribution curve of remaining residuals d x for the 1st variant of transformation.
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Fig. 5. Histograms and distribution curve of remaining residuals d x for the 2nd variant of transformation.
Fig. 6. Leica DS2000 Utility Detection Radar owned by University of Sarajevo – Faculty of Civil Engineering, Department of Geodesy and Geoinformatics.
4 Conclusion This research clearly demonstrates that the nonuniform positional errors in the process of utility map digitization (deformations of the map supporting material and scanning errors), cannot be eliminated with the transformation based on four common points placed at the neatline corners. It is obvious that the content of the map is not homogenous, even if it is made on material that has dimensional stability i.e., various forms of plastic materials, laminated paper (paper mounted on the aluminum plate, etc.). Therefore, very large distortions can be expected in the underground utility database. To fulfill strict requirements regarding the quality and reliability of the geospatial data, it is necessary to apply the transformation based on adequate tie points quantity. However, a specific
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problem with the digitization of the utility maps is that they are mostly drawn on a set of overlaying sheets. Each theme (electricity, natural gas, water, sewage, telephone lines, etc.) is drawn on a separate transparent map sheet. These map layers, together with a base map, form a composite image. Such thematic sheets contain only geometric information on underground utilities and four corners of the map neat line, so it is not possible to use the grid intersections and geodetic points as common points in the process of transformation. In this case, improvements in the positional accuracy of the utility map can be achieved with the inclusion of detailed points, collected during the underground utility data acquisition, in the transformation model. In this way, numerous errors that occur due to the plotting with drafting equipment and dimensional changes on the base materials of maps can be effectively eliminated. In order to achieve this, it is necessary to calculate the coordinates of detailed points from the original survey data. A sufficient number of these common points will enable the transformation of the map with the local transformation model. Local transformation parameters, specific to small areas, produce much better results than global parameters. This methodology is time-consuming since it requires a long-lasting search for original survey data, as well as its processing, but necessary if we wish to improve the geometric quality of the underground utility database. The authors plan to conduct further investigation to confirm the correctness of the described methodology. In this regard, the Leica DS2000 GPR system (Fig. 6) will be used to accurately detect the buried utility lines [24], and the obtained data will be compared to those from the improved database. Acknowledgments. This research was supported by the Ministry of Science, Higher Education and Youth of Sarajevo Canton, under contract number 27–02-11–4375-5/21.
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Data Science and Geographic Information Systems
Interactive Narrative Design and Storytelling, Use Case with Geospatial Data (B) ˇ Naida Congo
and Almir Karabegovi´c
Faculty of Electrical Engineering, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. Storytelling, as one of the main concepts of social behaviour, which existed in various shapes throughout history, is an essential part of human interaction with others. How one design and tell the story depends on the purpose of the story and the audience. Storytelling with the use of geospatial data is an additional challenge, but also helps to create an exciting user experience and provides better understanding of some complex structures or hidden data connections. This paper describes patterns that can help authors design a story using geospatial data and its usage in geographic visualization and data-driven storytelling. Also, its aim is to describe the design process of data storytelling system and an example of implementation which uses these patterns as assistance in it. It shows how these patterns can be used for designing stories, how that affects the tourism industry and what benefits such stories bring. Keywords: interactive narrative design · storytelling · interactive storytelling · geographic visualization · geospatial data
1 Introduction Since ancient times, it has been in human’s nature to tell stories, for different reasons but always with the same goal - “to tell a story”. Therefore, throughout history, we have different records of men’s creations. At the beginning of ancient civilizations, men used to paint the cave walls showing what they encountered during the day. Often there were pictures of animals or hunting scenes, very realistically presented, drawn for religious reasons or simply because some rites related to hunting. Further, throughout history, we can find different ways of telling stories. In Ancient Greece, the tomb walls were often filled with paintings, which was their way of telling the story. Later, The Epic of Gilgamesh, known as the earliest notable literature which survived, was a story inscribed by hand on clay tablets, which was another way of recording messages. In the next few centuries, the written word is becoming a main tool for telling stories. Later, with the development of television and music videos, storytelling is going through its development, but the goal is still the same. With the development of technology in 20. and 21. century, storytelling is getting another dimension. The next generation is putting the focus on “virtual reality” and we © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 145–156, 2023. https://doi.org/10.1007/978-3-031-43056-5_12
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can say that we’re getting back to an old way of storytelling – visual storytelling. That focus on visualization is placed because the visual system provides a very high bandwidth channel to our brains and a significant amount of visual information processing occurs in parallel at the preconscious level [1]. Visualization enables us to develop different kinds of systems for different kinds of data structures. Therefore, we can develop a system for data analysis where users don’t know the initial problem. Those systems can help users to even start researching and figuring out their initial problem. Also, we can develop systems that can help user to get a better and clearer view of the data before they switch to computing. Furthermore, we can develop a system whose purpose would be double-checking the computed data before the final decision is made. For all of these systems, one crucial element is interaction which handles the complexity of the data and enables the user to get to the point where they can go into details and even compare different sets of data. There are numerous papers which tried to define the term “interaction”. One of the definitions is that interaction is described as action-reaction dialogue between the user and the system [2]. What we can say for sure is that it consists of a few elements: the user who initiates interaction, the data as a user’s main object of interest and mediating entity with which the user interacts with the data visualization system [2]. One of the industries where visualization and interaction can be used is tourism. As mentioned in [3], one of the main characteristics of tourism is dynamics and the need for constantly “creating” new locations or refreshing existing destinations to create a more attractive “tourist product”. “A tourist product is defined as a set of goods and services that are the object of acquisition, use or consumption by people who are in the context of a tourist trip” [3]. Therefore, it makes it challenging to improve the tourist product and the question arises in what way visualization and interaction can help with that. This paper presents different patterns which are used in geographic visualization and data-driven storytelling and how they can help authors to create and develop stories. The aim of this paper is to describe the solution for geospatial data storytelling system in which these patterns are used and how that can affect tourism industry and what benefits such system brings.
2 Methodology As previously mentioned, data visualization, as one of the storytelling techniques, can be used for creating various applications, for various purposes and in various industries such as telecommunication, financial analysis, software engineering, information technology management, etc. Authors in [4] showed how interactive narrative design can be used in a positive way and what experience it provides to users when story plot is set to locations known to them. 2.1 A Review of Existing Tools In Sarajevo, which is the capital city of Bosnia and Herzegovina, as well as in the whole country, tourism is recognized as one of the main industries with enormous potential of using interactive narrative design in storytelling. To improve it, it is necessary to
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constantly refresh tourist product and present new offers, so that those locations would attract many more tourists, which would result with greater economic benefit at the end. If we search the internet for information about all tourist attractions we want to visit, we will come across a bunch of information that the human brain takes longer to process. Some of those systems are navigator.ba, Google Maps, visitsarajevo.ba. In each of these systems, we have a map of certain location, with all the attractions shown in one view. Also, we have a list of categories such as: accommodation, food, shopping, etc., by which data can be filtered. Amount of data being displayed on the screen is getting smaller after category is chosen. The advantage of these systems certainly is the amount of information existing in one place, and these categories can help those who want to visit the city. However, one of the disadvantages is that the user is overwhelmed by the amount of information in one view, and it may not attract users to become curious and explore the location further. Google Maps, one of the widely used web mapping platforms, first launched in 2005 [5], is used in numerous papers for developing trip planner systems, such as ones mentioned in [6, 7]. Authors in [6], used Google Maps API as layer for providing cartographic data, but adapted the system for more specific needs. Another way of presenting data in these systems is a text form where information is presented as text containing links to other websites for more information about attractions themselves. One of the disadvantages of that way of presenting data is that a large amount of text is decreasing the probability that user would be interested in further exploration. Also, what’s missing there is the element of placing the context in specific location. 2.2 Interactive Narrative Design Interactive narrative design represents a new form which promises to solve the division between active creators and passive audience and that it will announce the arrival of the connection between dynamic narrative creator and audience which represents one of the participants [8]. We can say that it is the new form of storytelling, which represents the intersection of digital technologies, literary and artistic vision – from hyper-text novels to online video games and virtual reality technology [9]. It is the form, which is still in development phase, unlike some classic forms of narratives such as theatre performances, books etc. Continuous technology development made it possible for us to visualize many things. Due to its specifications and possibility to execute commands in parallel, as well as to react on user’s commands, technology development completely changed the way in which the narrative content is created and distributed. Therefore, we can see a huge progress in graphic interpretations from the beginnings, when only text was shown on the screen, to the modern realistic and dynamic high-resolution 3D screens. As stated in [10], narrative visualizations are different from traditional forms of storytelling. It is concluded in [10] that stories appear to be more effective when the interaction in the story is occurring at few places within a narrative and allows the user to explore without disrupting the narrative.
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2.3 Visualization Patterns Some papers were introducing various narrative design patterns and techniques for data storytelling. In [10], authors analysed a few use cases for storytelling, explaining what techniques are used for what purpose. So, we have different techniques used such as point of comparison used for comparing data, annotation notes and details-on-demand used for sharing additional information, progress bar indicating length of visualization and providing navigation to user, time-slider etc. These design elements are divided into three divisions: genres, visual narratives, and narrative structure [10]. Visual narratives are divided into three sections: visual structuring section including elements such as establishing shot, consistency, progress bar, checklist progress tracker, then highlighting section including elements such as close-ups, feature distinction, character distinction, motion, audio, zoom, and transition guidance section including elements such as familiar objects, viewing angle, viewer motion, continuity, animated transitions [10]. Narrative structure is divided also into three sections: ordering section including elements such as random access, user directed path, linear, then interactivity section including elements such as hover highlighting, filtering, navigation, limited interactivity, explicit instruction, and messaging section including elements such as captions, annotations, introductory text, summary [10]. Combination of previously mentioned design elements forms different patterns used while developing the story. In [11], author summarized narrative structure based on the order of the events, the order of the narrative and the connection between them. There is a list of seven patterns: Chronicle – meaning events are narrated in chronological order, Retrograde – events are narrated in reverse chronological order, Zigzag – events from the past and the present are interleaved, i.e. narration alternating between them, Analepsis – events that occurred in the past are narrated in order to fill the backstory, they are commonly known as flashbacks, Prolepsis – narrative is temporarily taken forward in time, commonly known as flashforwards, Syllepsis – events are grouped based on some criteria and Achrony – events are randomly ordered. Narrative pattern can be defined as some device with specific purpose [12]. As stated in [12], there are some narrative design patterns which can help designers to think about the stories they want to tell and the best way to tell them. Every pattern has a special purpose such as engaging the audience, arousing empathy or creating rhythm and flow in the story. These patterns can be used individually or in combination with others, so the story could have a certain shape [12]. In [12] authors summarized these patterns into 5 groups: • Argumentation One of the patterns used is comparation used for comparing data. Another pattern is concretize, used for showing some abstract concept with concrete objects. Repetition pattern can also help argumentation in a way to increase data importance and increase the chance that the audience will remember that information. • Narrative flow One of the patterns in this group is reveal, based on which the data in the story is gradually revealed to the audience. Another pattern that can be used is previously
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mentioned repetition. In order for the story to have its time context and flow, and also to tune the speed of data reveal, speed-up and slow-down patterns can be used. • Framing One of the patterns used for framing is creating a familiar environment so the audience can relate to the content. For example, this pattern can be used when the creator wants the audience first to explore the data in their own region, before they move to exploring the data in other regions. Another pattern used is inviting users to make-aguess of the data or the outcome of the events. Defamiliarization is used for presenting familiar things in an unexpected way, e.g. setting map upside down. Convention breaking pattern is used in a same way, whether it is about colour, shapes or visual rhythm of the story. Also, using physical metaphors in visual perspective can help in reinforcing positive and negative feelings. Therefore, often used metaphors are: up = good, down = bad, left = withdraw, right = progress. • Empathy and emotion Story content can invoke empathy since the audience is the one who follow events, process data and make an interaction. Concretize can also be used for invoking empathy. For example, if we want to show war victims, we can show black and white image of children playing in ruins. That way we could connect carefree childhood with the horror of the war. One of the patterns is also “Humans-behind-the-dots”, which is similar to the concretize pattern and is often used in journalism to represent story topics with the concrete person. In the previous example, this pattern could be used to represent war victims with dots and clicking on every dot would show pop-up window with details about the victim. • Engagement Engagement implies creating the feeling of being part of the story, of being connected to it, and being in control over the interaction with the story’s content. One of the ways for engaging the audience is with rhetorical question. Rhetorical question can connect narrator with the audience and can arouse the desire, at least for a moment, to answer that question. Rhetorical question can sometime ensure “call for action” which can lead the audience to some of the actions. The collection of these patterns is presented on a website https://napa-cards.net, where authors wanted to show every pattern through example and in creative and interactive way. These patterns in combination can be useful for developers while creating the story, but three things they need to have, in order to use these patterns, are to have a story, to know who is the audience and to know how to affect the audience.
3 USE CASE: Solution for Geospatial Storytelling Designers and developers of visualization system should primarily understand visual interaction of humans, as well as how they perceive information, but also the psychological aspect. In [10] few principles are described how certain visual elements affect
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human brain, such as colour, size, orientation, reading order based on cultural factors, etc. Developers need to learn how to create flexible user interface, navigation tools, search methods, adequate for each type of user, application, and task itself. With all disadvantages of geoinformation systems mentioned in the Sect. 2, opportunity for using interactive narrative design and storytelling has opened up to improve those systems, so the user can adequately perceive information, understand and find the meaning of that information. Proposed solution is the system that contains a set of created stories told in an interesting way using interactive narrative design patterns with geospatial data, mainly focused on the city of Sarajevo. The purpose of these interactive stories designed to show attractions of the city is to make it easier for visitors to plan their tours as well as the development of tourism industry in a certain location – city and beyond. Users of these interactive stories would be: – all those who want to visit Sarajevo and get to know with everything that Sarajevo offers, – all those who want to get familiarized with cultural and historical heritage of Sarajevo, without having planned to visit the city, – all those who have already visited Sarajevo but want to refresh their knowledge gained during the visit. This solution has many goals. Some of those are to encourage people to explore the city map, to familiarize people with the history of the city and city attractions, as well as to encourage people to visit the city. That way they would help the development of tourism industry, all while spending less time planning the visit than it would be with the use of any existing solutions. 3.1 Architecture of Proposed Solution Proposed solution is a web application. Users could access it from any device using the internet. Web application would consist of frontend part of the application, the part that users see, which is loaded in their web browsers, and backend part of the application that serves application logic, as well as communication with the database. Application also communicates with a 3rd party system – ArcGIS system, for loading maps and any additional elements. Figure 1 shows architecture diagram of the proposed solution, which shows all parts of the system as well as communication between them. Frontend part of the application is implemented in Angular framework. Angular is framework based on Typescript programming language which implies: – framework based on components, used for creating scalable applications, – set of libraries used for creating various functionalities [13] For loading maps within the application, esri-loader library is used – library implemented to make usage of ArcGIS API for JavaScript easier [14]. The library allows
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Fig. 1. Architecture diagram.
loading required modules which are used to display maps within the application. It is suitable for use from different web frameworks. Backend part of the application is implemented in Django framework. Django framework is web framework based on Python programming language, which enables clean and pragmatic application development, as well as speed and scalability of the applications [15]. One of the advantages of this framework is built-in application admin panel which enables fast and simple content management. Therefore, there is no need for creating custom content management system, which makes it much easier for start-up projects. For content and data storage, relational PostgreSQL database is used. This database is used for storing content of stories, external content, as well as necessary data about elements which are loaded on maps. 3.2 Data Model Figure 2 shows only one part of a larger data model diagram of the application, which relates to stories, in which we can see what data is stored in the database. 3.3 Process Model Figure 3 shows one of the activity diagrams which refers to the story loading and user’s interaction with the application. After the user chooses one of the stories, few things are done in parallel – returning data from the database and loading map. After that, objects on map are created and story content is loaded. The user has an option to click and open some of the external contents or to scroll down through the story. If the user chooses some of the external content, 3rd party system will open and display that content (additional links, videos, etc.). If the user chooses to scroll down through the application, then the map view is changing and it is showing different locations and some additional content such as text, images, links etc. If the
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Fig. 2. Data model.
user has not reached the end of the story, he has the option to open some of the external content again, or to continue scrolling down through the application, until he reaches the end of the story. 3.4 Designing Interface Designing interface went through several stages. In first phase – gathering requirements, the goal was to collect requirements that need to be met, such as what needs to be displayed on the screen for application to have its purpose. In the next phase, requirements analysis took place to balance interface design and provide the best user experience. Since the audience targeted by the application is wide, i.e. no age limits nor other categorization, the goal was to harmonize the way of interaction to it. Also, design of the stories itself was considered. This means that colours of the design followed the theme of the story and emotions it should arouse in users. However, tendency was to maintain consistency, and stories were designed to follow movement flow between locations, i.e., to have a starting point and end point. Figure 4 and Fig. 5
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Fig. 3. Process model.
show one of the created stories. Figure 4 shows initial map view of some location, with all the landmarks shown on it, and Fig. 5 shows different map view after the user scrolls down from one point to another. Besides every location point, there is text shown with some additional information about the landmark, as well as few images.
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Fig. 4. Initial map view of the story.
Fig. 5. Map view after scrolling through the story.
4 Discussion If we compare existing solutions and proposed solution described in this paper, we can see that their difference is precisely the use of interactive narrative design patterns. Using these patterns fulfils the objectives described in Sect. 3. By using different patterns of interactive narrative design, developers can create stories that should satisfy wider audience. Since the proposed solution does not target only one group of people, yet several, target audience can vary – it can be both younger and older people, people who do not have much experience in using technology, as well as those who are experts, then people who travel often, as well as those who have never
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travelled before, people who like to explore, as well as people who have no desire for some interaction etc. For example, certain patterns of interactive narrative design such as “rhetorical question”, “guess” or “exploration” may influence certain group of users, while they may not have the same effect for another group. However, we have seen in the proposed solution that larger number of patterns can be used to present same information to many users. Since examples created in proposed solution, are related to the tourism industry, the non-use of spatial data is unimaginable. If the story was not placed in spatial context, a large part of the story would be lost. The cartographic elements relate to the presentation of data from spatial aspect, their presentation, and their use. Spatial data used in maps, affect the theme and the settings of the story, as well as the user’s emotions. Some data can be hidden, and some visible, and some data filtering can be done as well. The audience can identify with some of the stories based on their personal experience, which can lead to attracting users for additional exploration. As with other elements of the story, it is necessary to adapt cartographic elements to a larger number of users. In the proposed solution, balance is found for the number and type of applied elements on the map. Many diverse and playful elements could distract the user’s attention from the story itself. Therefore, the goal of the proposed solution was to show what number and which elements should be added on the map, so the users would not be distracted by them, but would follow the story. People tend to explore stories “on-the-fly”, and the map itself contributes to this and provides the user with dynamic environment. One of the goals of using interactive narrative design in proposed solution, which was met, is that the amount of information shown in one view is reduced, which makes planning the visit to the city much easier for the user, and time for making those plans is also reduced. Each of these stories encourages users for further interaction and exploration. Information in stories is presented in useful and entertaining way so that users are encouraged to return to previously seen information to refresh their knowledge. Increasing the number of visits to the city would mean a lot to the development both the city and the whole country, bringing huge economic benefit. On one hand, that is affected by the tourist offer and its improvement, and on the other hand, by presenting the current tourist offer in the best possible way.
5 Conclusion Creating interactive stories and storytelling as a technique that conveys the messages to a wider audience, is not something that was created recently, but something that extends through history and adapts to the medium, audience, time, and space. Nowadays, technology allows us new techniques for presenting information in the form of a story. This paper presents patterns that are used for adapting information to a wider audience. However, story designers still play a crucial role in deciding which patterns and how will they use them to present certain information. The paper presents which combination of patterns can help to maintain balance and not to put focus on just
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one of them. That depends on several factors such as the topic of the story, the audience, etc. Since connecting spatial data with the story and other elements such as audio and visual elements, people consider one of interesting topics to research, this paper shows how to combine these elements together with spatial data, and how to use cartographic elements in such way that they follow the story and not distract the user from it. This way of combining these patterns and elements for creating stories would make a great contribution to tourism industry. The focus is Sarajevo, as the capital city, but same patterns could be applied for other locations as well. After interactive narrative design patterns have been introduced, proposed solution using those patterns and its effect on users have been described, the solution is suitable for future upgrades using presented concepts.
References 1. Munzner, T., Maguire, E.: Visualization Analysis & Design. CRC Press, Taylor & Francis Group, Boca Raton, FL (2015) 2. Dimara, E., Perin, C.: What is interaction for data visualization? IEEE Trans. Vis. Comput. Graph. 26, 119–129 (2020) 3. Mínguez, C.: Teaching tourism: Urban routes design using GIS Story Map. Investigaciones Geográficas, 25–42 (2021) 4. Paay, J., et al.: Location-based storytelling in the urban environment. In: Proceedings of the 20th Australasian Conference on Computer-Human Interaction: Designing for Habitus and Habitat (2008) 5. Reid, E.: A look back at 15 years of mapping the world, Google, 06-Feb-2020. https://blog. google/products/maps/look-back-15-years-mapping-world/. Accessed 02 Feb 2023 6. Wasino, W., Arisandi, D., Andanwerti, N., Sridevi, G.T.: Tourism information systems with grouping using google maps. In: Proceedings of the 2nd International Conference on Industrial and Technology and Information Design, ICITID 2021, 30 August 2021, Yogyakarta, Indonesia (2021) 7. Sudarma, A., Piarsa, N., Buana, W.: Design and implementation of geographic information system on tourism guide using web-based google maps. Int. J. Comput. Sci. Issues (IJCSI). 10(1), 478 (2013) 8. Koenitz, H., Sezen, T.I., Sezen, D., Haahr, M., Ferri, G.: Interactive Digital Narrative: History, Theory and Practice. Routledge (Taylor & Francis Group), New York, NY (2015) 9. Barry M.: Interactive Digital Narrative – What’s the Story? [Review of: Koenitz, H., Sezen, T.I., Sezen, D., Haahr, M., Ferri, G., (eds.): Interactive Digital Narrative: History, Theory and Practice, New York, NY, 2015]. In: DIEGESIS. Interdisciplinary E-Journal for Narrative Research 5.1 (2016) 10. Segel, E., Heer, J.: Narrative visualization: telling stories with data. IEEE Trans. Vis. Comput. Graph. 16, 1139–1148 (2010) 11. Kim, N.W.: From Exploration to Explanation: Designing for Visual Data Storytelling, Ph.D. dissertation, The School of Engineering and Applied Science, Harvard University, Cambridge, Massachusetts (2019). https://dash.harvard.edu/handle/1/42029568 12. Bach, B., et al.: Narrative design patterns for data-driven storytelling. Data-Driven Storytelling. 107–133 (2018) 13. Official web page for Angular framework. https://angular.io 14. Official esri-loader npm package web page. https://www.npmjs.com/package/esri-loader/v/ 3.4.0” 15. Official web page for Django REST framework. https://www.djangoproject.com/
Geoinformation Crowdsourcing and Possible Applications in Croatia Robert Župan1(B)
, Fran Duboveˇcak1 , Stanislav Frangeš1
, and Ivana Racetin2
1 Faculty of Geodesy, University of Zagreb, 10 000 Zagreb, Croatia
[email protected] 2 Faculty of Civil Engineering, Architecture and Geodesy, University of Split, Balkans, Croatia
Abstract. Systems for geoinformation crowdsourcing are becoming an important source of spatial data. Alongside the exponential development of technology in the world systems for geoinformation crowdsourcing are also developing and finding a wider range of use. In research we present public participation GIS, participatory GIS, and volunteered geographic information as the three main systems for geoinformation crowdsourcing. These systems are based on GIS technology and realized by workshops, software, and web applications which have possibilities such as collecting, managing, and analyzing spatial data collected or submitted by a target group or so-called human sensors. For each of the three systems, there has been chosen and presented five different applications all around the world. It has been shown how systems for geoinformation crowdsourcing are used for various things such as the use of local spatial knowledge in urban planning, getting an insight about public opinion or an opinion of a target group, solving certain issues, etc. Also, there are six different scenarios which are showing possible useful applications of these systems in the Republic of Croatia. These scenarios show how there is a potential for using those systems in Croatia for purposes of urban planning, tourism, protecting biodiversity, handling crisis situations, etc. The geoquestionnaire has been conducted with the help of the web tool LOPI. LOPI proved itself useful when it comes to the creation, distributing, and analyzing the geoquestionnaire results. With the conduction and analysis of the geo-questionnaire results, it has been shown how the geo-questionnaire is a tool that can easily collect wanted geoinformation from a crowd. Keywords: geoinformation · geo-questionnaire · PPGIS · PGIS · VGI
1 Introduction Term “mass data collection” or “crowdsourcing” was created in 2005 as a combination of the word’s “crowd” and “outsourcing” by Jeff Howe and Mark Robinson, editors of Wired magazine, to describe the way companies used the Internet to “outsource work to the crowd”. Howe first published a definition for the term crowdsourcing in a blog that followed the publication of the article “The Rise of Crowdsourcing” in Wired magazine in June 2006: “Crowdsourcing is the act of a company or institution taking over functions that were once performed by employees and giving them to an undefined to a network © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 157–170, 2023. https://doi.org/10.1007/978-3-031-43056-5_13
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of people in the form of an open invitation. The most important requirement is to use an open call format and a large network of potential workers.” Although crowdsourcing was first described in 2005, the method itself goes back much further in history. In France, in the era before the French Revolution (around 1750), citizens were invited to present their proposals for solving certain social problems, and the best solution was rewarded. In the 18th century, the British Parliament was used crowdsourcing to find solutions to contemporary problems in science [1]. The power of the crowd was used by Napoleon in search of the most efficient way of delivering food to military troops on distant battlefields. One of the areas that are always interesting to geoinformatics experts is systems for the mass collection of geoinformation. Mass collection of geoinformation and its possible application in the Republic of Croatia should be investigated, but of course if there is a possibility of applying the mentioned systems in the Republic of Croatia [2–4]. The theoretical part of this paper deals with geographic information systems for public participation, participatory GIS, and volunteer geographic information as the three main systems for gathering geoinformation from the crowd. The research aims to show the ways in which systems for the mass collection of geoinformation are applied in different parts of the world and their purposes, such as using local knowledge of citizens in spatial planning, gaining insight into public opinion or the opinion of a target group of people, solving certain problems [5–7]. In addition to application in in different parts of the world, the paper considers the possible applications of systems for the mass collection of geoinformation in the Republic of Croatia. The work also aims to show the significant role of GIS in the process of public participation and the collection of spatial information, that is, geoinformation from the crowd [8, 9]. The data is analyzed after the implementation of the geo-survey. A geo-survey includes a combination of a markable map and map-related questions that target a specific group of people. Respondents can express their preferences on limited areas of the map in qualitative and/or quantitative terms by sketching and answering questions related to those areas. The objectives of the geo-survey implementation are the presentation of one of the many ways of collecting geo-information from the crowd, which in the case of this work was done by students, the presentation of the process of creating a geo-survey using the LOPI web tool, the presentation of the implementation of the geo-survey, the presentation of the processing of the collected geo-information from the crowd and drawing a conclusion based on processed results.
2 Possibilities of Application of the System for Geoinformation Crowdsourcing First, second and third PPGIS (Public participation GIS) scenario can be found at [2]. Those scenarios of the PPGIS system for mass collection of geoinformation has the following features: 1. emphasis on the process—application of local knowledge and preferences of citizens in spatial planning, 2. sponsor—city authorities or state bodies, 3. place context—urban area,
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4. data owner—city authorities, 5. method of data collection—PPGIS geo-survey. 2.1 First, Second and Third VGI Script In the first VGI (Volunteered Geographic Information) scenario, the realization of the VGI system is conceived, which uses the citizens of the Republic of Croatia as sensors during crisis situations. With the help of such a realization, citizens could report from the field during crisis situations such as fires, floods, earthquakes, and riots [10–13]. The result of public participation in this realization would be a set of geoinformation recorded by the public that could be of benefit to humanitarian organizations, emergency and rescue services in their interventions, subsequent repair of damage or provision of humanitarian assistance [14, 15]. 2.2 Conducting a Geo-survey The use of geo-surveys can be most observed in geographic information systems for public participation, whereby information on the spatial preferences of residents is predominantly collected, which is then considered and influenced decision-making, especially in spatial planning. A geo-survey includes a combination of a map on which it can be marked and with a map related issues that target a specific group of people. Respondents may express their preferences in limited areas of the map in qualitative and/or quantitative terms by sketching and answering questions related to these areas. The title of the conducted geo-survey is “Student activities in Poznan during the COVID-19 pandemic”, and since it was conducted in English the original title is “Student activities in Poznan during the COVID-19 pandemic”. The target audience at which the interrogation was conducted were students from Adam Mickiewicz University in Poznan, Poland. They answered questions related to their activities and their life in the city before and after the restrictions imposed due to the COVID-19 pandemic. The geo-survey itself is divided into three parts. The first part contains questions related to student life before restrictions, the second part contains questions related to student life during restrictions and the last part refers to their experience of studying and quality of life in Poznan. Sharing the geo-survey to respondents was conducted through social networks, such as Facebook, Instagram, and WhatsApp.
3 Geo-survey Results The LOPI web software allows you to check the number of completed geo-surveys in real time. When the geo-survey reached the completion deadline, the software showed how the geo-survey was filled out 51 times. Only fully completed questionnaires are registered. The URL invitation to complete the geo-survey was distributed to students at Adam Mickiewicz University of Poznan via Facebook groups, WhatsApp groups and Instagram. There are 47 questionnaires left that are credible and ready for further statistical processing. LOPI also gives us insight into the data collected and statistics (see Fig. 1). Given that LOPI includes all registered questionnaires in the statistics,
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not filtered ones, statistical representations of filtered responses were made using MS Excel program. The answers to tagging questions on the map were analyzed using LOPI software By GIS operation and presented as a heatmap), while answers to multipleanswer questions and answers to questions with range are displayed using column and circular diagrams. When asking open type questions, the software gives us a list of all the answers that respondents have written. It is also possible to export from the software .Csv file with all questions and answers.
Fig. 1. Example of a circular diagram with a legend obtained using LOPI software.
The geo-survey on student activities in Poznan during the COVID-19 pandemic is divided into 3 pages. The title of the first page translated into Croatian reads: “Your activities in Poznan before COVID-19 restrictions”, and the title describes what kind of questions the data subject can expect on this website (see Fig. 2). The first question on that page is the issue of marking on the map asking the respondent where his three favorite places to spend his free time in Poznan are before COVID-19 restrictions. Respondents were supposed to answer the question by drawing polygons on a map. The question is defined in the settings so that the data subject is instructed as indicated on the map and has a limit of a maximum of three answers. When the respondent marks the requested area on the map, a window with additional questions around the marked area appears on the respondent’s screen. The first related question is a question with multiple answers related to the activities of respondents in the marked area, and the second related question is an open-ended question that was optional. The two remaining questions from the first page are questions with a range that the respondent answered by selecting values on a predefined scale. The results of the marking question on the map are presented with a heat map (see Fig. 3) which does not accurately display the results in a finer scale (shown the entire marked area). In a larger scale, we can see more precisely the difference of individual marked areas (see Fig. 4). The red color indicates the areas over which most polygons are marked. In this case, we can see those areas of the old city center (Fig. 4 marked with the number two) and Citadel Park (Fig. 4 marked with the number one) were favored among students before COVID-19 restrictions.
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Fig. 2. First page of geo-survey with associated questions.
Fig. 3. Heat map of marked areas before restrictions.
Fig. 4. Heat map of marked areas at a larger scale.
The first related question to the tagging question on the map is the multiple-answer question. The question asks respondents to indicate the types of activities they have carried out in the previously designated area. The respondents had eight answers. They
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could choose between sports activities, shopping, bars and restaurants, walks, cultural events, socializing with friends, studying, relaxing, and if none of the offered answers suited them, they could write an arbitrary answer. The most recorded responses are related to socializing with friends, walking and going to catering facilities (see Fig. 5). Another related issue is an open-ended issue that asks respondents to describe what they liked best in designated areas. The question is optional and most of the answers included comments on building architecture, nature, or social life. On the first page there are two questions with a range. The first question asked students how much time they spent daily outdoors before the restrictions. It was possible to select integer values on a scale between 0 and 24 h. According to student responses (see Fig. 5), the average student spent 5.93 h a day outdoors before restrictions, and the most common response was 3 h. Nonrespondent responded with a response greater than 14 h.
Fig. 5. Time spent outdoors before COVID-19 restrictions.
On the second question with a range, respondents were asked to rate the quality of their student everyday life before COVID-19 restrictions. In the question settings, it is set that the lowest value of 0 is called “Terrible” (The highest value of 10 is called Perfect). Respondents were able to choose a value ranging from 0 to 10 with a range of values of 1 and express their level of satisfaction accordingly. The results showed that students were quite satisfied with their student life before the restrictions (see Fig. 6), as shown by the average quality score of students’ everyday life before COVID-19 restrictions in the amount of 7.80. The second page of the geo-survey has the title “Your activities in Poznan during COVID-19 restrictions”. The structure of the questions on the second page is equal to the structure on the first page, and the only difference between the pages is that the questions on the second page refer to student life during COVID-19 restrictions, and not before the restrictions as it was on the first page. The first question on the second page asked respondents to mark their three favorite areas for leisure time in Poznan during COVID-19 restrictions. This time, the respondents marked on the map an area of smaller scope compared to the same question related to
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Quality of student everyday life before COVID-19 restricons
Fig. 6. Quality of student everyday life before COVID-19 restrictions.
the period before the restrictions, given that, at a larger scale (a smaller scale does not precisely show the results) it is possible to simultaneously have the entire marked area and precisely displayed results. The heat map (see Fig. 7) shows how the most popular area for spending leisure time during the restrictions is Citadel Park (in Fig. 7 indicated by the number one). In addition to Citadel Park, two other popular areas can also be singled out, the center of Poznan (in Fig. 7 marked with the number two) and the Avenida shopping center (in Fig. 7 marked with the number three).
Fig. 7. Heat map of marked areas during restrictions.
The first related question to the tagging question on the map is the multiple-answer question. The question is the same as the question on the first page of the geo-survey that was related to the issue of marking on the map. The only difference is that it refers to areas of movement during restrictions, and not before restrictions as it was on the first page. The question asks respondents to indicate the types of activities they have carried
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Types of acvies during COVID-19 restricons
Sports Shopping Bars/Restaurants Stroll Cultural events Time with friends Learning Relaxing
Fig. 8. Types of activities during COVID-19 restrictions.
out in the previously designated area. The respondents had eight answers. They could choose between sports activities, shopping, bars and restaurants, walks, cultural events, socializing with friends, studying, relaxing, and if none of the offered answers suited them, they could write an arbitrary answer. The most recorded responses are related to socializing with friends and walking (see Fig. 8). Another related issue is an open-ended issue that asks respondents to describe what they liked best in designated areas. The question is optional and most of the answers included comments on building architecture and nature. The next question on the second page of the geo-survey is a range question that requires students to determine how much time they spent per day outdoors during restrictions. The question settings were identical to the questions on the previous page asking students about their time outdoors before restrictions. According to the responses of students (see Fig. 9) we can notice a change from the period before the restrictions. The average student spent 3.12 h a day outdoors before restrictions, and the most common response was 3 h. Nonrespondent responded with a response greater than 12 h. Then there’s another question with a range where respondents were supposed to evaluate the quality of their student everyday life during COVID-19 restrictions. The question settings were set in the same way as the questions on the previous page that required respondents to assess the quality of life before restrictions. The results showed that students were less satisfied with their student life during restrictions compared to student life before the restrictions (see Fig. 10). The average quality score of student everyday life during COVID-19 restrictions is Fig. 11. The last page of the geo-survey has the title “Experience studying in Poznan during the COVID-19 pandemic”. The structure of the question on the last page is different than on the previous two. The first question on this page is the issue of marking on the map, which is optional and does not have to be answered. The previous question is related to an open type of question that is mandatory to be answered if the respondent answered the
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Fig. 9. Spend time outdoors during COVID-19 restrictions.
Fig. 10. The quality of everyday student life during COVID-19 restrictions.
previous question by marking it on the map. The next two questions are multiple-answer questions, and the last question is a question with a range that the respondent answered by selecting values on a predefined scale. The first question on the last page of the geo-survey is optional by which respondents were asked if they discovered any interesting new places in Poznan during the restrictions and if so to mark them on the map. Solacki Park is a place that has been marked as newly discovered most of the time. Respondents marked locations by drawing dotted markers on the map. The results of the collected responses are shown by a heat map (see Fig. 11). The previous question of marking on the map is related to an open type of issue that asks respondents to describe what type of location they marked. Most of the responses
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Fig. 11. Heat map of marked newly discovered locations during COVID-19 restrictions.
were related to green spaces and catering establishments that worked despite the work ban. The next question on the last page of the geo-survey is a question with multiple answers offered. We asked respondents what activity they miss the most due to COVID19 restrictions. Respondents were able to choose from 8 offered answers, and they chose between sports activities, shopping, bars and restaurants, walks, cultural events, socializing with friends, relaxing, and not wearing a mask whose wearing was mandatory in open and indoors. According to the collected results (see Fig. 12), visiting catering facilities was the most lacking for respondents, and the second place with a significantly lower percentage of responses is occupied by not wearing masks.
Types of acvies most lacking in respondents due to COVID-19 restricons
Sports Shopping Bars/Restaurants Stroll Cultural events Time with friends Learning Relaxing
Fig. 12. Types of activities most lacking in respondents due to COVID-19 restrictions.
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This was followed by another question with multiple answers that asked respondents to express their opinion on how much COVID-19 restrictions affected their quality of life. The respondents had 5 answers: “yes, quite a bit”, “yes”, “I’m not sure”, “no” and “no, not at all”. According to the collected results (see Fig. 13), COVID-19 restrictions significantly affected the quality of life of the subjects.
Impact of COVID-19 restricons on the quality of everyday life
Yes, very much Yes Not sure No No, not at all
Fig. 13. Impact of COVID-19 restrictions on the quality of everyday life.
The last question asked in this geo-survey is a question with a range that requires students to give an overall grade to their experience of studying in the city of Poznan. In the question settings, it is set that the lowest value of 0 is called “Terrible”. Respondents were able to choose a value in the range from 0 to 10 with a step value of 1 and accordingly express the level of quality of their previous studies in the city of Poznan. The results show (see Fig. 14) that students rated their study experience most often with grades 6 and 7, while the average score is 6.67. 3.1 Discussion of the Conducted Geo-survey The implementation of this geo-survey showed the effectiveness of the LOPI web tool in the preparation of the geo-survey. The LOPI web tool has proven to be a tool that allows easy creation of arbitrary geo-surveys in five main steps as well as its rapid implementation. LOPI offers a number of options when creating a geo-survey while at the same time it is a user-friendly system for ease of use. It also offers a number of options when creating questions since it offers the possibility of creating eight different types of questions. The first noticed shortcoming of this web tool that was noticed during the processing of geo-survey results is the inclusion of all registered questionnaires in the production of statistical views, i.e., it is not possible to filter the results of the questionnaire directly in the software, but it is necessary to export.csv file and manually filter the results. Another noticed drawback is the creation of a heatmap that does not
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Reviews of the experience of studying in Poznan
Fig. 14. Reviews of the experience of studying in Poznan.
accurately display the results. Regardless of these two shortcomings, the web tool LOPI has proven to be very useful for the purpose of creating, conducting, and analyzing the results of the geo-survey. While social networks such as Facebook, Instagram and WhatsApp have proven useful for the purpose of sharing geo-surveys. The results of the geo-survey “Student activities in Poznan during the COVID19 pandemic” clearly show how COVID-19 restrictions have affected the lives of the students surveyed. Their activities and areas of their movement in the city of Poznan have changed significantly as has the amount of time they spend outdoors. During the restrictions, the area of movement of students decreased and oriented towards green areas, the old town and shopping centers. Activities before and during restrictions differ the most in the use of catering services, during the restrictions the percentage of students who spend time in catering facilities has significantly decreased compared to the situation before the restrictions. Before the restrictions, the average student spent 5.93 h a day outdoors, and during the restrictions the time spent outdoors changed to 3.12 h, which marks a 40% decrease in outdoor time due to COVID-19 restrictions. From the above it can be concluded that COVID-19 restrictions negatively affect the quality of student life, and this is confirmed with attached assessments of the quality of student life before and during restrictions. The implementation of the geo-survey has proven to be an effective method for collecting information related to space such as preferences on the use of public space and spatial organization of the city of Poznan. The results of such a geo-survey can be valuable to local authorities or universities that could make decisions or detect problems based on the collected geo-information and answers. Thus, based on this geo-survey, local authorities or universities could take measures to improve the lives of students during the PERIOD of COVID-19 restrictions. Different planning of the use of urban space could increase the quality of student life. Problems such as locations where COVID-19 restrictions are not respected could also be detected and, according to such knowledge, appropriately react and solve existing problems. It can be concluded that a geo-survey
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is a means by which one can easily collect the required geoinformation from the crowd represented by students in the example of conducted surveys.
4 Conclusion Systems for the multitude collection of geoinformation are increasingly becoming an important source of spatial information. Given the exponential advances of technology in the world, geoinformation collection systems from the crowd are also advancing and finding wider application year after year. The research presents and processes geographic information systems for public participation, participatory GIS, and volunteer geographic information as the three main systems for collecting geoinformation from the crowd and their differences are also presented. These systems include the public, i.e., citizens or certain groups of people who passively or actively contribute to the collection of spatial data that can be organized by government bodies, scientists, volunteers, local organizations, and commercial projects. This can allow citizens to be involved in laying the groundwork for the future of the community, determining how information is shared, defining goals and policies, allocating tax funds, and implementing programs. Three different types of systems for collecting geoinformation from the crowd, PPGIS (Public Participation Geographic Information System), PGIS (Participatory Geographic Information System) and VGI (Volunteered Geographic Information) are applied all over the world. For each of the three listed systems, five different realizations were selected and processed. Based on the presented realizations, we can conclude that these systems are applied in all parts of the world, from developed countries to developing countries. These systems for collecting geoinformation from the multitude are based on GIS technology and are realized through various workshops, software and web applications that may have opportunities such as collecting, processing, and analyzing spatial data collected or entered by the target group of respondents or human sensors (passive or active). Itis shown that systems for the multitude collection of geoinformation are applied for various purposes such as the use of local knowledge of citizens in spatial planning, gaining insight into public opinion or opinion of a target group of people, solving certain problems, etc. In addition to the presentation of fifteen realizations of systems for multipurpose collection of geoinformation that are applied for various purposes around the world, possible realizations in the territory of the Republic of Croatia have been considered. Given that PGIS systems for the multitude collection of geoinformation intended for use predominantly in developing countries, they were not considered. Three scenarios for PPGIS and VGI systems were considered, presenting realizations that could be useful in the Republic of Croatia. Based on the presented scenarios, it can be concluded that in Croatia there is a potential for application from the constitution for a multitude collection of geoinformation for the needs of spatial planning, tourism, biodiversity conservation, crisis situations, etc. In the practical part of the research, a geo-survey was conducted using the LOPI web tool. The web tool LOPI has proven to be very useful for the purpose of creating, implementing, and analyzing geo-survey results. By conducting and processing the results of the geo-survey, the objectives of presenting one of the many ways of collecting geoinformation from the multitude of students in the case of this paper were achieved,
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the presentation of the process of creating a geo-survey using the LOPI web tool, the presentation of the implementation of geo-survey, the presentation of the processing of collected geoinformation from the crowd and the adoption of conclusions based on the processed results. It can be concluded that a geo-survey is a means by which one can easily collect the required geoinformation from the crowd represented by students in the example of the conducted survey.
References 1. Njegovan, A.: Analiza slobodnih aplikacija za mjerenje buke. Master thesis. Faculty of Geodesy, Zagreb (2018) 2. Duboveˇcak, F.: Sustavi za mnoštveno prikupljanje geoinformacija i njihova mogu´ca primjena u Republici Hrvatskoj. Master thesis. Faculty of Geodesy, Zagreb (2022) 3. Bicksler, R.: What is Participatory GIS? (2017) 4. Griffin, G.P., Jiao, J.: Crowdsourcing bike share station locations. J. Am. Plann. Assoc. 85, 1 (2019) 5. Tulloch, D.: Public participation GIS (PPGIS), K. Kemp (Ed.), Encyclopedia of geographic information science (2008) 6. Zhang, S.: Public participation in the geoweb era: defining a typology for geo-participation in local governments. Cities 85, 38–50 (2019) 7. McCall, M.K.: Participatory GIS, PPGIS and participatory mapping utilising local spatial knowledge. A bibliography (2016) 8. Hettiarachchi, C.J., Priyankara, P., Morimoto, T., Murayama, Y.: Participatory GIS-based approach for the demarcation of village boundaries and their utility: a case study of the Eastern boundary of Wilpattu national park. Sri Lanka. ISPRS Int. J. Geo-Inf. 11(1), 17 (2022) 9. Jankowski, P., Czepkiewicz, M., Młodkowski, M., Zwoli´nski, Z., Wójcicki, M.: Evaluating the scalability of public participation in urban land use planning: a comparison of Geoweb methods with face-to-face meetings. Environ. Plann. B Urban Analy. City Sci. 46(3), 511–533 (2017) 10. Kantola, S., Uusitalo, M., Nivala, V., Tuulentie, S.: Tourism resort users’ participation in planning: testing the public participation geographic information system method in Levi. Finnish Lapland 27, 22–32 (2018) 11. Brown, G., Kytta, M.: Key issues in research priorities for public participation GIS (PPGIS): a synthesis based on empirical research. Appl. Geogr. 46, 122–136 (2014) 12. Brewer, T.D., Douglas, M.M.: Mapping Community Landscape Values and Development Preferences in and around Darwin Harbour (2015) 13. Aberley, D., Sieber, R.: Public Participation GIS (PPGIS) Guiding Principles (2003) 14. Goodchild, M.F.: Citizens as sensors: the world of volunteered geography. Geo. J. 2007(69), 211–221 (2007) 15. Goodchild, M.F.: Neogeography and the nature of geographic expertise. J. Locat. Based Serv. 32(2), 82–96 (2009)
Distribution of Medieval Necropolises and Tombstones in the Bosnia River Basin Edin Hadžimustafi´c(B) , Dževad Mešanovi´c, and Hamza Jašarevi´c Faculty of Natural Sciences and Mathematics, Department of Geography, University of Tuzla, 75000 Tuzla, Bosnia and Herzegovina [email protected]
Abstract. The subject of research in this paper is the analysis of distribution of the medieval necropolises and tombstones in the Bosnia river basin. Bosnia and Herzegovina inherits a unique cultural and historical heritage, which is reflected in numerous and unique medieval tombstones (in Bosnian is called “stecak”). Tombstones are dominantly distributed on the territory of Bosnia and Herzegovina (86%), there are much less of them in the neighboring countries: Croatia, Serbia and Montenegro (14%). The aim of the research is to determine the geographical position of the necropolis, altitudes, aspects and slopes of terrain in the Bosna river basin on which the necropolises are located. Terrain analysis is fundamental to relating the 717 necropolises sites to their altitudes, aspects and slopes. The entire analytical procedure will be carried out in the Geographical Information System. Research results of the distribution of the necropolises and tombstones in the Bosna river basin will help scientists researching medieval Bosnian tombstones for their better understanding, as well as introducing a potentially new method for their analysis. Keywords: Bosnian tombstone · medieval Bosnia · Bosnia river basin · terrain analysis
1 Introduction On the territory of Bosnia and Herzegovina, there are unique tombstones dating back to the Middle Ages. They are spread over the entire country and considering their age, they have been preserved in a fairly large number. They are special and unique in the whole of Europe, and even in the world, due to their various shapes, decorations, and inscriptions (see Fig. 1). However, they are not sufficiently researched and even less presented to the world scientific community. Tombstones in necropolises in Bosnia have a specific name – “stecak”, derived from the word “standing”. They are “standing” for almost a thousand years, witnessing the turbulent and mystical Bosnian past. Necropoli were placed near settlements, in more lonely and isolated places. Knowing the positions of necropolises will also help in discovering the areas that were inhabited in the Middle Ages and potentially finding the exact locations. In this paper, the geographical characteristics of the Bosnia river basin, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 171–185, 2023. https://doi.org/10.1007/978-3-031-43056-5_14
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the historical peculiarities of the medieval Bosnian state, introduction to the “stecak” necropolises through their presentation and description, and GIS analysis of the necropolises are sublimated. This area was chosen because it bears the name of the river after which the country bears its thousand-year-old name - Bosnia. The Central Bosnia region is located in the basin and it has always formed the core of the Bosnian state, which, despite the shifting of state borders throughout history, has always been an inseparable part of it. Important medieval cities were built in central Bosnia region also: Kreševo, Hvojnica, Visoki, Sutiska, Vranduk, Travnik and the capital Bobovac.
Fig. 1. Necropoli with tombstones - Ilijas municipality, Dragorade locality.
1.1 Geographical Characteristic of the Bosnia River Basin The catchment area of the Bosnia river is located in the central part of Bosnia and Herzegovina, which is located in the southeast of Europe, in the Balkan Peninsula. The Bosnia river originates from karst springs at the foot of Mount Igman. It is the right tributary of the Sava and flows into it near Bosanski Samac municipality, and therefore belongs to the Black Sea basin. The basin of the Bosnia river is fan-shaped and is bounded on the west by the basins of the Vrbas and Ukrina rivers, on the south by the basin of the Neretva river, and on the east by the basins of the Drina river and immediate tributaries of the Sava river. The easternmost point of the Bosnia river basin is located at 19° 3 33 E, on the mountain top of Djipa (1328 m above sea level). The westernmost point lies at 17° 26 5 E near the settlement of Hemici. The confluence of the Bosnia river with the Sava river is located in the village of Prud at 45° 04 17 N and is also the northernmost point of the basin. The southernmost point is located on Mount Treskavica, the top of the Pasina Mountain (2070 m above sea level), at 43° 35 23 N. The Bosnia river basin has an area of 10.786 km2. It stretches in the north-south direction for a length of 164.6 km, while in the east-west direction it stretches for a length of 129.9 km (see Fig. 2). The Bosnia river
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basin is mostly drained in the physical-geographical region of Mountain-valley Bosnia and to a lesser extent in the lower part of the stream in Peripannonian Bosnia.
Fig. 2. Geographical position of the Bosna river basin.
The Bosnia river basin is geologically extremely heterogeneous, where one can see the differentiation of magmatic, sedimentary and metamorphic rock complexes that are the result of various geotectonic and geodynamic processes from the Paleozoic to the present day. Geologically, it is located in the Central and Inner Dinarides. The Dinarides on the territory of Bosnia and Herzegovina are divided into three large structural-facies units: the Outer Dinarides, the Central Dinarides and the Inner Dinarides [1]. According to the structural-genetic, lithological, orographic and morphogenetic homogeneity of the relief units, the Bosnia basin is located in the area of two macroregional geomorphological units: Lowlands, low mountains and hills, valleys of northern Bosnia and second is Bosnian highlands [2]. Average temperatures increase from the source part of basin towards the mouth and ranges from 0 °C on the highest mountain peaks of Vranica, Bjelasnica and Vlasic to 10 °C in the lower parts at the confluence of the Bosnia river and the Sava river. The amount of precipitation decreases from the orographic-hydrographic divide on the mountain Bjelasnica, where it amounts to an average of 2000 mm, towards the confluence of the Bosnia river and the Sava river, where an average of 800 mm of precipitation is recorded. Two continental climate types are expressed in the Bosna river basin, namely: moderatecontinental, which covers the entire area of the basin, except for places with an altitude
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of more than 1000 m, where mountain climate type dominate. They are represented on the mountains: Bjelasnica, Vranica, Vlasic, Borja, Treskavica, Jahorina, Romanija, Javor and Konjuh. Hydrologically, the largest surface watercourse is the river Bosnia, which is formed by numerous permanent and occasional watercourses. Bosnia is a river with a source flow of about 3000 l/s. The length of the river is 282 km. The altitude at which the river originates is 500 m, while the altitude of the mouth is 83 m, which is a difference of 417 m, so we can say that the average drop is 1.51‰. Bosnia is a river with a large number of tributaries, near Sarajevo the Zujevina flows in from the left, while the Miljacka, Dobrinja and Zeljeznica flow in from the right. Other important tributaries are Ljesnica, Usora, Gostovic, Krivaja, Spreca and Lukavica [3]. The area of the Bosnia river basin is the most populated part of Bosnia and Herzegovina, where today about 1.8 million inhabitants live in slightly more than 1.600 settlements. 1.2 Historical Development of Medieval Bosnia Bosnia is mentioned for the first time in written form in the famous geographical and historical work “De administrando imperio”, by the Byzantine emperor Constantine Porphyrogenitus, from the middle of the 10th century. According to contemporary historians, Bosnia is the oldest South Slavic early feudal state. The oldest known Bosnian ruler by name was Prince Stjepan from the end of the 11th century, who was a protégé of the Duklja king [4]. The first known Bosnian Ban Boric ruled from 1154 to 1164. After Ban Boric, the next Ban that is mentioned by name is the “great Ban of Bosnia” as the “Roman court” called him, “the noble and powerful husband”, as the Pope’s envoy Ivan de Kazamaris called him in 1203, Ban Kulin. The reign of Ban Kulin was accompanied by political stability and strong economic development based on rich deposits of ore and mineral resources and flourishing trade. The Bosnian Banate had special trade relations with Dubrovnik, with which on 29.8.1189. Year, Ban Kulin also signed a special trade agreement (charter). At the end of the 12th century, a heresy appeared on Bosnian soil, which was accepted by Ban Kulin himself and his family. In order to suppress heresy, Pope Innocent III launched an action with the aim of eradicating it and preventing its spread to the surrounding countries. The close cooperation of the Roman Curia, the Hungarian kings, and various rulers from the Nemanjic family and their Orthodox Church in the joint fight against heretical Bosnia, and in fact against its independence and identity, resulted in the beginning of the fight against Bosnian heretics, which has never been interrupted [5]. Ban Kulin makes a wise move and addresses the Roman curia in a letter. He wrote that he accepted all the conditions set by the curia, he was not sure it was heresy because he could not distinguish heretics from Catholics, and on 8.4.1203. Signed the act of abjuration (rejection) of heretical teaching and practice. The thirteenth century was filled with dynastic and especially religious turmoil, which resulted in the formation of the independent Bosnian Church, which in the following centuries would leave deep traces in almost all spheres of life [4]. On the territory of the medieval Bosnian state, in addition to the Catholic and Orthodox, there was also a separate Bosnian Church. Its members mostly buried themselves under the world unique tombstones, as members of other confessions also did. The organized activity of the Bosnian Church began from
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the middle of the 13th century to the middle of the 15th century, while the tombstones appeared from the 12th and lasted until the 16th century [6]. John Fine came to the conclusion that the Bosnian Church, as an autonomous church, was created between 1234 and 1252, and its hierarchy used the existing religious organization [7]. During the time of the Bosnian ruler Stjepan II Kotromanic, Bosnia expanded significantly territorially in the south and southwest direction. Stjepan II Kotromanic supported the work of the Bosnian Church. During the reign of Ban Stjepan II Kotromanic, Bosnia experienced a strong economic development based on rich deposits of precious metals, the production of which in Europe at that time was in stagnation. Ban Stjepan II Kotromanic was succeeded by his nephew Tvrtko I Kotromanic. Tvrtko I Kotromanic stabilized Bosnia politically and expanded its territory: he made Bosnia the largest state in the Balkans and among the South Slavs at that time [5]. The borders of medieval Bosnia and Hum (region on south of Bosnia today’s Herzegovina) were different compared to today. During the reign of Ban Stjepan II Kotromanic, they reached the Adriatic coast and the Cetina river, including the city of Omis, and during the reign of King Tvrtko I, from 1378–1390, they covered the area as far as Zadar, including the cities of Split, Trogir and Sibenik, and the islands of Brac, Hvar and Korcula. For a long time, the Ston coast, as well as part of Konavle, and during the time of Tvrtko I even the cities of Kotor, Prijepolje, Bijelo Polje, Zabljak, Niksic and Herceg-Novi were in the possession of Bosnia, and before that Hum. These facts explain the appearance of Bosnian tombstones in the west as far as Trogir and Sibenik, in the south as far as Ston and Peljesac, then as far as the Dubrovnik coast, Zupa and Konavle, and in the east as far as Bijelo Polje and Prijepolje, because wherever the inhabitants of Bosnia and Hum lived and died, regardless of whether they were members of the Bosnian, Catholic or Orthodox Church, Bosnian tombstones (“stecak”) were also made [8]. Towards the end of Tvrtko’s rule, new conquerors, the Ottomans, came from the east and penetrated into Bosnia for the first time in 1386. With the fall of the hard city of Bobovac 1436, the capital of the Bosnian kingdom, the Ottomans occupied Bosnia. In the first nearly 150 years of Ottoman rule, Bosnian tombstones continued to be built with a gradual transition to Muslim tombstones. 1.3 General Characteristics of Medieval Bosnian Tombstones In general and quite simplified, it can be said that Bosnian tombstones, as the name says, are made of stone and they are characteristic of the area of the old Bosnian state [6]. Many folk beliefs and stories have been created about them in attempts to shed light on their origin. There is a common story in which tombstones are mentioned as Greek cemeteries. Bosnian tombstones are unique and authentic gravestones, which, unlike the rest of European medieval culture, have been preserved in large numbers and are a special cultural-historical phenomenon related only to the area of the medieval Bosnian state. Limestone rocks are most often used in making them. The territory of Bosnia and Herzegovina abounds with limestone rocks, so the widespread use of it is understandable. In addition, limestone rocks are steadily, they are resistant to erosion processes caused by weathering. Limestone is easy to work with also.
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The placement and arrangement of tombstones in the necropolis followed certain rules. The tombstone was placed lengthwise above the grave. Both graves and tombstones are mostly oriented in the west-east direction. The deceased, regardless of gender and age, is always laid in a grave in a lying position, on his back, so that his head was on the western side and his legs on the eastern side [6]. The orientation of the tombstone was directed to the sunrise and sunset azimuth points on the horizon [9]. Bosnia and Herzegovina represents the main area of geographical distribution of necropolises. There are 2.687 localities with tombstones in Bosnia and Herzegovina, which is 84.97% of the total number of localities. In those necropolises, 59.593 tombstones were recorded, which is 85.92% of the total number of tombstones. The most numerous form of tombstones in Bosnia and Herzegovina are crates, 37.955 or 63.69% of the total number of tombstones. The number of gables is relatively small, 5.606 or 9.41% of the total number of gables in the territory of Bosnia and Herzegovina [6]. Bosnian tombstones are divided by shape into: lying and standing. Lying forms are divided into types: slab, crate, crate with plinth, gable and gable with plinth. Standing forms include: a column, a cross and amorphous monuments (see Fig. 3).
Fig. 3. Differentiation of tombstones by shapes at the primary level.
With their specific artistic form, their reliefs, Bosnian tombstones attracted (and still do) the attention of both scientists and amateurs. Researchers of tombstones paid special attention to this specific phenomenon, sketching, drawing, photographing, recognizing and classifying relief motifs on tombstones (see Fig. 4). The inscriptions on tombstones were written exclusively in the vernacular in the old Bosnian Cyrillic, i.e. Bosnian script. We have different opinions about the time of creation of tombstones, but most historians believe that they were created in the 12th century.
2 Materials and Methods The research process of the necropoli in the Bosna river basin is divided into three phases. Research area was first defined. In the second phase, a database of necropoli was formed, and at the end, an analysis of the terrain in the Bosna river basin was carried out. Delineation of catchment area of the Bosnia river was determined by the Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global, Digital Elevation Model for
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Fig. 4. An example of various motifs on Bosnian tombstones.
Bosnia and Herzegovina with a spatial resolution of 30 m [10]. Catchment area of the Bosnia river is made up of 8 parts, so after taking over it was necessary to merge them into one part. Merged DEMs covered a larger area than the basin of Bosnia is, so the process of delineating the basin was done to determine the watershed. SRTM data is provided in the WGS 84 EPSG:4326 geographic coordinate system. Before starting the analysis, the DEM was reprojected from the geographic to the rectangular GaussKrüger coordinate system. Correction of DEM (Fill sinks) was done using the algorithm developed by Wang and Liu [11], Strahler stream ordering, flow direction for which the D8 method was used, channels, channel network and catchment area were determined. By determining the boundary of the catchment area, the research area of the necropoli was also defined. The river network in the Bosnia basin was downloaded from the Internet [12]. It was modified by adding names to certain more important river courses. Other geographic data such as roads, settlements were downloaded from the OpenStreetMap website [13]. The basis for researching the necropoli is the data collected from the Archaeological Lexicon of Bosnia and Herzegovina [14, 15]. In the lexicon, the area of Bosnia and Herzegovina is divided into 25 medieval Bosnia regions, 9 of which are the part of Bosnia river basin. Medieval localities in regions are described textually with different meanings such as: hoards of money, necropoli with tombstones, basilicas, medieval towns, mines and quarries. For the purposes of this research, necropolises with tombstones were extracted, for which a brief description of municipalities, settlements, necropoli names, number of tombstones, etc. was given. This was the basis for the formation of an attributive database in the Geographical Information System. All necropolises in the regions are also shown cartographically, which was the basis for positioning the necropolises in the geographical space. By combining attributive data with geographic data, the prerequisites for further analysis of the necropoli were achieved. Collecting necropolis locations from maps required georeferencing them. One of the definitions of georeferencing is: aligning geographic data to a known coordinate system so it can be viewed, queried, and analysed with other geographic data [16]. The maps were originally made in the Gauss-Krüger rectangular coordinate system,
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so it was also used during georeferencing. The Bosnia river basin is located in the 6th zone of the Gauss-Krüger coordinate system, and all 9 maps are georeferenced in the MGI/Balkans zone 6 coordinate reference system. According to Hackeloeer it is sufficient to correlate several (at least three) GCPs to sets of pixels within an image which have been classified accordingly. This way, the remaining pixels and also other identified features and objects can be georeferenced to positions on a map [17]. In order to increase the precision of the obtained results, an average of 50 GCPs were placed on each map representing the region. After placing the maps in a unique coordinate system, the locations of the necropoli were digitized. Necropoli positions are digitized with points. In this way, a geographic database was formed that contained locations without attributes. The necropoli attributes are summarized in a Microsoft Excel spreadsheet, which is associated with the locations, thus completing the database. Now it was possible to determine which necropolises belong to the researched area, because the area covering 9 regions is larger than Bosnia river basin. Previously, the watershed of the Bosnia basin was separated. By selecting data by geographic location, only necropolises located in the basin were chosen. This prepared the data for further analysis. In the third phase, terrain analysis or geomorphometric analysis was undertaken. The fundamental operation in geomorphometry is extraction of parameters and objects from DEMs. DEMs, i.e. digital land-surface models, are the primary input to morphometric analysis [18]. Altitudes, aspects and slope gradients of terrain in the Bosnia river basin were analyzed. All the listed parameters are extracted from the Digital Elevation Model. Contours with an interval of 100 m were generated, which resulted in height zones along which the necropolises and tombstones will be further analyzed. For the aspect analysis, the horizon is divided into 8 parts of 45°, and the value 0°, i.e. flat terrain. The division was made on the basis of the following criteria, where the directions and their angular values were determined: without aspect (−1°), north (0°−22.5° and 337.5°−360°), northeast (22.5°−67.5°), east (67.5°−112.5°), southeast (112.5°−157.5°), south (157.5°−202.5°), southwest (202.5°−247.5°), west (247.5°−292.5°) and northwest (292.5°−337.5°). The specified criterion was a form for reclassification of the aspect of slopes in the Bosnia river basin. In geomorphology, slope angles are divided into 6 categories: flat terrain (0°−2°), slightly inclined terrain (2°-5°), inclined terrain (5°−12°), significantly inclined terrain (12°−32°), very steep slopes (32°−55°) and cliffs (55°>). After obtaining the map of the slope of terrain, it was reclassified into the specified categories. The positions of the necropoli were obtained by digitalization, and now these locations could be linked to the terrain, i.e. altitudes, aspects and slopes. Then it was possible to start analyzing the necropoli in the Bosnia river basin and obtain numerous valuable results.
3 Results and Discussion By placing necropolises in a coordinate system, their geographic position was determined. Locating where the necropolises are positioned was the main prerequisite for further analysis. The distribution of necropolises by altitude was the first to be done. The altitudes of the necropolises were obtained using the Digital elevation model by
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extracting the values of the altitudes of the cells on which the necropolises lie. A lot of valuable statistical data was obtained for all analyzed parameters, and this paper will present the necessary basic statistical indicators that show the horizontal and vertical distribution of the necropoli. Altitudes were extracted for 716 out of a total of 717 localities. The lowest necropolis is located at 88 m above sea level (locality Crkvina in Bosanski Samac municipality) and the highest at 1799 m above sea level (locality of Brezov Rat in the municipality of Trnovo). The altitude range where the necropolises are located is 1711 m. The average altitude of the necropolis is 674 m. The necropolises and tombstones were arranged and analyzed according to altitude in a range of 100 m (see Fig. 5). Most necropolises are located in the range of 600 to 700 m above sea level and one necropolis is located 1400 to 1800 m. A total of 10.412 tombstones were recorded in 634 necropolises in Bosnia river basin. The least number of them were recorded at altitudes above 1200 m and at altitudes lower than 400 m. Most tombstones, 1.583 of them, are located at altitudes of 800 to 900 m. It could be concluded that the areas on these heights were the most populated. The fewest tombstones are in the highest altitude zones from 1300 to 1800 m because these are harsher areas for settlement.
Fig. 5. Distribution of necropolises by elevation in the Bosnia river basin.
Necropolises can be grouped into different categories. They are grouped according to the elevation zones of relief: lowlands (0–200 m), hills (200–500 m), low mountains (500–1000 m) and medium mountains (1000–2000 m). There are 16 necropolises with
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2000 1500 1000 500 0
88-200 200-300 300-400 400-500 500-600 600-700 700-800 800-900 900-1000 1000-1100 1100-1200 1200-1300 1300-1400 1400-1500 1500-1600 1600-1700 1700-1800
188 tombstones in the lowlands, 156 necropolises with 1524 tombstones on the hills, 374 necropolises with 6775 tombstones in the low mountains, and 88 necropolises with 1925 tombstones in the medium-high mountains (see Fig. 6).
Tombstones
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Fig. 6. Necropolises and tombstones according to relief classification by 100 m elevation zones.
After the elevations, the aspects of the slopes where the necropolises are located was determined. Out of a total of 717 necropolises, aspects were not determined for 4 necropolises, and for a total of 713 was determined. Through terrain analysis, the aspects of slopes in the Bosnia river basin were classified into 9 categories (see Fig. 7). According to this classification, distribution of the necropoli was made, that is, the necropolises were associated with the corresponding aspects. In the middle of the value range, the aspect is 198°, which represents the median. From 0° to 198° aspects were determined for 360 necropolises, and 322 of them contain 4.635 tombstones. In the second group of aspect values from the median to the end of the value range, there are 353 necropolises, where there are 5.761 tombstones in 310 necropolises. Necropolises have evenly distributed aspects without major deviations. The largest number of necropolises, 115 of them, are located on the southwestern slopes, and the smallest, 5 of them, on level terrains without aspect (see Fig. 8). Out of a total of 10.412 tombstones, 16 tombstones lie at 2 out of 4 necropolises that do not have a specified aspect, so the total number of analyzed tombstones is 10.396. The southwestern aspect, where the largest number of necropolises was recorded, is followed by the largest number of tombstones, 2.314. If the horizon was divided strictly into eastern with azimuth from 0° to 180° and western with azimuth from 180° to 360°, the result would be that 316 necropolises with 4.075 tombstones face east, while 397 necropoli with 6.321 tombstones is exposed to the west. It can be concluded that 81 necropolises face the western side of the horizon, or 11.36% more than the total number of necropolises. There are 2.246 tombstones in those 81 necropolises, which is 21.6% more than the total number of tombstones. The inhabitants of medieval Bosnia set up necropolises randomly, probably in accordance with the relief composition of the area they inhabited. Of the 6 official categories in which the slope gradients are classified, necropolises are found in 5 (see Fig. 9). There is no tombstones on the steepest terrains or cliffs with slopes (>55°). In the first category, there are 37 necropolises, with 555 tombstones at
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Fig. 7. Aspects of slopes in the Bosnia river basin and the dispersion of necropolises along them.
2500 2000 1500 1000 500 0 F
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Fig. 8. Necropolises and tombstones in relation to aspect categories.
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32 necropolises (the number of tombstones was not recorded in all necropolises). The second category has 106 necropolises, with 90 necropolises and 1.318 tombstones. There are 205 necropolises in the third category, where there are 182 necropolises with 3.156 tombstones. Slopes have been determined for 716 necropoli out of a total of 717. Three necropoli are located on flat terrain without slopes, two are located on the plain in northern part of Bosnia river basin with a total of 16 tombstones. The maximum value of the slope of the necropolis is 44.11°, it is located in the municipality of Vares, at the locality Pod, and 9 tombstones were recorded on it. Necropoli range in slope values from 0° to 44.1182°, with an average slope of 44.1182°. The median of the slopes is 12.4222°, which shows that there are 313 necropoli with 5.154 tombstones on slopes from 0°–12.4222°, that is, from 12.4222°–44.11° there are 321 necropoli with 5.258 tombstones. The standard deviation of the slope is 26.8404. A quarter of the necropoli or 179 localities, where 2.298 tombstones were recorded in 153 necropoli, are located on slopes of 0°–6.16423°. Three quarters and more necropoli or 179 localities, out of 155 necropoli with 2.490 tombstones, lie on slopes of 18.0517°–44.1182°.
Fig. 9. Terrain slopes in the Bosnia river basin with associated necropolises.
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The category of significantly inclined terrain (12°–32°) has the largest angular range, so it also contains the largest number of necropolises –345. In this category, there are 5.122 tombstones on 310 necropolises. The next category of very steep slopes (32°–55°) has the fewest necropolises, 23 of them. A total of 261 tombstones were distributed over 20 necropolises (see Fig. 10).
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Fig. 10. Slope categores with grouped necropolises and tombstones.
Empirically, it was determined that the total number of necropoli and tombstones recorded in the field is about 25% higher than the number recorded in the literature. So the number of necropoli in the Bosnia river basin is much higher than 717 and the total number of tombstones is much higher than 10.412. Although 717 necropoli are listed in the Archaeological Lexicon of Bosnia and Herzegovina, tombstones were recorded at 634 necropoli. The accuracy of the above data is conditioned by the accuracy of necropoli mapping in the Archaeological Lexicon of Bosnia and Herzegovina and the process of georeferencing maps in the Geographical Information System. The symbol for necropoli on maps is drawn out of proportion, and its dimensions also affect the accuracy of locating. We compared the results for the area of the municipalities of Breza and Vares, where positions of the necropoli were collected directly from the field [19] using GPS. 10 random necropoli from the field and their georeferenced counterparts were taken as a sample. The smallest distance between two necropoli is 254 m and the largest is 790 m with an average distance of 420.6 m. Data about necropoli and tombstones were published in 1988 and were collected much earlier. Many of these necropoli, especially tombstones, no longer exist. They were destroyed in different ways and for different needs of the local population. As an example, we will mention that in our GIS database, the necropoli at the location Kaursko groblje in the municipality of Breza is listed with 114 tombstones. That was the former number, and in nowadays that necropoli has 58 tombstones [19].
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4 Conclusion Necropolises and tombstones are scattered throughout of the Bosnia river basin, and despite being a thousand years old, a large number of them have been preserved. Through this research, almost exact locations where the necropolises lie have been discovered, which will help in discovering the areas that were inhabited in the Middle Ages and potentially finding the exact locations of the settlements. The altitudes at which people lived at that time were indirectly determined by the positions of necropolises. The most suitable heights for settlement are from 800 to 900 m. Middle Age inhabitants of Bosnia river basin avoided high mountain areas and lowlands. This guaranteed the safety of the inhabitants, a suitable climate, wealth of springs, forests, pastures and arable land. The placement of the necropolises did not follow any special rules because they are located in all directions. The relief features determined the placement of the necropolises and not some custom, belief, or superstition. It was found that the necropoli were positioned on relief features with different slopes, from flat terrain to a slope of 44°. Half of the necropoli are located on slopes of up to 12°, which shows that the necropoli were mostly located on sloping terrain. For the location of cemeteries, given the relief configuration of Bosnia and Herzegovina, such locations are chosen that cannot be used for agricultural production. Tombstones are crying out for organized and comprehensive multidisciplinary research. This work has shown a new way to make new discoveries related to the mysterious and poorly researched Bosnian tombstones.
References ˇ ci´c, S.: Geološki sastav i tektonika Bosne i Hercegovine. Earth Sci. Inst. Sarajevo, 249–252 1. Ciˇ (2002) 2. Lepirica, A.: Reljef geomorfoloških makroregija Bosne i Hercegovine. Zbornik radova Prirodno-matematiˇckog fakulteta. Svezak Geografija, Univerzitet u Tuzli, PMF, godina VI, broj 6, Tuzla, 7–52 (2009) 3. Grupa autora: Vojno-inžinjerski opis rijeke Bosne. Državni sekretarijat za poslove narodne odbrane. Uprava inžinjerije, Beograd (1961) - c, P.: Bosna i Hercegovina u praistoriji, antici i srednjem vijeku: Kasni srednji vijek. 4. Andeli´ In: Arheološki leksikon Bosne i Hercegovine, tom 1, Zemaljski muzej Bosne i Hercegovine, Sarajevo, pp. 44–49 (1988) 5. Imamovi´c, M.: Historija Bošnjaka. Bošnjaˇcka zajednica kulture Preporod, Matiˇcni odbor, Sarajevo (1997) 6. Bešlagi´c, Š.: Ste´cci – kultura i umjetnost. IRO „Veselin Masleša“, OO Izdavaˇcka djelatnost Sarajevo, 67 (1982) 7. Fine, J.: Bosanska crkva: novo tumaˇcenje: studija o bosanskoj crkvi, njenom mjestu u državi i društvu od 13. do 15. stolje´ca. Bosanski kulturni centar, Sarajevo, (2005) 8. Bešlagi´c, Š.: Ste´cci, kataloško-topografski pregled. Veselin Masleša, Sarajevo, 43 (1971) 9. Hadžimustafi´c, E., Smaji´c, S.: Geografska orijentacija ste´caka na podruˇcju Tuzlanskog kanˇ tona. Zbornik radova Cetvrtog kongresa geografa Bosne i Hercegovine. Sarajevo, 230–240 (2016) 10. USGS Homepage. https://earthexplorer.usgs.gov/. Accessed 23 May 2018
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11. Wang, L., Liu, H.: An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. Int. J. Geogr. Inf. Sci. 20(2), 193–213 (2006) 12. HydroSHEDS Homepage. https://www.hydrosheds.org/. Accessed 17 Dec 2019 13. OSM Homepage. https://www.openstreetmap.org/#map=9/44.5141/17.8967. Accessed 9 Feb 2022 14. Grupa autora: Arheološki leksikon Bosne i Hercegovine, tom 2., Zemaljski muzej Bosne i Hercegovine, Sarajevo (1988) 15. Grupa autora: Arheološki leksikon Bosne i Hercegovine, tom 3., Zemaljski muzej Bosne i Hercegovine, Sarajevo (1988) 16. Sommer, S., Wade, T.: A to Z GIS: An Illustrated Dictionary of Geographic Information Systems, 2nd edn. Esri Press, Redlands (2006) 17. Hackeloeer, A., Klasing, K., Krisp, J.M., Meng, L.: Georeferencing: a review of methods and applications. Ann. GIS 20(1), 61–69 (2014). https://doi.org/10.1080/19475683.2013.868826 18. Pike, R.J., Evans, I.S., Hengl, T.: Geomorphometry: A Brief Guide. Geomorphometry, 1st Edition, Concepts, Software, Applications. Elsevier Science, 3–30 (2009) ´ Hadžimustafi´c, E.: Mramorje Breze i Vareša. Bošnjaˇcka zajednica kulture 19. Omerhodži´c, C., Preporod Breza (2022)
Use of Augmented Reality to Present Archaeological Contents Vesna Poslonˇcec-Petri´c1(B) , Valentina Vukovi´c2 and Ivka Kljaji´c1
, Željko Baˇci´c1
,
1 Faculty of Geodesy, University of Zagreb, Kaciceva 26, Zagreb, Croatia
[email protected] 2 Clover Studio, 10000 Zagreb, Croatia
Abstract. Modern society has taken big steps in digital environment, which has caused ever growing usage of digital maps. However, conventional paper maps still remain very much present, especially in presenting tourist information about some city, region or even state. Irrespective of whether the maps are digital or paper maps, map producers are faced with the problem of including all necessary information on maps, and their users are facing the challenge of clearly reading and understanding their contents. The technology of augmented reality has been developed in order to remove such problems. The application ArheVinkovci has been developed for the purpose of re-searching the possibilities of the augmented reality technology on a concrete example of the archaeological sites in the town Vinkovci and it was tested in order to figure out the advantages and disadvantages of such technology. The paper presents the approach to the development of the application along with the problems encountered in the process, as well as the results of application testing in real conditions and the possibilities offered by this technology. For this purpose, two hypotheses have been set: 1. Augmented reality technology can be used to produce carto-graphic representations; 2. Existing analogue cartographic representations can be complemented with additional cartographic content using augmented reality technology. Keywords: augmented reality · archaeological sites · ArheVinkovci
1 Introduction The area of the present-day town Vinkovci and its immediate surroundings has been populated by people for at least 8,000 years. The oldest cultures originate from the period of Early Stone Age (Staˇcevo and Sopot), of Copper Age (Vuˇcedol), Bronze Age (Vinkovci). There are also the sites originating from the Iron Age (the Celts), and the sites of Roman provincial culture originate from the Roman period (Cibalea) lasting from the 2nd to 4th centuries. There are the remains of Gepid and Slavic culture originating from the Migration Period. The Early Middle Ages, High Middle Ages and Late Middle Ages came in the period from 7th to 15th centuries the heritage of which is also known in these areas [1, 2]. The continued habitation has resulted in archaeological sites below a significant part of the present-day Vinkovci that also overlap each other. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 186–194, 2023. https://doi.org/10.1007/978-3-031-43056-5_15
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There is a problem how to present the archaeological sites of the town Vinkovci on a town map along with the chronology of their development? Since certain archaeological sites originating from various periods are placed in partially the same locations, the method of presenting them on paper maps would require the overlapping in presenting single sites, which makes it difficult to read a map and requires more time to understand the contents of a map. Although digital contents and even digital maps are getting more and more available, the paper maps are still used, especially for tourist purposes, which opens the question how to provide the desired information about the archaeological sites without compromising the readability of the map by the amount of content. In order to find the solution, the technology of Augmented Reality (AR) has been used and its applicability in solving the mentioned problem tested.
2 Augmented Reality Technology The definition of AR was given by different authors like Reitmayr and Schmalstieg [3] or Milgram et al. [4]. Azuma [5] defines that AR allows the user to see the real world, with virtual objects superimposed upon or composited with the real world. Therefore, AR supplements reality, rather than completely replacing it. Azuma [5] defines AR as systems that have the following three characteristics: 1. Combines real and virtual 2. Interactive in real time 3. Registered in 3-D This definition allows other technologies besides head-mounted displays (HMDs) while retaining the essential components of AR like monitor- 3 based interfaces, monocular systems, holograms and various other technologies which satisfy the mentioned three characteristics. AR is enriching the reality with information and virtual content and can also be defined by the spectrum of mixed reality as visualised by Milgram and Kishino [4] (Fig. 1). Computer interfaces can be presented as a continuum between the real and virtual environment. The Augmented Reality is a part of Mixed Reality where the computergenerated content is included into the real world in order to supplement it with additional information, objects and other contents.
Fig. 1. Simplified representation of a “virtual continuum” [4].
Although the term Augmented Reality itself was coined only in the 90s of the 20th centuries [6], the idea of Augmented Reality dates back as far as the fifties of the
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last century when the cinematographer Morton Heilig engaged additional senses in the experience of watching a film. He built a prototype of a device that brought together film, sound effects, vibrations, air movement and odours and is called Sensorama [7]. A computer scientist Ivan Sutherland constructed the first head-mounted display (HMD) in 1968 named The Sword of Damocles (Fig. 2). HMD displayed computer graphics overlapped with the real world [8, 9].
Fig. 2. The Sword of Damocles by an inventor Ivan Sutherland [8].
In the following years, a few devices/systems were invented aiming to enhance user experience with augmented reality. The significant breakthrough of the augmented reality technologies happened in 2013 when Google [10] launched the augmented reality glasses called Google Glass. Google Glass was supposed to replace smart phones since it could be used hand-free and with voice commands. All information is presented to users on the glass screen. Google Glass was equipped with processor, camera, speakerphones, GPS, Internet, Bluetooth and the most important component – small glass prism with a mini-projector in it that displays an image.
Fig. 3. Experience of augmented reality on a smart phone [11].
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Three years later, Microsoft launched the augmented reality glasses to the marker called Microsoft HoloLens running under Windows 10 computer operating system. Numerous advantages and various possibilities of augmented reality glasses have been identified over time. More and more companies produce today the augmented reality glasses. They are still rather expensive; however, the augmented reality technology can be experienced also through smart phone. Using the camera on a smart phone, we can observe the real world through the device screen, and virtual elements overlay the real works by means of a developed application. It functions in the same way as smart glasses, but instead of looking through the glasses on our heads, we look at the screen of a smart phone (Fig. 3). Depending on the type of technology used and application purpose, there are a few categories of augmented reality [12] available: • Marker based AR – known also as Image Recognition, uses visual markers, such as QR or 2D code. • Markerless AR – also called location-based AR, uses GPS, digital compass, velocity meter or accelerometer which is embedded in the device to provide the data based on the location. • Projection based AR – projects artificial light onto some surface and provides the interaction of users with a system. • Superimposition based AR – replaces the original image with a virtual image, either partially or fully. It functions on the principle of object recognition that is being replaced and that does not need to be defined in advance as it is the case with the marker-based AR. For the purpose of the practical part of the paper, marker-based AR shall be used and discussed.
3 Augmented Reality Cartographic Application for Archaeological Sites in Vinkovci The technology of augmented reality is used for various visualisations of spatial data and objects, for easier and quick access to various data, and in learning and education. It has been found out that new cartographic presentations have been created that use tracking technology in order to “retrieve” a specific type of information such as in WELC Map application and tourist maps [13]. However, the mentioned application does not produce any additional content on the map itself. The existing cartographic presentations have been used to reproduce a virtual object as it is the case in Arch4maps application that present a 3D model of a building. There have been no data found about the use of augmented reality technology where an especially produced map or already existing map has been applied to reproduce only cartographic content offering the possibility to solve some problems in the production of paper maps. Such problems refer mostly to the limited availability of the space on the map, the inability to update the maps according to the changes in space, the selection of displaying the exactly defined map content according to the users’ needs.
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In order to test the possibilities of using the augmented reality technology for the purpose of addressing some limitations of paper maps, there was a practical problem discussed: how to present the archaeological sites in the town Vinkovci chronologically on the town map? Since some areas of the archaeological sites originating from various periods are located partially on the same locations, their presentation on paper maps would require the overlaying of single sites. It would make it difficult to read a map and require more time to understand the contents of the map. Therefore, two conditions were set in the implementation of the application: paper map should continue to be used as necessary for obtaining the desired information about the archaeological sites, and the readability of the map will not be compromised by the amount of the contents. The town map at the scale of 1:15,000 [13], was used for this purpose, Fig. 4. The free software Unity was used for non-commercial purposes in the creation of the display. Unity is a multiplatform programme intended for the development of computer games created by the company Unity Technologies [15]. Unity supports the development of AR applications together with Vuforia Engine software platform that facilitates the implementation of advanced functionalities of computer vision in any kind of application [16]. This allows the images and objects in the real world to be recognized. Since the existing cartographic presentation – the town map of Vinkovci will be supplemented by the augmented reality, the plan shown in Fig. 4 was used as the basis for recognition, i.e. as marker.
Fig. 4. Vinkovci, City map [14].
The smart phone Huawei P9 Lite and the smart glasses Vuzix M300 were used to test the application. The application using the described software to produce the map of archaeological sites to be used together with the town map of Vinkovci has been named ArheVinkovci. Since it is necessary to test before the creation of the application itself whether the town map is an appropriate marker for the creation of the application using the guidelines given in the Sect. 3, it cannot be concluded with certainty by visual inspection of the plan on paper whether it would be appropriate. Vuforia platform makes it possible to control and assess the suitability of some object to serve as a marker in AR applications.
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In order to overlap the map contents relating to the archaeological sites with the town map by means of the application and to present the location of individual archaeological cultures on the map, the layers with archaeological sites were first graphically processed. The data on the archaeological sites of the town Vinkovci taken over from [1, 17] along with the additional data on the locations of individual sites that were given by the employees of the Town Museum in Vinkovci. The received data processed in GIS (Fig. 5) [17] are the basis for the production of the cartographic presentation in the augmented reality.
Fig. 5. Archaeological sites: a) Early Stone Age, b) Copper Age, c) Bronze Age, d) Iron Age, e) Migration Age, f) Roman Period, g) Middle Ages, h) Early New Age [17].
Fig. 6. ArheVinkovci, the application on a smart phone [9].
Each archaeological period is shown in its own colour, and each culture is shown in the shades of the same colour in line with the period that it belongs to. Apart from the layers, there was also an external map description added: title and description of the
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archaeological sites in these areas (Fig. 6). The layers with cultures can be switched on and off depending on the users’ needs [9].
4 Testing and User Experience of the Application ArheVinkovci The application was tested on a smart phone and smart glasses. When opening the application on a smart phone, the rear camera is switched on. When the user points the camera to the map, virtual objects are shown (Fig. 7).
Fig. 7. The operation of the application ArheVinkovci on the smart phone [9].
The user moves the camera and inspects the area of archaeological sites more detailed. In the left part of the screen, there are options for switching on and off single layers. The user can move his smart phone forwards/backwards, left/right, away or closer to the map, which is equivalent to panning and zooming on web-maps. The user can also move, rotate, bring closer or away the town map itself. There were some failures in the operation of the application noticed where some virtual objects were not displayed, or more time was needed for them to be displayed, and they appeared when the map background was uneven, i.e. wrinkled. Apart from that, even the impeccable map background that reflected light caused certain difficulties. The work of the user with the mobile application is shown in Fig. 8 [9].
Fig. 8. Users inspect the archaeological sites with the application ArheVinkovci installed in smart glasses [9].
The application was also tested using smart glasses Vuzix M300. The glasses were developed to improve the working process and open new possibilities in industrial, medical and retail sector, remote help centres and various other fields [18].
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The looks at the town map pointing thus the camera integrated in the smart glasses to the map as well. After the town plan has been recognized as an AR marker, virtual objects are displayed. The application functions in the same way as the one used in a smart phone, and the user with the glasses on his head can perform all actions as it is possible with the smart phone (Fig. 7). The image of the application on the screen of smart glasses and work of the user with the application is shown Fig. 8. The advantages of the application on smart glasses offer the possibility to observe a map laid on the table hand-free. The user can easily perform other actions since his field of vision is not fully obscured. It was noticed that the textual description of the map in the lower left corner was more difficult to read than with the application for a smart phone. The user should bring his head much closer to the map in order to read the text, while the smart phone only needs to be brought closer to the map and one can read the text at the usual reading distance of about 40 cm [9]. The testing of the created application by users of various profiles (cartographers, citizens, tourist) has revealed numerous advantages of such a display: cartographically richer content, easier understanding and perception of the display, dynamism and selection of display layers, easier updating in the event of new site detections – no need for new map printing, the attractiveness of the application that arouses interest in the contents. The openness to solutions provides the integration of other functionalities into the application and the use of augmented reality for complex purposes, e.g. complete tourist information system based on paper or digital maps supported by the augmented reality application. The application once created on the existing cartographic presentation (e.g. town map) provides simple extension of functionality and the use of augmented reality technology for tourist purposes. The tourist content of some town is changing overtime, and the maps available to tourist very often do not contain all information they might need and find interesting. The application that would upgrade some tourist map or town map with various contents interesting to tourist, such as location, assessment, appearance, restaurant opening hours in some town, may surely make it easier for user to get along in a new town and choose the location they are interested in [9].
5 Conclusions Within the frame of the research and testing the possibilities of augmented reality technology, the application ArheVinkovci was created that uses a cartographic presentation of the archaeological sites in Vinkovci on a town map of the Tourism Association of the town Vinkovci and broadens the scope of information available to users in a simple and articulate way. The application ArheVinkovci can be used with smart phones or smart glasses. The entire solution was tested by the users who pointed to the advantages of using the concept of augmented reality by developing simple applications. The research has confirmed both hypotheses. The augmented reality technology can be efficiently used for the production of cartographic representation, i.e. it is possible to supplement the existing analogous cartographic presentations with additional cartographic contents using the augmented reality technology. The map contents can be updated by applying the augmented reality, which makes the whole process much quicker. The existing maps do not need to be update or new special purpose maps produced, but the complete additional content can be projected using the augmented reality technology. The possibilities
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are numerous and inexhaustible, and in the present, it is very important to receive high quality information as fast as possible. Acknowledgement. The research described in this paper and the ArheVinkovci application were created by Valentina Vukovi´c, M.Sc. Ing. Geod. et geoinf. in postgraduate studies at the Faculty of Geodesy, University of Zagreb.
References 1. Vinkovci, The City, History. http://www.tz-vinkovci.hr/en/city/history/. Accessed 15 Feb 2022 2. Durman, A. (ed.): The Oldest Town in Europe. Publisher, Vinkovci City Museum, Vinkovci, Croatia (2013) 3. Reitmayr, G.; Schmalstieg, D.: Location based applications for mobile augmented reality. In: Proceedings of the 4th Australasian User Interface Conference, Australian Computer Society, Adelaide, Australia (2003). ISBN: 0–909925–96–8, 65–73 4. Milgram, P., Kishino, F.: A taxonomy of mixed reality visual displays. IEICE Trans. Inf. Syst. E77-D (12), 1321–1329 (1994) 5. Azuma, R.T.: A survey of augmented reality. Pres. Teleoper. Virt. Environ. 6(4), 355–385 (1997) 6. Caudell, T.P., Mizell, D.W.: Augmented reality: an application of heads-up display technology to manual manufacturing processes. In: Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences, vol. 2, Kauai, Hawaii, USA, pp. 659–669 (1992). https:// doi.org/10.1109/HICSS.1992.183317 7. Billinghurst, M., Clark, A., Lee, G.: A survey of augmented reality. Found. Trends Hum. Comput. Interact. 8(2–3), 73–272 (2015). https://doi.org/10.1561/1100000049 8. Sutherland, I.E.: A head-mounted three-dimensional display. In: AFIPS Proceedings of the Fall Joint Computer Conference, part I, vol. 26, San Francisco, California, USA, pp. 757–764 (1968). https://doi.org/10.1145/1476589.1476686 9. Vukovi´c, V.: Application of Augmented Reality in Cartography. University of Zagreb, Faculty of Geodesy (2019) 10. Kuyper, A.: Google Glass: The role of augmented reality technology as a gatekeeper, Master thesis. University of Amsterdam (2014) 11. Springwise, Tech Explained: Augmented Reality. https://www.springwise.com/tech-explai ned-augmented-reality/. Accessed 13 Mar 2022 12. Making digital work real. https://medium.com/datadriveninvestor/making-digital-work-real7f5cc4b41fc0. Accessed 13 Dec 2021 13. WELC Map. Available online. http://welcmap.com/en/idea. Accessed 15 Jan 2022 14. Poslonˇcec-Petri´c, V.: Vinkovci, City map, Publisher: Vinkovci Tourist Bord, Vinkovci, Croatia (2017) 15. Unity Real-Time Development Platform|3D, 2D VR & AR Visualizations. https://unity.com. Accessed 15 Dec 2021 16. Vuforia Developer Portal. https://developer.vuforia.com. Accessed 15 Feb 2022 17. Živiˇcnjak, P.: GIS arheoloških nalazišta grada Vinkovaca (GIS of Archeological Sites of the City of Vinkovci), Diploma thesis, Faculty of Geodesy, University of Zagreb (2017) 18. Vuzix M300 Augmented Reality (AR) Smart Glasses. https://www.vuzix.com/Products/ m300-smart-glasses. Accessed 15 December 2021
Breast Cancer Classification Using Support Vector Machines (SVM) Jasminka Telalovi´c Hasi´c(B)
and Adna Salkovi´c
Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina [email protected], [email protected]
Abstract. When compared to all other malignancies, breast cancer is one of the most common among women. It is the second leading cause of death from cancer in women. Early and accurate diagnosis enables timely treatment and enhances prognosis. For disease outcome, it is crucial to be able to detect whether the tumor is malignant or benign which the most experienced and educated physicians can do with 79%. The purpose of this research is to construct a computational model that exceeds this accuracy. For this purpose, machine learning algorithms were considered. They enable us to learn from data that has known classification (training set), test the model performance using more known data (testing set), and finally use the developed model to classify unknown data (validation set). We constructed two such computational models using Support Vector Machines (SVM) computational approaches. The models were tested on breast cancer data with a total of 569 rows (samples) and 32 columns (features) coming from the Wisconsin dataset. The achieved accuracy is 97% which well exceeds the accuracy of well-trained human professionals. Keywords: Breast Cancer · Support Vector Machine (SVM) · Medical Decision Making
1 Introduction Aside from COVID19, civilization has been plagued for several years by a disease known as breast cancer, which comes in many forms and has the second highest mortality rate in women, next to lung cancer. As per clinical statistics, one in every eight women is diagnosed with breast cancer in her lifetime. Breast carcinoma is the most prevalent cancer among women worldwide, making up 25% of all cancer cases in 2020 and infecting two million people [9]. Breast tumors and lumps typically occur as dense spots on mammography. A benign lump typically has a round, smooth, and well-defined boundary, whereas a malignant tumor typically has a jagged, rough, and hazy borderline. This is a disease wherein breast cells rapidly grow and divide quickly. Breast cancer arises whenever a malignant (cancerous) cancer progresses in breast cells. Early detection of breast cancer can drastically enhance prognosis and survival by promoting timely clinical treatment to affected patients. The major challenge in early diagnosis is attempting to determine whether tumors are malignant or benign; machine learning algorithms can help with establishing this © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 195–205, 2023. https://doi.org/10.1007/978-3-031-43056-5_16
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diagnosis. Studies show that most experienced physicians can diagnose cancer with 79% accuracy. Additionally, computer-aided detection (CAD) systems are the second most popular opinion for radiologists as they are non-invasive. There is a large amount of data available on the research and assessment of CAD systems in mammography. Most of the suggested systems take a hierarchical approach. The CAD system first prescreens a mammogram for worrisome spots in the glandular tissue that serve as suitable locations for subsequent examination. The first stage in this is an algorithm of Gaussian smoothing filter, top-hat operation for image enhancement in which the combined operations are applied to the original gray tone image, and the improved images’ higher sensitivity lesion site selection is noted. The second stage then creates a thresholding approach for segmenting the tumor region. Other invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system arises. Early detection can give patients more treatment options. To detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Further accurate assessment of benign tumors from data can save patients from inappropriate treatment. Consequently, all research and observation are focused on the correct detection of breast cancer and the categorization of patients into malignant or benign groups [2]. Artificial intelligence can become a transformational force in healthcare and soon, computer vision models will be able to get even higher accuracy enabled by access to more medical imaging datasets. The application of machine learning models for the prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. Machine learning (ML) is universally acknowledged as the methodological approach of preference in Breast Cancer pattern classification due to its unique benefits in identifying critical features from sophisticated breast cancer datasets. This paper describes a comprehensive analysis of the execution of machine learning techniques using classification; thus, the primary objective is to use Python as a programming language to build a breast cancer classification model on a dataset that can precisely classify a histopathologic image (study of the microscopic structure of tissues) as malignant or benign. Classification algorithms predict one or more discrete variables, supported by the attributes’ variance within the dataset. Data processing software is required to run the classification algorithms. Classification aims to pick the simplest treatment. Classification is vital because it allows scientists to spot, group, and properly name organisms via a uniform system; it is intended to provide relevant information based on patient data and to assist medical workers in treating and curing patients with the best decision and treatment plan. The classification is built utilizing training and testing datasets, to predict the class of a new instance by examining the case categories and labels. With the computational model, we can describe the previous knowledge and classifications of experienced physicians and apply them to classify unknown data. If those models exhibit the reported accuracy of the experienced physicians (79%), we have a great decision-making tool that can help expedite the early detection and treatment of the patients.
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2 Literature Review Recent scientific literature showcases various applications of machine learning techniques for medical decision-making, such as investigating the role of the microbiome in Alzheimer’s disease [13], developing a decision support system for neuroendocrine tumor patients treated with somatostatin analog therapy [14], using data science to make decisions regarding the microbial community of the gut and autism spectrum disorders [15], and exploring the connection between the gut microbiome and multiple sclerosis [16]. Whilst using different deep learning methods, researchers are using images of diseased breast tissue to construct classification algorithms that can forecast the stage of breast cancer development. To perform image classification, neural networks can be integrated with feature selection techniques such as ridge and linear discriminant analysis. [10] These algorithms have a wide range of applications in medical screening since they may identify a patient as healthy or sick and determine the kind of breast cancer without prior knowledge of the absence or presence of breast cancer [3]. Such algorithms are also highly accurate at predicting the type of cancer, and they can be employed in addition to medical tests as an extra diagnostic tool. By reviewing patient medical records, machine learning algorithms are employed to extract text information regarding typical symptoms of aggressive breast cancer. For example, [5] investigated breast cancer features to predict its occurrence. Using neural networks, they attained an f1-score of 93.53%. This type of research expands medical knowledge about the origins and symptoms of breast cancer, increasing the odds of proper and timely identification. Classification algorithms are common machine-learning tools for investigating breast cancer. In contrast to prior research, classification approaches employ quantitative data to predict the kind of breast cancer. The Wisconsin breast cancer dataset [12] is the most utilized. It comprises quantitative data about the physical aspects of breast cancer, but the goal variable is a categorical variable that corresponds to benign or malignant cancer. The objective of categorization is to forecast cancerous cases; many authors have investigated this dataset using various methods. Based on the above-mentioned dataset, support vector machines, k-nearest neighbors, naive Bayes, and decision trees were used by [1] resulting in an accuracy of 97.13%. Support Vector Machines (SVMs) are the most extensively used algorithm for predicting malignant breast cancer with high accuracy. Previous research [4] demonstrated that SVMs with the RBF kernel are the best approach for detecting malignant breast cancer, attaining 96.8% accuracy. Even though there is a study on the same dataset that has achieved good results, it is already getting old and there are new extensions that can improve the classification performance. The results of the older papers have been replicated, which adds to the validity of the findings, however, the usage of linear SVC combined with SVM to find the best-fit hyperplane that can categorize the data into different classes is something that has not yet been implemented to the best of our knowledge and is, therefore, the main topic of this paper. Additionally, whereas Weka has a rigid structure and users are limited to the algorithms and filters provided within the software, Anaconda allows users to configure and use a wide range of third-party libraries and tools which allow the construction of more powerful computational models.
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3 Materials and Methods 3.1 Dataset Description In the first stage, we are considering any process which is simply extracting some of the cells out of the tumor. If it is a benign one, that means the tumor is not spreading around the body, therefore the patient is safe; however, if it is a malign one, that means intervention is needed to stop the cancer growth. Consequently, in this method, we will have two target classes: malignant and benign. Once the dataset is obtained, data needs to be analyzed and feature characteristics should be selected. In the machine learning aspect, we first need to execute all the available histology images to specify the cancer type which it represents. Afterward, all the extracted image features will be fed into the machine learning model. The main plan of principal component analysis (PCA) is to cut back the dimensionality of a dataset consisting of the many variables related to one another, either heavily or gently, whereas holding the variation present within the data set, up to the utmost extent. (Rathi and Pareek) This is achieved by remodeling the variables to a replacement set of variables, which are referred to as the principal elements. The data set contains 569 rows and 32 columns. ‘Diagnosis’ is the column that we are going to predict, which says if the cancer is M = malignant or B = benign. “1” denotes the cancer is malignant and 0 means benign. We can identify that out of the 569 persons, 357 are labeled as B (benign) and 212 as M (malignant). The tumor is classified as benign or malignant based on its geometry and shape. 3.2 Feature Extraction Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass, which is a type of biopsy procedure. Through thresholding at various levels, the improved mammography pictures are converted to binary images. They describe the characteristics of the cell nuclei present in the image. The features of the dataset include: • • • • • • • • • •
tumor radius (mean of distances from the center to points on the perimeter), texture (standard deviation of gray-scale values), perimeter, area, smoothness (local variation in radius lengths), compactness (perimeter2 /area—1.0), concavity (severity of concave portions of the contour), concave points (number of concave portions of the contour), symmetry, fractal dimension.
The mean, standard error, and “worst” or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features.
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4 Implementation 4.1 Data Preparation The dataset used for this project is publicly available and was created by Dr. William H. Wolberg, a physician at the University of Wisconsin Hospital in Madison, Wisconsin, USA [12]. To create the dataset they used fluid samples, taken from patients with solid breast masses and an easy-to-use graphical computer program called Xcyt, which can perform the analysis of cytological features based on a digital scan. The program uses a curve-fitting algorithm, to compute ten features from each one of the cells in the sample, then it calculates the mean value, extreme value, and standard error of each feature for the image, returning a 30 real-valuated vector. For the project implementation, utilization of the programming language Python in Jupyter Notebook has been done on the chosen dataset. First, the data must be imported altogether with the corresponding libraries. For example, pandas is used for data manipulation using data frames, which provide various options very helpful for concise analysis. On the other hand, numpy is used for data statistical analysis, matplotlib is used for data visualization and seaborn is used for statistical data visualization. After the library import, dataset import is the next step. Firstly, dictionary keys and information and the shape of the data were considered. This is important because it defines the next steps, helps us see the problem scope, and discards unnecessary data. The dataset was examined using the pandas’ head(), info(), and shape method, which serves for finding the dimensions of the dataset. All the missing or null data points of the dataset (if there are any) were found using the isnull() function, whereas all the extra columns were removed, resulting in the final 30 features. 4.2 Data Visualization There are now 30 features that can be visualized, where 10 features are plotted at a time. This led to 3 plots containing 10 features each. The means of all the features were plotted together, and so were the standard errors and worst dimensions. The first method of visualization was the seaborn pair plot, including variables such as radius, texture, area, perimeter, and smoothness. However, the problem is that the plot does not show the target class; there is no clue which of the samples are malignant or benign. Afterward, when doing the seaborn pair plot with the same elements, blue points show the malignant case, whereas orange points show the benign one, which is good enough to proceed with (Fig. 1). With the use of the count plot, we take one of these slide graphs and see what we can do with them. To check the correlation between the features, a correlation matrix was plotted (Fig. 2 shows some exemplary correlations of features). It is effective in summarizing a large amount of data where the goal is to see patterns. This map includes all the included features, their means, errors, and worst values. It is especially important to observe all the features whose correlation is close to 1. By now, we have a rough idea that many of the features are highly correlated with each other.
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Fig. 1. seaborn library visualization of correlation between pairs of features.
Fig. 2. Heatmap visualization of features’ correlation matrix.
5 Machine Learning 5.1 Model Training After feature selection, we proceed to the model training, aka finding the model solution. We are going to use a subset of data for training, and another subset for testing. Before the model training, we must take care of the categorical data. Categorical data are variables that contain label values rather than numeric values. The number of possible values is often limited to a fixed set. For example, users are typically described by country, gender, age group, etc. We will use Label Encoder to label the categorical data. Label Encoder
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is part of the SciKit Learn library in Python and is used to convert categorical data, or text data, into numbers, which our predictive models can better understand. On the other hand, most of the time, the dataset will contain features highly varying in magnitudes, units, and ranges. But since most of the machine learning algorithms use Euclidean distance between two data points in their computations. We need to bring all features to the same level of magnitude. This can be achieved by scaling. This means that data is being transformed so that it fits within a specific scale, like 0–100 or 0–1. For this, we will use the StandardScaler method from the SciKit-Learn library. The data we use is usually split into training data and test data. The training dataset contains a known output, and the model learns from this data to be generalized to other data later. We have the test dataset (or subset) to test our model’s prediction on this subset. 40% of the data was reserved for testing purposes. The dataset was stratified to preserve the proportion of targets as in the original dataset, and in the train and test datasets as well. From our dataset, a target and predictor matrix was created. Y is the feature we are trying to predict (output). In this case, we are trying to predict if our “target” is cancerous (malignant) or not (benign), i.e., we are going to use the “target” feature here. X represents the predictors which are the remaining columns (mean radius, mean texture, mean perimeter, mean area, mean smoothness, etc.). 5.2 Support Vector Machine (SVM) Two different classifications were done: one using the support vector classifier, and another one, using the support vector machine. The objective of a linear SVC is to fit the provided data by returning a best-fit hyperplane that divides, or in our case, categorizes the data. From there on, after getting the hyperplane, a classifier is fed with some features to see what the predicted class is [11]. This makes this specific algorithm rather suitable for use in this project. On the other hand, a Support Vector Machine (SVM) is a binary linear classification whose decision boundary is explicitly constructed to minimize generalization error. It is a very powerful and versatile Machine Learning model, capable of performing linear or nonlinear classification, regression, and even outlier detection. SVM is well suited for the classification of complex but small or medium-sized datasets, which is exactly what we have. SVM fits the “decision boundary” that is defined by the largest margin between the closest points for each class. This is commonly called the “maximum margin hyperplane (MMH)”. SVM is effective in high dimensional spaces and uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. However, SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation. With the above-mentioned features, the next step is to check the accuracy of our prediction by comparing it to the output we already have (y_test in both cases). For this comparison, a confusion matrix was used. Finally, for the examination results, a classification report has been generated.
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5.3 Implementation Details This section summarizes implementation details for the process described above that performs breast cancer classification using Support Vector Machines (SVM). The process of feature extraction involved selecting, combining, or transforming features that were most relevant. In the given context, feature extraction is the process of computing features from digitized images of fine needle aspirates (FNAs) of breast masses using a graphical computer program called Xcyt. The program uses a curve-fitting algorithm to compute ten features from each of the cells in the sample, then it calculates the mean value, extreme value, and standard error of each feature for the image, resulting in a 30 realvalued vector. The unnecessary columns such as frame, filename, and data_module were removed, and the numpy array was converted to a pandas dataframe using the pd.DataFrame method. The SVM model is imported from sklearn.svm and the default parameters are used for the SVC object. To implement this process, the dataset was loaded using the load_breast_cancer function from sklearn.datasets, and unnecessary columns were removed using pandas’ DataFrame. The dataset was checked for NaN values using the.isnull().sum() method. Data visualization was performed using seaborn’s pair plot method to plot pairwise relationships in the dataset, sns.countplot to plot the distribution of the target variable, and sns.heatmap to plot the correlation matrix of the dataset. The dataset was split into training and testing sets using train_test_split from sklearn.model_selection. The parameter test_size is set to 0.20, which means that 20% of the data is reserved for testing. The parameter random_state is set to 5 in the train_test_split method to ensure that the same random split is generated each time the code is run. An instance of SVC was created and fitted using the training set using svc_model.fit(x_train, y_train). The model was evaluated on the test set using svc_model.predict(x_test) to get the predicted values and confusion_matrix and classification_report from sklearn.metrics to calculate performance metrics. To improve the performance of the model, feature scaling was performed using minmax scaling to scale the training and testing data using the formula (x − min)/(max − min). The scaled data was then used to fit and predict using the SVC model, and performance metrics were calculated using confusion_matrix and classification_report. The implementation uses the default parameters for the SVC model, but different hyperparameters can be experimented with by passing them to the SVC constructor. For example, SVC(kernel = ‘linear’, C = 0.1) uses a linear kernel and a regularization parameter of 0.1. 5.4 Results and Discussion The model score for both SVC (Fig. 3) and SVM (Fig. 4) models is 97%, meaning that these machine learning techniques can classify tumors into malign/benign with 97% accuracy. The values of the model’s precision and recall performance are also very close which indicates that the model is not biased towards false positive or false negative decisions (Fig. 5). Additionally, the technique can rapidly evaluate breast masses and classify them in an automated fashion. Using this strategy, it is proven that SVM can improve the detection of breast cancers. The benefits of the suggested algorithms
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include increased accuracy and lower error rates. Without overfitting, the algorithm can distinguish between benign and malignant cells and classify them correctly.
Fig. 3. SVC model results.
Fig. 4. SVM model results.
Fig. 5. SVM model results heatmap.
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6 Conclusions In conclusion, compared with the 78% accuracy of mammography, both models achieved a higher accuracy of around 97% for breast cancer diagnosis. To improve the performance, we experiment with feature selection using a correlation matrix and feature importance. It results in better accuracies when removing certain features for a certain model. There is a drawback of this dataset being relatively small and containing skewed data, but there is a way to mitigate the issue by appending the same samples with additional Gaussian random noise. Future work could also include tuning the hyperparameters of the current model as well as testing other deep learning methods/architectures to increase the model’s accuracy. Since the accuracy measure is a broad metric, more research can be done into the precision, and recall measurements to monitor the model’s performance in each class. Additionally, improvements could be made on dataset images - a mammography enhancing scheme, which results in even better tumor region segmentation, and feature extraction from the segmented tumor area, combined with the usage of an SVM classifier could be implemented. The enhancement is described as the transfer of visual quality to a higher and more intelligible level and could be done using filtering and top hat operation, altogether with the contrast stretching technique which is then used to boost the image’s contrast. The quality of detection can also be improved by improving the training dataset. This can be done by gathering additional image data but also consolidating and normalizing the existing dataset. In addition, the dataset’s metadata could also detect a potentially useful guidance for model. This metadata could include demographics but also additional information about patients that can help describe the patients’ data with additional dimensions of interest. This is an additional avenue that should be considered for future work as well. Overall, we presented machine learning models that can be applied in breast cancer diagnosis to improve the accuracy and therefore assist in the early diagnosis of breast cancer. Breast cancer is found at an early stage and will help save the lives of thousands of women. These projects help real-world patients and doctors to gather as much information as they can. Machine learning algorithms can be used for medical-oriented research, advancing systems, reducing human errors, and lowering all types of manual mistakes. Finally, this project will be a part of a lot bigger healthcare Django application which is currently in the development phase and will not only serve as a result interpretation service but also as a reminder and additional tool to raise awareness about this problem among the bigger part of the female population in Bosnia and Herzegovina, as a part of the application.
References 1. Asri, H., Mousannif, H., al Moatassime, H., Noel, T.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci. 83, 1064–1069 (2016). https://doi.org/10.1016/J.PROCS.2016.04.224 2. Bustan, M.N., Poerwanto, B.: To cite this article: Kumar Sanjeev Priyanka. IOP Conf. Ser. Mater. Sci. Eng. 1022, 12071 (2021). https://doi.org/10.1088/1757-899X/1022/1/012071
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3. Chang, W.T., Chen, P.W., Lin, H.W., Kuo, Y.H., Lin, S.H., Li, Y.H.: Risks of aromatase inhibitor-related cardiotoxicity in patients with breast cancer in Asia. Cancers 14, 508 (2022). https://doi.org/10.3390/CANCERS14030508/S1 4. Chaurasia, V., Pal, S.: A novel approach for breast cancer detection using data mining techniques (2017). https://papers.ssrn.com/abstract=2994932 5. Li, N., et al.: Global burden of breast cancer and attributable risk factors in 195 countries and territories, from 1990 to 2017: results from the global burden of disease study 2017. J. Hematol. Oncol. 12, 1–12 (2019). https://doi.org/10.1186/S13045-019-0828-0/FIGURES/6 6. Agrawal, R.: Predictive analysis of breast cancer using machine learning techniques. Ingeniería Solidaria 15(3), 1–23 (2019) 7. Rathi, M., Pareek, V.: Hybrid approach to predict breast cancer using machine learning techniques 8. Siddappa, M., Kagalkar, R.M., Kurian, M.Z.: Methodologies for tumor detection algorithm as suspicious region from mammogram images using SVM classifier technique. Digit. Image Process. 3, 1202–1207 (2011) 9. Sun, Y.-S., et al.: Risk factors and preventions of breast cancer. Int. J. Biol. Sci. 13, 1387–1397 (2017). https://doi.org/10.7150/ijbs.21635 10. To˘gaçar, M., Özkurt, K.B., Ergen, B., Cömert, Z.: BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phys. A Stat. Mech. Appl. 545 (2020). https://doi.org/10.1016/j.physa.2019.123592 11. Zafiropoulos, E., Maglogiannis, I., Anagnostopoulos, I.: A support vector machine approach to breast cancer diagnosis and prognosis. In: Maglogiannis, I., Karpouzis, K., Bramer, M. (eds.) AIAI 2006. IFIP International Federation for Information Processing, vol. 204, pp. 500–507. Springer, Boston (2006). https://doi.org/10.1007/0-387-34224-9_58 12. UCI Machine Learning Repository: Breast Cancer Wisconsin (Diagnostic) Data Set. https:// archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic) 13. Telalovic, J.H., Baši´c, D.C., Osmanovic, A.: Investigation of the role of the microbiome in the development of Alzheimer’s disease using machine learning techniques. In: Ademovi´c, N., Mujˇci´c, E., Muli´c, M., Kevri´c, J., Akšamija, Z. (eds.) IAT 2022. LNNS, vol. 539, pp. 639–649. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-17697-5_48 14. Telalovic, J.H., et al.: A Machine learning decision support system (DSS) for neuroendocrine tumor patients treated with somatostatin analog (SSA) therapy. Diagnostics 11(5), 804 (2021) 15. Telalovic, J.H., Pasic, L., Cicak, D.B.: The use of data science for decision making in medicine: the microbial community of the gut and autism spectrum disorders. In: Hasic Telalovic, J., Kantardzic, M. (eds.) MeFDATA 2020. CCIS, vol. 1343, pp. 79–91. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72805-2_6 16. Hasic Telalovic, J., Music, A.: Using data science for medical decision making case: role of gut microbiome in multiple sclerosis. IEEE Trans. Med. Imaging 39(12), 4189–4199 (2020)
Development of Soil Type Valuation Model in GIS for Sustainable Agricultural Production ˇ Melisa Ljusa1(B) , Hamid Custovi´ c2 , Mirza Ponjavi´c3 and Almir Karabegovi´c4
,
1 Faculty of Agriculture and Food Sciences, University of Sarajevo, Zmaja od Bosne 8,
71000 Sarajevo, Bosnia and Herzegovina [email protected] 2 Academy of Sciences and Arts of Bosnia and Herzegovina, Bistrik 7, 71000 Sarajevo, Bosnia and Herzegovina 3 International Burch University, Francuske revolucije bb, 71210 Ilidza, Bosnia and Herzegovina 4 Faculty of Electrical Engineering, University of Sarajevo, Zmaja od Bosne, 71000 Sarajevo, Bosnia and Herzegovina
Abstract. In this research, a model was developed and tested which represents a conceptual approach for the identification of land with the potential for sustainable use in agriculture using a pedological map M 1: 25,000 and representative land profiles. The applied conceptual approach is based on six soil parameters (organic matter, pH reaction, cation exchange capacity, soil depth and terrain slope level), which determines the quality and potential use of soil following its ecological functions in agriculture. The suitability of the mentioned parameters is grouped into the following categories: bad (1), medium (2), good (3) and very good (4). The main goal of the research is the application of the model for assessing the condition and quality of available land resources. It was tested in the area of Ilijas and Trnovo municipalities in Sarajevo Canton to determine the real and potential land values for agricultural production and other purposes in the ecosystem. The results obtained by applying the mentioned conceptual approach show that in the area of Trnovo we have very good and good soil suitability categories, and in the area of Ilijas very good, good and medium one. We can say that soil types that have very good suitability are resistant to physical, chemical and biological disturbances. Where the suitability is assessed as good, intensification of agricultural production should be done carefully. In the medium category of suitability, intensification of land use is very risky. Keywords: agricultural soil · ecological functions of the soil · suitability categories · modelling in GIS
1 Introduction The land is used multifunctional, where its two basic functions are noted: ecological and technical functions [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 206–212, 2023. https://doi.org/10.1007/978-3-031-43056-5_17
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Soil functions are divided into two groups [2]: ecological and non-ecological functions. Ecological functions include production of biomass, the protection of people and the environment, and the habitat of genes. Non-ecological functions are the physical basis for human activities, source of raw materials and geological and cultural heritage. Due to its unique characteristics, the land is a key factor in sustainability, opportunities and quality of life and social development [3]. Soil functions are often not recognized, generally not understood, and therefore are not incorporated into the framework nor is there a link between soil natural capital and its services [4]. The impact of human activities on land especially affects the reduction of soil functions in the production of biomass [5]. Anthropogenic degradation implies land damage in their function of regular use in plant production. It arises as a result of irrational land use and manifests itself through structural deterioration, compaction, reduction of physiological depth, appearance of surface and furrow erosion, landslides, loss of nutrients, i.e. reduction of soil fertility. These impacts or threats refer to permanent soil loss due to urbanization and industrialization, erosion, reduction of soil organic matter, soil contamination, reduction of biodiversity, compaction, salinization, floods and landslides, nutrient depletion and desertification. The evaluation of soil functions needs to be based on measurable soil properties [6]. Control of soil functions requires accurate, credible, recognizable and precise quantitative spatial data on well-defined soil and its properties, processes on the soil and biogeochemical circulation, as well as current and/or potential impacts of human activities on the soil [3]. Land areas of the Sarajevo Canton are most threatened by the loss of agricultural land due to its conversion to other uses, the processes of natural and anthropogenic degradation [7]. Categorization of agricultural land in the Canton indicates the first three best quality categories (I-III) are very limited (3,159 ha) [8]. Lands of worse quality categories are shallow and prone to erosion and degradation in general. From the point of view of ecological characteristics, lands in the area of Sarajevo Canton have significant areas of acidic pH reaction and low buffer capacity, which should be especially taken into account when talking about potential pollutants and the damage they can cause in soil, water and the environment. In order to adequately plan land use in the Sarajevo Canton, it is necessary to evaluate the condition and quality of available land resources in order to determine the real and potential land values for agricultural production and other purposes in the ecosystem. In this research, a model was developed and tested, i.e., a conceptual approach for the identification of land with the potential for sustainable use in agriculture in the area of two cantonal municipalities, namely Ilijas and Trnovo to determine the real and potential values of land for agricultural production and other uses in the ecosystem.
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2 Data and Methodology In this research, the conceptual approach for the identification of land with the potential for sustainable use in agriculture was applied [9, 10]. The conceptual approach is based on six parameters that determine the resilience of the land and its potential use in accordance with the ecological functions of the soil (Table 1). The suitability of the parameters is grouped into the following categories: poor (1), medium (2), good (3) and excellent (4). Table 1. Categories of suitability of selected land parameters. Parameters
Categories of suitability Excellent
Good
Medium
Poor
Unit
Organic carbon
≥4
2–4
1–2
≤1
%
Clay+silt
≥50
35–50
15–35
≤15
%
pH
6,5–7,5
5,5–6,5; 7,5–8,5
≤5,5; ≥8,5
in H2 O
Cation exchange capacity
>25
10–25
≤10
cmol/kg
Depth
≥60
30–60
≤30
cm
Slope
≤8
8–15
15–25
%
The parameters presented above were determined for each type of land within agricultural areas, for which representative soil profiles were used. Data is prepared, processed, stored, and displayed in GIS. By summing the points assigned for each indicator in accordance with their values, the categories of land suitability were obtained according to the following division: 6–10 points – poor; >10 points – medium; 11–15 points – good; 16–20 points – very good. Data on agricultural areas of the fourth level of the CORINE nomenclature were taken from the project “Renovation of the land use value study for the area of Sarajevo Canton” [8].
3 Results and Discussion The applied modelling of the land space from the point of view of agricultural use is based on the analysis of six parameters (organic carbon, clay+silt content, pH, cation exchange capacity, depth and slope) which determine the resistance of the land and potential use in accordance with the ecological functions of the soil. Soil organic carbon is the basis of sustainable agriculture. Soil organic carbon is believed to play a crucial role in many soil functions and ecosystem services [11, 12] and affects the chemical and physical properties of the soil, such as the water infiltration ability, moisture holding capacity, nutrient availability, and the biological activity of microorganisms [13]. Soil texture is the relative percentages of sand, silt, and clay particles that make up the mineral fraction of soil. Soil texture influences soil fertility
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[14]. Additionally, soil texture affects the following soil properties: drainage, water retention capacity, aeration, susceptibility to erosion, organic matter content, cation exchange capacity, and buffer capacity. Soil reaction affects many physical and chemical processes in the soil, as well as the vital functions of plants and [14]. Cation exchange capacity (CEC) is one of the most important concepts in soil fertility [15]. Soil depth is important for ensuring the water and nutrients for plant growth. The area of the municipality of Trnovo is 34,007 ha, of which 10,826 ha are agricultural areas. The average value of organic carbon ranges from 2.3 to 17%. According to the defined values, organic carbon has two categories of suitability (good 2–4%, very good ≥4%). The average content of clay+silt ranges from 36.2 to 96.5%, out of which 35–50% falls in the good and ≥50% in the very good category. The categories of convenience for pH values in water range from poor (≤5.5; ≥8.5), medium (5.5–6.5; 7.5–8.5) to good (6.5–7.5), considering pH values ranging from 4.3 to 7.68. Cation exchange capacity, soil depth and terrain slope have the same suitability categories as pH value, so from poor to good benefit. Average values for the cation exchange capacity range from 0 to 118.25 cmol/kg, soil depth from 6 to 130 cm, while terrain slope ranges up to 74%. The area of the municipality of Ilijas is 30,856 ha, of which 7,697 are agricultural areas. According to the defined values, organic carbon has three categories of suitability (medium 1–2%, good 2–4%, very good ≥4%) considering the average value of organic carbon ranging from 1.14 to 9.6%. The average content of clay+silt ranges from 22.2 to 96%. It means that this parameter has three suitability levels from medium to very good suitability. The categories of convenience for pH values in water range from poor (≤5.5; ≥8.5), medium (5.5–6.5; 7.5–8.5) to good (6.5–7.5), considering pH values ranging from 4.28 to 7.7. Cation exchange capacity, soil depth and terrain slope have the same suitability categories as pH value, so from poor to good suitability. Average values for the cation exchange capacity range from 0 to 53.17 cmol/kg, soil depth from 10 to 124 cm, while terrain slope ranges up to 80%. The applied model indicates that in the area of Ilijas, we have three, and in the area of Trnovo, two categories of land suitability from the point of view of agricultural use (Table 2, Figs. 1 and 2). Table 2. Categories of suitability from the point of view of purpose in agriculture. Category of suitability
Area (ha) Municipality of Ilijas
Municipality of Trnovo
Very good (16–20 points)
3,896.1
3,650.3
Good (11–15 points)
3,551.9
7,175.7
Medium (>10 points) Total area
249.0 7,697.0
10,826.0
Lands that can be recommended as very good for agriculture are generally those lands that can compensate for environmental impacts and that can achieve good agricultural production. Lands with very good suitability for agriculture are represented on
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flatter and slightly sloping terrains where and integral arrangement of agricultural land is possible. For lands that have very good suitability of the above factors, we can say they are resistant to physical, chemical and biological disturbances such as erosion, soil compaction, pollution, plant and water pollution, and loss of biodiversity [10]. Lands in this suitability category are recommended for intensive agriculture, but it is an important and a precondition that this process is done in a sustainable manner. By applying technical measures, the productivity of the land of this suitability category increases significantly, and yields can be doubled. Lands of good convenience (11–15 points) have a good potential for sustainable intensification, which means that these lands should be approached cautiously when intensifying agricultural production. In general, this category of suitability includes lands of moderate potential that are used less as arable land, and mostly as meadows, pastures and orchards that can be rotated in the production system. The category of suitability which is the least represented is medium suitability. In principle, in this category, the intensification of the way these areas are used is very risky.
Fig. 1. Agricultural land suitability for sustainable intensification in Municipality of Trnovo, Canton Sarajevo.
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Fig. 2. Agricultural land suitability for sustainable intensification in Municipality of Ilijas, Canton Sarajevo.
4 Conclusions In this research, the conceptual approach for the identification of land with the potential for sustainable use in agriculture was applied to the areas of the municipalities of Trnovo and Ilijas. The obtained results indicate that 33.7% of agricultural land in Trnovo is of very good suitability, and 66.3% is of good suitability, which generally coincides with the state of the land, given that pastures and meadows prevail in this municipality. On the territory of the municipality of Ilijas, the land categories of very good and good suitability are equally represented. In this municipality, the results also indicate smaller areas that can be very risky from the point of view of their usage. Cation exchange capacity, depth, pH and terrain slope appear as the most common constraints in the given areas. The application of this model indicates the potential and limitations of land for more intensive use in agriculture from the point of significant ecological functions. The model can be very applicable both at local and other administrative levels. Due to the very diverse local conditions in Bosnia and Herzegovina and the soil degradation problems, the recommendation is to apply and test this model in more municipalities
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or regions. Assessing the state and quality of available land through the application of this model may help us to better understand the limiting factors for more intensive agricultural production in the country. Studies in new research areas may also consider involving other soil parameters that could be considered very important in certain areas. An important precondition for application of this model is the existence of accurate and credible soil data. Lack of soil data may be seen as a weakness for further research. Assessing the potential of the land in this way can help decision makers determine the space suitable for the purpose of agricultural development, increasing yields, or more intensive food production. Considering the defined suitability, adequate measures of sustainable management of agricultural land can be proposed.
References 1. Resulovic, H., Bukalo, E., Kraisnik, V.: Nacini koristenja zemljista-suprotnosti i mogucnosti harmonizacije u funkciji odrzivog razvoja. Prvi naucni simpozijum agronoma sa medjunarodnim ucescem, pp. 102–110. Agrosym, Jahorina (2010) 2. Blum, W.E.H.: Functions of soil for society and the environment. Rev. Environ. Sci. Biotechnol. 4, 75–79 (2005) 3. Várallyay, G.: Soil, as a multifunctional natural resource. Columella J. Agric. Environ. Sci. 2(1), 9–19 (2015) 4. Stolte, J., et al.: Soil threats in Europe. EUR 27607. Publications Office of the European Union, Luxembourg (2016) 5. Blum, W.E.H.: Soil and land resources for agricultural production: general trends and future scenarios-a worldwide perspective. Int. Soil Water Conserv. Res. 1(3), 1–14 (2013) 6. Vogel, H., et al.: Quantitative evaluation of soil functions: potential and state. Front. Environ. Sci. 7 (2019) 7. Custovic, H., Ljusa, M., Taletovic, J., Tvica, M.: Application of land categorization in spatial planning of urban and suburban areas of Sarajevo. Növénytermelés (Crop production), vol. 64, pp. 131–135 (2015) 8. Custovic, H., et al.: Novelacija studije upotrebne vrijednosti zemljišta za podruˇcje Kantona Sarajevo. Poljoprivredno-prehrambeni fakultet Univerziteta u Sarajevu, Sarajevo (2011) 9. Schiefer, J., Lair, G.J., Blum, W.E.H.: Indicators for the definition of land quality as a basis for the sustainable intensification of agricultural production. Int. Soil Water Conserv. Res. 3(1), 42–49 (2015) 10. Blum, W.E.H., Schiefer, J., Lair Georg, J.: European land quality as a foundation for the sustainable intensification of agriculture. Works of the Faculty of Forestry University of Sarajevo, Special edition, vol. 21, pp. 9–15 (2016) 11. Schjønning, P., et al.: The role of soil organic matter for maintaining crop yields: evidence for a renewed conceptual basis. Adv. Agron., 35–79 (2018) 12. Scharlemann, J.P.W., Tanner, E.V.J., Hiederer, R., Kapos, V.: Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag. 5(1), 81–91 (2014) 13. Gan, Y., et al.: An innovative technique for boosting crop productivity in semiarid rain-fed environments. Adv. Agron. 118, 429–476 (2013) 14. Resulovic, H., Custovic, H.: Pedologija. Univerzitet u Sarajevu, Sarajevo (2002) 15. Chowdhury, S., et al.: Role of cultural and nutrient management practices in carbon sequestration in agricultural soil. Adv. Agron. 166, 131–196 (2021)
Geospatial Analysis of Residential Zones from the Aspect of Natural Limitations – A Case Study of the Urban Territory of Sarajevo Jasmin Taletovi´c and Nataša Pelja Tabori(B) Sarajevo Canton Institute of planning and Development, Branilaca Sarajeva 26, 71000 Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. From the ancient Roman architect Vitruvius, we know that the selection of zones for residential purpose was conditioned by so-called healthy sites, in which case four elements or Stoikheîa (in Greek τoιχε‹α) were crucial namely heat, moisture, earth and air. Climate change requires revising the cause-andeffect relationship between natural conditions and residential zones in our cities. European policies of the 21st century imply a green transition towards energy efficient housing, and green planning starting from strategies, spatial and urban plans, regulatory plans, construction permits, and finally process of construction and management of residential buildings. This paper provides essential insight on topographic features, altitude, slope, and terrain orientation of Sarajevo’s residential zones. The areas are evaluated by establishing a relation between the natural and built conditions in order to question the demand for housing and rational landuse of the evaluated areas. An overview is given of the existing spatial database showing the potential use of residential building stock within the adopted Urban plan for Sarajevo urban territory (The Concept of Urban plan for the urban territory of Sarajevo for the period 2016–2036 is currently in drafting process.). The research approach is based on data provided by the Sarajevo Canton Institute of Planning and Development. Cartographic representation and geospatial analysis were created by using tools in GIS software (Esri ArcGIS). The aim of this paper is to show the significance and possibilities of exploring geospatial analysis in monitoring the process of planning the residential zones as well as the existing situation of residential construction in the urban territory of Sarajevo. Keywords: geospatial analysis · DTM · aspect · slope · urban plan · residential zones
1 Introduction Understanding the linkage between the geospatial features and residential construction is a significant factor in urban research, and until today many tools have been developed for conducting spatial analysis of such a kind. The paper provides an overview of the spatial distribution of residential zones and available tools in geospatial analysis, as well as a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 213–229, 2023. https://doi.org/10.1007/978-3-031-43056-5_18
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review of the more significant methods used in urban planning. The most commonly used geospatial methods are distance estimation and spatial clustering estimation. The development of new technologies facilitates the implementation of spatial-temporal analysis. New simulation methods of geospatial analysis that will become very important in the future for monitoring housing construction and implementation of zoning and development plans are also mentioned in the paper. Geospatial analysis concerns what happens where and makes use of geographic information that links features and phenomena on the Earth’s surface to their locations. This sounds very simple and straightforward, and it is not so much the basic information as the structures and arguments that can be built on it that provide the richness of spatial analysis. In principle there is no limit to the complexity of spatial analytic techniques that might find some application in the world and might be used to tease out interesting insights and support practical actions and decisions [1]. For planning and managing spatial processes, it is significantly appropriate to use sophisticated GIS (Geographic Information System) methods as a tool for spatial and strategic planning. Using GIS methods, it is possible to identify inadequate areas for urbanization and development, and therefore to direct development strategies to other more suitable places. An important part of the planning process is to identify and evaluate suitable locations for new planned urban (human) activities. The outputs of the simulation of urban processes in the researched area can be, for example, proposals of several development scenarios and their comparison. If spatial planners use these methods, a sophisticated spatial development policy can be applied [2]. The development of information technology, especially GIS, has brought us the possibility to quantitatively measure and evaluate spatial factors, in this case residential buildings, depending on the orientation, slope and altitude of the terrain. The development of computer technology has imposed evolution of number of programs (GIS, CAD), which can be used to represent terrain shapes mathematically, using numbers and different algorithms, and we take x, y, z coordinates as input data. We call such a representation of a part of the Earth’s surface a digital terrain model (DTM) [3] (see Fig. 1). Presenting the shape of the terrain of the urban territory of Sarajevo is a complex task because it is a continuous three-dimensional object. The display had to ensure sufficient geometric accuracy and good visibility, so that three-dimensional objects, displayed in a plane, would be easier identified. DTM is extremely useful in all types of spatial analysis, in drafting and implementation of design projects of various buildings on the surface of the terrain, for visibility analysis, 3D visualizations, erosion assessment, earthworks, cubic contents calculation, orientation and slope of the terrain [4]. The shape of the terrain is significant in urban planning, for construction of buildings and roads in the urban territory of Sarajevo. A steeper terrain requires an increase in earthworks, although it does not have to be eroded by the flow of ravines. Also, the scope and schedule of investigative work increases with the slope of the terrain. Slope affects the seismic characteristics of the terrain, as well as the stability of terrain.
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Fig. 1. Digital Terrain Model of the Urban territory of Sarajevo (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation).
2 Data Structure The influence of the climate, as well as other natural limitations affecting the choice of optimal zones for housing construction, has been known to us since the time of Vitruvius: “If our designs for private houses are to be correct, we must at the outset take note of the countries and climates in which they are built” [5]. Climate change, on the other hand, two millennia after Vitruvius’ treatise The Ten Books on Architecture, remind us that the connection between the natural characteristics of the four elements he spoke about, the so-called Stoikheîa, namely heat, moisture, earthy and air, and built environment must be established even today. The development strategies of the European Union, such as the European Green Plan, speak of a green transition that goes a step beyond the connection of natural and superficial conditions for the construction of housing in the direction of energy efficiency and rational consumption and use of the resources on our disposal. “The European Commission new initiative on renovation in 2020 will include an open platform bringing together the buildings and construction sector, architects and engineers and local authorities to address the barriers to renovation. This initiative will also include innovative financing schemes under Invest EU. These could target housing associations or energy service companies that could roll out renovation including through energy performance contracting. An essential aim would be to organize renovation efforts into larger blocks to benefit from better financing conditions and economies of scale. The Commission will also work to lift national regulatory barriers that inhibit energy efficiency investments in rented and multi-ownership buildings. Particular attention will be paid to the renovation of social housing, to help households who struggle to pay their energy bills” [6]. Therefore, from EU strategic documents to local policies for the renovation of housing stock in the private and public sector, green planning should strengthen the resilience of cities to various risks that may arise in the future. How to recognize these risks and plan appropriate measures for Sarajevo Canton?
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The urban territory of Sarajevo covers 187 km2 and is a naturally limited space. All natural restrictions, as well as residential zones, are entered in the DTM (Digital Terrain Model) and the database for the urban territory of Sarajevo. The terrain elevation data is integrated into the database from the data collected during the development of the DTM. For the purposes of the Project for creating a 1:5000 topographic map of the urban territory of Sarajevo, i.e., obtaining up-to-date bases for the creation of urban plans and urban documentation, rectification of orthophotos and performing various spatial analyses, a DTM of the urban territory of Sarajevo with a resolution of 2.5 m was created (see Fig. 1) [7]. Sarajevo Canton institute of Planning and Development database was created in GIS format based on the following: – – – –
database of the real estate cadaster (BPKN), orthophoto from 2017, databases created for the purpose of the 2013 Population Census, development plans, and their section containing the existing situation maps, and has provided platform for the database of residential buildings that was used in this research. Esri ArcGIS software and tools were used to create the database and geospatial analysis.
3 Data Processing and Presentation The parameters of morphometric terrain are those that can be derived directly from DTM using some local operations such as slope, aspect, competition index, hill shade. 3.1 Hypsometric Scale of the Terrain The Hypsometric scale hypsometric range of altitudes of the urban territory of Sarajevo (Table 1) is distributed in such manner that most of the space in the urban territory of Sarajevo is covered with altitudes between 500 m and 550 m (26.5%). Table 1. Area percentages of slope bands (%) (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation). Hypsometric scale (m)
>500
500–550
550–600
600–700
700–800
>800
(%)
17,7
26,5
18,0
22,2
10,9
4,7
The terrain of the urban territory of Sarajevo is a combination of the lowland terrain in the Sarajevo Field, the hilly area above 550 to 700 m on the edge of the field and the mountainous area above 700 m (see Fig. 1). 3.2 Aspect Slope is the first derivative of area and has both magnitude and direction (i.e., Aspect). In other words, slope is a vector consisting of gradient and aspect. The term aspect is
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defined as the direction of the greatest slope of the vector on the tangent plane projected onto the horizontal plane. Aspect is the azimuth of the slope direction, and its angle ranges from 0 to 360°. Figures 2 and 3 show examples of aspect and slope maps of the urban territory of Sarajevo.
Fig. 2. Terrain orientations of the urban territory of Sarajevo (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation).
The best capture of the terrain in the urban territory with its strongly expressed erosion directions is shown in the analysis of the terrain according to exposure or cardinal orientation. Flat parts of the urban territory of Sarajevo occupy 19.3% of the terrain, the north facing (NW, N, NE) terrains occupy 26.8%, the east facing terrain occupy 8.5%, and the south facing slopes (SE, S, SW) occupy most of the area (34.0%), while 11.4% of the terrain are facing west (Table 2). Table 2. Aspect classes in the Urban territory of Sarajevo (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation). Aspect
Flat
North
East
South
West
%
19,3
26,8
8,5
34,0
11,4
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3.3 Slope The slope of the terrain surface is significant in spatial and urban planning, construction of buildings and roads, water supply and sewerage networks, as well as other facilities. In the urban territory of Sarajevo (see Fig. 3), the degree of terrain suitability in relation to surface slopes is divided into four categories (Table 3): – – – –
Optimally favorable terrain - I (1–5% slope), Favorable terrain-II (5- 10%), Conditionally favorable terrain – III (10–20%) and Unfavorable terrain – IV (>20% slope).
Table 3. Slope gradient in the Urban territory of Sarajevo (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation). Slope
I 1–5%
II 5–10%
III 10–20%
IV > 20%
(%)
33,1
6,5
20,4
40,0
Optimally favorable terrains – I, with slope gradient 1–5%, occupy 33,1% of the urban territory of Sarajevo. Favorable terrains – II, with slope gradient 5–10%, are identified at the edge of the flat terrains, where they traverse towards the hilly terrain and on ridges and plateaus of hilly terrain. They occupy a small area of urban territory, around 6,5%. Conditionally favorable terrain – III, with slope gradient 10–20% occupies lateral spaces along and around the previous categories and are mainly represented on the hillside parts of the urban territory. They occupy 20.4% of the urban territory of Sarajevo. Unfavorable terrain – IV, with slope gradient 20%, occupies the largest area of the urban territory, which represents the limiting factor for further urbanization. They occupy 40,0% of the urban territory of Sarajevo. 3.4 Terrain Stability Based on the above-mentioned criteria (slope) three categories of terrain stability were distinguished: – stable terrains, – conditionally stable terrains and – unstable terrains. Stable terrains are the most represented in the urban territory of Sarajevo with 55,3% (103,2 km2 ) of the territory, and in terms of convenience for construction, these terrains are favorable for construction. Conditionally stable terrains occupy 29,2% (54,6 km2 ) of the territory and are conditionally favorable for construction.
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Fig. 3. Slope of the urban territory of Sarajevo (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation).
Unstable terrains are represented with 15,5% (28,9 km2 ) of the urban territory of Sarajevo. These terrains are classified as unfavorable for construction/extremely unfavorable for construction and are expected to contain areas with a high likelihood of landslide initiation. In the database in the urban territory of Sarajevo there are 839 registered landslides, of which 150 have been sanitized. In the urban territory of Sarajevo there is 2.263 ha of unstable terrains (landslides), with 9.678 buildings situated on those terrains (see Fig. 9). According to the morphological characteristics (slope), the terrain can be divided according to the degree of convenience (see Fig. 3): • • • •
Optimally favorable terrain for construction - slope gradient 0–5% Favorable terrain for construction - slope gradient 5–10% Conditionally favorable terrain for construction - slope gradient 10–20% Unfavorable terrain - slope gradient >20%
Terrains with the slope gradient 0–5% are optimally favorable for residential construction because small volumes of earthworks are required during construction, and removal of water from the slope is carried out easily because of gravity removal of surface and usage of underground water. If the slope of the terrain is 20% are considered unacceptable for residential construction unless they are built of solid rock masses. The terrains with this kind of slope are unfavorable for the construction of streets and communal facilities, and it is dangerous for buildings to be erected at their foot. The slope parameters of terrain surfaces have a significant place in the assessment of geological risks and the proper assessment of suitability for construction. We can conclude from the analysis of the terrain slope maps (see Fig. 3 and Table 3) that in the urban territory of Sarajevo, unfavorable terrains with slopes over 20% occupy 40% of the territory. Optimally favorable terrains, with slopes of up to 5%, occupy 33.1% of the urban territory of Sarajevo. Favorable terrains, with slope gradient 5–10% occupy 6.5% of the territory, while conditionally favorable terrains, with slope gradient 10–20% occupy 20.4% of the urban territory. 3.5 Analysis of Residential Zones The analysis is focused on residential zones, which are subdivided into three typologies: zones of single house residential zones1 , collective residential zones2 and mixed residential zones3 (Fig. 4). Further detailed analysis is linking the spatial distribution of different residential zones typologies and natural conditions and limitations previously explained such as shape of terrain, orientation, slope, and stability. The analysis is finalized with identification of specific residential buildings characteristics in terms of construction period, construction method and materials with which they were built. By correlating natural conditions (soil type, terrain slope, orientation, altitude, terrain stability) with the characteristics of residential buildings, it is possible to simulate optimal areas, as well as areas at risk from natural disasters, seismic and flooding hazard, the effects of climate change, and define protection and regulation measures. This research did not include economic estimation of potential costs of sustainable development management (necessary amenities provision, optimization of energy efficiency and maintenance) of residential buildings, which would highly contribute, in authors opinion, to strengthen the main hypothesis of the paper that construction of residential zones on hazardous terrains needs to be controlled in the future by land use implementation management instruments and measures. 3.6 Current Situation of Residential Buildings According to the Spatial basis of the Urban Plan for Urban territory Sarajevo for the period 2016–2036, from the total urban territory of Sarajevo which covers the area of 18,674 ha, construction land occupies 8,804 ha or 47.14%, while residential zones occupy 5,010.14 ha or 26.82% of the urban territory. Out of total residential zones, area of 500 ha or 9.94% is covered with collective residential zones, 257 ha or 5.14% is 1 Single house residential zones are zones of single-family houses. 2 Collective residential zones are zones of condominium housing (community buildings). 3 Mixed residential zones are combined zones of single house and collective residential buildings.
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Fig. 4. Residential zones, subdivided in three typologies, distribution on the digital terrain model of the Urban territory of Sarajevo (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation).
covered with mixed residential zones (combination of collective and single house zones) and 4,249 ha or 84.78% is covered with single family house zones (see Fig. 5).
collecve residenal zones (ha)
mixed residenal zones (ha)
single house residenal zones (ha)
Fig. 5. The ratio between different typologies of residential zoning (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation).
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Such uneven distribution of single-family house residential zones in relation to collective and mixed residential zones, can be associated with our previous analysis including shape slope and stability of the terrain, as well as the analyses of unstable terrains or landslides. Since the Ottoman period Sarajevo has had the tradition of constructing single family house residential zones on the slopes, while the commercial center remains in the valley [8]. The first collective housing buildings were constructed in the valley as block typology during Austro-Hungarian period, while the first high rise collective housing buildings were erected during socialist Yugoslavia. In the same period the slopes in the western part of the city began to be covered with so-called informal settlements, mainly single house, on average one-story neighborhoods, built as expression of ‘what people want’ [9]. In the urban territory of Sarajevo, according to the data of the Ministry of Spatial Planning, Construction and Environmental Protection of the Canton of Sarajevo, there are 27,692 informal buildings4 registered in the period from 2006 to 2016 (the building does not imply only houses, but also parts of buildings, such as auxiliary buildings and other) of which a total of 6,938 buildings, or 25.05%, were legalized5 . Most of those buildings are single house residential buildings situated partly on slopes and in the protected zones in the valley of Sarajevsko polje. Stability of the terrain in the valley enabled construction of high-rise buildings (Fig. 7), while hills, unstable or landslide areas, and flooding areas in Sarajevsko polje limited the construction of, on average, one-story single house residential buildings (Fig. 8). Better insulation of the southern slopes affected better health conditions of their population, while the humid northern slopes over the history evidenced more tuberculosis diseased inhabitants [8]. Our research continues from the residential zones to more detailed analysis of residential buildings in order to strengthen the hypothesis that the natural conditions affected building typology in Sarajevo, implying distinction between possibility to build multi story residential buildings only on stable terrains in the valley, while the limitations considering landslides, unstable terrains, and their seismic characteristics on the hills, flooding areas near the major rivers in Sarajevsko polje affected construction of on average one-story residential buildings. The database of the residential buildings in the Spatial basis of the Urban Plan for Urban territory Sarajevo for the period 2016–2036, identified 67,822 buildings, which occupy 711.7 ha of residential zones. There are 63,212 single house residential buildings, that occupy an area of 566 ha, and 4,610 collective residential buildings that occupy the area of 145.6 ha (see Fig. 4). These buildings are situated on slopes above altitude for which there are limitations in terms of water supply, terrain stability, infrastructure and traffic, as well as in the flooding area of Sarajevsko polje. The ratio between collective and single house residential buildings in the year 2016 was 20.5%:79.5% in favor of single house residential buildings compared to the Urban
4 Informal building is a building constructed without construction permit. 5 Legalization is a legal term defining process of obtaining construction permit after the building
was constructed.
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plan 1986–20156 projection for the year 2015 which was 40%:60% in favor of single house residential buildings and the existing situation in 1985 when the ratio between collective and single house typology was 25%:75%. Therefore, we can distinguish the trend of increasing construction of single house residential zones associated with, according to spatial distribution analysis of residential zones, negative urban development in the form of uncontrolled urban sprawl. The city sprawled towards the hinterland, burdening construction of single house residential buildings mostly on hills in the nonresidential land-uses and in the protected zones, on terrains that represent challenge in terms of construction, while collective housing continued to be interpolated to the existing residential zones in the valley, mostly on stabile and less challengeable terrains when considering natural hazards. 3.7 Geospatial Analysis of Residential Buildings Spatial Distribution The overlapping method is a combination of several spatial layers (raster and/or vector) whereas in an output we receive new data (layer) with associated geometry and attributes. By using overlap analysis, we can combine the characteristics of several data sets into one. Then, we can find certain locations or areas that have a certain set of attribute values – meaning that they match the criteria we specify. This approach is often used to find locations suitable for a particular use or sensitive to certain risk factors [2].
Fig. 6a. Spatial distribution of buildings in the urban territory of Sarajevo (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation).
6 Urban plan for the urban territory of the City of Sarajevo for the period 1986–2015 was adopted
in 1990.
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We have used the overlapping method in this research, and it was conducted overlapping all the attributes that vectors (polygons) have. Thus, for example, by overlapping the polygons of elevations, slopes, orientations (obtained from DTM) and residential buildings, we obtain a new set of data (polygons), which outputs are shown below. Figures 6a and 6b show the spatial arrangement of residential buildings in the urban territory of Sarajevo. It is visible that the concentration of buildings is bigger on the hills than in the valley. Our analysis identifies 24.061 buildings or 35.48% of buildings situated over the altitude of 600 m (Table 4) for which there are limitations in terms of water supply, terrain stability and infrastructure and traffic. These zones are particularly on risk, bearing in mind that they overlap with unstable terrain and landslides (Fig. 9).
Fig. 6b. Spatial distribution of residential buildings in the urban territory of Sarajevo (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation).
Digital surface models (DSM) represent the Earth’s surface and all objects on it. DSM is applied in urban and spatial planning. With the help of 3D surface models, the understanding of complex urban scenarios caused by relatively rapid changes in built-up areas under the influence of urban sprawl can be increased (Figs. 4, 6a and 6b). Taletovi´c and Kljuˇcanin [10] analyzed the creation of a 3D model of the urban territory of Sarajevo as a basic map for applying the content. The database of the residential buildings contains heights and number of floors as attributes and can be analyzed by
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gross construction area, as well as correlation between building typologies, year of construction and construction materials to natural conditions and limitations (Figs. 7 and 8). Figures 7 and 8 show 3D models of residential buildings in the urban territory of Sarajevo with the level of detail LoD1 [4].
Fig. 7. LoD1 for Sarajevo city – high-rise typology in the valley [10].
Analysis of the spatial distribution of the residential buildings in the urban territory of Sarajevo according to the altitude (see Fig. 6b), we obtained the outputs shown in table 4. The total number of residential buildings (collective and single house typology) in the urban territory of Sarajevo is 67,822. The number of residential buildings located in the areas lower than 500 m is 7,459, in the areas from 500 m to 550 m is 18,429, in the areas from 550 m to 600 m is 17,873, in the areas from 600 m to 650 m we have 13,508, in the areas from 650 m to 700 m we have 5,696 buildings, in the areas from 700 m to 750 m we have 2,652 buildings, and 2,204 residential buildings built at an altitude of over 750 m. Analysis of the water protection zones and spatial distribution of residential buildings in the urban territory of Sarajevo according to altitude resulted with 3850 registered residential buildings above the water supply zone. The analysis of the spatial distribution of residential buildings in the urban territory of Sarajevo according to the terrain orientation (see Fig. 2) results with figures expressed in Table 5. The total number of analyzed buildings is 67,822. There are 18,660 residential buildings on the north facing slopes, 12,592 on the east facing slopes, 17,481 on the south facing slopes and 19,089 residential buildings on the west facing slopes. The northern orientation is less favorable for residential zones than the southern, eastern, and western one, due to the humidity and insufficient insulation of these areas. In the urban territory of Sarajevo, 27.5% of the buildings are located on the northern slopes, 18.5% of the buildings are located on the eastern slopes, 25.7% of the buildings are located on the southern slopes, and slightly larger portion of buildings, i.e. 28%, is located on the western, predominantly flat part of Sarajevo. It should be noted that
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Fig. 8. 3D City Model of Sarajevo – the difference between high-rise buildings in the valley and on average one-story residential buildings on the hills [10].
Table 4. Spatial distribution of residential buildings according to the altitude (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation). DTM (m) 750
Sum
Number of buildings
18.429
17.873
13.508
5.696
2.652
2.204
67.822
7.459
Table 5. Spatial distribution of residential buildings according to the terrain orientation (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation). DTM (Aspect)
North
East
South
West
Sum
Number of buildings
18.660
12.592
17.481
19.089
67.822
completely flat terrains (slope 0%) were not analyzed here, because we did a terrain analysis (DTM) according to the orientation of the slopes on the four sides of the world (north, east, south, and west). The analysis of the spatial distribution of residential buildings in the urban territory according to the terrain slope (see Fig. 2) results with figures represented in Table 6. The number of residential buildings located on a terrain with slope gradient 0–5% is 13,002, the number of residential buildings on a terrain with slope gradient 5 to 10% is 8,337, the number of residential buildings on a terrain with slope gradient 10 to 15% is 11,687, the number of residential buildings on a terrain with slope gradient 15 to 20%
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is 12,911 and the number of residential buildings on a terrain with slope gradient over 20% is 21,874. Table 6. Spatial distribution of the residential buildings in the urban territory according to the terrain slopes (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & PeljaTabori own presentation). DTM (slope %)
0–5
5–10
10–15
15–20
>20
Sum
Number of buildings
13.002
8.337
11.697
12.911
21.874
67.822
From the table we see that 19% of residential buildings are located on terrain with a slope gradient 0–5%, 12% on a terrain with slope gradient 5–10%, 17% on a terrain with slope gradient 10–15%, 19% on a terrain with slope gradient 15–20% and even 32% on a terrain with slope gradient more than 20%.
Fig. 9. Landslides in the urban territory of Sarajevo (Source: Sarajevo Canton Institute of planning and Development, Taletovi´c & Pelja-Tabori own presentation).
4 Conclusions The paper proved the hypothesis that geospatial analysis of residential zones in the urban territory of Sarajevo from the aspect of natural limitation, conducted with the overlapping GIS based method can be of a great significance in spatial and urban planning.
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There is a strong correlation between the natural conditions and limitations and the spatial distribution of residential zones in the urban territory of Sarajevo. The analyses have proven that single house residential zones are mostly situated on the hills, above the altitude of 600 m, highly at risk because of the landslides and unstable terrain. The single house residential zones in the valley of Sarajevsko polje are at risk of flooding, albeit the terrain is stable. In both cases there is a correlation between the typology of single house residential zones and hazardous natural conditions that need special dedication, zoning regulation, and planning implementation instruments and measures in the future planning of Sarajevo Canton. The analysis of the spatial distribution of residential buildings in the urban territory of Sarajevo leads us to the following conclusions: – Only 11% of the total number of residential buildings are located at an altitude of less than 500 m, and as much as 19.9% at an altitude of 600–650 m, for which there are limitations in terms of water supply, terrain stability and infrastructure and traffic. – 27.5% of the total number of residential buildings are located on the north facing slopes, which suffer from conditions of poor insulation and increased humidity. – On conditionally stable terrains with slope gradient 15–20% there are 19% of the total number of residential buildings and 32% of residential buildings are located on unstable terrains or landslides with a slope gradient of more than 20%. – In the urban territory of Sarajevo there is 2.263 ha of unstable terrains (landslides), with 9.678 buildings situated on those terrains. – The ratio between collective and single house residential buildings in the year 2016was 20.5%: 79.5% compared to the Urban plan 1986–2015 projection for the year 2015 which was 40%: 60% and the existing situation in 1985 when the ratio between collective and single house typology was 25%: 75%. Therefore, we can distinguish the trend of increasing single house housing zones associated with, according to spatial distribution analysis of residential zones, negative urban development in form of urban sprawl. – There are 27,692 registered informal buildings (not only residential) in the urban territory of Sarajevo, out of which 6,938 or 25.05%, were legalized in the period 2006–2016. From our analysis arises that in the future, in the processes of creating strategies and policies for residential construction in the urban territory of Sarajevo, in the process of drafting the Urban Plan, all available digital tools, including digital simulations should be used to arrive at concrete measures to reduce and prevent risks for construction and maintenance of residential zones as well as residential buildings in the urban territory of Sarajevo.
References 1. De Smith, M.J., Longley, P.A., Goodchild, M.F.: Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools, 4th edn. (2013) 2. Taletovi´c, J., Pleho, J., Ljuša, M.: GIS u prostornom planiranju, ARCH DESIGN d.o.o. Sarajevo (2018)
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3. Li, Z., Zhu, Q., Gold, C.: Digital Terrain Modeling: Principles and Methodology. CRC Press, Boca Raton (2005) 4. Taletovi´c, J., Kljuˇcanin, S.: Geodetski planovi, UniverzitetDžemal Bijedi´c u Mostaru, Gradevinski fakultet, Mostar (2022) 5. Vitruvius, M.: The Ten Books on Architecture, p. 170. Harvard University Press, London (1914) 6. European Commission: Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Comettee and the Comettee of the Regions – The European Green Deal, Brussels 11.12.2019 (2019) 7. Ažuriranje osnovne topografske karte 1:5000 urbanih podruˇcja Kantona Sarajevo. Zavod za planiranje razvoja Kantona Sarajevo, novembar (2016). https://zpr.ks.gov.ba/sites/zpr.ks.gov. ba/files/7.1_projektni_zadatak_otk5_up_ks_24112016.pdf. Accessed 15 Jan 2023 8. Pelja-Tabori, N.: Rechtfertigung für die Wiedereinführung einer Bauordnung im Kanton Sarajevo. Doctoral dissertation. TU Wien (2021) 9. Bruegmann, R.: The causes of sprawl from sprawl: a compact history. In: Le Gates, Stout (eds.) The City Reader, pp. 211–221. Routledge, Taylor & Francis Group, London and New York (2005) 10. Taletovi´c, J., Kljuˇcanin, S.: A modern base map and 3D city model production - a case study “city of Sarajevo”. In: Ademovi´c, N., Mujˇci´c, E., Akšamija, Z., Kevri´c, J., Avdakovi´c, S., Voli´c, I. (eds.) IAT 2021. LNNS, vol. 316, pp. 684–693. Springer, Cham (2022). https://doi. org/10.1007/978-3-030-90055-7_55
Computer Science and Artificial Intelligence
Credit Card Fraud Payments Detection Using Machine Learning Classifiers on Imbalanced Data Set Optimized by Feature Selection Admel Husejinovi´c(B)
, Jasmin Kevri´c , Nermina Durmi´c , and Samed Juki´c
International Burch University, Francuske revolucije bb, 71210 Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. Card payment fraud is a global problem that continues today. This study uses machine learning algorithms to predict the outcome of regular and fraudulent transactions on real-world credit card payment transactions. The performance of algorithms is evaluated through accuracy, sensitivity, specificity, Matthews Correlation Coefficient, and Receiver Operating Characteristic (ROC) Area rates. Three different experiments have been conducted. In the first stage, we used the original dataset. In the second stage, Synthetic Minority Oversampling Technique (SMOTE) is introduced to an imbalanced dataset to increase the performance of algorithms. Finally, we optimized the algorithms with feature selection methods in the last stage. In the second stage, we succeeded in increasing the specificity rate from 0.77 to 1.00 by using a voting classifier. In the third stage, we managed to keep the rate at a rate of 1.00 while decreasing the number of features from 31 to 5 features subset. Keywords: Credit card fraud · machine learning algorithms · SMOTE · Feature Selection
1 Introduction Card payments are still a very popular payment type nowadays globally. In 2020 total of USD 28.58 billion was due to card fraud, equal to 6.8 cents per USD100 in purchase volume [1]. According to the Federal Trade Commission (FTC), credit card fraud increased by 44% in 2019 and 2020 [2] as a result of the pandemic and the increase in the use of online payments in the USA. The European Central Bank (ECB) reported EUR 1.87 billion in fraudulent transactions using cards issued by the Single Euro Payments Area (SEPA). Fraudulent transactions include card-present fraud and card-non-present fraud [3]. ECB also reported an increase in credit card fraud in 2019. Despite an increase in detection techniques, online payment fraud is constantly increasing nowadays [4]. The main purpose of the online fraud detection model is to decrease online fraud to the lowest possible level, increase customer confidence in online payments and decrease fraud attempts [5]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 233–250, 2023. https://doi.org/10.1007/978-3-031-43056-5_19
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In the days before internet card payments, frauds commonly required physical theft of the cards from wallets. Nowadays most card payments do not even need a card to be made. There are several key types of card fraud according to Australian Payments Network: Card-not-present (CNP) fraud, Counterfeit/skimming fraud, Lost/stolen card fraud, Card-never received fraud, and Fraudulent application fraud [6]. “Card, not present” fraud happens without the use of a physical card only by the necessary information from cards, mostly from the internet or over the phone. Counterfeit and skimming frauds are those that occur when card details are illegally replicated from magnetic stripes from valid cards. Lost and stolen card fraud occurs from the loss or theft of a valid credit card and transactions are performed without cardholders’ consent. The card never received fraud occurs on cards ordered by a customer and intercepted in the delivery period.
2 Literature Review Online payment fraud is a very widespread problem among researchers [7]. Artificial Neural Networks (ANN), Decision Trees, and Support Vector Machines (SVM) are the most frequently used supervised machine learning techniques while genetic algorithm (GA) is the most popular unsupervised technique used for online fraud detection [7–9]. Datasets used in research are blurred because of financial data confidentiality issues. Another problem of datasets is highly imbalanced as in real-world cases fraudulent transactions are tiny compared to non-fraudulent transactions. In Bagga et al. [10] study authors compare the performance of logistic regression, K-nearest neighbors, random forest, naive Bayes, multilayer perceptron, ada boost, quadrant discriminative analysis, pipelining, and ensemble learning using accuracy, precision, recall, F1 score, and confusion matrix to compare the performance of 9 different techniques to find best performing one. They concluded that Pipelining method was found to be the best based on the performance of different techniques. In Patil et al. [11] research, the authors use a big data collection framework in Hadoop and send it to an analytical server for fraud detection. They run tree models and evaluate their performance based on a confusion matrix concluding that the Random Forest model surpassed other models. In similar studies conducted by other authors [12–15] The Random Forest model surpassed other tested models. Opposing them in a few other studies [16–18] other models surpassed The Random Forest model. The author in research [19] experimented with credit card fraud detection on an imbalanced dataset and showed that the best-performing algorithm is bagging with the C4.5 decision tree. As base learner algorithms are evaluated through precision, recall, and precision-recall (PR) curve area rate performance matrices. Dornadula and Geetha [15] used the clustering method to separate the cardholders into different clusters corresponding to transaction amounts. Extracted some features to find cardholders’ behavior patterns. In this model active change in constraints is conducted to adjust new customer behavior. They used SMOTE (Synthetic Minority Over-Sampling Technique) operation to deal with the imbalance dataset problem. The authors applied classification algorithms for each cardholder group separately. Experimental results showed that Logistic regression, decision tree, and random forest are the algorithms that gave better results in terms of accuracy, precision, recall, and MCC rates.
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Kovach and Ruggiero [20] proposed an online banking fraud detection system based on customers’ local and global activities. Authors revile that common characteristics of fraudulent real-world transactions are many different accounts accessed by a single fraudster, fraudulent transactions in small amounts in different accounts, and an increased number of password identification failures in attempting to access payment accounts. The proposed model profiles expected transactions, a significant deviation from expected values indicates fraud transactions occurrence. Nancy and colleagues [21] proposed 5 steps hybrid model compose of CNN for feature selection and k-NN algorithms for clustering data. CNN was used in the first phase of the hybrid model with an accuracy of 87% followed by the KNN algorithm that increased predictive accuracy to 90%. Finally, the proposed hybrid model accuracy increased to 98%. Xuan et al. [22] experimented with data from an e-commerce company in China using two kinds of random forests to train the behavior features of normal and abnormal transactions. Also, applying conventional machine learning algorithms for credit card fraud detection is hamstrung due to their design, which involves a stationary mapping of the input vector to output vectors. Thus, they cannot acclimatize to the dynamic shopping actions of credit card guests. Esenogho et al. [23] propose an effective approach to descry credit card fraud using a neural network ensemble classifier and a hybrid data resampling approach. The ensemble classifier is attained using a long, short-term memory (LSTM) neural network as the base learner in the adaptive boosting (AdaBoost) fashion. The performance of the offered approach is compared to the following algorithms multilayer perceptron (MLP), support vector machine (SVM), traditional AdaBoost, decision tree, and LSTM. The experimental results show that the classifiers performed better when trained with the resampled data. Malik et al. [18] of the proposed exploration examined seven hybrid machinelearning models to discover fraudulent exertion with a real word dataset. The developed hybrid models comported of two phases, state-of-the-art machine learning algorithms were utilized first to discover credit card fraud, also, hybrid approaches were constructed grounded on the smart single algorithm from the initial phase. Research findings indicated that the mongrel model Adaboost LGBM is the champion model as it showed the uppermost performance. In this research, the author used a real-world dataset with 432 features before applying a feature selection method called SVM-Recursive Feature Elimi nation (SVM-RFE) which significantly reduced the number of features. Alarfaj et al. [24] used the European credit card dataset for machine learning algorithms’ performance evaluation. Lately, deep learning algorithms were performed to increase performance. In the final stage, three structural designs based on a convolutional neural network are applied to improve fraud detection performance. Research results showed that significantly improved results were achieved, such as accuracy, f1score, precision, and AUC Curves having values of 99.9%, 85.71%, 93%, and 98%, respectively. Other authors Asha and Suresh Kumar [25] have proposed deep learning methods to identify fraud in credit card transactions. They first compare it with machine learning algorithms such as k-Nearest Neighbour, Support vector machine, and Neural Networks.
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Research revealed that Neural Networks outperformed other algorithms in terms of accuracy but not in terms of precision and recall. The Deep Convolution Neural Network (DCNN) scheme using a deep learning algorithm was proposed by Chen and Lai [26]. The proposed schema was tested on a real-time dataset where over 45 s accuracy of 99% was achieved. Nandi et al. [27] designed a Behaviour-Knowledge Space (BKS) model to analyze multiple classifier results for the prediction of fraudulent credit card payment transactions. They used statistical tests for the evaluation of model effectiveness. For the imbalanced dataset problem, the authors used a combination of over-sampling and under-sampling techniques. The deficiency of this research is a noisy dataset used in this research makes BKS resalts leading to erroneous predictions. Dhankhad et al. [28] employed many machine learning algorithms analyzed their performance, and formed a super classifier using ensemble learning methods. Nami and Shajari [29] introduced Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors that were tested on a real-world dataset and showed an increase in financial prevention of damage. Faraji [17] used a real-world dataset to demonstrate voting ensemble method supremacy over other single ML methods. Hussein et al. [30] used the stacking ensemble technique with the fuzzy-rough nearest neighbor and sequential minimal optimization as base learners and logistic regression as a meta-classifier. The proposed method experimented with Australian credit approval and German credit datasets and showed promising results in terms of detection rate, false alarm rate, specificity, positive predictive value, f-measure, ROC curves, and AUC area. Ileberi et al. [13] used a genetic algorithm (GA) to do feature selection on the European credit card holder dataset. After feature selection models were tested on several machine learning algorithms like Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). The authors used accuracy, precision, recall, and F1-score rates to compare model performances. Results showed that the best overall accuracy of 99.98% was achieved using GA and RF algorithms. A novel approach text2IMG conversion technique proposed by Alharbi et al. [31] to solve the credit card fraud problem by converting text data to the image. In the second stage, they used CNN design with class weights applying the inverse frequency approach to answer the class imbalance problem. In this study deep learning and machine learning approaches are used to verify the robustness of the proposed approach. Despite being a novel approach model achieved promising results in terms of model performance accuracy. 2.1 Data Imbalance Issues in Credit Card Fraud Detection Studies Many authors used real-world datasets in their experiments and naturally had problems with imbalanced detests [32–34]. To solve this problem proposed model called CostSensitive Neural Network (CSNN) based on a misuse detection approach. Zhu et al. [35] applied a Weighted Extreme Learning Machine (WELM) to handle the dataset imbalance problem. Boughorbel et al. [36] proposed a new method based on Matthews Correlation Coefficient (MCC) to solve data imbalance trouble. According to Saito and Rehmsmeier [37] while ROC is commonly employed as a performance measure for
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imbalanced datasets by numerous researchers, they propose that PRC plots are more informative and therefore should be preferred. Madhurya et al. [16] examine the issue of class imbalance in their research and explore potential solutions to address it. Authors showed that all tested algorithms despite their calculation at some point show imbalance. They conclude that there is no universally best data mining technique for all cases. They point out that dataset imbalance and real-time action are important problems in the implementation of any machine-learning model. Some authors in researches [12, 38] point highly imbalance and confidentially of financial data as two main problems of credit card fraud detection with data-driven models. Singh et al. [14] research focuses on dataset imbalance as the main problem. They use several dataset balancing techniques like Over-sampling techniques, under-sampling techniques, and oversampling followed by under-sampling techniques. For validation purposes precision, recall, and accuracy after applying state-of-art machine learning algorithms on the European cardholder dataset. Another similar research was conducted by Alfaiz and Fati [39] proposed a two-stage fraud detection model. In the first stage, a real-world credit card fraud detection dataset of European cardholders is used in each model employing nine machine-learning algorithms to test the detection of fraudulent transactions. The top 3 best-performing algorithms were nominated for the second stage. In the second stage, 19 resampling techniques were used with each one of the best three algorithms. Researchers reported that the results point out that the proposed model outperforms previous models with an AUC value of 97.94%, a Recall value of 95.91%, and an F1-Score value of 87.40%. Ahmad et al. [40] mainly focus on the credit card dataset imbalance problem. They tested a few different under-sampling techniques comparing them with novel Fuzzy C-means based on clustering and similarity-based selection (SBS) approach. According to the experimental results, Random Under-Sampling (RUS) approach was outperformed by the SBS approach. Experimental research results confirmed that Oversampling followed by Under-sampling methods can increase the performance of the classifier significantly. Experimenting with different machine learning algorithms Rathore et al. [9] concluded that the Random Forest model works better than SVM and Logistic Regression with a highly imbalanced dataset. They also determined that SVM and KNN perform better with small datasets than with large datasets. Also, researchers say that Neural Networks perform with the best precision, but they are expensive to train.
3 Methodology In this study, we employed 8 machine learning and statistical methods (Logistic Regression, Random Forest, Decision Tree, Naïve Bayes, Multilayer Perceptron, k-nearest neighbors, Support vector machine, and Voted Perceptron) to test their predictive performances in fraud detection. In the second step, we combined the three best-performing algorithms in a voted manner to increase their performance. Finally, we manage to improve the algorithms’ performance with feature selection methods.
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3.1 Dataset In this study, a dataset of 284,807 credit cardholder transactions carried out by European users in September 2013 was users [41]. The dataset consists of 30 features, including a target class that indicates whether a transaction is classified as fraud or legitimate (1 or 0). However, the dataset is highly imbalanced, with only 492 instances of fraud transactions. The dataset used for this research has undergone data preprocessing, and its features, except for time and amount, have been transformed using Principal Component Analysis (PCA) into numerical inputs represented by V1 to V28. This transformation was done to address confidentiality concerns. 3.2 Synthetic Minority Oversampling Technique (SMOTE) When working with real-world datasets we frequently have “normal” status classes presented more in the dataset. While the “abnormal” class is a lot less presented in the dataset. In other words, we have a problem with imbalanced datasets if classification categories are not similarly represented. In the case of credit card transactions, the misclassification costs of an abnormal class are higher than the misclassification of a normal transaction. SMOTE is a technique that generates artificial samples of instances for the minority class. SMOTE uses a k-NN algorithm to create artificial data points in a dataset. The difference between a randomly selected feature vector and its nearest neighbor multiplied by a random number between 0 and 1 is an approach to how SMOTE works [42]. 3.3 Feature Selection Methods Feature selection methods help us in reducing computational time, increase algorithm performance, have a better understanding of data, and reduce the dimensionality of data [43]. Wrappers, filters, embedded, and hybrid is the main categories of feature selection [44]. Wrapper methods are computationally intensive as they test specific machine-learning algorithms for all possible combinations of subsets to select the bestperforming subset [45]. Filter methods are less computationally intensive as they are independent of specific machine learning algorithms. They use various statistical tests for correlation with class variables. They are mostly used in pre-processing of data. Embedded methods are done in the process of the algorithm’s implementation or as a part of its extensive functionality. Hybrid methods combine valuable characteristics of both Filter and Wrapper methods [46]. 3.4 Machine Learning Methods for Classification In this study, we investigate the performance of Logistic Regression, Random Forest, Decision tree, Naive Bayes, Multilayer perceptron, k-nearest neighbor, Support vector machines, voted perceptron methods to test performance through accuracy, sensitivity, specificity, an area under ROC curve, and Matthews Correlation Coefficient (MCC).
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For data imbalance problems we used Synthetic Minority Oversampling Technique (SMOTE). Naive Bayes Naive Bayes is a statistical method estimating the concept of the conditional probability that a feature fits class based on utilizing Bayes’ theorem. It helps us to realize how often A happens given that B already happened. We can write it as: Pr
c X
=
(Pr( Xc ) ∗ Pr(c)) Pr(x)
(1)
The reason to be called naïve is for the sake of the assumption that each feature’s probability is independent of the other which is difficult to occur in the real world. Support Vector Machines It is a supervised machine-learning technique that is used for regression and classification problems. In two-dimensional space, the idea is to find a hyperplane that separates two classes linearly. Data points that are closest to the hyperplane are called support vectors. The distance between the closest data point and the hyperplane is called the margin. The ideal hyperplane is the one with the longest margin from support vectors [47]. C4.5 Decision Tree The decision tree is a supervised machine-learning algorithm. Used for classification and regression problems. Considered one of the simplest machine learning algorithms. Training data is arranged in a series of if-then statements with yes-no answers [48]. Data classification is arranged from the topmost node called the root node to the leaves created on the yes/no outputs. Splitting is done using information gain and entropy [49]. Entropy quantifies result class impurity with ps attributes in some D datasets as shown in the formula: s 1 (pi log log( )) (2) H (p1, p2, . . . ps) = i=1 pi While information gain calculates the difference between the entropy of the whole dataset and the entropy of the splitting attribute as we see in the formula: s Gain(D, S) = H (D) − p(Di )H (Di ) (3) t=1
Random Forest Random Forest is a popular supervised machine learning algorithm used for classification and regression problems. The algorithm is based on the concept of ensemble learning that produces multiple decision trees from randomly selected subsets from the training dataset [50]. Random Forest is solving variance problems from Decision tree algorithms [51]. Classification results from the randomly selected tree are finally classified by majority voting among each decision tree. Logistic Regression Logistic Regression is a classification algorithm that uses the logistic sigmoid function
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to assign a discrete set of classes based on their probabilities. f (x) =
1 1 + e−(x)
(4)
where f(x) is a value between 0 and 1, X is input to the function and e is a base of the natural log. The prediction function returns a probability score between 0 and 1. So if the prediction value p is greater than 0.5, we predict a class 1 outcome, and if the prediction p-value is less than 0.5 we predict an outcome of class 0. K-Nearest Neighbors KNN is a type of supervised machine learning algorithm. It is used to make predictions on the test dataset based on characteristics from the training dataset. Distance between the data point in the training dataset and the new data point from the test dataset is used. K is a parameter that is used to represent the number of the nearest neighbors. It is recommended to be a small positive integer and an odd number. The value of k needs to be an odd number to avoid tie-in voting results of different classes. KNN calculates the distance between data points using some of the common methods used for distance calculation like Euclidian. k (xi − yi )2 (5) i=1
Or Manhattan. k i=1
xi − yi ||
(6)
3.5 Proposed Method This section describes the main steps of the research methodology as shown in the following Fig. 1: 1. Dataset gain: There are several publicly available credit fraud datasets. 2. Data Preprocessing: That may involve oversampling methods of the minority class or under-sampling of the majority class. 3. Feature Selection: applying recent state-of-the-art wrapper methods. That also may involve filter methods. 4. Evaluate results on 3 stages of datasets: • the initial dataset • SMOTE and under-sampling applied dataset. • feature selection applied. 5. Propose a new model for: • New ensemble method for 3 best-performing algorithms at 1. Stage • New ensemble method for 3 best-performing algorithms at 2. Stage • New ensemble method for 3 best-performing algorithms at 3. Stage
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Fig. 1. Proposed Method.
6. Evaluate Method Performance 7. Discuss results. The same research methods will be repeated for different dataset sizes (1,000, 3,500, 5,000, 7,500, and 10,000 instances) to find optimal algorithm performance dependent on dataset size. In this research SMOTE over-sampling technique together with some undersampling techniques will be used on the unbalanced dataset to test the performance of classifiers. Datasets with a few groups by instance size (1,000, 3,500, 5,000, 7,500, and 10,000 instances) will be tested. According to the results optimal dataset size will be defined for further research. The data set used is unbalanced and some oversampling techniques need to be used. One of the most used is Synthetic Minority Oversampling Technique (SMOTE) [42]. A dataset contains many different attributes that may be redundant and irrelevant to the application of machine learning algorithms. Wrappers and filters feature selection methods will be applied to reduce overfitting and computational demand [46].
4 Results 4.1 Performance Evaluation The confusion matrix recaps the performance of algorithms we have been applying to our testing dataset. For this case we have two possible classes 0 and 1. 0 represents a regular payment transaction while 1 represents a fraudulent transaction. In our example, Confusion matrix columns represent the predicted class, while rows represent the actual class (Table 1). The Sensitivity represents the ratio of true positives (TP) and actual positives (TP + FN). Measures the fraction of actual positives that are correctly recognized by the algorithm. Sensitivity =
TP TP + FN
(7)
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A. Husejinovi´c et al. Table 1. Confusion Matrix.
Confusion matrix
Predicted class
Actual classes
1
0
1
True positive (TP)
False negative (FN)
0
False positive (FP)
True negative (TN)
The Specificity is the ratio of true negatives (TN) and actual negatives (TN + FP). Measures the fraction of actual negatives that are correctly recognized by the algorithm. Specificity =
TN TN + FP
(8)
The area under the ROC curve represents the usefulness of the test in general. As we get results from 0.5 (no apparent accuracy) closer to 1.0 (perfect accuracy) we get better performing tests. It is a representation of the probability of correctly identifying classes [52]. Matthews Correlation Coefficient (MCC) is defined in terms of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) as follows: MCC(θ ) = √
TP ∗ TN − FP ∗ FN (TP + FP)(TP + FN )(TN + FP)(TN + FN )
(9)
Originally developed by Matthews in 1975 for the comparison of chemical structures [53] MCC was re-proposed by Baldi and colleagues [54] in 2000 as a standard performance metric for machine learning with a genuine extension to the multiclass case. An effective solution to overcoming the class imbalance issue comes from the Matthews correlation coefficient [55, 56] Authors compared MCC and Confusion Entropy (CEN) as performance measures of a classifier in multiclass problems. They showed empirically and analytically that they have consistent behavior in practical cases. Both MCC and CEN improve over Accuracy (ACC), by far the simplest and most common measure in scientific literature. The point with ACC is that it weakly copes with unbalanced classes. 4.2 Experiment Result In this study, 8 classifier algorithms were used to train and test performance. The dataset used in this paper was highly imbalanced because only 0.17% of data was classified as fraudulent transactions. As an example, if all transactions were predicted to be classified as class 1 by an algorithm, an overall accuracy of 99.83% would be achieved. In other words, all fraudulent transactions would be missing. Three stages were conducted according to the dataset. In the first stage, the original dataset was split up into 70% data for training and 30% for testing. In the second stage, SMOTE was conducted on the minority class of the original dataset and the majority class was under-sampled. This resulted in the production of 5 balanced datasets with 1,000 instances, 3,500 instances, 5,000 instances, 7,500 instances, and 10,000 instances for each class.
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The overall best performance was shown by the balanced dataset with 7,500 of each class. The original dataset was used for testing purposes. In stage three, the feature selection algorithm is performed to increase performance and optimize results, based on the results from stage 2. The classifier models are evaluated using accuracy, sensitivity, specificity, ROC Area, and MCC. Non-Fraudulent transactions are represented by Class 0, while fraudulent transactions are represented by Class 1 (Tables 2 and 3). Table 2. Confusion Matrix for the Vote Models. Predicted class Before applying SMOTE
Actual class
1
0
1
134
40
0
3
85,265
After applying SMOTE
1
492
0
0
271
284,044
After applying the Feature Selection
1
492
0
0
15,015
269,300
Table 3. Algorithm Performance on initial dataset 70/30 split. Initial dataset
Accuracy
Specificity
Sensitivity
ROC
MCC
Logistic Regression
0.99910
0.99994
0.58621
1.000
0.74717
Random Forest
0.99951
0.99995
0.78161
1.000
0.87114
Decision Tree
0.99949
0.99991
0.79310
0.885
0.86558
Naïve Bayes
0.97732
0.97758
0.85057
0.964
0.24339
Multilayer Perceptron
0.99943
0.99989
0.77011
0.975
0.84923
k-nearest neighbors
0.99937
0.99973
0.77586
0.880
0.81384
Support vector machine
0.99940
0.99988
0.76437
0.882
0.84288
Voted Perceptron
0.99794
0.99998
0.00000
0.500
-0.00022
Vote (3 algorithms)
0.99950
0.99996
0.77011
0.885
0.86767
After addressing the issue of an unbalanced dataset, authors proceeded to conduct the experiment again using the same algorithms, but this time with a balanced dataset created through the application of under-sampling techniques on the majority class and SMOTE on the minority class. Table 4 presents the evaluation metrics for the resulting balanced dataset. During the third stage of our analysis, we utilize feature selection techniques to create subsets of the dataset that exhibit improved performance, thereby enhancing the model’s relevance for business purposes. The evaluation metrics for the algorithms’ performance are presented in Table 5.
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A. Husejinovi´c et al. Table 4. Algorithm Performances on the testing dataset.
+SMOTE
Accuracy
Specificity
Sensitivity
ROC Area
MCC
Logistic Regression
0.99310
0.99329
0.88415
0.983
0.40331
Random Forest
0.99777
0.99777
1.00000
1.000
0.65999
Decision Tree
0.98750
0.98755
0.95528
0.980
0.33236
Naïve Bayes
0.97673
0.97694
0.86366
0.962
0.22314
Multilayer Perceptron
0.99305
0.99316
0.92886
0.982
0.41879
k-nearest neighbors
0.99266
0.99265
1.00000
0.997
0.43483
SVM
0.99275
0.99297
0.86789
0.930
0.38900
Voted Perceptron
0.99615
0.99787
0.00407
0.501
0.00174
Vote (3 algorithms)
0.99905
0.99905
1.00000
1.000
0.80263
Table 5. Algorithm Performances on testing dataset optimized for feature selection. +Feature Selection
Accuracy
Specificity
Sensitivity
ROC
MCC
Logistic Regression
0.99222
0.99240
0.88821
0.980
0.38457
Random Forest
0.93252
0.93241
1.00000
0.988
0.15256
Decision Tree
0.98776
0.98781
0.95528
0.977
0.33552
Naïve Bayes
0.98310
0.98327
0.82110
0.969
0.26867
Multilayer Perceptron
0.99265
0.99276
0.93293
0.985
0.41065
k-nearest neighbors
0.91858
0.91844
1.00000
0.962
0.13813
SVM
0.99356
0.99377
0.87398
0.934
0.41151
Voted Perceptron
0.98969
0.98987
0.88211
0.936
0.33771
Vote (3 algorithms)
0.94728
0.94719
1.00000
0.974
0.17336
Table 6 presents the results of the feature selection process for all algorithms, revealing that not all of them have the same set of best-performing features. After the initial result was reviewed, it was decided to combine the three best algorithms in terms of accuracy, specificity, and sensitivity. The Vote with majority voting decision on Random Forest, k-NN, and Voted Perceptron was used. The best first search method was applied to the Wrapper methods of feature selection. The features of the best-performing subsets were presented in Table 6.
5 Discussion If we look at the results in the first stage, accuracy is very high for all algorithms, but let’s not forget that the data set on which we trained the algorithms is extremely unbalanced. Random Forest showed the best performance with an accuracy of 99.951%.
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Table 6. Feature Selection Subsets Feature List. +Feature Selection
No. of Features in Subset
Features
Logistic Regression
6
V1, V4, V7, V12, V14, V20
Random Forest
7
V1, V3, V4, V6, V9, V14, Amount
Decision Tree
11
V1, V4, V5, V9, V10, V12, V14, V17, V21, V22, V27
Naïve Bayes
7
V4, V11, V12, V14, V15, V17, V24
Multilayer Perceptron
10
V4, V8, V10, V11, V12, V13, V14, V17, V18, V20
k-nearest neighbors
6
V1, V4, V7, V12, V14, V20
SVM
10
Time, V2, V4, V7, V11, V12, V14, V22, V24, V28
Voted Perceptron
4
V4, V12, V14, V25
Vote (3 algorithms)
4
V4, V11, V17, V23
The specificity of the algorithms is also extremely high between 97.758% and 99.998% of the Vote Perceptron algorithm. The specificity of the algorithms vary between 0.000% for the Voted Perceptron and 85.058% for the Naïve Bayes. Specificity is perhaps the most important individual indicator of the performance of the algorithm from the aspect of not losing money and client and bank satisfaction. ROC Area shows the best performance for Logistic Regression and Random Forest with 1.000 and worse for Voted Perceptron 0.500. MCC shows best for Random Forest with 0.87114 and worse for Voted Perceptron with −0.00022. In the second stage, we try to increase specificity by applying SMOTE and undersampling to better train algorithms for the accuracy of class 1. The vote had the best performance with an accuracy of 99.905%. The specificity of the algorithms is also extremely high between 97.694% and 99.787% of the Vote Perceptron algorithm. Specificity is generally increased in the second stage; it varies in the range between 0.407% for the Voted Perceptron and 100.000% for the Vote, Random Forest, and k-NN. ROC Area shows the best performance for Random Forest with 1.000 and worse for Voted Perceptron with 0.501. MCC rates slightly decreased in the second stage showing best for Vote with 0.80263 and worse for Voted Perceptron with 0.00174. In the third stage, we try to optimize models by feature selection methods to increase models’ performance and practical usage by decreasing the number of features in the model. When we talk about features’ significance in feature selection subsets for 9 algorithms, we can say that feature V4 is the only feature that is shown in all tested subsets. V14 is presented in 8 out of 9 subsets. V12 is presented in 7 out of 9 subsets, V11, V17, V1 is shown in 4 out of 9 subsets. In terms of model accuracy, SVM had the best performance with an accuracy of 99.356%. The specificity of the algorithms is also high between 91.844% and 99.377% of the SVM algorithm. Specificity is generally at a high range in the third stage it varies in the range between 82.110% for the Naïve Bayes and
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100.000% for the Vote, Random Forest, and k-NN. ROC Area decreased in the third stage showing the best performance for Random Forest with 0.988 and worst for SVM with 0.934. MCC rates slightly decreased in the third stage showing best for SVM with a rate of 0.41151 and worse for k-NN with a rate of 0.13813 (Table 7). Table 7. Accuracy and AUC scores for research frameworks are discussed in this thesis work. Reference Paper
Algorithms Used
Best Classifier
Sensitivity
Specificity
MCC
Accuracy
[10]
Pipelining and Ensemble Learning
Pipelining
0.86
1.00
>0.8
0.99
[21]
Hybrid Model
CNN and k-NN
0.98
0.97
–
0.98
[57]
Hybrid Model
ANN
0.76
–
–
0.99
[11]
LR, Decision Tree, Random Forest
Random Forest
0.77
0.69
–
0.76
[19]
Bagging
C4.5
1.000
0.797
–
–
Our Work on the original dataset
Voting
Random Forest
0.78161
0.99995
0.87114
0.99951
Our Work with SMOTE
Voting
Vote
1.00000
0.99905
0.80263
0.99905
Our Work – Feature Selection
Voting
SVM
0.87398
0.99377
0.41151
0.99356
In comparison with other works using the same dataset [10, 15, 19], our vote model achieved superior performance in terms of sensitivity, as we reached the score of 1.000 sensitivity rate in the second and third stages. They [10] reported a sensitivity rate of 0.86. We also performed better compared to similar work [11, 21, 57] authors achieved 0.76 with ANN, 0.98 with CNN, and 0.77 with Random Forest. In terms of MCC value, our models in the third stage performed significantly weaker than in the second stage, achieving a 0.803 rate similar to results reported in a similar study [10].
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6 Conclusion We used a three-stage approach to test performance on the initial dataset. In the second stage, we introduced SMOTE and under-sampling methods and in the third stage, we managed to improve the model by the feature selection method. As a result of an extremely imbalanced dataset, overall accuracy is not a good way to interpret the results and performance of models. We used other parameters like MCC and ROC Area to do so. We see a slight increase in ROC area parameter values in the second and third stages whereas MCC parameter values decreased sharply in the second and third stages. From the business side of view, an individually more important single parameter most likely would-be specificity rate as it shows the ability of the model to identify fraudulent transactions. In the second and third stages, we increased the specificity rate for most of the models. For a better understanding of results implications on the business side of fraud transaction prevention, we have limitations on dataset hidden attributes. Similar experiments would give better insides into a dataset with undisguised attributes. That would lead to saving credit card users from financial losses.
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Document-Based Sentiment Analysis on Financial Texts Admel Husejinovi´c(B)
and Zerina Mašeti´c
International Burch University, Francuske revolucije bb, 71210 Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. Public opinion plays an important role in economic activities like investors’ decisions, and stock price changes. This study uses Sentiment Analysis as the technique of Natural Language Processing to identify economic texts’ sentiment. Sentiment Analysis is performed at the document level for each text. Term frequency and inverse document frequency (TF-IDF) algorithms are used for text summarization and document vectorization. Random Forest, SVM, and MLP algorithms are used to test the predictive performance of the model through accuracy, precision, recall, and F1-score. In the final stage, the Voting Ensemble of individual algorithms is combined using the majority vote rule for classification. The best-performing model is the Voting Ensemble with an overall accuracy of 71%. The need for sentiment analysis in economic activities will continue in the future as data generated from different sources rapidly grows. Keywords: sentiment analysis · NLP · financial news · opinion mining · machine learning algorithms
1 Introduction Financial and economic news from Bloomberg, The Wall Street Journal, Financial Times, etc. affects society’s opinion on economic activities. General sentiment toward economic outlooks is very important for investors’ decisions, and the movement of stock prices [1]. With the expansion of the Internet, information has become more accessible to a larger number of people in a shorter period. People form opinions based on available information every day. People share their opinions about various products, services, and topics in forums and blogs on the Internet. The collected data is used to form opinions about products, services, as well as expectations in financial markets. Fields like Machine learning and Natural Language Processing (NLP) help analyze text data and categorize it into predefined categories based on a person’s opinion. A person’s opinion about something generally is categorized as positive, negative, or neutral. In this research Sentiment Analysis for NLP and Machine Learning classification algorithms are used to categorize economic texts and predict opinion categorization for unseen economic texts. Economic texts’ opinion categorization is performed on the document level for the entire text. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 251–262, 2023. https://doi.org/10.1007/978-3-031-43056-5_20
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The paper is organized as follows: the next section presents the background of the sentiment analysis used in economics and finance, the literature review, and the research objectives. In Sect. 3 research methodology with dataset and proposed methods were used to answer the research objectives. Research results are introduced in Sect. 4 with a tabular representation of machine learning algorithms’ performance presented through accuracy, precision, recall, and F1-score and discussion. Section 5 conclusion summarizes the achieved results from this research.
2 Background and Literature Review 2.1 Sentiment Analysis An opinion is a personal point of view on something, not necessarily based on facts or knowledge. Sentiment analysis and opinion mining is the field of study that analysis people’s opinions, sentiments, judgments, and emotions, toward something using written language [2]. The growth of social media, blogs, and discussion forums results in the increasing importance of sentiment analysis. Sentiment analysis or opinion mining is an active research field in NLP [3]. There are three levels of document sentiment analysis extraction: document level, sentence level, and aspect/feature level analysis [2]. On the document level sentiment is extracted for the entire document as one classified opinion. The goal is to classify the review as positive, neutral, or negative. Sentence level involves two steps, classifying sentences into subjective or objective. In the second step, subjective sentences are classified for sentiment opinion. Aspect/feature level sentiment is focused on extracting object features of the review and determining whether the opinion is positive, neutral, or negative. 2.2 Literature Review Antweiler and Frank [4] in their study investigate can Internet stock messages affect stock market activities, they used 1,5 million finance-related messages from Yahoo! Finance and Raging Bull. Using the linguistics method, they found out that stock messages support market volatility. Tetlock [5] used the daily content of the Wall Street Journal column to test media pressure on stock market activities. The author found that unusually high or low pessimist media attitudes toward the market result in high market trading volume. There is some research regarding opinion mining in finance news and headlines [6– 11]. Nopp and Hanbury [12] used bank CEO letters and the report’s outlook section for opinion mining. The most popular subfield is the stock market for news and headlines sentiment classification correlation with stock price prediction. Yoosin, Seung, and Imran in their study [7] used news headlines classification to predict the movement of stock prices. In this study, they used Korean-language news to predict changes in stock prices on the KOSPI (Korea Composite Stock Price Index). Firstly, they categorize opinions from headlines and lately find the correlation between stock market news and market price fluctuation. Meanwhile, Mohammad et al. [13] introduced a Novel Text Mining Approach Based on TF-IDF and Support Vector Machine for News Classification.
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Bollen, Mao, and Zeng [8] investigated how tweeter’s collective feeds correlate to the Dow Johns Industrial Average (DJIA) over time. They analyzed Twitter moods using two mood tracker tools: Opinion Finder and Google Profile of Mood States. In further analysis, Self-Organizing Fuzzy Neural Network is used to measure the predictive abilities of mood states evaluated by Opinion Finder and Google Profile of Mood States. They predicted the closing value of the DJIA with an accuracy of 87%. Salas-Zarate et al. [6] researched aspect-based sentiment analysis on financial texts. They presented an ontology-driven method and used N-Gram After, N-Gram Before N-Gram Around, and All-Phase methods for word polarity estimate. With the proposed method they achieved an accuracy of 66.7% and an F-measure of 64.9% for feature polarity classification and an accuracy of 89.8% and an F-measure of 89.7% for news polarity classification. Nopp and Hanbury [12] investigated the way that banking supervisors could utilize sentiment analysis to risk assessment. They used CEO letters and report outlook sections from banks. In their study, the lexicon-based analysis showed that the sentiment score indicates major economic events between 2002 and 2014. Results suggested that 79% of CEO letters are correctly classified using the supervised risk classification approach. Opinion mining in the finance domain is still in the early stage [14] because many of the authors focused only on the course-grained market sentiment analysis, but finance as the other industries have different products and services. Banks have products like loans, deposits, credit cards, etc. that can be a focus of sentiment analysis. 2.3 Research Objectives This research aims to address inquiries related to the effectiveness of machine learning algorithms in conducting opinion mining on financial texts. Opinion mining is conducted on the document level for generating one opinion on the whole document. After data preparation and text summarization using the TF-IDF algorithm three machine learning algorithms (Random Forest, Multilayer Perceptron - MLP, and Support Vector Machine - SVM are used to perform the classification of economic headlines’ sentiments into negative, neutral, and positive. The paper provides empirical evidence that sentiment analysis techniques can be effectively applied to financial texts, which could lend credibility to sentiment analysis as a valid approach to financial analysis. The paper presents a Voting Ensemble model that achieves higher accuracy in sentiment analysis on financial texts, this could be a significant contribution to the field of natural language processing and financial analysis.
3 Research Methodology In this study, we employed 4 machine learning and statistical methods (Random Forest, Naïve Bayes, and Support vector machine algorithms and Voting algorithm) to test their predictive performances on sentiment prediction. In the second step, we combined the three best-performing algorithms in a voted manner to increase their performance.
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3.1 Proposed Method A dataset with financial tests is used in this research. In data, preprocessing regular expressions are used for cleaning data. For the text summarization step, the TF-IDF algorithm is used for removing strange data and data vectorization. Furthermore, the dataset is split into train and test datasets. In this paper, authors use machine learning algorithms such as SVM, Random Forest, Naïve Bayes, and Voting machine algorithms to predict sentiment on financial text datasets. In Sect. 3.5 each algorithm is explained in more detail. Finally, accuracy, precision, recall, and F1 score are used for performance evaluation as shown in Fig. 1.
Fig. 1. Proposed Method
3.2 Dataset In this research, two sentiment analysis datasets are used. The first dataset used in this study is presented 4.846 documents from financial news and company releases that are tagged with sentiment [9]. Sentiment distribution is disposed of 2.879 neutral, 1.363 positive, and 604 negative sentiments. Second dataset on sentiment analysis regarding economic headlines [15]. The second dataset’s sentiment distribution is disposed of 3.130 neutral, 1.852 positive, and 860 negatives. Both datasets have two features: sentiment and text.
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3.3 Data Preparation Initially, datasets are imported and concatenated to a mutual dataset. As both datasets have the same features, they are easily concentrated into a mutual dataset. Then the dataset is checked for missing values. Further analysis of the dataset is done by counting sentiments in a dataset. For a better presentation a bar chart is used. Python was used for data analysis in this research, as it offers stable numerical libraries with great quality in open-source documentation access. More specifically, the authors used the pandas’ library for financial data manipulation and analysis and Natural Language Toolkit (NLTK) to work with human language data [16] (Fig. 2).
Fig. 2. Dataset sentiment distribution.
Dataset sentiment distribution is highly imbalanced, and the authors used a resampling method for balancing the dataset to make better predictive performance [17]. Sklearn.utils.resample [18] is a Python library provided by the Scikit-learn (sklearn) machine learning framework. This library provides a method for resampling imbalanced datasets. Resampling refers to the process of creating new samples by duplicating or deleting existing samples from the dataset. As observed previously, the negative sentiment has the lowest frequency in the dataset. One approach to balancing the dataset is to convert the counts of the other two sentiments to match that of the negative sentiment (Fig. 3). As real-life data collected from different people’s comments contain wrong spellings, special characters, and there is a need to remove non-useful information from text [19]. Authors used regular expression [20, 21] sub-function to remove non-useful characters. 3.4 Text Summarization Natural Language Processing (NLP) text summarization is the process of automatically generating a reduced version of a longer text while preserving the most important information and the overall meaning of the original content. This technique is used to extract the most relevant information from a large amount of text data, making it easier for users
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Fig. 3. Resampled dataset sentiment percentage presentation.
to read and comprehend. There are two main approaches to NLP text summarization: extractive and abstractive. Extractive summarization involves selecting the most important sentences or phrases from the original text and combining them to create a summary. This approach relies on statistical methods, such as TF-IDF, to identify the most relevant sentences or phrases in the text. Abstractive summarization, on the other hand, involves generating a summary that is not necessarily based on the exact sentences or phrases in the original text. Instead, it relies on advanced machine learning techniques, such as deep neural networks, to understand the meaning of the text and generate a summary that captures its essence. Term Frequency-Inverse Document Frequency (TF-IDF) is an algorithm used in natural language processing to measure the importance of a term in a document or corpus. It is commonly used in information retrieval and text mining to extract relevant information from a large amount of text data [22]. Term Frequency-Inverse Document Frequency (TF-IDF) is a statistical measure used to evaluate how important a word is to a document in a collection of documents. TF-IDF is calculated by multiplying two measures: Term Frequency (TF) and Inverse Document Frequency (IDF). The formula for calculating the TF-IDF score of a term t in document d is as follows: TF − IDF(t, d ) = TF(t, d ) ∗ IDF(t)
(1)
where TF (t, d) is the term frequency of the term t in document d. It is calculated as the number of times the term appears in the document divided by the total number of terms in the document. IDF(t) is the inverse document frequency of the term t. It is calculated as the logarithm of the total number of documents in the collection divided by the number of documents containing the term t. Inverse Document Frequency (IDF) measures how important a term is to the entire collection of documents. The TF-IDF algorithm is useful for various natural language processing tasks, including information retrieval, text classification, and keyword extraction. It helps to identify the most important terms in a document or corpus and provides a way to rank them based on their relevance to a specific topic or query.
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3.5 Machine Learning Models After data vectorization is applied the dataset is divided into training (80%) and testing (20%). Predictive accuracy is tested on three machine learning algorithms individually and in an ensemble using the voting classifier. Naïve Bayes A Naive Bayes classifier is a probabilistic machine learning model that’s utilized for a class assignment. It is based on Bayesian Theorem and helps us to realize the probability that A happens given that B previously happened. We can write it as: Pr(
(Pr( Xc ) ∗ Pr(c)) c )= X Pr(x)
(2)
The premise made the predictor’s features independent. That presence of one point doesn’t affect the other. Therefore, it’s called naïve and rarely can be found in real-world examples. Support Vector Machines It’s a supervised machine-learning technique that is associated with regression and class problems. The idea of the support vector machine algorithm is to find a hyperplane in an N-dimensional space by maximizing the margin. Hyperplanes are decision frontiers that assist classify the data spots. The dimension of the hyperplane depends on the number of data features. Support vectors are data spots that are nearer to the hyperplane and impact the hyperplane’s location and direction. Random Forest Random Forest is a popular supervised machine learning technique that is based on the ensemble of many individual decision tree classifiers. Individual decision trees are used as a majority voting method for making the Random Forest model simple but very powerful [23]. Voting Ensemble A voting ensemble is a machine-learning technique that combines the predictions of multiple models to produce a final prediction. In a majority voting ensemble, the final prediction is determined by the majority vote of the individual model predictions. Here’s how it works: 1. Train multiple machine learning models on the same training data. 2. When making a prediction, each model predict the outcome. 3. Determine the final prediction by taking the majority vote on the individual model predictions. The advantage of using a voting ensemble is that it can help improve the accuracy and stability of the final prediction by combining the strengths of multiple models.
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4 Results 4.1 Performance Evaluation The confusion matrix recaps the performance of algorithms we have been applying to our testing dataset. In our research, there are three possible output classes (positive, negative, and neutral). The confusion matrix classification output is presented in Table 1. Table 1. Confusion Matrix. Confusion matrix
Predicted class
Actual class
Positive
Negative
Neutral
Positive
True positive (TP) False negative 1 (FNg1)
False neutral (FNt1)
Negative
False positive 1 (FP1)
True negative (TNg)
False neutral 2 (FNt2)
Neutral
False positive 2 (FP2)
False negative 2 (FNg2)
True neutral (TNt)
The accuracy gives an overall accuracy of the model, that is a fraction of the total items that are correctly classified by the model. Accuracy =
(TP + TNg + TNt) (TP + TNg + TNt + FNg1 + FNt1 + FP1 + FNt2 + FP2 + FNg2) (3)
The precision represents the ratio of true positives (TP) and actual positives. Precision =
TP TP + FP1 + FP2
(4)
The recall or true positive rate (TPR) is the ratio of true positives (TP) and actual positives, which measures the fraction of actual positives that are correctly recognized so. Recall =
TP TP + FNg1 + FNt1
(5)
The F1 score combines precision and recall to a single measure. It can be calculated by the formula below. F1 − score = 2 ∗
Precision ∗ Recall Precision + Recall
(6)
F1-score is one of the most used algorithm performance matrix rates [24] for evaluating machine learning algorithm performance.
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Table 2. Machine learning algorithms’ evaluations. Algorithm
Accuracy
Sentiment
Precision
Recall
F1-score
Random Forest
0,71
negative
0.71
0.76
0.74
neutral
0.65
0.75
0.70
positive
0.76
0.61
0.68
negative
0.70
0.72
0.71
neutral
0.63
0.80
0.71
positive
0.79
0.58
0.67
negative
0.57
0.78
0.66
neutral
0.58
0.59
0.58
positive
0.66
0.40
0.50
negative
0.69
0.79
0.74
neutral
0.65
0.78
0.71
positive
0.80
0.56
0.66
SVM
Naïve Bayes
Voting Ensemble
0.69
0.59
0.71
4.2 Experimental Results Table 2 results of machine learning algorithms are presented using accuracy, precision, recall, and F1-score.
5 Discussion Results of machine learning algorithms performance for accuracy rates are between 0.59 for NB to 0.71 for Voting Ensemble and SVM. Precision rates for negative opinion are between 0.57 for NB to 0.71 for Random Forest. Neutral opinions’ precision rates are between 0.58 for NB to 0.65 for Random Forest and Voting Ensemble. Positive opinions’ precision rates are rated between 0.66 for NB to 0.80 for Voting Ensemble. Recall rates on negative opinion are between 0.72 for SVM to 0.79 for Voting Ensemble. Recall rates for neutral sentiments are distributed from 0.59 for NB to 0.80 for SVM. Positive sentiment recall rates are presented between 0.40 for NB to 0.61 for Random Forest. Results of the algorithms’ performance showed that F1-score rates for negative sentiment are presented between 0.66 for NB and 0.74 for Random Forest and Voting Ensemble. F1-score rates for neutral opinion are organized between 0.58 for NB and 0.71 for SVM and Voting Ensemble. Positive opinion F1-score rates are distributed between 0.50 for NB to 0.68 for Random Forest. The analysis confirmed the research objective that the Voting Ensemble algorithms slightly increased performance of individual algorithms’ performance. Semantic analysis the implicit to be an important tool to enhance decision-making in finance because it excerpts and identifies patterns in economic news. One of the operations of understanding the feelings of economic textbooks consists of forecasting the outcomes of fiscal indicators. This reduces threats and queries for investors [25].
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The contribution of papers on sentiment analysis in financial texts is of utmost importance for various reasons. Firstly. Financial markets are highly dynamic and sensitive to the slightest changes in economic political and social factors. Sentiment analysis techniques can help identify the underlying sentiment of financial texts. Which can aid in predicting market trends and making better investment decisions. Moreover sentiment analysis can also help identify and mitigate financial risks. By analyzing the sentiment of financial news. Reports and social media companies can detect negative sentiment toward their products or services and take corrective measures before any significant damage occurs. The paper’s contribution to the field of sentiment analysis on financial texts is essential because it provides a more accurate and efficient way of analyzing sentiment. Traditional methods of sentiment analysis. Such as manual annotation or lexicon-based approaches are time-consuming and often unreliable. The paper’s proposed approach can analyze sentiment more accurately and efficiently by leveraging advanced natural language processing and machine learning techniques. The paper’s contribution is also significant because it provides a framework for sentiment analysis on financial texts that can be applied in various domains. Such as banking investment and insurance. By providing a standardized approach. The paper can help promote consistency and accuracy in sentiment analysis across different applications and industries.
6 Conclusion The rapid growth of Internet use increased faster information distribution among people in society. People’s opinion affects many economic activities like stock market prices and investors’ decisions. In this research Sentiment Analysis for NLP and Machine learning algorithms are used to collect. Preprocess economic new texts and categorize them into positive negative and neutral categories. TF-IDF algorithm is used for text summarization. In testing the predictive performance of Machine Learning algorithms Ensemble Voting classification algorithm showed the best overall accuracy in predicting the economic text opinion category. As data collected from different sources is exponentially growing the need for sentiment analysis will continue to increase in the future. In conclusion the paper’s contribution to the field of sentiment analysis on financial texts is of critical importance. By providing a more accurate and efficient way of analyzing sentiment. The paper can aid in predicting market trends mitigating financial risks and promoting consistency and accuracy in sentiment analysis across different domains.
References 1. Dogru, H.B., Tilki, S., Jamil, A., Ali Hameed, A.: Deep learning-based classification of news texts using Doc2Vec model. In: 2021 1st International Conference on Artificial Intelligence and Data Analytics CAIDA 2021, pp. 91–96, April 2021 2. Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, vol. 5. no. 1, pp. 1–184. Springer Cham (2012). https://doi.org/10.1007/9783-031-02145-9
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Emotion Detection Using Convolutional Neural Networks Abdullah Bjelak(B)
and Ahmed Selimovi´c
International Burch University, Francuske Revolucije Bb, 71210 Ilidža, Bosnia and Herzegovina [email protected]
Abstract. Machine learning is growing every day, with improvements in existing algorithms, thus making them more applicable for real life scenarios. It has made huge impact on everyday life that the majority of mobile applications have some kind of machine algorithm integrated in their structure. Convolutional neural networks are a great tool for image processing, and in this work we developed the model for emotion detection. Every machine learning algorithm requires a dataset in order to be trained and validated. For this specific case, we utilized publicly available FER-2013 dataset that contains seven emotions: angry, disgust, fear, happy, neutral, sad and surprise. Ratio between train and validation images is 80:20, respectively. For model optimization we proposed Adam optimizer, alongside other techniques for preventing overfitting during training and saving the best weights. Model achieved the training accuracy of 71.55%, and the validation accuracy of 61.4%. We utilized NVIDIA GeFroce MX350 GPU to train and validate the model, resulting in much shorter training time, which was cca 20 min. Keywords: Mobile Applications · Machine Learning · Image Processing · Deep Learning · Convolutional Neural Networks · Adam Optimizer · FER-2013 Dataset · Emotion Detection · GPU
1 Introduction One of the reasons for the growth of artificial intelligence (AI) is to enable an easier and more natural human-computer interaction (HCI) [9]. 1.1 About Machine Learning Machine learning (ML) is one of its many branches, where computing devices are enabled to make conclusions without human interaction. ML algorithms found their application in almost every branch known today and due to their efficiency, the analysis processes can be conducted in much shorter time. There are two main types of ML: supervised and unsupervised. In supervised learning the algorithm processes labeled input data, with each instance belonging to a specific category. During the training process, based on the input data, an output is produced in form of a vector. The goal is to make proper connections between inputs and outputs. An error is taken as a measurement of the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 263–279, 2023. https://doi.org/10.1007/978-3-031-43056-5_21
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difference between the output score and the desired pattern of scores. In order to reduce the error, the inner adjustable parameters, often called weights, require adjusting [3]. Bigger part of ML algorithm deployment is used in designing preprocessing and data transformations, resulting in data representation being able to support ML [1]. ML algorithms such as support vector machines, Bayesian classifiers, etc. do not have the desired flexibility to perform classification on images outside of the database they were previously trained on [22]. Neural networks (NN) have the ability to extract undefined features from the training database, thus making them perfect candidates for image processing [16]. Common NN consists of neurons, connected processors that produce sequences of real-valued activations. Input neurons are activated through environment perceiving sensors, and remaining neurons are activated through weighted connections from previously active neurons. The main task is to calculate weights that will enable neural networks to operate in a desired way [2]. 1.2 Deep Learning Deep learning (DL) is utilized as a representation-learning method with multiple representation levels obtained by composing simple, non-linear modules, each transforming the representation from lower to higher level [5]. This enables learning of very complex functions [3]. Compared to other ML algorithms, DL achieves robustness and far better performance. Its other dominant characteristic is scalability on new data types. Deep neural networks (DNNs) are composed of multiple levels with non-linear operation components, thus enabling models to learn more useful features, resulting in improved classification and prediction accuracy. In order to minimize cross-entropy loss, the softmax activation function for prediction is commonly used [9]. 1.3 Convolutional Neural Networks Convolutional neural networks (CNNs) process the input data in the form of multiple arrays, such as images which are 2D arrays. Due to their local connections, shared weights, pooling, and the many layers usage, they are a perfect choice for natural signal processing [3]. The aforementioned layers are: convolutional layers, max-pooling layers, and fully-connected layers, each consisting of diminutive neurons for image processing in portions that are the receptive fields. CNNs have three-dimensional neuron characteristics: height, weight, and depth (number of layers). First two stages of CNNs are composed of convolutional and pooling layers, performing feature extraction. They bring the nonlinearity into the network and reduce feature dimension. Inside the convolutional layer, units are organized in feature maps, with each unit being connected to local patches in the feature maps of the previous layer through a filter bank (set of weights). This result is passed through a non-linearity line Rectified Linear Units (ReLU) [18]. The feature map performs discrete convolution, thus the name for this type of NN. Convolutional layer detects local conjunctions of features from the previous layer, and the pooling layer merges semantically similar features into one, i.e. input classification, which are extracted features by other layers [9].
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Due to a large number of filters in convolutional layer, the pooling layer reduces the increased amount of processed data in the network [4]. The coarse features are learnt in the first couple of layers, while more complex features are learnt in the deeper layers [19]. 1.4 Facial Expression Recognition One way to make human machine interaction systems rich and robust is to enable them to recognize facial expressions [16]. Humans interact with each other mainly through speech, body language, and facial expressions. Considering all types of communications, facial expressions are the most natural, meaningful and important type of communication [9], and can be interpreted much faster than verbal communication. They are highly dependent on the current emotional state of the individual [19, 20]. In further text, we will refer to this as just “emotion”. Emotions can be described as biological states that are caused by thoughts, feelings, behavioral responses, and a degree of pleasure or displeasure [6, 7]. They reflect the inner state of a living being, and are influenced by mood, temperament, creativity [8], personality, disposition, and motivation [10]. In most research, seven emotions have been labeled as general: happy, shocked, angry, sad, fearful, disgusting and natural [18]. Due to its complexity and subtlety of facial expressions and their relations to emotions, emotion detection is quite a demanding task. For these models, deriving an effective and robust facial representation from raw faces is crucial [17]. Due to its effective feature extraction, and later application on the unknown data, CNNs are the perfect candidates for this type of classification.
2 Problem Definition In this chapter, we divided the emotion detection problem into several diminutive problems, for the purpose of explaining the process of building, training, and validating CNN model. The first step is acquiring labeled image dataset containing the seven basic emotions and feeding it to the CNN model. The second step is choosing the proper model architecture. Instead of using pretrained model, we decided to build our own. This task requires establishing order of layers, choosing activation functions and parameters, and applying optimizers. Choosing development environment that offers valuable and functional modules can assist in gaining better understanding of how training and validating CNN model is being conducted, as well as easier application of effective methods for obtaining better results, hence its choice is very important.
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Optimizers, as their name indicates, assist in optimizing training process, resulting in higher accuracy and lower loss values. Choosing the proper optimizer is also one of the problems to be solved. CNNs tend to overfit during the training, and this is the problem that occurs frequently as a result when a classifier tries to memorize the training data, rather than determining the underlying function. The classifier loses its ability to perform generalization on data not present in the training dataset [22]. CNNs demand large amount of resources, thus limiting them for wider application [4]. With the recent development of hardware components with enhanced computational performances, we can overcome this problem. In the next chapter, we analyzed related work that assisted us with solving aforementioned problems. After Literature Review, solutions to the aforementioned problems are presented in chapter 4. We presented the application of these solutions in chapter 5, alongside training and validation results, confusion matrix and AI metrics results. At the end of chapter 5, we gave comparisons of results from this paper with the ones from the literature review in terms of accuracy only. In the conclusion, we gave an overview of the entire work and recommendations for improvements and future work.
3 Literature Review In the work of Giannopoulos et al. [9], there can be found a very good retrospective of related work. Some of the most interesting studies in their work were very useful for our research. Five human emotions are recognized in the system made by Aung and Aye [10], and they are as follows: happiness, anger, sadness, surprise, and neutral. Emotion recognition is based on the geometrical characteristics of the human mouth. Recognition accuracy reached a total of 81.6%. In this survey, the expression classification method was based on histogram sequence of feature vectors. Murthy and Jadon [11] created a method for facial recognition that recognizes six emotions: sadness, anger, disgust, fear, happiness, and surprise. By utilizing a dimension reduction technique and Eigen spaces, they achieved a recognition accuracy of 83%. Thai et al. presented a new approach for facial recognition using Canny, principal component analysis technique for local facial feature extraction. Besides this, an artificial neural network was also used, achieving an average classification accuracy of 85.7% [12]. Utilization of a multilayer perceptron in neural network approach was used to specify and extract facial features in the research done by Perikos et al. [13]. Pˇrinosil et al. [14] utilized preprocessing techniques: Gabor filters, linear discrimination analysis, and principal component analysis for feature extraction. These techniques were utilized for recognition of four basic emotions, achieving outstanding 93.8% of recognition accuracy. Tang in his work [15] presented an L2-SVM algorithm, with the main objective of training deep neural nets. Lower layer weights are learned by back propagating the
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gradients from the top layer linear SVM. An accuracy of 71.2% was achieved on the FER-2013 dataset. A deep network that consists of two convolutional layers, each followed by max pooling and then inception layers is presented in the work of Mollahosseini et al. [16]. The network is a single component architecture that registers facial images as the input and classifies them into seven facial expression groups. Using the FER dataset, an average accuracy of 66.4% was achieved. In the research conducted by Yanan Guo et al. [17], a method called Deep Neural Networks with Relativity Learning (DNNRL) was applied which directly learns mapping from original images to a Euclidean space, where relative distances correspond to a measure of facial accuracy of more than 70%. Ramdhani et al. [18] developed an emotion recognition system using Convolutional Neural Network and training it on two datasets that are FER-2013 and their own dataset. They recognized four emotion expressions: happy, disappointed, angry, and natural. With the batch size of eight, they achieved better results on the configuration made by the researchers with accuracy of 73.89% while achieving 58.25% accuracy using FER-2013 dataset. Raksarikorn and Kangkachit [19] proposed a deep CNN model, inspired from XCEPTION, for classifying seven facial expressions: angry, disgust, fear, happy, sad, surprise, and neutral. Using the batch size of 128, they achieved an accuracy of 69.1% on the FER-2013 dataset. In the study conducted by Shafira et al. [20] two datasets were used, namely the FER-2013 and CK + datasets. This study utilized the Histogram of Oriented Gradient (HOG) feature and Local Binary Pattern (LBP) feature in the feature extraction stage. In the classification stage, the Extreme Learning Machine (ELM) classifier was used. The greatest accuracy achieved was 63.86% using HOG features with the FER-2013 dataset. The greatest accuracy by using LBP features was 55.11% for the FER-2013 dataset. Kingma et al. [21] proposed an Adam optimizer, a stochastic gradient descent for optimizing neural networks. They analyzed the local and global convergence of Adam. A series of conducted experiments confirmed that Adam performs well and even better compared to other stochastic optimization techniques. Mayer et al. [22] emphasized the problems that commonly occur in facial recognition systems and focused their research on preventing generalization and overfitting. They applied Candide-III face model, an effective facial extracting method. Due to the generalization that occurs during the application of facial extractors on one dataset, they applied them on multiple datasets in order to achieve robustness. They used SVM for performing final facial expression recognition.
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4 Problem Solution 4.1 Choosing Development Environment We decided to use Python programming language as a programming language for training and validating emotion detection CNN model. It has its applications as a tool for web development, process automation, data visualization, machine learning, etc., with many open-source modules such as TensorFlow which is common and useful with NN, and we highly recommend it. Jupyter Notebook is a programming environment where we compiled the python code. It is very interactive and user friendly, enabling users to compile pieces of code separately, thus saving us the trouble of compiling the entire program after every modification. 4.2 FER-2013 Image Dataset Kaggle website offers many publicly available datasets, along codes and examples, and it is this very website where we found image dataset used in this work. It is called FER2013, and was introduced by Pierre-Luc Carrier and Aaron Courvill at the International Conference on Machine Learning (ICML) in 2013. The dataset consists of grayscale images, 48 × 48 pixels in size, with seven emotions: angry, disgusted, fearful, happy, neutral, sad, and surprised. The ratio of training and validation images is 80:20, respectively, with 28709 training images, and 7178 validation images. Number of images per classes, as well as per training and validation dataset is given in Table 1. Table 1. Distribution of images in FER-2013. Emotion
Train
Angry
3995
Disgusted
Validation 958
436
111
Fearful
4097
1024
Happy
7215
1774
Neutral
4965
1233
Sad
4830
1247
Surprised
3171
831
“Happy” emotion contains the most images, while “Disgusted” emotion contains the least images. At the commencing point, it is expected of classification of “Happy” emotion to reach the highest accuracy, since it contains the most images, while for classifying “Disgusted” emotion is expected to reach the lowest accuracy, since this class contains the least amount of images. It does not necessarily have to turn out this way, however, uneven distribution can affect in higher, or lower classifying accuracy.
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In order of us to present a brief insight in FER-201, some of the images are shown in Fig. 1, three for every emotion.
Fig. 1. FER-2013 Image Dataset.
4.3 Creating CNN Architecture We created model from the scratch. The model was built as a Sequential model, where the input layer is convolutional layer, with shape of 48, 48, 1. First two numbers represent image size, and the last number indicates processing of grayscale images. Processing grayscale images requires less resources compared to RGB images. We set the batch size of the input layer to 32, with kernel size of 3 by 3. After convolution, the activation layer uses a nonlinear ReLU activation function in order to eliminate the negative value by using a threshold of 0 to infinity. The equation of ReLU activation function is given in (1), where x represents the input value [18]. f (x) = max(0, x)
(1)
The ReLU operates as the half-wave rectifier, and it learns much faster in multilayer networks, thus allowing training of a deep supervised network without unsupervised pre-training [3]. It assists in avoiding the vanishing gradient problem caused by other activation functions [16]. The next layer is another convolutional layer, with a larger batch size of 64. It is followed by the MaxPooling layer, used for reducing the amount of processed data due to large number of filters inside convolutional layer, followed by the Dropout layer which randomly sets input units to 0 with specific frequency in order to prevent overfitting. Neurons for elimination and temporary elimination are randomly selected. The similar analogy is applied in the second part of the network, with convolutional layer with batch size of 128, followed by MaxPooling layers, and again in this order, ending with the Dropout layer. The third and final part of architecture consists of the Flat layer for flattening the inputs, followed by the Dense layer. After this Dense layer, there is one final Dropout
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layer, followed by the Dense layer at the end with softmax activation function which performs emotion classification, based on inputs received from previous layers. Softmax activation function is commonly used in the output layer for representation of category distribution [18], with the following equation shown in (2) (Fig. 2). ex
yj = K
Tw j
k=1 e
xT wk
(2)
Fig. 2. Visual representation of the used CNN architecture.
4.4 Saving Best Weights There are four factors describing the CNN model training and validation: training accuracy, validation accuracy, training loss, and validation loss. The loss indicates how well the model is doing for training and validation, representing the sum of errors made for each instance in training and validation sets. For classification tasks, this function resembles negative logarithm. Lower values of the loss results indicate better network performance. With the decrease of training loss and validation loss values, the model understands the image data pattern. The pattern’s training encounters difficulties with the increase of the loss value. Accuracy is determined after learning and fixating parameters, where no further learning is conducted. Validation samples are fed to the model and the recording of
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mistakes begins, i.e. comparing the model’s calculations with the true values. The higher value of accuracy indicates better performance of the model. We focused on validation loss, observing in which epoch this value is at its minimum. There is a method in the TensorFlow module which enables us to perform this operation, and it is called Model Checkpoint. This method compares the value of validation loss in the current epoch during the training process with the values of validation loss from previous epochs, and if that one turns out to be lowest so far, the weights calculated in that epoch will be stored. This is an efficient way of saving model weights during its peak performance. An example of this method used in Python programming language is given in the following line: ModelCheckpoint(‘emotion_best_model.h5’, monitor = ‘val_loss’, mode = ‘min’, save_best_only = ‘True’, verbose = 1) The model will be saved if the boolean function parameter save_best_only is set to True, under the name of emotion_best_model in the.h5 format on the local device, if the validation loss value that is being monitored is at its minimum (determined by the mode variable in function parameters). 4.5 Prevent Overfitting One of the ways to prevent overfitting, besides aforementioned usage of Dropout layer is terminating the training process as soon as no improvements have been made in terms of loss and accuracy. We utilized the method called Early Stopping to automatically terminate the training process, without supervisor’s interference, in the case the validation loss value has not dropped through 15 consecutive epochs. The following line shows how this function is utilized in Python: EarlyStopping(monitor = ‘val_loss’, patience = 15, verbose = 1, mode = ‘min’) 4.6 Choosing Computing Device CNNs process data in batches, performing large matrix operations, thus affecting the memory and energy consumption. In this work, we trained and validated CNN model on the GPU. Their massive parallelism, alongside the ability to process data in batches make them desirable for working with CNNs. TensorFlow enables usage of NVIDIA GPUs for training CNNs, thus speeding up the entire process. GPU achieves outstanding performances at the fast matrix and vector multiplications, resulting in 50 times and even more faster learning process. GPU-based computers nowadays have a million times the computational power of desktop machines at the beginning of the 1990s. Due to this, propagating errors a few layers further down within reasonable time is possible, even in traditional NNs [2]. The rapid development of GPUs with affordable prices made usage of DL realistic [18]. Most modern laptop devices have integrated NVIDIA GPUs, and we used the NVIDIA GeForce MX350 that comes with flat laptop devices. With 2.0 GB of dedicated memory storage, it proved itself to be sufficient for processing low quality image data such as FER-2013. However, when working with bigger data and higher quality images, we recommend utilizing GPUs with more dedicated memory storage.
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4.7 Configuring Training and Validation Parameters We set our emotion detection model to be trained through 70 epochs, and validated at the end of each epoch. During the model training, we applied an optimization technique called Adam optimizer, a stochastic gradient descent method. This optimizer is based on adaptive estimation of first and second-order moments. It is computationally efficient, requiring little memory, invariant to diagonal rescaling of gradients, and very well suited for large data or parameters [21]. The example of compiling our model is given in the following line: emotion_model.compile(loss = ‘categorical_crossentropy’, optimizer = Adam(learning_rate = 0.0001, decay = 1e-6), metrics = [‘accuracy’]) We named our model emotion_model. Categorical crossentropy is a function used for multi-class classifications tasks representing loss, and Adam is determined by the learning_rate and decay values. The python code that demonstrates how our model is trained, then validated at the end of each epoch is given in the following line: emotion_model.fit(train_generator, steps_per_epoch = 28709 // 64, epochs = 70, validation_data = test_generator, validation_steps = 7178 // 64, callbacks = [ec, mc]) We set steps_per_epoch to the total number of training images divided by the batch size and applied the same principle on validation_steps. We previously loaded train_generator and test_generator and preprocessed training and validation images, respectively, where each pixel is dived by the value of 255, thus obtaining the 2-D vectors with the values between 0 and 1. Convolutional layers extract features by using vast number of filters, and we did not apply any additional feature extraction methods. Aforementioned GPU is used for enhancing the training speed. We set the batch size to 64, following the recommendations of setting this number to the power of 2 in order to fully utilize the GPU.
Emotion Detection Using Convolutional Neural Networks
Algorithm 1 Model Training and Validation Process while current_epoch < 70 do Train Model Validate Model if val_loss is minimal then Save Model Weights Restart Patience Counter else Increase Patience Counter
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#Using training images #Using validation images #ModelCheckpoint
if patience_counter > 15 then Terminate the Process
#EarlyStopping
5 Results and Discussion 5.1 Training and Validation Results The best validation loss (red dotted line) value was calculated in the 27th epoch as shown in Fig. 3, and it is equal to 1.06024. The training loss value (blue dotted line) calculated in this epoch was equal to 0.7824. Regarding the training accuracy (blue dotted line) and validation accuracy (red dotted line) the values of 71.55% and 61.40% were calculated, respectively, as shown in Fig. 4. It is noticeable that after the 27th epoch the model overfits too much, and the gap between training accuracy and validation accuracy, as well as the gap between training loss and validation loss keeps increasing. The training process was terminated in the 42nd epoch, due to EarlyStopping, thus saving on time and energy consumption. The average time per epoch was around 27 s, resulting in a total of cca 20 min of model training. 5.2 Confusion Matrix and AI Metrics The confusion matrix defines performance of a classification task, where predicted values (columns) are compared to the true values (rows). We used validation images for plotting confusion matrix and obtaining the precision for every emotion. Diagonal values are more highlighted compared to other values, confirming that most of the predicted values match with the true values (see Fig. 5). “Angry” emotion had most misclassifications as sad. These emotions can be classified as “negative” that share common characteristics, such as shape and position of eyebrows and lips. The other factor that contributes to this misclassification is a low-quality image dataset.
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Fig. 3. Training and validation loss through epochs.
Fig. 4. Training and validation accuracy through epochs.
“Disgusted” showed surprisingly well results, despite having the least images. This emotion was often incorrectly detected as angry, again due to the common features and low quality of images.
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Fig. 5. Confusion matrix.
“Fearful” was classified most of the time properly, while also being very often misclassified as sad. “Happy” emotion shows characteristics that are easier to detect compared to characteristics of other emotions, such as gentle wrinkles around eyes, slightly risen eyebrows followed by a smile. Not just that, however this class has the greatest number of images, thus enabling our model to learn better. The AI metrics results for every class is given in Table 2. While verifying the predictions, four values determine AI metrics: • • • •
True Positive – the instance was positive, and predicted as positive True Negative – the instance was negative, and predicted as negative False Positive – the instance was negative, and predicted as positive False Negative – the instance was positive, and predicted as negative
Precision is defined as the number of true positive predictions, divided by the sum of true positives and false positives. This attribute indicates the model’s ability to identify only the relevant data points. The highest precision occurred while classifying “Happy” emotion, while the lowest prediction occurred during classification of “Sad” emotion. Recall is the result of true positives divided by the sum of true positives and false negatives. It represents the model’s ability to find all the relevant cases within a dataset. “Happy” emotion achieved the highest value of recall, while the “Fearful” achieved the lowest value of recall. F1-Score calculates the harmonic mean of both precision and recall, thus establishing balance between the precision and the recall. It punishes the extreme values. “Happy” emotion shows the highest value of F1-Score, due to the high and equal values of recall and precision for this emotion, i.e. our model does not show any sign of issues while detecting relevant and truly relevant features of “Happy” emotion. The biggest differences between recall and precision occurred in “Disgusted” and in “Fearful” emotions, i.e. the emotion model experienced difficulties while detecting relevant and truly relevant features during classification of these two emotions. Support represents the number of appearances of specific class inside the dataset. Imbalanced support such as one present in Table 2 indicates the need for stratified sampling or rebalancing.
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Recall
F1-Score
Support
Angry
0.51
0.56
0.54
877
Disgusted
0.69
0.43
0.53
103
Fearful
0.52
0.34
0.41
931
Happy
0.82
0.82
0.82
1591
Neutral
0.56
0.59
0.57
1114
Sad
0.46
0.56
0.51
1102
Surprised
0.76
0.74
0.75
746
0.62
6464
Accuracy macro avg
0.62
0.58
0.59
6464
weighted avg
0.62
0.62
0.61
6464
5.3 Comparisons with Related Work In Table 3, we present comparisons between results in this work and results in related works. We made comparisons based on the accuracy alone, since not all AI Metrics is provided in related work. We considered only papers that utilized CNNs. Models that are trained on JAFFE dataset achieved higher accuracy compared to the ones trained on the FER-2013 dataset. The JAFFE (Japanese Female Facial Expression) database consists of 213 images of 10 Japanese female models [12], 256 × 256 pixels in size. This dataset contains 6 basic emotions: happiness, anger, fear, sadness, surprise, and disgust, plus a neutral expression [14]. Higher quality images require more resources for processing, however the better results are obtained compared to low quality images. Canny_PCA_ANN improved the classification accuracy, with Canny and PCA being applied for extraction of local features (eyebrows, eyes, and mouth), reducing the space for image representation. This space reduction focused on important features only, resulting in better accuracy. Training time is reduced by using ANN for classifying PCA representations. This method required a high calculating cost for the training process [12]. It is shown how important feature extraction is for classification results. PCA and LDA are used for feature extraction, however they are affected by illumination variations, which was overcome by applying Gabor filters in [14]. Classification was performed on only 4 emotions: anger, happiness, surprise, and sadness. Alongside having higher quality images in the dataset, decreasing the number of classes, as well as using powerful feature extraction tools, delivers higher accuracy. Deep neural networks with relativity learning (DNNRL) [17] contain inception layers, thus increasing the width and depth of the network, and improving local feature recognition without increasing computational cost. In [18], the researchers applied similar methods to one in [17], with batch sizes of 8 and 128, reaching the accuracy of 58.25% and 69.10%, respectively, i.e. increase in batch size impacts the accuracy in the positive way. They trained their model through higher number of epochs, thus increasing overfitting.
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Unfortunately, due to a restricted access to JAFFEE dataset, we were not able to obtain it and train our model on it. Many datasets have restricted access and are publicly not available, due to their importance and confidential information they contain. The obtained model does not require large amount of memory, only 26.9 MB, and it is easy to load for later application in detecting emotions on images, or in real time footage from a camera. The problem regarding uneven distribution of images or a diminutive number of them could be solved by applying data augmentation. Table 3. Comparisons with related works. Work
Dataset
Utilized Methods
Accuracy
This paper
FER-2013
CNN (proposed architecture) + Adam Optimizer
61.4%
Thai et al. [12]
JAFFE
Cany_PCA_ANN
85.7%
Pˇrinosil et al. [14]
JAFFE
Gabor (PCA & LDA)
94%
Yanan et al. [17]
FER-2013
DNNRL
71.33%
Ramdhani et al. [18]
FER-2013
Inception Layer CNN Architecture (batch sizes of 8 and 128)
58.95% and 69.1%
6 Conclusion Our motivation for conducting this research was to write an introductory paper for audiences interested in ML, and to also present a huge potential of CNNs in image classification. We explained the entire process of developing CNN model: building CNN model architecture, training and validating it on a certain data, finding best parameters, optimizing the training process, and preventing overfitting. By presenting confusion matrix and AI Metrics result we observed how well our model classified each class. It was also shown how the GPU affects model training by speeding up computationally demanding matrix operations. The model achieved decent accuracy, which was 61.4%. This accuracy could be improved through several ways, such as applying more advanced feature extraction algorithms, or by obtaining higher quality image dataset with evenly distributed images. However, higher quality images demand more complicated matrix computations, thus we recommend using advanced GPUs. This work is open for further improvements, like applying more advanced and effective feature extraction methods, post training optimization methods such as network pruning or network quantization, etc. Its applications in real life could be as a tool for improving customary service by detecting customers’ emotion, making HCI more natural e.g. in automated vehicles for making the travel more enjoyable to travelers, etc.
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From Olympics to War – Pursuing Sarajevo Identity Using Sentiment Analysis Emina Zejnilovi´c1 , Erna Husuki´c1 , Zerina Mašeti´c2 and Dželila Mehanovi´c2(B)
,
1 Department of Architecture, International Burch University, Ilidža, Bosnia and Herzegovina 2 Department of Information Technology, International Burch University, Ilidža, Bosnia and
Herzegovina [email protected]
Abstract. In the case of the 1984 Sarajevo Winter Olympic Games, the legacy evokes sentiments that range from hopefulness to dejection, playfulness to anxiety, since what was to follow only 8 years after this mega event was the most violent destruction of any city in modern European history. Using sentiment and data analysis the research analyses existing international academic discourse on the mentioned topics and questions their impact on the city identity. The results of the world cloud analysis show the almost identical ratio between Olympic games and war identity, with slightly higher association with Olympic games identity. Occurrence and bigram analysis confirm that and results in favor of the Olympic games. Finally, sentiment analysis shows that 87.5% of articles are classified as war sentiment. The presented study concludes that the academic discourse obscures the identity of Sarajevo, maintaining the status quo and polarizing it between the opposing memory debate of the two analyzed happenings. Keywords: City Identity · Natural Language Processing (NLP) · Sentiment Analysis · Winter Olympic Games · 1992–1995 war
1 Introduction The (re)creation of cities as natural, geographical, cultural, and social products is a continuous process. Accumulation of socio-cultural components turns spaces into places that hold a variety of meanings and impact urban and collective identities. Issues of city character and collective memory have become particularly fragile, due to rapid globalization and massive urbanization, questioning matters of local originalities, cultural heritage, and urban readability. In a city like Sarajevo, its constructed environment reflects the fractured memory elements that continue in space through disjointed mosaics of countless historic events. Merging in its urban organism diverse architectural epochs in several centuries, Sarajevo suffers from a competing memory syndrome, associated with two sequential mega events that radically transformed the urban form and image of Sarajevo at the end of the 20th century: the 1984 Sarajevo Winter Olympic Games (SWO) and the 1992–1995 war. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 280–299, 2023. https://doi.org/10.1007/978-3-031-43056-5_22
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Sarajevo is an exemplary case of an Olympic city that went through the longest siege in modern history, only 8 years after it joined the Olympic family. It is also the only contemporary divided Olympic city, and an unprecedented case of the changing identity related to how the city identified, captured, and utilized the Olympic legacy for local purposes prior – during – and after the war. A significant number of Olympic heritage structures are still derelict sites, forgotten and ruinous associated, in the memory maps of the contemporary city, with a dual set of memories: the nostalgic reminiscence of the city’s magnificence, and the materialization of Sarajevo’s traumatic experience [1, 2]. Understanding the multidimensional nature of the Olympic Games’ effects on their host cities has been pinpointed as a subject of paramount importance, as the awareness of achieving positive, long-lasting, tangible impact of organizing the Games becomes increasingly important. In the last few decades, academic attention has been put on the changing urban impacts of the event, on the variety and of potential outcomes, and the insufficient positive effect on host cities and nations. In the case of Sarajevo, the Olympic city, the analysis on the topic is additionally challenging, as the elements of opposing memories of the two sequential mega events, form intense moments of often conflicting communion history, slowing down the development of common post-war socio-cultural setting. Aiming to understand the character, nature, and impact of the XIV Winter Olympic Games legacy on the city identity, the research analyzes existing internationally published academic discourse on the 1984 Sarajevo Winter Olympic legacy, in contemporary context, using data and sentiment analysis. Specifically, the research investigates if the existing scholarly discourse associates the city of Sarajevo and its Olympic legacy with the greatest rise (Winter Olympic Games) or with the greatest fall of the city (the 1992– 1995 war). The number of scientific publications covered is somewhat lower than the number of the articles used in the similar studies, due to the limited number of published works on the topic, in English, related to the SWO and the 1992–1995 war. The rest of the paper is organized as follows. In Sect. 2, a summary of previous works and the applied technique within their studies is given, which includes overview of the articles covering the similar topic and general description of the natural language processing (NLP) and text analysis. Section 3 explains the methodology, starting with the data collection and data preprocessing steps, including word tokenization, stemming and removal of the stop words. In the same section, the sentiment analysis step is explained. Section 4 presents the obtained results. The research is finalized with Sect. 5, in which the general discussion and the discussion of the obtained results is given, ending with conclusions.
2 Literature Review Natural language processing (NLP) is defined as the ability of computer machines to understand the human (natural) language through a combination of statistical and machine learning methods. One of the tasks within the NLP is sentiment analysis, used to identify the emotions or opinions expressed in a given text. The sentiment is analyzed by examining their scores, and can be done in number of levels [3]: document level [4], sentence level [5], word/term level [6] or aspect level [7] Emotions or opinions are
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usually expressed as “positive”, “neutral” or “negative”, even though the polarity can be expressed as the range. The sentiment analysis is mostly associated with topics such as product and movie reviews [8, 9]; or social network opinions on a topic [10], however, it is equally applicable in any field in which the opinion or emotion towards a given topic is required. This paper aims to identify how the city of Sarajevo is branded through academic discourse, related to the two mega events that greatly marked its history at the end of the 20th century: the 1984 Winter Olympic Games and the 1992–1995 war. Investigation of the city identity through similar methods have been conducted in numerous studies. Authors in [11] used a database of 298 scientific works obtained from Web of Science. The VOS viewer package was used to create figures that represent the topics mostly occurring within titles, abstracts and keywords of the articles. Moreover, authors obtained statistical information such as what are the journals with the highest number of articles related to the city marketing area and what are most frequently cited papers. Besides that, the results of the countries and areas with the highest frequency of addressing city marketing were investigated. The research resulted in presenting the terms with highest occurrences such as City Branding, Image, Identity, Policy, Culture and Resident. The research presented in [12] investigated the impact of the legacy of events such as the Olympic games. The selection criteria applied to scientific works was to review papers published in journals requiring a peer-review process. Literature was retrieved from the Olympic Studies Center in Lausanne and from Web of Science. The articles are collected by searching for keywords such as Olympics, Olympic Games, legacy and leverage and, in total, authors analyzed 322 publications. They found out that most of these publications are most frequently investigated topics related to behavior which is connected with behavioral changes after hosting Olympic Games. Also, they were able to detect that 42% of analyzed research papers represented positive impacts of the Games, 50% did not find impact to the city and 8% found negative impact. Moreover, the study presents the perception of the city residents which is 33% positive, 33% negative and the remaining part which is a mixture of those two. Authors in [13] investigated the way of how the Winter Olympics in Sarajevo were contextualized during announcements about the Olympics Games in 1994, ten years after the Olympics Games in Sarajevo and during a war. In this study, the database was a collection of the 87 videotaped hours of the Winter Olympic Games in 1994. To find valence of the speeches, authors applied coding in such a way to replace a city with another city or year with another year. If the update did not change a meaning, it is considered as non-valent, but if the meaning is changed then the key term is considered as significant. Images accompanied with text description were used as input to dataset by [14] to investigate collective memory of victims of political repression. Authors used textual descriptions to detect if collective memory exists. Alceste classification analysis was used with an array of more than 53000 words. The most frequently detected topics are related to the environment and materials from which monuments should be made. At the bottom, there are topics related to the monument symbolism. Authors detected words that are occurring at least once in the 5% text segments related to human and historical dimensions. Based on this part, it is concluded that correlation between the number of
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text segments related to human and historical dimensions is small and there is a place to consider these two dimensions as separate inputs. Analyzing the previous works in the field of city identity within the Olympic Games context, the sentiment and connotation of these two terms could be detected, by employing various analysis techniques. This research aims at investigating the sentiment associating the city of Sarajevo and its Olympic legacy with the greatest rise (Winter Olympic Games) or with the greatest fall of the city (the 1992–1995 war), by analyzing the existing scholarly discourse on the Sarajevo Olympic legacy. The study approach is focused on the keyword analysis and the context meaning detection, using sentiment analysis within the natural language processing tasks.
3 Methodology 3.1 Data Collection To perform the sentiment analysis, the total of 24 scientific publications were collected and a final dataset was created. It is noticeable that the number of publications analyzed in this study is lower than the number of publications analyzed in related research works on the city identification topic. The reason for that is the limited number of publications covering the topic of Sarajevo city, and its association to the Olympic Games and the war. The data such as title, type and text are extracted from the articles using Python script that is written specifically for this purpose and is stored as a.json file. The text contains abstract and the text from other titles and subtitles within the publication, excluding the list of references. Majority of the included publications are journal articles, however, there is a portion of book chapters included in the final dataset. The text read from the publications contained characters, words and segments that are not useful for further analysis. Therefore, the obtained text needs additional processing and data cleaning. Figure 1 shows a snapshot of a portion of the dataset. It is a.json array which contains text and titles of the collected articles.
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Fig. 1. The snapshot of a portion of a dataset.
Articles used within the dataset are listed within Table 1. Each article has a number assigned to it which is used through the paper when referring to the article. 3.2 Data Preprocessing One of the tasks within the data cleaning step is removal of the special characters, URLs or tags. However, the obtained text was clean from special characters, URLs or tags and no further steps needed to be performed within this process. The whole text was converted to lowercase letters, to avoid the appearance of the same words as different, due to the case sensitivity issue. So, there was not a lot to do rearranging the data cleaning part. Text was converted all to lowercase letters. 3.3 Word Tokenization The next step was to perform the word tokenization, to convert text into a list of words. This step was performed with the help of Natural Language Toolkit (NLTK) [15] and its method word_tokenize(). The obtained lists of words are later used to perform natural language processing and sentiment analysis of the given article. 3.4 Stemming and Stop Words Removal In natural language, it is normal to use the same words in different forms, i.e. there are words with the same base, but different suffix values. To be able to perform valid analysis of text, it is necessary to return all words to their base format. This process is called stemming. Here, we applied Porter Stemmer [16]. Porter Stemmer is one of the most popular stemming algorithms which is based on the idea that the suffixes in the
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Table 1. List of articles analyzed within the study. Article #
Title
Authors
1
An Integrative Symbol for a Divided Nicolas Moll Country? Commemorating the 1984 Sarajevo Winter Olympics in Bosnia and Herzegovina from the 1992–1995 War until Today
2
The Rebirth and Demise of Sarajevo’s Holiday Inn
3
The Targeting of the Holiday Inn
Kenneth Morrison
4
The Hazards of Living on the Frontline
Kenneth Morrison
5
Urban development after the Bosnian War: Inés Aquilué, Estanislao Roca The division of Sarajevo’s territory and the construction of East Sarajevo
6
Beyond the Sarajevo 1984 Olympicscape: An evaluation of the Olympic Villages
Erna Husuki´c, Emina Zejnilovi´c
7
The 1984 Sarajevo Winter Olympics and Identity-Formation in Late Socialist Sarajevo
Zlatko Jovanovi´c
8
Sarajevo Memories – the City of Sublime Disorder
Emina Zejnilovi´c, Erna Husuki´c
9
City and soul Sarajevo, Johannesburg, Jerusalem, Nicosia
Scott A Bollens
10
Olympic Environmental Concerns as a Legacy of the Winter Games
Jean-Loup Chappelet
11
Imaging Sarajevo Recomposing the city and territory
Valentina Bandieramonte, Chiara Cavalieri, Irene Guida, Kaveh Rashidzadeh
12
People and things in the ethnography of borders: materialising the division of Sarajevo
Stef Jansen
13
Yugoslav Unity and Olympic Ideology at the 1984 Sarajevo Winter Olympic Games
Kate Meehan Pedrotty
14
Sarajevo’s ambivalent memoryscape: Spatial stories of peace and conflict
Stephanie Kappler
15
The Construction of Sarajevo’s ‘Olympic Hotel’
Morisson
16
Phoenix or Phantom: Residents and Sarajevo’s Post-War Changes
Marple-Cantell, Katherine
Kenneth Morrison
(continued)
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Article #
Title
Authors
17
Recent urban development and gentrification in post-Dayton Sarajevo, Bosnia and Herzegovina
Alma Pobri´c, Guy M. Robinson
18
Post-traumatic city: Marijin dvor and mount Trebevi´c, two urban spaces in transition in Sarajevo
Caterina Borelli
19
‘Sniper Alley’: The Politics of Urban Violence in the Besieged Sarajevo
Mirjana Risti´c
20
Intangible Borders
Mirjana Risti´c
21
Sarajevo´s Modernist Olympic Ruins – A Future for the Vanishing Past? Sarajevo´s Modernist Olympic Ruins – A Future for the Vanishing Past?
Bojana Bojanic, Sonja Ifko
22
‘Not Welcome at the Holiday Inn’: How a Sarajevan Hotel Influenced Geo-politics
Lisa Smirl
23
˘ Sarajevo – a border city caught between its Miruna TRONCOTA multicultural past, the Bosnian war and a European future
24
Olympic ghosts in a former warzone: what the legacy of 1984 means for Sarajevo today
Juliet Walker
English language are combinations of smaller and simpler suffixes. Even though it is popular due to its speed and simplicity, it is limited only to English words. Additionally, there are small words that do not add to the meaning and that even the search engines are ignoring when indexing entries for searching, called stop words (e.g. “the”, “in”, “so”, etc.). Before stemming, all stop words were removed from the text using the already available corpus of stop words in NLTK. 3.5 Sentiment Analysis As stated, the sentiment analysis is used to determine the opinion of emotions of the studied group about a given topic. Here, the research intends to identify the character of the sentiment communicated through international academic discourse, on the topic of Sarajevo, more precisely, determine if the identity of Sarajevo and its Olympic legacy is associated with the greatest rise (Winter Olympic Games) or with the greatest fall of the city (the 1992–1995 war) Based on the stated aim, we determined two sentiments: (1) Olympic Games and (2) war, where (1) is connotated positively and (2) is connotated negatively. The connotation is determined with the help of the field experts. Keywords that are listed within the Olympic Games and war dataset are found by using the bag of words method [17], which represents the text in form of word occurrences
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within it, to find the most related words. Beside this approach, keywords are additionally determined by the literature screening, with the help of the field expert. The keywords listed in Table 2 shows the list of the words used for positive (Olympic games) and negative (war) sentiment. There are in total 17 words in positive and 13 words in negative corpus of the words. Table 2. List of the words used for positive (Olympic games) and negative (war) sentiment. Olympic Games
War
Olympic city
Joy
War
Violence
Sarajevo 1984
Better times
Conflict
Attack
History of Bosnia
Glory
Destruction
Damage
History of Yugoslavia
Solidarity
Ruined
Fragmented
Integrative
Our town
Symbol of war
Shared heritage
Our country
Divided
Shared memory
Symbol of the city
Division
Connecting
Symbol of Sarajevo
Abandoned
Unity
Suffer
Listed words are used as features to build a dataset for the sentiment analysis. Each article is broken to word tokens which are compared with the features from the dataset. This was followed with assigning each feature with a given True or False value, depending on which group it belongs, 1984 SWO or 1992–1995 war related group.
4 Results The results section is divided into five subsections: 1) 2) 3) 4) 5)
Text preprocessing Analysis of word clouds Word frequency analysis Bigram analysis Sentiment analysis
4.1 Text Preprocessing In this section, the results of the conducted study are present. The result of all the preprocessing steps, starting from the word tokenization, stop word removal to stemming. Figure 2 shows the snapshot of the text from one article used in the study.
Fig. 2. The snapshot of the text in input article before preprocessing.
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All the html tags, number and punctuations were removed, and the entire text is converted to lowercase letters. Figure 3 shows the same text after preprocessing was performed.
Fig. 3. The snapshot of the text from Fig. 1. After preprocessing step.
After the word tokenization step in which the text is converted to a list of words, data is obtained as shown in Fig. 4.
Fig. 4. List of word tokens after the tokenization.
As it is noticeable, the word list contains all words from the text, including the stop words. Therefore, the stop word removal is performed to obtain only the words that are useful for further analysis. The snapshot of the obtained results is shown in Fig. 5.
Fig. 5. List of word tokens without stop words.
The remaining step was to perform stemming, which results in a text, whose snapshot is given in Fig. 6.
Fig. 6. The snapshot of the text after the stemming was performed.
4.2 Word Cloud Analysis Beside text preparation, the analysis of the words using word clouds is performed. Below are resulting word clouds for the analyzed articles. The analysis of individual cases shows that the prevalence of words associated with the two events are almost equally distributed, with slightly higher association of Sarajevo with the Olympic games in the summarized word cloud image. Word clouds are analyzed individually (Table 3.), for each article and generally for all the articles together (Fig. 7.).
From Olympics to War Table 3. Word clouds for each article.
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However, the general word cloud showed that the academic discourse is biased towards Sarajevo as an Olympic city.
Fig. 7. General word cloud of all articles.
4.3 Word Frequency Analysis Word frequency analysis is applied to find the most frequent words within articles under the inspection. Figure 8. Presents the summary of the frequency distribution, confirming that the frequency of word Olympic across all analyzed articles is higher than the frequency of word war.
Fig. 8. Frequency distribution of the top 20 frequent words.
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4.4 Bigram analysis Table 4 below shows the most occurring bigrams, or two words that frequently appear together, per each article. After the analysis of the results, bigrams that are the most important to the study are identified and bolded. Table 4. Bigram analysis per individual article. Article 1
Article 2
Article 3
(‘olymp’, ‘game’) 26 (‘oc’, ‘bih’) 17 (‘east’, ‘sarajevo’) 17 (‘took’, ‘place’) 13 (‘olymp’, ‘heritag’) 13 (‘thirtieth’, ‘anniversari’) 12 (‘winter’, ‘olymp’) 11 (‘olymp’, ‘committe’) 10 (‘memori’, ‘swo’) 8 (‘narr’, ‘around’) 7 (‘sport’, ‘event’) 7 (‘integr’, ‘potenti’) 7 (‘within’, ‘bih’) 7 (‘town’, ‘sarajevo’) 7 (‘sarajevo’, ‘east’) 7 (‘question’, ‘integr’) 6 (‘anniversari’, ‘swo’
(‘holiday’, ‘inn’) 39
(‘holiday’, ‘inn’) 45 (‘sniper’, ‘fire’) 11 (‘around’, ‘holiday’) 6
Article 4
Article 5
Article 6
(‘holiday’, ‘inn’) 62 (‘sniper’, ‘fire’) 11 (‘flak’, ‘jacket’) 11 (‘bosnian’, ‘govern’) 8 (‘armour’, ‘car’) 8 (‘side’, ‘build’) 6 (‘wear’, ‘flak’) 6 (‘car’, ‘park’) 6
(‘east’, ‘sarajevo’) 22 (‘novo’, ‘sarajevo’) 15 (‘istoˇcno’, ‘novo’) 14 (‘republika’, ‘srpska’) 13 (‘urban’, ‘area’) 13 (‘citi’, ‘sarajevo’) 13 (‘bosnia’, ‘herzegovina’) 12 (‘canton’, ‘sarajevo’) 10 (‘urban’, ‘plan’) 8 (‘citi’, ‘centr’) 7 (‘new’, ‘urban’) 7 (‘new’, ‘citi’) 7 (‘istoˇcna’, ‘ilidža’) 7 (‘residenti’, ‘area’) 6 (‘centr’, ‘sarajevo’) 6
(‘sarajevo’, ‘olymp’) 35 (‘olymp’, ‘legaci’) 29 (‘winter’, ‘olymp’) 23 (‘olymp’, ‘game’) 20 (‘olymp’, ‘villag’) 18 (‘host’, ‘citi’) 15 (‘villag’, ‘mojmilo’) 12 (‘urban’, ‘develop’) 9 (‘olymp’, ‘urban’) 9 (‘olymp’, ‘resid’) 9 (‘olymp’, ‘site’) 7 (‘winter’, ‘sport’) 7 (‘olymp’, ‘citi’) 6 (‘citi’, ‘sarajevo’) 6 (‘winter’, ‘game’) 6 (‘residenti’, ‘area’) 6
Article 7
Article 8
Article 9 (continued)
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E. Zejnilovi´c et al. Table 4. (continued)
Article 1
Article 2
Article 3
(‘sarajevo’, ‘olymp’) 29 (‘winter’, ‘olymp’) 17 (‘bosnia’, ‘herzegovina’) 16 (‘our’, ‘days’) 14 (‘alpin’, ‘ski’) 11 (‘sarajevo’, ‘yugoslavia’) 10 (‘olymp’, ‘tradit’) 9 (‘winter’, ‘sport’) 9 (‘exist’, ‘research’) 7 (‘research’, ‘topic’) 7 (‘tempor’, ‘cultur’) 7 (‘olymp’, ‘host’) 7 (‘brotherhood’, ‘uniti’) 7 (‘sens’, ‘yugoslav’) 6 (‘cold’, ‘war’) 6 (‘olymp’, ‘sarajevo’) 6 (‘bosnia’, ‘yugoslavia’) 6 (‘herzegovina’,’yugoslavia’) 6
(‘olymp’, ‘legaci’) 9 (‘collect’, ‘memori’) 6 (‘site’, ‘memori’) 6
(‘contest’, ‘citi’) 10 (‘bosnian’, ‘serb’) 10 (‘buffer’, ‘zone’) 8
Article 10
Article 11
Article 12
(‘winter’, ‘game’) 45 (‘sustain’, ‘develop’) 17 (‘organ’, ‘committe’) 15 (‘lake’, ‘placid’) 13 (‘olymp’, ‘game’) 12 (‘ski’, ‘jump’) 9 (‘took’, ‘place’) 8 (‘luge’, ‘run’) 8 (‘winter’, ‘sport’) 7 (‘bobsleigh’, ‘luge’) 7 (‘olymp’, ‘movement’) 6 (‘environment’, ‘issu’) 6 (‘legaci’, ‘olymp’) 6 (‘host’, ‘citi’) 6 (‘game’, ‘first’) 6 (‘summer’, ‘game’) 6 (‘salt’, ‘lake’) 6
(‘boundari’, ‘line’) 16 (‘dobrinja’, ‘iv’) 12 (‘imag’, ‘process’) 11 (‘republ’, ‘srpska’) 10 (‘inter’, ‘entiti’) 10 (‘entiti’, ‘boundari’) 10 (‘bosnia’, ‘herzegovina’) 8 (‘two’, ‘entiti’) 7 (‘dayton’, ‘peac’) 6 (‘citi’, ‘territori’) 6 (‘interent’, ‘boundari’) 6 (‘peac’, ‘agreement’) 6
(‘human’, ‘practic’) 18 (‘east’, ‘sarajevo’) 18 (‘bodili’, ‘movement’) 16 (‘peopl’, ‘thing’) 14 (‘nonhuman’, ‘actant’) 13 (‘human’, ‘nonhuman’) 10 (‘materialis’, ‘border’) 9 (‘focu’, ‘thing’) 9 (‘critic’, ‘potenti’) 8 (‘certain’, ‘thing’) 7 (‘differ’, ‘scale’) 7 (‘mani’, ‘peopl’) 7 (‘thing’, ‘peopl’) 6 (‘sieg’, ‘line’) 6 (‘flight’, ‘simul’) 6 (‘feder’, ‘dobrinja’) 6 (‘side’, ‘street’) 6 (‘warrel’, ‘affect’) 6
Article 13
Article 14
Article 15 (continued)
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Table 4. (continued) Article 1
Article 2
Article 3
(‘olymp’, ‘game’) 34 (‘organ’, ‘committe’) 19 (‘winter’, ‘olymp’) 14 (‘sarajevo’, ‘game’) 13 (‘final’, ‘report’) 12 (‘tourist’, ‘industri’) 12 (‘tran’, ‘foreign’) 12 (‘foreign’, ‘broadcast’) 12 (‘broadcast’, ‘inform’) 12 (‘daili’, ‘report’) 12 (‘report’, ‘eastern’) 12 (‘eastern’, ‘europ’) 12 (‘foreign’, ‘currenc’) 11 (‘brotherhood’, ‘uniti’) 9 (‘olymp’, ‘movement’) 8 (‘sarajevo’, ‘olymp’) 8 (‘republ’, ‘provinc’) 8 (‘sarajevo’, ‘winter’) 7 (‘foreign’, ‘tourist’) 7 (‘sarajevo’, ‘organ’) 7 (‘yugo’, ‘slavia’) 7 (‘olymp’, ‘financ’) 7 (‘inform’, ‘servic’) 7 (‘polit’, ‘econom’) 7 (‘olymp’, ‘committe’) 6 (‘olymp’, ‘era’) 6 (‘intern’, ‘olymp’) 6 (‘decemb’, ‘tran’) 6
(‘stori’, ‘around’) 10 (‘around’, ‘monument’) 8 (‘peopl’, ‘relat’) 6 (‘local’, ‘intern’) 6 (‘person’, ‘interview’) 6
(‘holiday’, ‘inn’) 79 (‘ivan’, ‘štrau’) 20 (‘winter’, ‘olymp’) 17 (‘inn’, ‘hotel’) 9 (‘bosnia’, ‘herzegovina’) 8 (‘inn’, ‘sarajevo’) 8 (‘sarajevo’, ‘holiday’) 7 (‘olymp’, ‘committe’) 7 (‘sarajevo’, ‘olymp’) 6 (‘winter’, ‘game’) 6
Article 16
Article 17
Article 18 (continued)
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E. Zejnilovi´c et al. Table 4. (continued)
Article 1
Article 2
Article 3
(‘person’, ‘interview’) 29 (‘sarajevo’, ‘canton’) 16 (‘canton’, ‘plan’) 11 (‘plan’, ‘institut’) 10 (‘citi’, ‘sarajevo’) 9 (‘experi’, ‘chang’) 9 (‘director’, ‘sarajevo’) 9 (‘sarajevo’, ‘today’) 9 (‘world’, ‘war’) 8 (‘chang’, ‘citi’) 7 (‘physic’, ‘social’) 7 (‘citi’, ‘war’) 7 (‘prewar’, ‘sarajevo’) 7 (‘sarajevo’, ‘sieg’) 7 (‘war’, ‘sarajevo’) 7 (‘sarajevo’, ‘war’) 6 (‘life’, ‘sarajevo’) 6 (‘republika’, ‘srpska’) 6 (‘ottoman’, ‘empir’) 6 (‘war’, ‘ii’) 6
(‘stari’, ‘grad’) 19 (‘inner’, ‘citi’) 14 (‘citi’, ‘center’) 13 (‘sarajevo’, ‘canton’) 10 (‘new’, ‘develop’) 9 (‘sarajevo’, ‘citi’) 9 (‘novo’, ‘sarajevo’) 7 (‘urban’, ‘renew’) 7 (‘privat’, ‘investor’) 7 (‘east’, ‘sarajevo’) 6 (‘novi’, ‘grad’) 6
(‘bosnia’, ‘herzegovina’) 9 (‘marijin’, ‘dvor’) 9 (‘doubl’, ‘transit’) 8 (‘urban’, ‘space’) 6 (‘mount’, ‘trebevi´c’) 6
Article 19
Article 20
Article 21
(‘sniper’, ‘alley’) 17 (‘urban’, ‘space’) 15 (‘spatial’, ‘violenc’) 14 (‘ethnic’, ‘mix’) 11 (‘citi’, ‘centr’) 10 (‘public’, ‘space’) 9 (‘sniper’, ‘view’) 8 (‘sieg’, ‘line’) 7 (‘sniper’, ‘posit’) 7 (‘residenti’, ‘suburb’) 7 (‘part’, ‘sarajevo’) 6 (‘socialist’, ‘town’) 6
(‘east’, ‘sarajevo’) 49 (‘street’, ‘name’) 17 (‘sarajevo’, ‘east’) 15 (‘two’, ‘citi’) 14 (‘urban’, ‘space’) 13 (‘spatial’, ‘discours’) 13 (‘ethnic’, ‘divis’) 9 (‘ethnic’, ‘group’) 8 (‘bosnia’, ‘herzegovina’) 8 (‘architectur’, ‘urban’) 7 (‘form’, ‘spatial’) 7 (‘spatial’, ‘practic’) 7 (‘traffic’, ‘arteri’) 7 (‘border’, ‘zone’) 6 (‘colour’, ‘urban’) 6
(‘cultur’, ‘heritag’) 16 (‘bosnia’, ‘herzegovina’) 10 (‘ski’, ‘jump’) 7 (‘ivan’, ‘štrau’) 6
(continued)
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Table 4. (continued) Article 1
Article 2
Article 3
Article 22
Article 23
Article 24
(‘holiday’, ‘inn’) 64 (‘intern’, ‘commun’) 11 (‘ivan’, ‘štrau’) 11 (‘brock’, ‘binder’) 6
(‘bosni’, ‘hercegovini’) 24 (‘in’, ‘besieged’) 24 (‘besieged’, ‘sarajevo’) 23 (‘bosn’, ‘hercegovin’) 18 (‘death’, ‘life’) 18 (‘srbian’, ‘nationalists’) 26 (‘under’ ‘control’) 12 (‘bosnian’, ‘srbs’) 11 (‘under’, ‘siege’) 9 (‘nacionalists’, ‘in’) 15 (‘during’, ‘war’) 8 (‘whole’, ‘war’) 7 (‘unprofor’, ‘is’) 7 (‘citizens’, ‘sarajevo’) 6
(‘bosnia’, ‘herzegovina’) 16 (‘citi’, ‘sarajevo’) 14 (‘world’, ‘war’) 11 (‘sieg’, ‘sarajevo’) 8 (‘invis’, ‘border’) 7 (‘capit’, ‘citi’) 7 (‘bosnian’, ‘war’) 6 (‘melt’, ‘pot’) 6 (‘sarajevo’, ‘citi’) 6
After looking at each article separately, we analyzed the most frequent bigram and created the list of most occurring bigrams for all articles together (Table 5). From the bigram analysis in the academic discourse analyzed here, Sarajevo is associated both with the Olympic Games and the war. The most common bigram of the interest is (‘sarajevo’,’olymp’), (‘memori’,’swo’), (‘olymp’,’heritag’), (‘game’,’sarajevo) associating Sarajevo with its greatest rise – Olympic Games, but also (‘sieg’,’sarajevo’), (‘two’,’entiti’), (‘ethnic’,’mix’), associating Sarajevo with its greatest fall – 1992 – 1995 war. 4.5 Sentiment Analysis The results of the sentiment analysis identify the most significant difference between the opposing identity of the city, greatly leaning towards Sarajevo being associated more with the 1992–1995 war than with the Olympic Games, as it is noticeable in Table 6. Out of 24 researched articles, only three leaned towards Sarajevo being associated more with the Olympic Games, which is 12.5% out of the total number of included articles. Even though the frequency of the words such as “olympics” and “games” were among top 4 frequencies, based on the word cloud in Fig. 7. And frequency distribution in Fig. 8., which could lead to the opinion that Sarajevo and the Winter Olympic Games are the predominant identity factor, sentiment analysis showed a completely different perspective, with 87.5% of the articles associating Sarajevo and its Olympic legacy with the war.
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(‘holiday’, ‘inn’) 316 (‘olymp’, ‘game’) 150 (‘east’, ‘sarajevo’) 147 (‘winter’, ‘olymp’) 128 (‘bosnia’, ‘herzegovina’) 128 (‘sarajevo’, ‘olymp’) 92 (‘citi’, ‘sarajevo’) 77 (‘winter’, ‘game’) 71 (‘took’, ‘place’) 53 (‘organ’, ‘committe’) 51 (‘urban’, ‘space’) 48 (‘world’, ‘war’) 44 (‘olymp’, ‘legaci’) 43 (‘ivan’, ‘štrau’) 42 (‘oc’, ‘bih’) 41 (‘in’, ‘sarajevo’) 40 (‘olymp’, ‘committe’) 38 (‘republika’, ‘srpska’) 37 (‘sarajevo’, ‘east’) 37 (‘winter’, ‘sport’) 35 (‘olymp’, ‘heritag’) 35 (‘sarajevo’, ‘canton’) 35 (‘bosnian’, ‘serb’) 35 (‘boundari’, ‘line’) 34 (‘sarajevo’, ‘citi’) 34 (‘sieg’, ‘sarajevo’) 33 (‘olymp’, ‘villag’) 30
(‘brotherhood’, ‘uniti’) 26 (‘thirtieth’, ‘anniversari’) 26 (‘sniper’, ‘fire’) 26 (‘stari’, ‘grad’) 26 (‘sniper’, ‘alley’) 25 (‘sarajevo’, ‘yugoslavia’) 24 (‘olymp’, ‘movement’) 24 (‘olymp’, ‘citi’) 24 (‘bosnian’, ‘war’) 24 (‘novo’, ‘sarajevo’) 24 (‘host’, ‘citi’) 24 (‘citi’, ‘center’) 24 (‘public’, ‘space’) 24 (‘bosni’, ‘hercegovini’) 24 (‘in’, ‘besieged’) 24 (‘sarajevo’, ‘winter’) 23 (‘sarajevo’, ‘game’) 23 (‘ethnic’, ‘group’) 23 (‘ski’, ‘jump’) 23 (‘biographi’, ‘p’) 23 (‘besieged’, ‘sarajevo’) 23 (‘sport’, ‘event’) 22 (‘sieg’, ‘line’) 22 (‘olymp’, ‘museum’) 22 (‘dayton’, ‘peac’) 22 (‘peac’, ‘agreement’) 22 (‘two’, ‘entiti’) 22 (‘cold’, ‘war’) 21 (‘olymp’, ‘site’) 21 (‘ethnic’, ‘divis’) 21 (‘cultur’, ‘heritag’) 21 (‘town’, ‘sarajevo’) 20 (‘olymp’, ‘sarajevo’) 20 (‘end’, ‘war’) 20 (‘canton’, ‘sarajevo’) 20 (‘urban’, ‘develop’) 20
(‘urban’, ‘plan’) 20 (‘sustain’, ‘develop’) 20 (‘integr’, ‘potenti’) 19 (‘memori’, ‘swo’) 19 (‘two’, ‘citi’) 19 (‘inn’, ‘hotel’) 19 (‘sarajevo’, ‘holiday’) 19 (‘polit’, ‘econom’) 19 (‘ethnic’, ‘mix’) 19 (‘bosn’, ‘hercegovin’) 19 (‘econom’, ‘polit’) 18 (‘sarajevo’, ‘bosnia’) 18 (‘sarajevo’, ‘sieg’) 18 (‘banja’, ‘luka’) 18 (‘game’, ‘sarajevo’) 18 (‘hotel’, ‘holiday’) 18 (‘dobrinja’, ‘iv’) 18 (‘residenti’, ‘area’) 18 (‘human’, ‘practic’) 18 (‘smrt’, ‘život’) 18 (‘yugoslav’, ‘republ’) 17 (‘eastern’, ‘europ’) 17 (‘urban’, ‘area’) 17 (‘socialist’, ‘yugoslavia’) 16 (‘line’, ‘iebl’) 16 (‘bosnian’, ‘govern’) 16 (‘develop’, ‘citi’) 16 (‘inner’, ‘citi’) 16 (‘war’, ‘ii’) 16
5 Discussion and Conclusion City identity is closely related to the concept of collective memory, and is communicated through unique spatial and architectural meanings, visuals, and rhetorical systems. The image of the city is therefore constructed and reconstructed over time, continuously questioning who the city is, and what are its perceived and hidden distinctiveness and values. There are numerous categorizations under which the contemporary city of Sarajevo is classified: Olympic city, divided city, post-war city, post-socialist city, a city in transition. Torn between these socio-spatial realities, turbulent past and uncertain future, Sarajevo
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Table 6. Sentiment analysis of included articles. Article #
Sentiment
Article #
Sentiment
1
Olympic Games
13
Olympic Games
2
War
14
War
3
War
15
War
4
War
16
War
5
War
17
War
6
War
18
War
7
Olympic Games
19
War
8
War
20
War
9
War
21
War
10
War
22
War
11
War
23
War
12
War
24
War
repetitively keeps failing to establish contemporary city character, as it struggles with ambiguous visual expression and persistent disregard to the complex-built setting. Deeply etched in the collective memory, Sarajevo 1984 Winter Olympics and the 1992–1992 war have a remarkable place in the local culture of remembrance, with SWO being identified as a rare socio-spatial non-polarizing, unifying factor for a divided city. In a situation where the Olympic sites are also the sites of war, the research questions the character of the academic paradigm on the topic in the context of identity making. 24 international scientific publications addressing the topics related to SWO and the 1992–1995 war are used as input to the analysis. These publications are collected and textual content from them is extracted to json format of dataset. Later, the dataset is processed and natural language processing techniques such as stemming, stop words removal, and word tokenization are applied. The word cloud analysis of the selected research indicates that the prevalence of words associated with the two events are almost equally distributed with slightly higher association of Sarajevo with the Olympic games. This is confirmed in the analysis of the occurrence frequency, where the repetition of the word ‘Olympic’ is higher. Additionally, bigram analysis results of most occuring word sets validates that Sarajevo is predominantly associated with its Olympic identity (‘sarajevo olymp’ 92 occurrences, ‘host city’ 24, ‘olymp city’ 24, ‘olymp sarajevo’ 20, ‘memory SWO’ 19, ‘olympic heritage’ 35, and ‘game sarajevo’ 18). The integrative aspects of Sarajevo Olympics has been somewhat communicated with following incidents: ‘brotherhood, unity’ 26 and ‘integrative potential’ 19 repetitions. Interestingly the recurrence of words and phrases that associate Olympic sites and Sarajevo with the 1992–1995 war, is numerous and versatile ( ‘boundary line’ 34 iterations, ‘siege sarajevo’ 33, ‘sniper fire’ 26, ‘sniper alley’ 25, ‘bosnian war’ 24, ‘in besieged’ 24, ‘besieged sarajevo’ 23, ‘siege line’ 22, ‘two entities’ 22, ‘ethnic division’ 21, ‘two cities’ 19, ‘sarajevo siege’ 18, ‘iebl’ – inter
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entity boundary line 16.) Nevertheless, the research recognizes the integrative potential of the SWO through the conclusion that the words ‘bosnia and herzegovina’ have been used more frequently 128 occurrences, than the words ‘republika srpska’ which has been repeated 37 times, suggesting that the concept of Sarajevo Olympics is discussed more within the context of the country rather than its entities. Furthermore, the research conducted sentiment analysis using two defined corpus’, categorized as SWO related and 1992–1995 war related. After application of the classifier, results indicate that out of 24 analyzed articles 3 were recognized as SWO related and 21 were related to the 1992–1995 war, which is a ratio of 12.5% vs. 87.5% in favor of the 1992–1995 war sentiment. This implies that the sentiment of the academic discourse strongly recognizes Sarajevo as a post-war city, leaning more towards the acknowledgement of Olympic sites as places of war and locations of destruction, and speaking in favor of their divisive potential. Comparison of the obtained results, as well as consideration of the already existing social research where Olympic legacy has been promoted for its integrative potential, further confirms the hypothesis of the dual, opposing, competing nature of Olympic remembrance: on one side their argued potential in being commutual and connecting inheritance that transgresses political, ethnic and social differences; and on the other side, their evident identification as symbols of destruction and socio-spatial division. Subsequently, the city identity maintains the status quo, polarized between the opposing memory debates of the two analyzed happenings. As previously mentioned, the sample used within this study and the created dataset is significantly smaller than the number of articles analyzed in the related literature, due to the limited amount of published research on the topics investigated. Thus, the study itself, and the obtained data are a significant contribution to the insufficiently researched topic. Moreover, this is the first investigation on the topic where the natural language processing method – sentiment analysis is used to identify the tone of the city character in the related academic literature. In the wider context, the research contributes to the knowledge on the development of the city of Sarajevo, the academic discourse of Winter Olympic Legacy, as well as to the study of Olympic legacy and the goals of the International Olympic Committee. Acknowledgments. This work was supported by the Olympic Studies Centre under the Advanced Olympic Research Grant Programme.
References 1. Zejnilovi´c, E., Husuki´c, E.: Sarajevo memories – the city of sublime disorder. Architektúra urbanizmus 54(3–4), 166–179 (2020). https://doi.org/10.31577/archandurb.2020.54.3-4.2 2. Husuki´c, E., Zejnilovi´c, E.: Beyond the Sarajevo 1984 Olympicscape: an evaluation of the Olympic Villages. Cities 106, 102924 (2020). https://doi.org/10.1016/j.cities.2020.102924 3. Thomas, B.: What Consumers Think About Brands on Social Media, and What Bunesses Need to do About it Report, Keep Social Honest (2013) 4. Ainur, Y., Yisong, Y., Claire, C.: Multi-level structured models for document-level sentiment classification. In: Proceedings of the 2010 Conference on Empirical Methods in Natural
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The Employment of a Machine Learning-Based Recommendation System to Maximize Netflix User Satisfaction Dinko Omeragi´c1(B) , Dino Keˇco2 , Samed Juki´c1 , and Be´cir Isakovi´c2 1
International Burch University, Sarajevo, Bosnia and Herzegovina [email protected] 2 University of Sarajevo, Sarajevo, Bosnia and Herzegovina
Abstract. This study compared two content-based filtering methods for creating a Netflix recommendation system. Two functio601586ns were developed and evaluated, both of which take the name of a movie or TV show as input and return a list of similar movies or TV shows based on the filtering strategy. The first method compared movie or TV show descriptions based on keywords, while the second method considered several features such as title, director, cast, and genre to determine the degree of similarity between two movies or TV shows. Different metrics such as cosine similarity, Euclidean distance, and Pearson’s correlation were used to measure the degree of resemblance. The results of the study show that the technique in which data from various specific features has been used has proven to be more useful and trustworthy than the approach in which only description keywords have been employed. The study also reveals that the Naive Bayes model delivered superior results to the KNN model in terms of categorising movie and TV show features. These findings provide insight into the connection between movie and TV show features in terms of their similarity, which can be useful when selecting features for recommender systems. However, the study does not establish whether the content-based strategy is the optimal method for developing Netflix’s recommendation algorithm due to a lack of data on user interactions. Nonetheless, the recommendation systems produced accurate suggestions without spoilers, achieving the main goal of this study.
Keywords: Machine learning Content-based filtering
1
· Recommender systems ·
Introduction
Recently, recommender systems have become an integral part of our daily lives. The large volume of available information necessitated the creation of systems to filter this data and extract the essential information. Many of us typically rely on external knowledge to assist us in making judgments regarding a course of action c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´ c et al. (Eds.): IAT 2023, LNNS 644, pp. 300–328, 2023. https://doi.org/10.1007/978-3-031-43056-5_23
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or an item of interest. For instance, if we intend to purchase a CD or visit a new club. When deciding whether to purchase a book, we can rely on the opinion of a person with similar reading preferences. On the other hand, we sometimes examine only the information available about the thing and our preferences. Researchers have examined the several factors that influence decision-making. They are attempting to model as many as possible in order to improve the accuracy of recommender systems [1,2]. In the 1990s, when the first publications on collaborative filtering were published [3], interest in recommender systems grew. Even though there are numerous methodologies and algorithms employed in recommender systems, they are often divided into three categories based on their mode of operation. This includes content-based, collaborative, hybrid, and more ways [4]. The contentbased approach attempts to propose a movie to a user based on the movie’s description, the user’s interests, and past behavior. The content-based technique considers the preferences of a single user, whereas the collaborative filtering approach [5–8] takes into account how various users rank movies to determine what to recommend. The primary objective of this strategy is to locate comparable consumers (commonly referred to as “neighbors”) and recommend similar products to them. The advantage of the collaborative approach is that subject expertise is unnecessary, as user comments on various issues are used to generate recommendations [9]. This strategy is rather prevalent these days, and large organizations such as LinkedIn, Facebook, Spotify, Amazon, Google News, Twitter, and Last.fm, whose success relies on providing excellent customer service, employ it [10]. Due to the shortcomings of the previously stated methodologies, researchers have developed hybrid algorithms that combine the most advantageous aspects of content-based and collaborative filtering. Researchers strongly suggest these strategies. Recommendations are built on various user reviews, ratings, likes, dislikes, and comments and are generated by recommendation engines. In this research, we are going to work with one type of ratings [11,12], but there are also other reviews, such as camera reviews [9], book ratings [13], video game reviews [11], and restaurant ratings [13]. The main contribution of this paper is the development of a content-based recommendation system that utilizes two distinct methods to compare and recommend movies and television programs that are streamed on Netflix. Our proposed system employs the similarity between several features as well as the description keyword similarity to determine the degree of resemblance between two items. We have utilized several approaches, including Euclidean distance, cosine similarity, and Pearson’s correlation, to compute these similarities. Furthermore, we have evaluated our system’s performance by examining the top 10 recommendations from both systems for their actual similarity to other movies or TV shows. Our experiments have demonstrated that our proposed system, which utilizes multiple attributes, produces more accurate recommendations than other content-based recommendation systems. Additionally, we have used KNN and Naive Bayes classification models to categorize movie and television program
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characteristics, such as the rating, in order to strengthen the comparison between our two methods. Our study shows that utilizing various and more specific item characteristics could improve the interrelationship of items in content-based recommendation systems. By adopting our proposed recommendation system, both movie and television producers and the rest of the movie-loving community can benefit from more precise and dependable recommendations, which are not biased towards any specific individual’s tastes or preferences. In the following sections, we present the details of our proposed movie recommendation system. Section 2 provides a literature review of previous research on movie recommendation systems and compares our approach with those works. Section 3 describes our proposed recommendation system in detail, including the features used for similarity calculations and the classification models employed for categorizing movies and television programs. We also explain the data set and the pre-processing steps involved in preparing it for the experiments. Section 4 presents the experimental results and a comprehensive evaluation of our proposed system. Section 5 provides a comprehensive discussion of the main findings and conclusions drawn from the experiments, as well as limitations and potential areas for improvement. It serves as both the conclusion and discussion of the paper. Finally, in Sect. 6, we present potential future work that can be done to improve the performance of the proposed system, such as exploring more advanced algorithms or incorporating additional sources of data to enhance the recommendation process.
2
Related Work
This section of the paper discusses the research conducted on the subject of recommender systems over the past two decades by diverse writers. When the first publications on collaborative filtering were published in the mid-1990s [3], interest in recommender systems increased. Even though there are a variety of approaches and algorithms utilized in these systems, we have taken into account the most popular and well-known methods. In order to determine which classification method is appropriate for our problem, we will discuss and contrast contemporary classification methods in the following section. The content-based approach tries to suggest a movie to a user based on what the movie is about and what the user likes. SRS Reddy et al. (2019) came to the conclusion that by utilizing movie genres and user movie ratings, they are able to create a system that accurately recommends movies. This method is simple to comprehend because it only considers one user and does not require information from other users to make suggestions. In reality, it takes into account the movies that a user has rated with higher marks and then recommends movies of the same genre to the user [14]. But collaborative filtering, which is another way to make recommender systems, has been shown to have problems. The content-based technique considers the preferences of a single user, whereas the collaborative filtering approach [15,16] considers how multiple users
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rank movies to determine which ones to recommend. The primary objective of this strategy is to locate comparable consumers (commonly referred to as “neighbors”) and recommend similar products to them. The collaborative approach is helpful because it doesn’t require topic expertise because it makes suggestions based on what users say about different things [17]. This strategy is quite prevalent nowadays, and many significant organizations that rely on providing the appropriate service to clients, such as LinkedIn, Facebook, Spotify, Amazon, Google News, Twitter, and Last.fm [10,18], employ it. Unfortunately, even this strategy has drawbacks. The cold-start problem [19] is one of the most well-known issues, which means that this approach cannot produce recommendations based on encounters with fresh things. Since this is a significant issue, researchers [20,21] have attempted to determine its scope and develop a solution. In addition to the already-mentioned two strategies, there are also a number of hybrid methods [21,22] that incorporate the most advantageous aspects of each. Researchers mix these methodologies in a variety of ways. While some use the outcomes of one way as the input for the second, others attempt to blend the outcomes of both approaches. In their study [21], S. Natarajan, S. Vairavasundaram, S. Natarajan, and A. H. Gandomi discovered that linked open data can be utilized to resolve data sparsity and cold start problems and increase the accuracy and performance of collaborative filtering and. In addition, additional studies [23,24] have asserted and experimentally demonstrated that these findings are fruitful. Other approaches, in addition to the three previously stated ones, take other elements into account and correspondingly provide more specific methods. Methods based on emotions [25], rules-based [26], tag-based [27] approaches, tailored recommenders [28,29], etc., can be employed. Recommendations are generated by recommendation engines based on numerous user reviews, ratings, likes, dislikes, and comments. This paper will focus on one rating type [30,31], but there are other types of reviews, such as camera reviews [31], book ratings [32], video game reviews [31], and restaurant reviews [32]. Since the goal of our project is to make a Netflix recommendation system and find out whether filtering by movie features (genre, director’s name, actor’s name, and rating) or filtering by movie description keywords gives more accurate results, we can say that the content-based approach is the best choice.
3 3.1
Materials and Methods Data
Since we have opted to create a bespoke Netflix movie recommendation system, we need a data set containing reliable, up-to-date information about Netflix movies and TV episodes. This is why we used the Netflix Movies and TV Shows data set [33] from Kaggle. This data set contains a constantly updated list of Netflix-accessible movies and television shows. There are 8807 rows and 12 columns (features) in this data collection. Depending on the importance of the
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features, we will determine which ones will be employed in this project. These decision-making and pre-processing phases are outlined in the text that follows. The 12 features of the data set are: – – – – – – – – – – – –
show id: a standard unique ID for a record type: there are two values for type: TV show and movie title: the name of the movie or TV show director: the director(s)’ name(s) cast: actors involved in the movie or TV show country: the country in which the movie or television show was made date added: date when the movie or TV show was added to Netflix release year: the actual release year of the movie or TV show rating: the TV rating of the movie or TV show (such as PG-13, R, etc.) duration: total duration (in minutes for movies and in seasons for TV shows) listed in: movie or TV show genre(s) description: standard description or summary Table 1. Total number of missing values in columns. Column name Number of missing values show id
0
type
0
title director
0 2634
cast
825
country
831
date added
10
release year
0
rating
4
duration
3
listed in
0
description
0
Table 1 reveals that the columns director, nation, cast, date added, rating, and duration contain null data. Since null values can lead to issues and erroneous outcomes when processing data and attempting to make sense of it, our first pre-processing steps will involve dealing with these values. There are many alternative ways that we can investigate, but we have only employed a handful. Even though the columns “cast”, “nation”, and “rating” include critical information that we require, we have decided to eliminate the rows that have null values in these columns. As depicted in Table 2, the majority of missing director name values are present in rows containing TV show information. Aside from that, almost one-fourth of the data set rows (over 90% of TV shows) lack these variables, and removing them could be detrimental. Therefore, we have opted to populate these cells with “unknown” and have removed this characteristic from the calculation of the similarity between television programs. After that, we separated the data set into “movies” and “TV shows” sets. In both of these data
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Table 2. Number of missing values in columns grouped by type (movie and TV show).
Column name Number of missing values Movies TV shows show id
0
0
type
0
0
title
0
0
director
188
2446
cast
475
350
country
440
391
date added
0
10
release year
0
0
rating
2
2
duration
3
0
listed in
0
0
description
0
0
Fig. 1. Distribution of movie ratings according to age categories.
sets, the all-content column has been added. The information in this column comes from the Movie data set columns “director”, “listed-in”, “cast”, “country”, and “rating.” In the meantime, it comprises data from the TV Show data set’s “listed-in”, “nation”, “cast”, and “rating” columns. Figure 1 depicts the distribution of movie ratings according to the age groupings of their audiences. As can be seen, the most prevalent movie rating is TVMA, which denotes programming intended for adults and hence unsuitable for children under the age of 17. On the other hand, movies with ratings of NC-17 (not for kids under 17), TV-Y7-FV (for older kids and may have more extreme fantasy violence), and G (general audience) aren’t very common [34].
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Fig. 2. Distribution of TV show ratings according to age categories.
In Fig. 2, the distribution of TV show ratings is obvious, and we can affirm that the majority of these programs are designed exclusively for adolescents and adults, as they are typically the target audience. On the other hand, TV shows with ratings of TV-7Y-FV [for older kids and may have more extreme fantasy violence] and R [no one under 17 without a parent or adult guardian present] are rarely seen [34]. 3.2
Developing the Recommendation System
In this section, we will discuss the methodologies and algorithms utilized in the creation of recommender systems. We will also explain how we have used their benefits and tried to work around their problems. We used the TF-IDF (term frequency-inverse document frequency) method to build the recommender systems and figure out how similar movies and TV episodes were. TF-IDF Algorithm. This algorithm can be understood as the computation of a word’s importance to the entire text inside a corpus or series. It states that the term’s meaning grows according to the number of times it appears in the text but is offset by the frequency of the word in the corpus (data set). Tf-idf is one of the most effective metrics for calculating the importance of a term (or word) to a text in a corpus or series, which is why we have chosen to utilize it in our project [35]. The approach is frequently implemented in natural language processing (NLP), information retrieval, and text mining [36,37].
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It assigns each word in a document a weight determined by the term frequency (tf) and inverse document frequency (idf) of that word. As the weight of a word increases, so does its importance. To comprehend the algorithm, we must first become acquainted with its vocabulary. In the following section [35], term frequency, document frequency, and inverse document frequency are explained in greater detail. Term Frequency: Frequency represents the number of times a certain word (w) appears in a document (d). This is the reason why we may claim that when a word occurs in the text, its significance increases. We can use a vector to describe the text, as the order of the words is irrelevant. According to Eq. 1, the term frequency can be determined for all words (terms) that are present in the text. tf (w, d) = count of w in d/number of words in d
(1)
Document Frequency: While tf is the number of occurrences of a word (w) in a text (t), document frequency is the number of papers (independent texts) that contain the term, as stated in Eq. 2 [35]. df (w) = occurrence of w in documents
(2)
Inverse Document Frequency: The number of documents in the corpus divided by the frequency of the text represents the inverse document frequency (IDF) of a given word. IDF’s primary objective is to determine the relevance of a word by locating the most pertinent documents. Term frequencies alone cannot be used to calculate the importance of a word in a document because TF considers all words to be equally significant. Using Eq. 3, we can figure out the idf [35] after we have found the document frequency of a word (df(w)) by counting the number of documents in which the word appears and the total number of documents (N). idf (w) = log(N/df (w))
(3)
Equation 4 [35] can be used to calculate the tf-idf metric (tf-idf(w, d)) from the term frequency (tf(w, d)) and inverse document frequency (idf(w)) that have already been found. tf − idf (w) = tf (w, d) ∗ idf (w)
(4)
Before computing tf-idf values, the data contained in the targeted documents must undergo a series of pre-processing processes. These procedures include the elimination of common English stop words and all non-letter characters, the conversion of all words to lowercase, and the addition of a single space between each pair of syllables. So, we can get the most out of the data and do searches that don’t care about case for each key phrase. After defining the TF-IDF values, keyword vectors can be created for each of them. There are numerous ways for calculating the similarity between these vectors, including Euclidean distance, cosine similarity, and Pearson’s correlation [38]. The system’s recommendations are based on the most similar docu-
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ments, which in this instance are movies and television programs. In this project, all three strategies were implemented and compared. Euclidean Distance. Euclidean distance is the distance between two points in Euclidean space. Equation 5 [39] can be used to compute the distance between two points in a plane with coordinates p = (x1, y1) and q = (x2, y2). (5) d(p, q) = d(q, p) = (x1 − x2 )2 + (y1 − y2 )2 To calculate the Euclidean distance between two vectors with real-valued components, we must utilize Eq. 6, which is a more generic form of the Euclidean distance formula. Even though Euclidean distance is a typical method for calculating distance, it may result in unbalanced distances since it does not function the same way for objects of different sizes. We have therefore utilized the TF-IDF technique to calculate the feature values. This algorithm generates the normalized data required by the Euclidean distance. While working with this distance measurement, we must also account for the curse of dimension. As the dimension of the data rises, Euclidean distance becomes less useful. This is related to the fact that higher-dimensional space behaves differently from two- or threedimensional space [40]. n (6) d(p, q) = (qi − pi )2 i=1
Cosine Similarity. Cosine similarity is the cosine of the angle between two n-dimensional vectors in a space of n dimensions. It is the dot product of the two vectors divided by the length product of the vectors (or magnitudes). These vectors will be the previously computed keyword vectors in our example. One of its applications is to determine the degree of similarity between two things. This commonality can then be incorporated into a recommendation query. For example, a user may receive movie recommendations based on the preferences of other users who have given similar ratings to previous movies that the user has seen. The cosine similarity values range from −1 to 1, where 1 represents perfect similarity and −1 represents perfect dissimilarity [41]. Equation 7 can be utilized to calculate the vectors.
similarity(A, B) = A ∗ B/(||A|| ∗ ||B||) =
n i=1
n n Ai ∗ Bi /( A2i ∗ Bi2 ) i=1
(7)
i=1
Although this distance measurement is well known, it has some drawbacks. While cosine similarity determines the distances between two vectors, it does not consider the vectors’ magnitudes. This indicates that the differences in values are not fully considered, which could lead to inaccurate results [40]. On the other hand, this method may prove superior to the Euclidean distance for calculating
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the distance between huge objects. The Euclidean distance between two objects may be great, but the angle between them may be modest. The greater the resemblance, the smaller the angle. Pearson’s Correlation. Pearson’s correlation is among the most prevalent correlation techniques. This approach yields a score that can range between −1 and 1, the same output range as cosine similarity. Two extremely comparable objects would have a score close to 1. Unrelated items would have a Pearson score close to zero. Two variables with a Pearson score close to −1 are inversely correlated; one increases while the other decreases. Equation 8 is used to compute the Pearson’s correlation between two items having paired qualities. In this equation, the product of their differences from the item mean is added and then divided by the product of their differences squared from the item mean [42]. This similarity measure is more time-consuming than the two previously stated measures due to its increased computational complexity [43]. n n n [(xi − x ¯) ∗ (yi − y¯)]/( (xi − x ¯) ∗ −¯ y2 ) (8) r= i=1
i=1
i=1
After assessing all of these commonalities, we were able to create multiple recommendation systems for both of our methodologies so that we could evaluate their performance and the resulting suggestions. Taking into account how the different similarity metrics are similar and how they are different, we expect similar results, at least among the top recommendations. 3.3
Movie and TV Show Feature Classification
This section of the study discusses the methods and algorithms used to classify movies and television programs, including ratings and cast members. In addition, we will attempt a brief comparison of various methodologies and describe our anticipated outcomes. K-Nearest Neighbors (KNN) and Naive Bayes Classifiers are two of the most renowned classifiers. Both of these procedures have been applied, and their outcomes are compared. Naive Bayes Algorithm. The Naive Bayes classifier is based on the Bayesian theorem and operates under the premise of a strong independence assumption [44]. This indicates that the presence of one characteristic in a class is unconnected to the presence of other characteristics. This classifier was given the name “naive” [45] because all features of an item of interest independently contribute to the probability that the item belongs to some class. To calculate the posterior probability P (c—x) using P (x—c), P (x), and P (c), the Bayesian theorem provides Eq. 9. In Eq. 9, P (c—x) is the posterior probability of class (c, target) given predictor (x, attributes), P (x—c) is the likelihood, which is the probability of the predictor given class, P (x) is the prior probability of the predictor, and P (c) is the prior probability of class [46].
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P (c|x) = (P (x|c) ∗ P (p))/P (x)
(9)
Since the Naive Bayes classifier often requires a large number of records to provide accurate results, other classification approaches may provide more precise classifications for smaller data sets. Alternately, the computation speed of the training is relatively quick, and we can achieve decent performance by deleting unnecessary features. Taking into account the size of the data set, we expect this classifier to produce less accurate results than the KNN classifier, but results that are still good (above 75% accuracy). K-Nearest Neighbors. The easiest way to explain KNN is to say that the class of an unknown data point is the same as the class of its nearest neighbor, whose basic point is known. This technique computes the nearest neighbor based on the k-value, which determines the number of nearest neighbors to consider. As a result, the class of sample data is defined [47]. The fact that the classification of a given data point may depend on the use of more than one nearest neighbor is one of the reasons for employing KNN. This technique is known as a memory-based algorithm because data points must be stored in memory during execution. The detailed pseudocode of the KNN classifier: 1. Load the training data. 2. Prepare data by scaling, missing value treatment, and dimensionality reduction as required. 3. Find the optimal value for K 4. Predict a class value for new data: (a) Calculate distance(X, Xi) from 1 = 1, 2, 3, ..., n. where X = new data point, Xi = training data, distance as per your chosen distance metric. (b) Sort these distances in increasing order with corresponding train data. (c) From this sorted list, select the top ‘K’ rows. (d) Find the most frequent class from these chosen ‘K’ rows. This will be your predicted class. KNN is one of the most popular machine learning algorithms due to its simplicity. Numerous uses are possible. KNN can be utilized in online marketing, pattern recognition, cluster analysis of image data sets, and similar applications. A single number k must be supplied in order to determine the number of neighbors utilized for classification. KNN requires an integer k, a training data set, and a metric for measuring proximity [45]. The performance of a KNN classifier is mostly determined by the selection of k and the distance metric employed. Due to the possibility of sparse data and mislabeled or ambiguous sites, the local estimate is likely to be somewhat inaccurate if k is set to a small value. One might smooth the estimation by increasing the k value and taking the broad region surrounding the query into account. Unfortunately, a large k number may over smooth estimations and
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degrade classification performance. This is why numerous similar efforts [48–51] have been conducted to enhance this classifier. The greatest benefits of this classification technique are its ease of implementation, rapid training, quick execution speed for small training data sets, and accuracy with many classes. On the other hand, there are constraints that must be taken into account. The two greatest drawbacks are the slow execution speed when working with huge data sets and the memory cost because all data points must be in memory during runtime [46]. Due to the relatively modest size of the data set, this classifier’s expected output would be more accurate than that of the Naive Bayes Classifier, and it would be computed faster.
4
Experimental Results
In this section, we will describe the experimental procedure and present the experimental outcomes. In addition, we will compare the performance of our methods and evaluate them properly. Our experiments are intended to address the following major questions: Q1: Is it true that a large number of specific feature similarities give more accurate movie and TV show suggestions than a single description keyword similarity? Q2: Does using several specific characteristics make more accurate predictions about movies and TV shows than using description keywords? Please note that the “description keyword similarity” refers to the similarity between movies or TV shows that is calculated based on the similarity between the keywords present in their descriptions. On the other hand, the “specific feature similarity” is the similarity that involves using various specific characteristics of movies or TV shows, such as title, director, cast, and genre, to determine the degree of resemblance between them. 4.1
Experimental Setup
Computing Resources and Frameworks. The experiments were done on a Dell (Latitude E5470) laptop, where the operating system was Windows 10 Pro 64-bit (10.0.19043, Build 19043). The processor was an Intel(R) Core(TM) i56300U running at 2.40 and 2.50 GHz (2 CPUs). The memory of the laptop was 8 GB of RAM. For program execution, an Intel (R) HD Graphics 520 graphics card was utilized. The Pandas and Numpy frameworks were utilized for data analysis, cleaning, and feature engineering; the Matplotlib and Seaborn frameworks were used for data visualization; and the Scikit-Learn and NLTK frameworks were used for data analysis. The code is accessible to the public on Github [52]. Model Training. In the initial portion of this study, we did not separate the data set into training and testing data sets but instead used all of the data to determine the similarity between movies and television episodes. In order to
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maximize the efficiency of the method, it was necessary to do the pre-processing stages associated with computing the tf-idf values for column descriptions and all-content following the initial pre-processing steps. We have employed three correlation methods (Euclidean Distance, Cosine Similarity, and Pearson’s Correlation) that have been thoroughly examined to calculate similarity and create recommender systems. In the second stage of our assignment, we have divided the data set into two sections (training and testing data sets). The data has been divided in an 80:20 ratio, meaning that 80% of the data set has been allocated to the training set and 20% to the testing set. During training for both the K-nearest neighbor and Naive Bayes classification models, we used the TV show or movie rating column data as the output and the all-content and description columns as inputs, depending on the technique. This allowed us to implement the two techniques and compare them later on. In addition, we have sought the ideal combination of their hyperparameters that minimizes a preset loss function in order to achieve superior results. This procedure is elaborated upon in the section on parameter adjustment.
Fig. 3. Average cross-validation scores for different k values in the KNN classification model for predicting movie ratings based on the description.
Evaluation Methods. In order to evaluate the accuracy of our movie and TV show suggestion methodologies and compare their performance in the first stage of the study, we have opted to examine the top 10 recommendations for their actual similarity to the movie or TV show that was previously offered. In addition, the results from the second half of the studies have been used to reinforce
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this comparison. Regarding the classification of movie and TV show features in the second part of the work, the evaluation is performed using accuracy ((TP + TN)/(TP + TN + FP + FN)) and precision (TP/(TP + FP)), recall (TP/(TP + FN)), and F-measure (2 PrecisionRecall Precision+Recall), where TP (i.e., true positive instances) is the number of instances predicted correctly; FP (i.e., false positive instances) is the number of instances predicted incorrectly; and FN (i.e., false negative instances) stands for the number of actual instances which are not predicted by our approaches. To test the KNN and Naive Bayes classification models during training, we employed 5-fold cross validation and compared the models’ average crossvalidation scores.
Fig. 4. Average cross-validation scores for different k values in the KNN classification model for predicting TV show ratings using many features.
Parameter Tuning. Due to the fact that the performance of the K-Nearest Neighbors Classifier is largely dependent on the number of nearest neighbors k, we have performed some parameter adjustments in order to achieve its optimal performance. During the training phase, 5-fold cross-validations of KNN classification models with k values ranging from 1 to 100 were conducted. As shown in Figs. 3 and 4, the cross-validation (CV) average scores in numerous models with distinct k values are not identical. To determine the k value for our model, we selected the k value with the highest average CV score. This would have equated to 35 in Fig. 3 and 44 in Fig. 4, respectively.
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Fig. 5. Average cross-validation scores for different alpha and fit-prior values in the Naive Bayes Classification Model for predicting movie ratings based on the description.
On the other hand, fit-prior and alpha were the hyperparameters we desired to optimize in our Naive Bayes model. Fit-prior indicates whether class prior probabilities are to be learned or not, while alpha is an additive smoothing parameter. In addition, we have conducted 5-fold cross-validation on the Naive Bayes classification models. Fit-prior has been assigned the values True and False, whereas alpha has been assigned the values 0.0, 1.0, 2.0, and 3.0. Using the average cross-validation scores illustrated in Figs. 5 and 6, we determined the ideal parameter choices for both models. The best parameter values for the models in Fig. 5 are false for fit-prior and 1.0 for alpha. Figure 6’s alternative optimal model parameters are false for fit-prior and 2.0 for alpha. 4.2
Results
Tables 3, 4, 5, 6, 7, and 8 depict the recommendations generated by the three similarity calculation methods (Euclidean Distance, Cosine Similarity, and Pearson’s Correlation) using the similarity between multiple specific features for the television show “13 Reasons Why” and the movie “My Little Pony Equestria Girls: Forgotten Friendship”. As shown in the preceding graphs, there are relatively minor differences between the suggestions provided by the three approaches for the television series “13 Reasons Why”. For instance, “Sintonia” and “The Walking Dead” were ranked seventh and eighth, respectively, by Pearson’s Correlation, although their rankings were reversed in the other two lists. Even though
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Fig. 6. Average cross-validation scores for different alpha and fit-prior values in the Naive Bayes Classification Model for predicting TV show ratings based on numerous features. Table 3. Top ten recommendations for the TV show “13 Reasons Why” based on Euclidean distance with multiple feature similarity. Rank TV show title 1
13 Reasons Why: Beyond the Reasons
2
Private Practice
3
The Day I Met El Chapo
4
Frequency
5
Grey’s Anatomy
6
Imposters
7
The Walking Dead
8
Sintonia
9
MeatEater
10
Shopkins
there were minor discrepancies throughout the lists, the top 10 television programs on all of them were identical. In contrast, the recommendations generated for the movie “My Little Pony Equestria Girls: Forgotten Friendship” were identical. While the similarity calculation approaches provided the top 10 suggestion lists with modest variances in the first approach (using multiple feature simi-
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Table 4. Top ten recommendations for the movie “My Little Pony: Equestria Girls: Forgotten Friendship” based on Euclidean distance with multiple feature similarity. Rank Movie title 1
My Little Pony Equestria Girls: Rollercoaster
2
My Little Pony Equestria Girls: Friendship Games
3
My Little Pony Equestria Girls: Legend of Ever
4
My Little Pony Equestria Girls: Rainbow Rocks
5
My Little Pony Friendship Is Magic: Best Gift
6
Barbie in A Mermaid Tale
7
Barbie & Her Sisters in a Pony Tale
8
Barbie: Princess Charm School
9
LEGO Marvel Spider-Man: Vexed by Venom
10
Beat Bugs: All Together Now
Table 5. Top ten recommendations for the TV show “13 Reasons Why” based on cosine similarity with multiple feature similarity. Rank TV show title 1
13 Reasons Why: Beyond the Reasons
2
Private Practice
3
The Day I Met El Chapo
4
Frequency
5
Grey’s Anatomy
6
Imposters
7
The Walking Dead
8
Sintonia
9
MeatEater
10
Shopkins
Table 6. Top ten recommendations for the movie “My Little Pony: Equestria Girls: Forgotten Friendship” based on cosine similarity with multiple feature similarity. Rank Movie title 1
My Little Pony Equestria Girls: Rollercoaster
2
My Little Pony Equestria Girls: Friendship Games
3
My Little Pony Equestria Girls: Legend of Ever
4
My Little Pony Equestria Girls: Rainbow Rocks
5
My Little Pony Friendship Is Magic: Best Gift
6
Barbie in A Mermaid Tale
7
Barbie & Her Sisters in a Pony Tale
8
Barbie: Princess Charm School
9
LEGO Marvel Spider-Man: Vexed by Venom
10
Beat Bugs: All Together Now
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Table 7. Top ten recommendations for the TV show “13 Reasons Why” based on Pearson’s correlation with multiple feature similarity. Rank TV show title 1
13 Reasons Why: Beyond the Reasons
2
Private Practice
3
The Day I Met El Chapo
4
Frequency
5
Grey’s Anatomy
6
Imposters
8
Sintonia
7
The Walking Dead
10
Shopkins
9
MeatEater
Table 8. Top ten recommendations for the movie “My Little Pony: Equestria Girls: Forgotten Friendship” based on Pearson’s correlation with multiple feature similarity. Rank Movie title 1
My Little Pony Equestria Girls: Rollercoaster
2
My Little Pony Equestria Girls: Friendship Games
3
My Little Pony Equestria Girls: Legend of Ever
4
My Little Pony Equestria Girls: Rainbow Rocks
5
My Little Pony Friendship Is Magic: Best Gift
6
Barbie in A Mermaid Tale
7
Barbie & Her Sisters in a Pony Tale
8
Barbie: Princess Charm School
9
LEGO Marvel Spider-Man: Vexed by Venom
10
Beat Bugs: All Together Now
Table 9. Top ten recommendations for the TV show “13 Reasons Why” based on Euclidean distance with description keyword similarity. Rank TV show title 1
13 Cagaster of an Insect Cage
2
Twice Upon a Time
3
Hidden Worlds
4
Arrested Development
5
The Untamed
6
Entangled
7
Graceful Friends
8
Mind Game
9
Tango
10
Silvana Sin Lana
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larity), their lists for the input TV program “13 Reasons Why” were identical in the second approach (using description keyword similarity). This is demonstrated by Tables 9, 10 11, 12 13, and 14. In addition, the generated suggestion lists for the movie “My Little Pony Equestria Girls: Forgotten Friendship” were identical as well. Despite the fact that Euclidean distance, cosine similarity, and Pearson’s correlation generated comparable, and sometimes identical, suggestion lists using the same data, there was a discernible difference between the two lists. As seen in Tables 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, and 14, the movies and television shows advised by the first approach were entirely different from those suggested by the second. For instance, the top recommendation in the initial pitch for the Table 10. Top ten recommendations for the movie “My Little Pony: Equestria Girls: Forgotten Friendship” based on Euclidean distance with description keyword similarity. Rank Movie title 1
Line Walker
2
My 100 Things to do Before High School
3
Rock the Kasbah
4
Just In Time
5
Hell and Back
6
Orbiter 9
7
Scream 2
8
Spirit Riding Free: Spirit of Christmas
9
4th Man Out
10
¡Ay, mi madre!
Table 11. Top ten recommendations for the TV show “13 Reasons Why” based on cosine similarity with description keyword similarity. Rank TV show title 1
13 Cagaster of an Insect Cage
2
Twice Upon a Time
3
Hidden Worlds
4
Arrested Development
5
The Untamed
6
Entangled
7
Graceful Friends
8
Mind Game
9
Tango
10
Silvana Sin Lana
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Table 12. Top ten recommendations for the movie “My Little Pony: Equestria Girls: Forgotten Friendship” based on cosine similarity with description keyword similarity. Rank Movie title 1
Line Walker
2
My 100 Things to do Before High School
3
Rock the Kasbah
4
Just In Time
5
Hell and Back
6
Orbiter 9
7
Scream 2
8
Spirit Riding Free: Spirit of Christmas
9
4th Man Out
10
¡Ay, mi madre!
Table 13. Top ten recommendations for the TV show “13 Reasons Why” based on Pearson’s correlation with description keyword similarity. Rank TV show title 1
13 Cagaster of an Insect Cage
2
Twice Upon a Time
3
Hidden Worlds
4
Arrested Development
5
The Untamed
6
Entangled
7
Graceful Friends
8
Mind Game
9
Tango
10
Silvana Sin Lana
television series “13 Reasons Why” was the show’s sequel, “13 Reasons Why: Beyond the Reasons”. In the second approach, the same sequel was not among the top 10 recommendations. The same thing occurred in the movie “My Little Pony Equestria Girls: Forgotten Friendship”. In the first method, the top five choices for this movie included prequels and sequels, but not in the second. Aside from this, some of the movie and TV program recommendations created by the second approach are not in the same genre or have stories similar to the reference movie or TV show. In the initial part of the study, the focus was on exploring and comparing different correlation methods to calculate the similarity between movies and TV shows. However, after evaluating the results, it was found that the differences among the correlation methods were minor, meaning that the choice of correlation method may not have a significant impact on the accuracy of the
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Table 14. Top ten recommendations for the movie “My Little Pony: Equestria Girls: Forgotten Friendship” based on Pearson’s correlation with description keyword similarity. Rank Movie title 1
Line Walker
2
My 100 Things to do Before High School
3
Rock the Kasbah
4
Just In Time
5
Hell and Back
6
Orbiter 9
7
Scream 2
8
Spirit Riding Free: Spirit of Christmas
9
4th Man Out
10
¡Ay, mi madre!
recommender system. Therefore, in the second part of the study, the focus was shifted to implementing and comparing the KNN and Naive Bayes algorithms. These algorithms are popular and widely used in the field of machine learning and have been shown to perform well in various applications. By focusing on these algorithms, the study can direct its attention to more important aspects, such as evaluating the accuracy of the recommendation system, identifying the strengths and weaknesses of each algorithm, and optimizing the hyperparameters to improve the performance. This will provide a more focused and informative analysis of the results and allow for more meaningful conclusions to be drawn from the study. To compare the performance of KNN and Naive Bayes classification models, detailed performance reports were prepared. Tables 15, 16, 17, and 18 illustrate these reports. In addition to the previously indicated performance metrics (precision, recall, f1 score, and accuracy), the column “Support” is also included in the figures. Support is the number of real occurrences of a class inside a given data set. As depicted in Tables 15, 16, 17, and 18, the Naive Bayes model exhibited comparable or superior performance measure values in the majority of instances. In three of four instances, the Naive Bayes model exhibited greater accuracy and precision than the KNN model, while its recall and f1 score were once equal and twice as high. Overall, the Naive Bayes model outperformed the KNN model and was more dependable. Even though the Naive Bayes Classification model produced better results than the KNN Classification model, the performance gap between the two techniques was evident in both models. For both models, the accuracy, precision, recall, and f1 score were higher in the first approach than in the second. Using the same data, our experiments demonstrated that all three similarity computation methods (Euclidean Distance, Cosine Similarity, and Pearson’s Correlation) generated comparable and sometimes identical top-10 recommenda-
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Table 15. KNN and Naive Bayes Classification report over the Movie test data by using multiple feature similarity. Output variable of the rating column
KNN Classification Naive Bayes Classification Support Precision Recall F1-score Precision Recall F1-score
G
0.50
0.11
0.18
0.00
0.00
0.00
9
PG
0.53
0.38
0.45
0.54
0.12
0.19
60
PG-13
0.39
0.37
0.38
0.69
0.12
0.20
93
R
0.45
0.60
0.51
0.35
0.77
0.48
158
TV-14
0.54
0.62
0.58
0.55
0.59
0.57
252
TV-G
0.00
0.00
0.00
0.00
0.00
0.00
16
TV-MA
0.54
0.58
0.56
0.55
0.61
0.58
338
TV-PG
0.33
0.12
0.18
0.36
0.05
0.08
84
TV-Y
0.75
0.21
0.33
1.00
0.36
0.53
14
TV-Y7
0.44
0.21
0.29
0.67
0.21
0.32
19
TV-Y7-FV
0.00
0.00
0.00
0.00
0.00
0.00
Accuracy
0.50
1
0.49
1044
Macro average
0.41
0.29
0.31
0.43
0.26
0.27
1044
Weighted average
0.49
0.50
0.48
0.51
0.49
0.45
1044
Table 16. KNN and Naive Bayes Classification report over the Movie test data by using description keyword similarity. Output variable of the rating column KNN Classification Naive Bayes Classification Support Precision Recall F1-score Precision Recall F1-score G
0.00
0.00
0.00
0.00
0.00
0.00
7
PG
0.00
0.00
0.00
0.57
0.08
0.14
49
PG-13
0.00
0.00
0.00
0.60
0.03
0.06
102
R
0.38
0.14
0.21
0.33
0.12
0.18
155
TV-14
0.32
0.40
0.36
0.35
0.39
0.37
245
TV-G
0.00
0.00
0.00
1.00
0.05
0.09
21
TV-MA
0.40
0.74
0.51
0.40
0.78
0.53
357
TV-PG
0.50
0.01
0.02
0.10
0.01
0.02
81
TV-Y
1.00
0.07
0.12
1.00
0.07
0.12
15
TV-Y7
0.25
0.08
0.12
1.00
0.08
0.15
12
0.39
1044
Macro average
0.28
0.14
0.14
0.54
0.16
0.17
1044
Weighted average
0.32
0.37
0.30
0.41
0.39
0.31
1044
Accuracy
0.37
tions, and the Naive Bayes Classification model produced superior results than the KNN Classification model. Furthermore, in all circumstances and across the entire project, the technique that considered multiple feature similarity outperformed the approach that simply used description keyword similarity.
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Table 17. KNN and Naive Bayes Classification report over the TV Show test data by using multiple feature similarity. Output variable of the rating column KNN Classification Naive Bayes Classification Support Precision Recall F1-score Precision Recall F1-score R
0.00
0.00
0.00
0.00
0.00
0.00
1
TV-14
0.40
0.61
0.48
0.43
0.54
0.48
99
TV-G
0.00
0.00
0.00
0.00
0.00
0.00
14
TV-MA
0.58
0.67
0.62
0.54
0.74
0.63
180
TV-PG
0.61
0.28
0.38
0.73
0.22
0.34
50
TV-Y
0.60
0.10
0.17
1.00
0.16
0.28
31
TV-Y7
0.47
0.31
0.38
0.36
0.17
0.23
29
0.51
404
Macro average
0.38
0.28
0.29
0.44
0.26
0.28
404
Weighted average
0.51
0.51
0.48
0.54
0.51
0.48
404
Accuracy
0.51
Table 18. KNN and Naive Bayes Classification report over the TV Show test data by using description keyword similarity. Output variable of the rating column KNN Classification Naive Bayes Classification Support Precision Recall F1-score Precision Recall F1-score TV-14
0.32
0.33
0.32
0.38
0.35
0.36
TV-G
0.00
0.00
0.00
0.00
0.00
0.00
18
TV-MA
0.49
0.76
0.60
0.52
0.80
0.63
162
TV-PG
0.50
0.09
0.15
0.35
0.13
0.19
46
TV-Y
0.89
0.52
0.65
0.62
0.48
0.54
33
TV-Y7
0.60
0.18
0.28
0.70
0.21
0.33
33
0.49
404
Macro average
0.47
0.31
0.33
0.43
0.33
0.34
404
Weighted average
0.46
0.46
0.42
0.46
0.49
0.45
404
Accuracy
5 5.1
0.46
112
Conclusion and Discussion Discussion of Results
The study reveals that the link between many features and those with simple description keywords is distinct. This research has answered both of our questions regarding the approach one should take when constructing a movie and TV show recommender system based on item similarity and interrelationships and the approach one should take when attempting to predict specific characteristics, such as the movie or TV show rating. In constructing a recommendation system and attempting to categorize certain movies and television programs, the technique in which data from various specific features has been used has proven to be more useful and trustworthy than the approach in which only description keywords have been employed. The importance of these results comes from the fact that it’s possible that more detailed characteristics may make a stronger link between things than description terms.
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Contrary to the expected relationship, certain approaches have performed differently and produced findings that differ from our anticipations. On the other hand, several procedures were executed in accordance with our previously stated hypotheses. Despite the fact that all three similarity computation methods displayed hints of correspondence and minor variances, they all produced nearly identical answers, as anticipated. This was likely due to the relatively modest size of the data set and the fact that we only considered the top 10 suggestions out of a much larger number. Contrary to our assumptions, the movie and TV show feature categorization portion of the findings revealed that the Naive Bayes model delivered superior results to the KNN model. Despite the modest amount of the data set, the KNN approach required more time to train the model and classify the movies and television series from the test sets. This was not due to the hyperparameter choices, as 5-fold cross-validation was used to evaluate the models on training data and select those with the best parameters. Even though Naive Bayes worked well, it could only predict the rating of a movie or TV show with an average accuracy of 50%, which is not enough to be called a good classification model. These results should be considered when determining how to select features that will serve as the building blocks of recommender systems, as they provide new insight into the link between movie and television features in terms of their similarity. Due to a paucity of data on user interactions, the results cannot establish whether the content-based strategy is the optimal method for developing Netflix’s movie and television show recommendation algorithm. But the recommendation systems that came out of this study gave correct suggestions without spoilers, which was the main goal of this study. 5.2
Limitations and Possibilities for Extension
After talking about the results that our study has shown, there are still several limitations that need to be addressed. One limitation is the small size of our data set, which can result in limited accuracy and diversity of recommendations. To address this limitation, we plan to expand our data set by incorporating additional user feedback and incorporating data from multiple sources. Another limitation is the lack of contextual information, such as time or location, in our current recommendation system. Incorporating this information can provide a more personalized experience for users and lead to more accurate recommendations. We plan to explore ways to incorporate this contextual information into our recommendation system to improve its effectiveness. Additionally, while our proposed recommendation system is transparent in terms of its feature selection process, it lacks transparency in terms of how recommendations are generated. To address this limitation, we plan to explore the use of more explainable recommendation models that can provide users with a better understanding of how recommendations are generated. In terms of future extensions, there is potential for incorporating more advanced machine learning algorithms such as deep learning and reinforcement
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learning. These algorithms have shown promise in other areas of machine learning and could potentially improve the accuracy and diversity of recommendations in recommendation systems. 5.3
Conclusion
In recent decades, the movie business has undergone substantial expansion, particularly due to the presence of streaming platforms such as Netflix. With so many movies and television programs on the market, it is becoming increasingly difficult to discriminate between the good and the terrible. Therefore, recommendation algorithms have gained popularity in recent years. Although there are several suggestion websites, many of them include spoilers or are prejudiced since they cater to one person’s tastes. Therefore, these websites may be ineffective or even harmful to their visitors. For this proposed system, a movie recommendation system is constructed utilizing two distinct methods with the aim of comparing them. The first method employs the similarity between several features, while the second method employs the description keyword similarity to determine the degree of resemblance between two movies or television programs. Netflix Movies and Television Shows is the data set used for all tests. Several approaches (Euclidean distance, cosine similarity, and Pearson’s correlation) have been utilized to compute these similarities. The top 10 recommendations they generated were close and occasionally identical. Taking into account the recommendations that resulted, it can be stated that the strategy that utilized numerous attributes produced more accurate findings. The KNN and Naive Bayes classification models were used to categorize movie and television program characteristics, such as the rating, in order to strengthen the comparison between our two methods. Accuracy, precision, recall, and the f-measure have been used for evaluating their performance. Experiments have demonstrated that the Naive Bayes model outperformed the KNN classification model in both ways. In addition, the first method produced more precise and dependable results, confirming the previously indicated comparison findings. This study demonstrates that various and more specific item characteristics could improve the interrelationship of items in content-based recommendation systems. In conclusion, our proposed recommendation system could potentially benefit both movie and television producers and the movie-loving community. By utilizing this methodology, users can discover content that caters to their preferences, leading to more personalized viewing experiences.
6
Future Work
While our study has produced positive outcomes, there is still room for future research that could improve the efficiency and customization of recommendation systems. For instance, we plan to explore additional classification models to further improve classification accuracy and make a deeper comparison between different approaches. One of the main limitations that we have come across is the
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modest size of our data set. To address this limitation, we plan to incorporate multiple sources of data, such as user reviews and ratings, as well as improved data collection and pre-processing techniques, such as feature engineering and data augmentation. Additionally, we intend to develop more explainable recommendation models to enhance transparency and address issues of trust and user privacy. For instance, we plan to explore the use of interpretable machine learning algorithms, such as decision trees and rule-based models, that can provide users with a better understanding of how recommendations are generated. We also plan to incorporate user feedback into the recommendation process to improve user satisfaction and address issues of trust. For example, we could provide users with options to provide explicit feedback on recommended items and use this feedback to update the recommendation models. Moreover, we plan to incorporate social and contextual information into the recommendation process to provide a more personalized experience for users. For instance, we could consider users’ social networks, location, and time of day when making recommendations. This could help us identify more relevant items and provide a more personalized experience. We also plan to explore the use of more advanced machine learning algorithms such as deep learning and reinforcement learning, which have shown promise in other areas of machine learning and could potentially improve the accuracy and diversity of recommendations. Finally, we plan to explore the use of hybrid approaches that combine contentbased and collaborative filtering methods to provide a more comprehensive recommendation system. For example, we could use content-based approaches to recommend items based on their features and attributes, and collaborative filtering approaches to recommend items based on users’ preferences and behavior. This could help us provide more diverse and personalized recommendations that take into account both item features and user preferences.
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A Recommendation System for Movies by Using Hadoop Mapreduce Dinko Omeragi´c1(B) , Aldin Beriˇsa1 , Dino Keˇco2 , Samed Juki´c1 , and Be´cir Isakovi´c2 1
International Burch University, Sarajevo, Bosnia and Herzegovina [email protected] 2 University of Sarajevo, Sarajevo, Bosnia and Herzegovina
Abstract. Recommendation systems have become an integral component of the sales strategies of many businesses. Due to the immense size of data sets, however, innovative algorithms such as collaborative filtering, clustering models, and search-based methods are utilized. This study intends to demonstrate the benefits of the Hadoop MapReduce framework and item-to-item collaborative filtering by developing a userratings-based recommendation system for a larger movie data set. The resulting system offers information on movies filtered by year, director name, or comparable movies based on user reviews. Thus, we have been able to deliver credible movie suggestions based on these lists. The evaluation indicates that the recommended approaches are accurate and reliable. Keywords: Machine learning · Big Data systems · Collaborative filtering
1
· Hadoop · Recommender
Introduction
Recently, recommender systems have become an integral part of our daily lives. The large volume of available information necessitated the creation of systems to filter this data and extract the essential information. Many of us typically rely on external knowledge to assist us in making judgments regarding a course of action or an item of interest. For instance, if we intend to purchase a CD or visit a new club. When deciding whether to purchase a book, we can rely on the opinion of a person with similar reading preferences. On the other hand, we sometimes examine only the information available about the thing and our preferences. Researchers considered the numerous factors that influence decisionmaking. They are attempting to model as many as possible in recommender systems in order to achieve high accuracy [1,2]. In the 1990s, when the first publications on collaborative filtering were published [3], interest in recommender systems grew. Even if there are a lot of different approaches and algorithms used in recommender systems, they are typically categorized into three groups when evaluating the way in which they work. c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´ c et al. (Eds.): IAT 2023, LNNS 644, pp. 329–340, 2023. https://doi.org/10.1007/978-3-031-43056-5_24
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These include content-based, collaborative, hybrid, and other strategies [4]. The content-based approach attempts to propose a film to a user based on the film’s description, the user’s interests, and past behavior. While the content-based approach considers the preferences of a single user, the collaborative filtering approach [5–7] takes into account the rating behavior of multiple users when proposing movies. The primary objective of this strategy is to locate comparable consumers (commonly referred to as “neighbors”) and recommend similar products to them. The advantage of the collaborative approach comes from the fact that domain expertise is unneeded as it uses user feedback on different topics for recommendations [8]. This strategy is quite widespread these days and is adopted by many huge firms that depend on providing excellent service to their clients, such as LinkedIn, Facebook, Spotify, Amazon, Google News, Twitter, and Last.fm [9]. Due to the problems with the above methods, researchers have come up with hybrid methods that combine the best parts of content-based filtering and collaborative filtering. Researchers highly recommend using these techniques. Recommendation engines produce recommendations based on numerous user reviews, ratings, likes, dislikes, and comments. In this research, we are going to work with one sort of rating-movie ratings [10,11], although there are also other reviews, such as camera reviews [8], book ratings [12], video game reviews [10], and restaurant ratings [12]. The aim of the research paper is to develop a movie recommender system using machine learning techniques and Hadoop MapReduce to process a large data set of movie ratings. The system will take into account various attributes and user reviews to generate personalized movie suggestions for users. The paper aims to contribute to the field of recommender systems by evaluating the effectiveness of the proposed method in terms of accuracy and scalability. In the following sections, we present the details of our proposed movie recommendation system. Section 2 provides a literature review of previous research on movie recommendation systems and compares our approach with those works. Section 3 describes our proposed recommendation system in detail, including the features used for similarity calculations. We also explain the data set and the preprocessing steps involved in preparing it for the experiments. Section 4 presents the step-by-step guide on how we have performed our experiments. Section 5 provides a comprehensive discussion of the main findings and results that we have got in our study. Finally, in Sect. 6, we present the conclusions drawn from the experiments.
2
Related Work
This section of the study discusses the research on recommender systems conducted by various writers over the past two decades. Midway through the 1990s, when the first publications on collaborative filtering were published [3], there was a rise in interest in recommender systems. In spite of the fact that these systems employ a variety of algorithms and methodologies, we have focused on
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the most well-known and widely used strategies. In the next section, we’ll talk about and compare different modern classification methods to figure out which one is best for our problem. Reddy et al. [13] drew the conclusion that by using movie genres and users’ movie ratings, they could create a system that recommends movies with a high degree of accuracy, after researching numerous elements that can be employed for developing a movie recommendation system. This strategy has the advantage of being easy to comprehend because it focuses on a single user and does not require data from other users when making recommendations. It offers movies of the same genre to the user [13] based on the movies that the user has rated with higher grades. However, collaborative filtering, an alternative technique for constructing recommender systems, has revealed its shortcomings. As indicated in the works of Herlocker et al. [7] and Cui et al. [5], the fundamental component of collaborative filtering systems is locating comparable users (sometimes referred to as “neighbors”) to recommend similar items to them. The benefit of this strategy is that topic expertise is not required, as user feedback on various products is used to generate recommendations [8]. This strategy is rather prevalent nowadays, and many significant firms that rely on providing the appropriate service to their clients, such as LinkedIn, Facebook, Spotify, and Amazon [9], employ it. Unfortunately, even this strategy has drawbacks. The “cold-start problem” [14], which indicates the lack of interactions with new objects on which this technique relies when producing recommendations, is a well-known disadvantage. Since this is a serious problem, researchers [15,16] have attempted to explain its severity and provide a solution. In addition to the two methodologies already stated, there are other hybrid methods [16,17] that combine the most advantageous aspects of these two approaches. Researchers integrate these methods in various ways. While some use the results of one way as input for the second, others attempt to blend the outcomes of both approaches. Wang et al. [16] found that clustering and genetic algorithms can be utilized to optimize and enhance the precision of collaborative filtering. In addition, additional studies [18,19] have asserted and experimentally demonstrated that this finding is fruitful. Other approaches, in addition to the three previously described ones, consider other elements and provide more specific ways correspondingly. Methods based on emotions [20], rule-based [21] and tag-based [22] approaches, tailored recommenders [23,24], etc., can be employed. Different strategies and approaches for implementing recommender systems are discussed in the papers and articles covered in this part. As can be observed, one of the most important elements is that our selection of collaborative filtering for our movie data set was influenced by the data contained in the data set.
3 3.1
Materials and Methods Data
Since we’ve decided to create a movie recommendation system, we need a database with current information on movies. Therefore, we selected the MUBI
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SVOD Platform Database for Movie Lovers [25] data set. This data collection contains more than 15 million movie ratings, 745,000 movie critics, and 225,000 movies. After examining the data in the other files in the set, we chose to only use mubi-ratings-data.csv and mubi-movie-data.csv. There are 15.5 million rows in the first file. The thirteen columns or features are as follows: – – – – – – – – – – – – –
Movie ID - Movie ID related to the rating Rating ID - Rating ID on Mubi Rating URL - URL to the rating on Mubi Rating score - Rating score ranging from 1 (lowest) to 5 (highest) Rating timestamp UTC - Timestamp for the movie rating made by the user on Mubi Critic - Critic made by the user rating the movie. If value = “None”, the user did not write a critic when rating the movie Critic likes - Number of likes related to the critic made by the user rating the movie Critic comments - Number of comments related to the critic made by the user rating the movie User ID - D related to the user rating the movie User trialist - 1 = the user was a trialist when he rated the movie; 0 = the user was not a trialist when he rated the movie User subscriber - 1 = the user was a subscriber when he rated the movie; 0 = the user was not a subscriber when he rated the movie User eligible for trial - 1 = the user was eligible for trial when he rated the movie; 0 = the user was not eligible for trial when he rated the movie User has payment method - 1 = the user was a paying subscriber when he rated the movie; 0 = the user was not a paying subscriber when he rated
On the other hand, the second file has 227 000 rows and ten columns/features, they are: – – – – – – – – – –
Movie ID - ID related to the movie on Mubi Movie title - Name of the movie Movie release year - Release year of the movie Movie URL - URL to the movie page on Mubi Movie title language - By default, the title is in English Movie popularity - Number of Mubi users who love this movie Movie image URL - Image URL to the movie on Mubi Director ID - ID related to the movie director on Mubi Director name - Full Name of the movie director Director URL - URL to the movie director page on Mubi
In this work, the data collection has been preprocessed to accommodate the recommender system under construction. Important information is contained in the rating-score, user-id, and movie-id columns of the mubi-ratings-data.csv file. Rows containing null values in these columns will be discarded. The presence of these values may result in inaccurate outcomes. Due to the size of the data
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sets, we attempted to exclude unnecessary information and reduce the time and resources required to load and process these files. This is why we removed columns from the mubi-movie-data.csv file that we did not require, leaving only movie-id, movie-title, movie-release-year, movie-popularity, and director-name intact. In addition, the mubi-ratings-data.csv file is missing several additional columns. The remaining columns were as follows: user-id, movie-id, rating-id, and rating-score. Figures 1 and 2 depict the previously described preprocessing steps and the dataset’s specifics.
Fig. 1. Block Diagram of the mubi-movie-data.csv file preprocessing.
Fig. 2. Block Diagram of the mubi-ratings-data.csv file preprocessing.
3.2
Hadoop MapReduce
Due to the scale and complexity of the data collection, we have chosen Hadoop MapReduce to process the large amount of data in a dependable, fault-tolerant manner. Hadoop MapReduce is the most widely used open-source implementation of Google’s MapReduce architecture [26]. Typically, a Hadoop MapReduce task consists of two user-described functions: map and reduce. The set of keyvalue pairs (k, v) is the input for a Hadoop MapReduce job, and for each of these pairings, the map function is invoked. The map function produces zero or more intermediate key-value pairs (k’, v’) as its output. The reduce function is then invoked for each group of key-value pairs grouped by the intermediate key k’. Lastly, the reduction function returns zero or more aggregated results [27]. Using Hadoop MapReduce has the advantage that users typically only need to define the map and reduce functions. Concurrently, frameworks handle the remainder, including failover and parallelization. The Hadoop MapReduce framework utilizes a distributed file system in order to read and write its
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data. Typically, the Hadoop Distributed File System (HDFS), which is the opensource equivalent of the Google File System [28], is deployed. Therefore, the I/O performance of Hadoop MapReduce jobs is highly dependent on HDFS. 3.3
Item-to-Item Based Collaborative Filtering
Linden et al. [29,30] were the first to use the phrase “item-to-item collaborative filtering method.” They presented an algorithm for developing a precise and scalable recommendation system for Amazon’s large user data sets. The algorithm works by comparing the purchased and rated items of a user to similar items, as opposed to matching similar users. To do this, they employ the concept of cosine similarity, where each vector represents a product and the dimension of the vector indicates the number of people who have purchased the product. In Section III-D, cosine similarity is explained in further depth. This method allows the algorithm to scale regardless of the goods or users. It depends solely on the quantity of goods a user has purchased or rated. Since the algorithm works with strongly connected, comparable things, it is able to generate effective recommendations. In comparison to other collaborative filtering algorithms, it performs well with limited data, such as one or two items. 3.4
Cosine Similarity
Cosine similarity is the cosine of the angle between two n-dimensional vectors in a space of n dimensions. It is the dot product of the two vectors divided by the length product of the vectors (or magnitudes). In our case, those vectors will be the previously computed keyword vectors. One of its applications is to determine the degree of similarity between two things. This commonality can then be incorporated into a recommendation query. For example, a user may receive movie recommendations based on the preferences of other users who have given similar ratings to previous films that the user has seen. The cosine similarity values vary from −1 to 1, where 1 represents complete similarity and -1 represents perfect dissimilarity [31]. Use the Eq. 1 to calculate the vectors. similarity(A, B) = A ∗ B/(||A|| ∗ ||B||) =
n i=1
n n Ai ∗ Bi /( A2i ∗ Bi2 ) i=1
(1)
i=1
Although this distance measurement is well known, it has some drawbacks. While cosine similarity determines the distance between two vectors, it disregards the magnitude of the vectors. This indicates that the differences in values are not fully considered, which could lead to inaccurate outcomes [32]. However, when dealing with large items, this method outperforms other similarity computing algorithms, including the Euclidean distance [33].
4
Experimental Setup
After preparing the data, we were able to implement the aforementioned approaches and begin constructing the recommendation system. In order to
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develop the final recommendation program, we have split our system into two pieces following the preprocessing step. These components were subsequently integrated into a tiny application that makes recommendations based on the user’s preferences using the obtained datasets. In the next section, we’ll talk about the frameworks and computing resources used to build the system. 4.1
Computing Resources and Frameworks
The first portion of the research was done using a Dell (Latitude E5470) laptop, where the operating system was Windows 10 Pro 64-bit (10.0.19043, Build 19043). The processor was a 2.40 GHz (2 CPUs) and 2.50 GHz Intel(R) Core(TM) i5-6300U. The memory of the laptop was 8 GB of RAM. For program execution, an Intel (R) HD Graphics 520 graphics card was utilized. For the second part of the trials, we employed Amazon Web Services (AWS) in order to handle massive amounts of data and speed up the process. The cluster of virtual machines (VMs) that we utilized consisted of eight nodes (1 master and seven slaves). Pandas and Numpy were utilized for data analysis, cleaning, and feature engineering, while Hadoop MapReduce was employed for data processing. The first section of the system was written in Java, while the second and final sections were written in Python. The construction of our system will be described in further detail in the following section. 4.2
Part 1
After preprocessing the datasets, we mapped the movie ID to each rating in the mubi-ratings-data.csv file. The remaining pairings are then minimized by grouping them by movie ID and averaging their ratings. The reduction function’s result is exported to the calculated average.txt file. The duration of its execution was twenty seconds. 4.3
Part 2
After preprocessing the dataset, we mapped the user ID to the movie ID and rating in the mubi-movie-data.csv file. To decrease the number of generated pairs, we sorted them by user ID. By mapping every pair of movies a user has viewed with their respective ratings, we have been able to obtain information about the interrelationships between various films. These pairs have been reduced by calculating the similarity score between rating vectors for each pair of films viewed by several individuals. cloudProcessing.csv is the filename for the output that has been mixed and exported. This section was carried out for 9 h and 15 min. 4.4
Final Program
By combining the outputs of the two previously described components, the final application is able to present users with movie suggestions depending on their
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preferences. As shown in Fig. 3, the completed software can display three distinct outputs depending on the choice selected by the user. By entering the year, the name of the director, or the name of the movie, the user can find the best movies from that year, movies directed by that director, or movies that are most like the given movie.
Fig. 3. Block diagram of the final system workflow. Table 1. Recommendations for the movies released in year 1999. Index
Movie id
Movie title
Movie release year
Movie popularity
Director name
Average grade
31748
469976
Welcome To Australia
1999
6
Alan Lowery
5
27475
422284
Green Desert
1999
2
Anno Saul
5
53130
94461
No ran lejos de Adnromeda
1999
2
Juan Vicente Araya
5
55015
96639
Geri
1999
2
Molly Dineen
5
77040
124119
Shooting the Past
1999
2
Stephen Poliakoff
5
128779
223390
In the Surroundings 2
1999
0
Stjepan Sabljak
1
133194
233953
Culture
1999
0
Ari Gold
1
137413
245676
Rosalinda
1999
0
Karina Duprez, Beatriz Sheridan
1
137418
245682
For Your Love
1999
0
Luis Eduardo Reyes
1
139857
253363
Now and Then, Here and There
1999
0
Masahiko Otsuka
1
5
Discussion and Results
As previously stated, we used various film characteristics to create recommendation systems depending on user interests. On the basis of the data processing
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Table 2. Recommendations for the movies directed by Steven Spielberg. Index
Movie id
Movie title
Movie release year
Movie popularity
Director name
Average grade
3116
3401
Schiendler’s List
1993
5199
Steven Spielberg
4.061894
3112
3397
Raiders of the Lost Ark
1981
4263
Steven Spielberg
4.017621
3153
3439
Jaws
1975
3193
Steven Spielberg
3.838067
3114
3399
Saving Private Ryan
1995
2293
Steven Spielberg
3.780391
3156
3442
Close Encounters of the Third Kind
1977
1082
Steven Spielberg
3.764647
112063
184903
Columnbo
1971
16
Steven Spielberg, Vincent McEveety
3.746269
5162
10613
Duel
1971
264
Steven Spielberg
3.717331
112766
185984
Night Gallery
1969
2
Steven Spielberg, Jeannot Szware
3.714286
6871
19690
Jurassic Park
1993
1087
Steven Spielberg
3.693591
6028
15144
Indiana Jones and the Last Crusade
1989
508
Steven Spielberg, Boris Sagal
3.686742
Table 3. Recommendations for “Deadpool” generated by using the similarity between movies according to the user reviews. Index
Movie title
Similarity score
795546
Deadpool 2
0.96806469
Movie popularity 797
1184085
Spider-Man: Into the Spider-Verse
0.95851828
649
1086603
Captain America: Civil War
0.95609404
1097
214688
Spider-Man: Homecoming
0.95500660
871
214587
Avengers: Infinity War
0.95378048
911
408312
Guardians of the Galaxy
0.95257781
1427
1184084
Ant-Man
0.951545784
996
1086602
Logan
0/951545784
1107
311132
The Dark Knight
0.95148406
1656
795546
Captain America: The Winter Soldier
0.95107499
991
script, two new data sets have been constructed. The first one featured information about the films, including film ID, film title, film release year, film popularity, director name, and average grade. Alternatively, the second data collection featured information regarding the similarity of movies based on the preferences of individuals who purchased similar things or viewed similar films. To demonstrate the true utility of our results, we developed a simple program that uses our results to deliver personalized recommendations to users. Java and Python were used to write the code.
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We indicate that user ratings organized by diverse movie criteria, such as year and director, may be reliable sources for recommending movies. Taking into account the similarities between movies based on user reviews, we can also create a dependable method for recommending movies. In our study, we took advantage of the Hadoop MapReduce architecture to process the large amounts of data in our datasets and then used the generated lists to make movie suggestions based on user preferences. Table 1 shows the top movie recommendations based on the films released in 1999, along with their average user ratings. This table demonstrates how our recommendation system can provide personalized suggestions based on user preferences. For instance, if a user is interested in films released in a particular year, they can use this recommendation system to discover new titles. In this example, the top recommendation is “Welcome To Australia,” which has an average user rating of 5 out of 5. Table 2 displays the most recommended films directed by Steven Spielberg. This table demonstrates how our recommendation system can provide recommendations based on a specific director or filmmaker. Users who are interested in a particular director can use this recommendation system to discover new titles that match their interests. In this example, the top recommendation is “Schindler’s List,” which has an average user rating of 4.06 out of 5. In Table 3, we are showing the top recommended movies based on their similarity to “Deadpool,” a movie about a superhero with a particular sense of humor. As we look at the list, we can see that the first recommendation is “Deadpool 2,” which is the sequel to the first movie and is considered to be very similar to the original “Deadpool.” As we move down the list, we can see that the other movies are also in the same genre, specifically movies about superheroes. This means that the movies on the list have similar plotlines and characters, and would likely be appealing to someone who enjoyed “Deadpool.” Overall, these tables showcase how our recommendation system can provide personalized movie suggestions based on a variety of user preferences and characteristics. Users can use our system to discover new films that match their interests and explore different genres and directors.
6
Conclusion
The purpose of this academic work was to demonstrate the advantages of Hadoop MapReduce for developing movie recommendation systems. In addition, we discussed how item-to-item collaborative filtering may be used to calculate the similarity of movies based on user ratings and reviews. Due to the massive amount of data contained within our data sets, we have deployed the Hadoop MapReduce framework to process the data. The processing of the data yielded multiple lists, including information about movies filtered by year, director, or comparable films based on user reviews. The lists were then utilized to generate movie suggestions based on user interests. The evaluation demonstrated that our recommendation methods are trustworthy and reliable. In the future, we intend to
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do more thorough formal studies of the data and attempt to compute additional recommendation systems based on other movie characteristics, such as film genre and cast members.
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Business-Driven Analysis of Website Traffic: A Case Study Aldin Kovaˇcevi´c(B)
, Amela Mudželet Vatreš , and Samed Juki´c
Faculty of Engineering, Natural and Medical Sciences, International Burch University, Ilidža, Bosnia and Herzegovina [email protected]
Abstract. The “Information Age” changed the business industry, not only in the sense of creating, but also in the sense of promoting and delivering content to the end customers. Almost every business – regardless of its size – has its own website. However, not all customers and visitors intend to buy a product – or are even humans. The main purpose of this research is to provide statistics on web page visitors, with a special focus on user demographics, conversion rates and fraudulent behaviors or non-human visits, using the data provided by the tribeOS company. This research uses Python tools for data mining, analysis, and visualization. Among other things, the results show the exact number of bot visits and their sources, visit session durations, device types, operating systems and browsers used, as well as the average conversion rate. Therefore, the results presented in this research can serve as a guideline for improving website traffic and generating more sales results. All of the analysis results are compared to the ones published in previous research efforts, which serves to further corroborate our conclusions. Keywords: visit sessions · visitors · device type · browser · operating system · conversion rate · fraudulent behavior · bot · website traffic
1 Introduction In today’s extremely competitive business market, having an online presence in the form of a website (or even a simple landing page) has become a necessity [1]. Apart from enriching the image of a business, a corporate website also provides benefits in terms of increasing electronic product sales, or e-commerce [2], information retrieval, branding and services [3]. Awareness of the World Wide Web and related Internet technologies is driving more and more traditional companies to conduct their operations online [4]. The Internet allows businesses to interact with their customers in several ways. One of the more popular and useful use cases (other than sales and e-commerce) is website analytics, or the analysis of the information usage of various web sites. By analyzing website visitor activities, it is possible to extract a plethora of useful customer information. Website analytics can give insight into users’ demographics, device and browser information, favorite pages, average visit duration, conversion rate, the reliability of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 341–358, 2023. https://doi.org/10.1007/978-3-031-43056-5_25
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website design and architecture, etc. [5]. Depending on the size and popularity of a website, collected visitor data can range from a few thousand records to several million records, requiring specialized database systems and gigabytes of storage space. Most commonly, this data is kept in its original raw format, requiring further processing and transformation to yield useful information. The gathered information can then be analyzed to shape further business decisions, based on user behavior and preferences. Business and commercial web sites have been using website traffic information and data mining techniques to ascertain and capitalize on the behavior of their customers for a long time [6]. In order to drive higher quantities of relevant traffic to their web sites, many businesses and organizations employ various third-party marketing and advertising tools, such as Google AdSense, Google Analytics, Facebook Insights, etc. One company offering such tools is tribeOS [7], a digital advertising platform that delivers real-time ad traffic by using comprehensive tracking scripts. For the purposes of this research, tribeOS provided access to a subset of their anonymized tracking information about the visitors that had accessed websites utilizing tribeOS services. Web analytics can serve as one of the most powerful BI tools at a company’s disposal. However, this field has not attracted much interest in academia, mainly due to the difficulty of obtaining access to such type of data [5]. Therefore, this research is structured as a case study that utilizes a unique opportunity to analyze website customer data, with the aim of extracting relevant business information and drawing useful conclusions. After obtaining the appropriate data set and transforming it for the purposes of the paper, the processed information is analyzed and compared to existing results, backed by various other research. Logical explanations for expected and unusual occurrences in the data have been found, with a focus on business aspects and relevance of collected information. The main contributions of this paper can be summarized as follows: (1) performing a set of exploratory and descriptive data analyses on the visit session data set, with the goal of extracting useful business conclusions and (2) comparing the obtained results to previous known research, in order to corroborate similarities and compare differences in visit behaviors.
2 Literature Review As previously mentioned, website analytics can bring considerable and lasting benefits to companies with an online presence [5], as already demonstrated by numerous businesses [6]. However, due to the difficulty of data retrieval and the procedures involved with comparing similar patterns between different data sources, this academic field has garnered relatively little interest [5]. The conducted research in web analytics covered a variety of distinct topics. Certain website analytics studies focused on improving the design of web pages, such as the one by Spiliopoulou [8], or to better understand information flows, like in Thelwall’s 2011 study [9]. However, several studies were focused more on data mining and the definition of web sessions hits, such as Cooley et al. [10], Pitkow [11] and Wang & Zaïane [12]. Moreover, a number of techniques have been used to improve website session extraction and processing methods, such as the statistical language model by Huang et al. [13] and Markov models by Deshpande & Karypis [14].
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With the rising number of internet users, it has become evident that website design and analysis should be carried out in a competent and professional manner to achieve an increase in profit. In his article [15], Venkatesh C.R. discussed the power of web analytics for business growth. Venkatesh emphasized the importance of setting clear business goals, tracking website metrics, and using website data to make informed decisions that drive business growth. According to Omidvar et al. [16], without concrete metrics available through website analytics, website optimization (WSO) is a “guessing game”, representing a considerable financial risk. Additionally, huge quantities of data are gathered using web analytics tools, making it difficult to process them all effectively. Therefore, the Web Analytics Association Standards committee defined the most crucial website analytics data and knowledge that is required for successful website development [17]. They define the three most important metrics as: unique visitors, visits/sessions, and page views. Additionally, the Web Analytics Association gave prominence to search engine marketing (SEM), focusing on counts (visits), ratios (page views per visit) and key performance indicators [17]. As outlined by King [18], continual improvement of SEM campaigns, conversion rates and website performance leads to increased profits, happier customers and higher return on investment (ROI), in comparison to competitors. Additionally, Kim, Shin and Yoo in their paper [19] explored the use of website analytics to improve customer experience. They highlighted the importance of using website data to better understand pain points and improve user experience through website optimization. All of this reaffirms the statement that website development is a dynamic and ongoing process guided by the gathered and inferred knowledge about its visitors. Zheng & Peltsverger [20] have defined two major methods of collecting website traffic data: web server logging and page tagging (with the third approach, application logging, becoming more and more popular recently). Web server logging is a traditional method of data collection, in which textual logs produced by web servers (Apache, Nginx, etc.) are extracted and analyzed. These web log files typically record server information and activities, and the visitors’ HTTP headers [21]. The second, more recent and widely employed, approach known as “page tagging” uses client-side programs such as embedded JavaScript code, invisible pixels or browser plugins. The embedded program collects the information about a visitor and sends it to a processing server, where it will be stored and/or analyzed [21]. This method is commonly offered by third-party service providers such as Google Analytics, Open Web Analytics and similar businesses. Whereas web server logging is less invasive and does not require website modifications [22], page tagging is generally considered more advantageous [23], providing access to additional client-side data such as screen size, color depth, mouse movement and keyboard presses, etc. This paper utilizes the information provided by tribeOS, which offers website analytics data through page tagging. The company provides a custom JavaScript code which needs to be embedded into a client website. This research attempts to cover the common metrics established by the Web Analytics Association [17]. In addition to that, the research paper plans to establish a supplementary analysis of various other potentially useful visitor metrics, such as visitor devices (operating systems and browsers), visit sources and sales. This is performed in hopes of gaining more insight into the habits and profiles of recurring website visitors.
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3 Materials and Methods The following chapter outlines an overview of the utilized dataset, the steps performed during data preprocessing, and the detailed explanation of the business and research analytics performed in this research. 3.1 Dataset Overview The examined dataset comprises a subset of website visits recorded by tribeOS. The data has been made available by the company for the purposes of research, academia and business analytics. The dataset comprises 1,000,000 visit session records collected between April 21st and June 7th, 2020. The records in the dataset belong to various tribeOS clients (119 unique websites in total), who utilize the tribeOS tracking script. Most of clients are sale-driven businesses that want to increase their conversion rate and improve marketing decisions. Each “visit session” represents one visit to a website, whether organic or ad-driven, during which the user performed various online activities on said website. Each record contains several key fields, such as a unique MongoDB identifier, ID of the advertising party, the name of the website, a user’s fingerprint and cookie (which can be utilized to recognize unique and recurring visitors), date of visit and session duration (how much time a user spent on the site). Moreover, the records contain geographical data, such as country, region, city and ZIP code information, and parsed user agent details, which include the device type from which the visits originated, device model and manufacturer (when available), operating system and browser details. Lastly, the visit session rows contain other useful analytical and marketing data, such as information about whether a visit is associated with a sale – conversion [23], visit source (organic or ad-driven) and whether the visit is fraudulent (resulting from various aspects of ad fraud). All in all, even though the available dataset represents only a subset of the entire tribeOS collection, it is still full of valuable information which can serve as a basis for various interesting analyses. Data Cleaning and Preprocessing. All incoming data is cleaned, processed and formatted as JSON on the application side of tribeOS, meaning that the database already contains clean data. For that reason, there was not much cleaning or preprocessing to be done before analysis. One operation that was performed was feature selection of relevant features (all of which are explored in the upcoming Chapter), since not all data stored by tribeOS was relevant for the purposes of this research. The flow graph in Fig. 1 showcases the key steps of the research process outlined in this paper.
3.2 Utilized Technologies The collection subset was obtained in a comma-separated value (CSV) format, for easier compatibility with Python data analysis tools. For the purpose of data processing and three different frameworks were used: Pandas, NumPy and Matplotlib. Pandas is an opensource Python library widely used in data munging and wrangling [24]. Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms [25]. Building on
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Fig. 1. Flow graph with key steps.
top of NumPy, Basemap is another useful graphical library for modeling geographical maps [26]. NumPy is a powerful Python library for scientific computing, namely linear algebra, Fourier transformations and random number capabilities [27].
4 Results of Analysis The primary focus of this research is on data analysis, which is usually defined as a process of cleaning, transforming and modeling data to discover useful information for decision making or deriving conclusions [28]. Following paragraphs represent the results of in-depth data analysis which will later be compared to previously conducted research results. 4.1 Exploratory Data Analysis The dataset provided by tribeOS contains 1,000,000 records, obtained in period between April 21st and June 7th, 2020. It features 38 attributes – containing information about user cookies, country/region/city of the visitor, ISP and IP of the visitor, whether the visitor is a “crawler” (bot) and why they are thought to be one, whether a sale occurred during the visit, user agent and device information, etc. Among the million visit sessions, there were 877,202 unique users. Since the website is mostly intended for the US market, the highest number of visit sessions comes from the United States – 873,558 of them, representing 90.4% of overall traffic. It is followed by Canada, Germany and United Kingdom visitors, whose numbers are considerably lower. The graphical representation of countries from which website visits are recorded can be seen on Fig. 2. When all visit sessions are taken in consideration, 3,733 of them have identified themselves as bot visits. Therefore, almost 0.4 percent of the total visit number belongs to known bots, which can be seen in Fig. 3. It should be noted that these are “selfidentified” bots – visitors that clearly specify in their user agent that they are not a human visitor.
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Fig. 2. Countries with most originating visits.
Fig. 3. Human vs. bot visits.
Most of the website visits occurred from mobile phones, with a total number of more than 600,000 mobile phone visits. After mobile devices, customers prefer to use personal computers. A small number of visits originated from tablets, TVs or other devices. In percentages, mobile users comprise 60.7% of total visits, 34.26% belongs to personal computer users, while only 5% of visitors used other device types. The distribution of device types is presented on Fig. 4. On the other hand, Fig. 5 represents the distribution of operating systems used while visiting the websites. The highest percentage of visitors used the iOS operating system – 40.63%. They are followed by Android users – which is expected due to the total number of mobile phone visits. When it comes to PC visits, Windows users represent the most common group – and are followed by Mac OS and Chrome OS users. Most of the visit sessions occurred using the Safari browser. Therefore, it can be assumed that those browser visits mostly belong to iOS users. Chrome is the second most preferred browser, followed by Edge, Facebook Browser and Samsung Browser. In total, there are 89 different browser types from which the visits were recorded. For an easier preview, the distribution of the 10 most used browsers is presented in Fig. 6. The highest number of visits are the actual browser visits, taking up more than 50% of the total number. The second most common application type is the WebView, or mobile applications which simply display web content as part of the activity layout. On the other hand, specific native application types are less common. Top 10 most used browser/application types can be seen in Fig. 7.
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Fig. 4. Distribution of device types.
Fig. 5. Distribution of operating systems.
Fig. 6. Distribution of browsers.
The most common visit type by far is a “page visit”, which means that the user arrived at a website organically. There are close to 980,000 organic visits – 97.35%. A small number of page visit sources are ad impressions or ad clicks (“purchased” visits), as seen on Fig. 8. One of the most important parameters for decision making might be the number of sales that occurred from page visits. Among a million visits, only 4,130 ended up with a sale. In tribeOS metrics, sales are known as “conversions”. As seen in Fig. 9, the average conversion rate across all tribeOS client websites is 0.41%. The longest visit session lasted for 1,665,316 s – which is more than 19 days. The most likely explanation for a session this long is that the user left the website open in a browser tab, and closed it after several days. There are also several sessions that lasted for
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Fig. 7. Distribution of browser and application types.
Fig. 8. Page visit sources.
Fig. 9. Web page visits vs. sales.
0 s, known as zero-length sessions. A possible explanation is the lack of user interaction with the website; the tracking script starts recording the session duration when the user first interacts with the page (clicking, scrolling, etc.). Zero-length sessions could be due to users opening a page, but not performing any actions on it before closing it. A part of these zero-length sessions has also been proven by tribeOS to be bot visits, by comparing them to results in Fig. 3. This fits into the previous explanation, as bots do not directly interact with the page as a user would do. According to Fig. 10, there is a high number of 0-s lasting sessions, accounting for more than ¼ of recorded visits. The highest number of sessions lasted between 0 and 150 s, while very few of them were more than 300 s long. Figure 11 represents the same data with a different duration range used – 0 to 300 s
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(not counting the zero-length sessions). The figure showcases a peak of visit durations lasting 120–150 s. The comparison of session durations can be seen in the following two figures.
Fig. 10. Distribution of session durations.
Fig. 11. Distribution of session durations (without zero-length sessions).
The results of session duration analysis also show that the session duration median in general is 33 s. However, the session duration median for the sessions that ended with a sale is 234 s, meaning that the sessions which result in a sale last longer than the usual “visit-only” sessions. The longest “sale” session lasted for 88,369 s, which is a whole day. Figure 12 shows the distribution of session durations with sales.
Fig. 12. Distribution of session durations (with sales).
Violations are visits which are considered fraudulent or abnormal by the tribeOS AdProtector algorithm, such as bot visits, visits from IP addresses associated with malicious activities, visits from spam websites, etc. The number of violator visits is 229,536, and the ratio can be seen in Fig. 13. More violations come from ad-driven sources than the organic ones. This conclusion can be drawn from the comparison graphs presented on Fig. 14, where the right side represents the ad-driven traffic.
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Fig. 13. Legitimate vs. fraudulent visits.
Fig. 14. Fraud distribution in organic vs. ad-driven traffic.
It is interesting to observe the distribution of visits per weekday, represented on Fig. 15. The days with the most traffic are Wednesday, Thursday and Friday. On the other hand, Monday and Sunday are the days with the least traffic.
Fig. 15. Distribution of weekday visits.
When it comes to the time of day (hours) when the websites are most visited, the highest visit rate is between 16:00 and 19:00. The second highest accumulation of visits occurs in the interval between 0:00 and 5:00, while the lowest visit rate is between 8:00 and 12:00. Figure 16 represents the distribution based on the hour of the day. Since
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tribeOS and their clients are based in the EST (GMT-5) time zone, all time data was presented in EST time and the timestamps were processed server-side.
Fig. 16. Distribution of hourly visits.
The dataset contains data starting from April 2020, when the highest number of visits was recorded. Afterwards, tribeOS most likely lost some of its clients, representing the drop seen in Fig. 17.
Fig. 17. Website visitors over time.
4.2 Analysis of US Visit Sessions As previously stated, among all visit sessions, the highest number came from the United States – 87.36%, which accounts for 873,558 visit sessions. The next part of the research focuses on the analysis of US visit sessions. As can be seen in Fig. 18, in most US states, mobile phones are the preferred device type. The map is homogenous, with only a few different results – namely, the users from Alabama and District of Columbia utilize personal computers more. Figure 19 shows the operating systems used in the United States. The map does not differ much from the map in Fig. 18 and is homogeneous as well. In a few states,
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operating systems other than iOS are dominant – Washington users prefer Android and District of Columbia citizens mostly use the Windows operating system. Figure 20 represents the browser distribution by state. It is less homogeneous than the other two maps, as visitors use different browser types. However, the most used browser in most of the states is Safari, with a few states whose dominant is Google Chrome.
Fig. 18. Most common device types by state.
Fig. 19. Most common operating systems by state.
5 Discussion The analysis of this dataset provided a lot of useful conclusions that can be compared to the previously published results. One of the main reference sources was StatCounter and all its comparison data was extracted from the same period covered by the tribeOS dataset.
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Fig. 20. Most common browsers by state.
According to StatCounter, the population worldwide prefers to use the Android operating system among all the others. It is followed by Windows and the third most used operating system is iOS. The results in Fig. 21 are slightly different from the results of our analysis, as the tribeOS platform mostly recorded visits by iOS users [29].
Fig. 21. Operating system market share worldwide [29].
However, this research uses data provided by a platform intended for the US market. Thus, after checking the operating system usage in the US only, the existing results correlate better to the results of this research. Namely, iOS is the most used operating system in the US, closely followed by Windows. This can be seen in Fig. 22 [30]. As for the US browser usage presented in Fig. 23, the data differs from the results of this research in a similar way to operating system data. Namely, Safari is the second most used browser in the United States, while the first place belongs to Chrome [31]. This discrepancy could be explained by StatCounter data covering the entire US population, while this research only covers the visitors from businesses utilizing tribeOS services. Therefore, tribeOS customers might have similar profiles – using the iOS operating system which mostly comes with Safari as the default browser. Taking this into
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Fig. 22. Operating system market share in the USA [30].
consideration, it can be expected to have Safari as the most used browser for the iOS users. According to a different study by Digital Trends, Chrome is also the most widely used browser, which further corroborates the findings of this case study [32].
Fig. 23. Browser market share in the USA [31].
TribeOS visits were organic (regular) visits most of the time, not originating from an ad. A similar result can be observed in webpage visit analyses conducted by other sources, as they also show that most of the traffic is organic [33]. Zero Limit Web presented the same results in 2020, showing that 67,60% visits belong to organic traffic [34]. According to WordStream, a good conversion rate is between 2 and 5% [35]. Conversion is the key element in paid strategy and includes turning lookers into buyers. When it comes to tribeOS, the conversion rate of its clients was 0.41%, which cannot be considered as a good conversion rate. This low conversion rate can be interpreted in two ways: either the tribeOS clients did not set appropriate targeting options (leading
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to a small number of relevant visitors), or there is a problem with the tribeOS targeting algorithm. Since information about client settings and the inner workings of the tribeOS algorithm is not available, no further conclusions can be drawn at the moment. Various research shows that around 55% of web page visitors spend less than 15 s on a specific page [36]. These results are unusual, considering that the entire web page content of an average website cannot be adequately read in just 15 s. The session duration median according to this research is 33 s, slightly more than the previously conducted results – but still not enough to read the page completely. It is important to note that the visit sessions that ended up with a sale usually lasted longer than 4 min. Unfortunately, this research could not ascertain the number of visits that took place before a sale occurred on a specific website. Figure 17 shows a drop in the visitors over time, but as mentioned earlier, this is likely due to the loss of tribeOS advertisers. The so-called “Internet Rush Hour” is typically between 19:00 and 21:00, regardless of time zone [37]. Figure 16 shows that this time interval was also one of the most active periods for tribeOS, indicating that the “Internet Rush Hour” affects tribeOS traffic as well. Figure 24 represents a visual breakdown of web traffic presented by Voluum [38]. However, their results do not match the findings of this research. Their figure shows that bot traffic is more present on the Internet than human usage. On the other hand, tribeOS data indicates below 32% of bot usage.
Fig. 24. Traffic breakdown: human vs. bot visits [38].
Figure 13 showcases the ratio of legitimate and fraudulent visits and is a result of tribeOS data analysis. These results can be corroborated by the ones produced by BusinessOfApps, which show that 28% of web traffic comes from non-human actors [39]. Finally, the device usage results show that the mobile phone users are dominant. This can be seen on Fig. 25, which was taken from Statista. There, it can be seen that mobile phones are the most used devices, followed by desktop devices and tablets [40].
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Fig. 25. Distribution of website visits by device [40].
6 Conclusion The results derived from this study could mostly be corroborated by the previously conducted research. However, there were a few differences present in some of the cases, but we assume it is because of the specific nature of tribeOS-affiliated clients and their visitors. Here is a brief summary of the most important conclusions derived from the analysis. The total number of unique visitors is 877,202, with the majority coming from the United States at 873,558 visitors. Self-identified bot visits were present in a small number (less than 4,000). Mobile phones were the most used device types (>60%), followed by personal computers and tablets. iOS is the most present operating system among unique tribeOS users. The second place goes to Android and Windows users, whose number is almost the same. Among 89 different browser types, Safari is in first place, with a total number of 371,880 visit sessions. The highest number of visits were organic visits, or visits not originating from ads. The conversion rate is 0.41% – meaning that only 4,130 visit sessions ended up with a sale. The median session duration for all visits is 33 s, whereas the median duration of sessions that concluded with a sale is 234 s. 23% of all visits were fraudulent visits, and they can either be bot visits, visits from malicious IP addresses, referrals from spam websites, etc. Wednesday, Thursday and Friday are the days with most traffic, while the time interval between 16:00 and 20:00 is the most active period of the day. This research can serve as a case study that is applicable to similar situations or problems. The above-mentioned conclusions can be used for calculating useful statistics and deriving business-related decisions. A business could track and analyze the criteria mentioned in this research on their own website(s) to formulate future business strategies and goals. Additionally, we believe that this field of research is an important one in investigating fraudulent internet behavior and preventing it as much as possible. One of the future plans regarding this research is to perform additional analyses of client websites in order to extract the type of market they belong to, type of CMS used, number of external ads embedded in the content, SEO features of the sites, etc.
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These features could then be correlated to tribeOS data to derive more relevant results. In addition, we also intend to build precise models that can be used to predict bot visits or fraudulent behavior on the website. Moreover, we are planning to analyze data from more recent periods and compare it to historical data. This way, we believe we are going to derive potential improvements that result in higher visitor rates and most importantly – higher conversion rate.
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Composable ERP – New Generation of Intelligent ERP Dražena Gašpar1(B)
´ c2 , Ivica Cori´
, and Mirela Mabi´c1
1 Faculty of Economics, University of Mostar, Mostar, Bosnia and Herzegovina
[email protected] 2 HERA Software Company, Mostar, Bosnia and Herzegovina
Abstract. Companies in the modern digital era need infrastructures that can adapt to rapid change and the inherent unpredictability it brings. As the rate of upheaval and unpredictability rises, businesses must adapt by placing greater emphasis on business design and architecting composability across numerous interrelated perspectives. The process of transforming a company into a composable firm requires creating a framework out of modular, replaceable parts. AI integration with ERP systems has the ability to transform the way businesses run by automating procedures, boosting decision-making, and increasing operational efficiency. A composable ERP approach entails combining numerous ERP components, each serving a distinct purpose, that may be joined to produce a tailored solution for the specific needs of a particular firm. In this configuration, AI’s purpose is to analyze data, discover patterns, and make predictions, allowing the various ERP components to collaborate more effectively. Composable ERP solutions may enable enhanced agility, scalability, and adaptability while lowering costs and improving overall performance by leveraging the power of AI. This article aims to investigate the importance of establishing a new generation of ERP systems, the architecture on which such systems are constructed, and how integrating AI with the ERP system leads to intelligent ERP systems or composable ERP. Keywords: Artificial Intelligence · Composable ERP · ERP
1 Introduction Significant shifts are occurring in today’s societies and businesses as a result of globalization and the rapid development of ICT. Digital technology has become an integral part of modern business and private life. It enables everyday business and personal communication, execution of business tasks, entertainment, and finding answers to various business and social challenges. The COVID-19 pandemic has further accelerated digital transformation, both in business and society, focusing on technologies like cloud computing, artificial intelligence (AI), Big data analytics, blockchain, augmented and virtual reality, the Internet of Things (IoT), robotics, etc. The digital business future promises firms virtually limitless opportunities to generate business value. However, digitalization irreversibly changes the way of doing business. More clouds, more devices connecting © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 359–375, 2023. https://doi.org/10.1007/978-3-031-43056-5_26
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to the network, and more requirements at the network’s edge are driving the evolution of enterprise infrastructures. Cloud-native applications create visibility gaps, complicating monitoring and management. Organizations that cannot use machine learning (ML) and artificial intelligence (AI) technologies to understand the relationships and performance of distributed systems jeopardize their digital business transformation initiatives. In the digital age, business architectures should be built to withstand uncertainty and constant change. In responding to increasing disruption and uncertainty, organizations should shift their focus to business design and architecting composability across multiple interconnected viewpoints to succeed. Converting a business to a composable business entails constructing an organization out of interchangeable building blocks. According to Gartner [1], there are three building blocks of a composable business: 1. Composable thinking. It should boost creativity, making anything composable. When the principles of change, modularity, orchestration, and autonomy are combined with composable thinking, a framework and timeline for idea conceptualization are provided. 2. Composable business architecture. It promotes organizational flexibility and resilience. Businesses will have mechanisms to adapt and repurpose their offerings if their structural capabilities are improved. 3. Composable technologies. They are today’s and tomorrow’s tools. These interconnected parts propel technology in support of product design goals and the concept of composability. Gartner [2] defines a composable enterprise as “an organization that delivers business outcomes and adapts to the pace of business change. It does this through the assembly and combination of packaged business capabilities (PBCs). PBCs are application building blocks that have been purchased or developed.” The composable enterprise refers to the practice of using software components that can be swapped out for one another to create, innovate, and modify corporate operations in response to both internal and external changes. It allows companies to adapt and expand. Building a composable enterprise architecture necessitates close coordination between many parties since it removes traditional barriers between business and IT, permitting businesses to provide clients with more individualized application experiences. Because business is based on technology, technology must be composable in order to run composable businesses. Composability must be extended throughout the technology stack, from infrastructure that allows for the rapid integration of new systems and partners to workplace technology that facilitates the exchange of ideas. The Enterprise Resource Planning (ERP) systems could not avoid the abovementioned changes. Namely, since the 1990s, ERP systems have been recognized as information systems that manage all business processes (accounting, sales, production, finance, human resources, etc.) and serve as a single source of data in the organization based on a unified database [3, 4]. ERP systems create a continuous flow of real-time knowledge for the entire organization. It employs a single interface as well as the databases required for organizational processes [5].
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Over the last few years, organizations that want to streamline their ERP modernization have recognized and adopted a composable ERP approach packaged with artificial intelligence (AI). Integration platforms as a service (iPaaS) assist businesses in transforming IT complexity into connectivity, synchronizing the processes, applications, and data required to provide customers with the integrated experiences they expect. Composable ERP delivers a core of modular applications as well as software platforms as a service that is highly configurable, interoperable, and adaptable to future modern technology. The aim of this paper is to research the significance of developing a new generation of ERP systems, the architecture on which such systems are built, and how the integration of AI with the ERP system leads to intelligent ERP systems – composable ERP. The paper is organized as follows. Section one describes how ERP systems have developed through time, beginning with MRP and progressing to the more modern concept of composable ERP. The following part then discusses the primary features of the composable ERP. Next, a road map to composable ERP architecture is concerned, and the AI-driven ERP is evaluated. The paper comes to a close with a conclusion, challenges, and next steps.
2 ERP Evolution ERPs have evolved dramatically over the previous six decades. The debate over the evolution of ERPs exposes varying timescales and the assumption of continuous progress. The naming of the time may alter depending on the historical period on which a researcher has commented [6]. In this section, the authors describe the evolution of ERP, starting from the Material Requirements Planning (MRP) systems, which represent their predecessors. Further, the evolution of ERP is followed through four ERP generations recognized and described by Gartner. Although Gartner invented the term Enterprise Resource Planning (ERP) in the 1990s, manufacturers’ practice of using inventory control systems to maintain sufficient stock levels dates back to the 1960s [7]. Engineers in the software industry developed systems to track raw materials and notify manufacturing plant managers of when and how much to order for restocking. These applications grew into what is now known as Material Requirements Planning (MRP) systems in the 1970s and are used by manufacturers to determine precisely when and how much of various raw materials, subassemblies, and component parts will be needed to keep the production schedule for finished goods on track. In contrast to MRP, which stops at the point where products are received, MRP II continues to incorporate the value stream right up until the point where the finished goods are dispatched to the client. Planning production, scheduling machine capacities, and ensuring quality are all part of this value stream [7]. Throughout the 1990s, MRP II morphed into its successor, enterprise resource planning (ERP). Gartner coined the name – ERP to emphasize that many businesses, not just manufacturing, could benefit from ERP and that they were starting to use ERP to increase the efficiency of all their processes. ERP systems enhanced MRP II capabilities by adding many other modules, such as human resources, fixed assets, sales and distribution, and project management. MRP II systems handle inventory management in a manufacturing
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setting, whereas ERP systems oversee all operations and resources (and support many more industries). Enterprise resource planning (ERP) is defined as the ability to deliver an integrated suite of business applications. ERP tools share a common process and data model, covering broad and deep operational end-to-end processes, such as those found in finance, HR, distribution, manufacturing, service, and the supply chain [Gartner]. At that point, ERP systems acquired their current identity: a unified database for information from across the organization. ERP systems integrate other business functions such as accounting, sales, engineering, and human resources (HR) to provide all employees with a single source of accurate data. Some authors [8] defined ERP as a concept and a system. As a concept, ERP systems “involve the integration of business processes within an organization, with improved order management and control, accurate information on inventory, improved workflow, and SCM, and better standardization of business and best practices” [8]. In this view, the ERP idea stresses institutional changes that occur as a result of the phenomenon’s introduction and maintenance. Throughout the 1990s, ERP systems developed further. The introduction of cloud ERP was a huge step forward. It is generally agreed that cloud ERP is superior to onpremises systems because it allows companies to access mission-critical data via the web from any device with an internet connection. With cloud computing, businesses no longer have to invest in expensive gear or employ dedicated IT departments to manage it. Because of the cloud computing approach, ERP solutions, previously only affordable by large corporations, are now available to SMEs who lack the resources to implement and maintain an on-premises solution. Automation of procedures, enhanced data accuracy, and increased efficiency, are all potential gains for small and medium-sized enterprises across all sectors. ERP II, as coined by Gartner in the year 2000, describes web-enabled systems that can integrate data from a wide variety of departments and functions across an organization [8]. That includes front-office applications such as CRM, e-commerce, and marketing automation, as well as back-office functions such as supply chain management (SCM) and human capital management (HCM) (HCM). This was a significant step forward because the ERP system can better detect problems, correct them, and capitalize on possibilities for growth the more data it receives [9]. Gartner coined the term “postmodern ERP” to describe the third phase of ERP, which began in the 2010s and reflected the fact that ERP had morphed into more of a strategy than a discrete technology. Postmodern ERP is defined as a technology strategy that automates and links administrative and operational business capabilities (such as finance, HR, purchasing, manufacturing, and distribution) with appropriate levels of integration that balance the benefits of vendor-delivered integration against business flexibility and agility [10]. Starting with the third generation of ERP, postmodern ERP, the focus of ERP changes from control to value (Fig. 1). At least two related phenomena have spurred this shift toward a more value-oriented strategy. Most businesses, for example, have to deal with dwindling funds while simultaneously meeting rising demands for an ever-greater variety and quantity of goods and services. Robotic process automation (RPA) and artificial intelligence (AI), on the other hand, are two examples of the kinds of digital advancements that have emerged in the 21st century [6].
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Fig. 1. ERP evolution [11]
RPA and AI were among the digital advances that led to the fourth generation of ERP. Gartner coined the term “composable ERP” to describe this new generation of ERP systems in the 2020s. The fourth generation of ERP introduces a more fundamental change, expanding ERP’s scope from internal corporate resources and planning to external ecosystems. Customers and vendors are just two parts of an ecosystem that also includes partners, rivals, and other stakeholders.
3 The Composable ERP The state-of-the-art ERP systems of today are massive data repositories that can produce reports highlighting the efficiency of any department in the business, from sales and marketing to product development to human resources and operations. ERP serves as the nerve center of what might be a complex software network tailored to different industries, business models, and challenges [12]. As explained in the previous section, the composable ERP is the fourth generation of ERP that brings a new radical shift related to the concept of ERP and the technologies behind ERP. Overall, Gartner’s study emphasizes the ongoing development of ERP systems, focusing on delivering ever-better integrated, intelligent, and cloud-based solutions. The composable ERP is defined as “an adaptive technology strategy that enables the foundational administrative and operational digital capabilities required for an enterprise to keep up with the pace of business change” [13]. There are six hallmarks of this fourth generation of ERP [14]: 1. AI-driven. Existing patterns of interaction between businesses and their customers, vendors, products, software, and each other are being radically altered by AI. 2. Data-centric. In the digital age, data is crucial. It’s increasingly derived from various programs, websites, services, and physical objects. 3. Consumable. The goal of a fourth-generation ERP system is to make its use as seamless as possible. Buyers of ERP systems aren’t concerned with the vendors behind the scenes as long as the product works “out of the box.” 4. People-augmented. As robots have replaced humans on factory floors, AI is now transforming other human-centric activities by supplementing judgment-based processes.
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5. Enabling. The failure of ERP in its early days was due to the system’s inflexibility. An essential factor in ERP’s metamorphosis into an enablement tool is the general trend toward agile implementation and support in software. 6. Customer-facing. Everything in the information era is customer-facing. Fourthgeneration ERP must assist firms in understanding and meeting the needs of their consumers before their competitors can. That is a shift from the previous ERP, which was focused on creating internal value. Composable ERP provides a core of composable applications as well as software platforms as a service that is highly configurable, interoperable, and adaptable to future modern technology. To keep up with the speed of business, organizations must devise a strategy that maintains them agile, adaptable, and capable of delivering innovation as soon as their consumers need it. To put these into practice, they must be able to seamlessly disconnect and merge apps in order to continue meeting business expectations. A large majority of businesses have already begun the process of upgrading to the composable ERP. The top 5 modernization priorities on that way are [15]: 1. 2. 3. 4. 5.
Standardization/Consolidation of applications Application modernization Migration applications to the cloud Migrating infrastructure to the cloud Consolidating current legacy IT infrastructure.
The composable ERP’s success depends on the organization having the necessary integration tools and procedures in place. The value of modular ERP should be found in the business outcomes enabled by ERP ecosystems of applications, integration platforms, and data fabrics [16]. The essential characteristics of the composable ERP are the following [16]. • • • • •
Integration Composition Orchestration Business continuity (BC) development User experience (UX) development
Having the proper integration tools and techniques in place is crucial to the success of the Composable ERP approach. Supporting enabled, self-service integration by business lines, subsidiaries, application development teams, and ultimately business users are the goals of a Hybrid Integration Platform (HIP) strategy. Composable ERP systems are composed of separately built application components and composite applications that connect and integrate information flows. In such an architecture, the application pieces are modular and serve a single purpose. That allows for the continual integration of new features into the composite application as well as the rapid adaptation of business strategy and architecture changes to the requisite software components [17]. The orchestration layer will serve as the main hub for all technologies to connect to the ERP. If a vendor doesn’t work out or a specific division decides it wants to switch technologies, the user can undock the current system and replace it with a newer,
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compatible one. Adopting a composable ERP approach requires clearly defining the master database and the mechanisms through which data from many systems interact with and contribute to that single, authoritative data store [18]. Business continuity (BC) can also be better controlled with finer-grained application development. A single point of failure in the network may bring down existing application dependencies. However, application components might combine process orchestration and choreography to alleviate the load on any one point of interaction [19]. Organizations need to give a variety of routes to the same goal, which necessitates a wide range of user experiences (UX). More UXs inevitably lead to more technologies and more loosely coupled, compartmentalized systems. Improving the customer experience is challenging when processes don’t allow for iteration. Constructing several modern UXs from legacy software (traditional ERP, for example) might result in disconnected touchpoints that lose customers along the customer journey due to a lack of context and uniformity. All user data sources, systems, and touchpoints must be integrated with the backend to create a modern, seamless customer experience. Leaders in the application space must ensure that the user interface can be easily customized to meet the needs of a wide range of end users [20]. Composable ERP provides a core of composable applications as well as software platforms as a service that is highly configurable, interoperable, and adaptable to future modern technology. To stay up with the rapid pace of business, companies must devise a plan that allows them to remain agile, flexible, and capable of delivering innovation as rapidly as their consumers expect. Putting them into effect requires a simple means of decoupling and integrating applications to maintain continuous delivery to satisfy business needs. However, the path to composable ERP is not always easy. Nonetheless, ERP modernization is no longer a choice but rather a need. Globally, businesses are putting their faith in iPaaS and Communities of Practice as the backbones of composable ERP and hybrid integration initiatives. The synergy of these solutions will allow them to keep up with the rapid pace of business, integrate with more agility, and link all stakeholders for a more cohesive user experience.
4 Roadmap to Composable ERP Architecture The development of ERP systems and the ways in which businesses conduct their operations are inextricably linked, as seen by the history of these tools and methods. Each successive iteration of ERP systems is mainly a response to business operations changes. With the rise of the composable enterprise comes a new breed of ERP systems. Rather than being driven by technological decisions, composable ERP provides a method for using technology to support business outcomes. Composable applications are the obvious choice for businesses in this uncertain era because of their flexibility and ability to adapt to the unique requirements of each industry. The reference architecture for intelligent composable ERP is shown in Fig. 2 [21]. Components (PBCs) are “living” in the composable enterprise; they are developed, assembled with other components, utilized in multiple products, updated, and finally deprecated or replaced. The process of creating components and APIs have been “legalized,” so teams can now deliver them directly to one another. Internal and thirdparty components combine to form an economy – Catalog/Marketplace (Fig. 2). Each
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implemented component generates an exponentially rising compounding value. It is the governed democratization and legalization of components. Product teams are both component creators and consumers. They participate in a “give and take” economy in which they benefit from the components of other teams and have “clients” who benefit from their components. A platform is required to act as a marketplace for teams to host, discover, exchange, and collaborate on components in a component ecosystem [22].
Fig. 2. Reference architecture for composable ERP [21]
Packaged business capabilities (PBCs) are fundamental software components of the composable ERP architecture (Fig. 3). They encapsulated and reflect a well-defined business capability recognizable by a business user and packaged for programmatic access [21]. As applications start breaking down the components and defining the borders, it’s essential to consider what will be on the inside and what will be on the outside. The key is to use APIs to connect the pieces (for example, PBCs) with other types of IT components such as Micro Services, Apps, and Macro Services. Not everything should be a PBC, but connectivity is expected to increase across the board. Because PBCs are software components that implement well-defined and autonomous business capabilities, they must also contain internal data and metadata required to carry out business requirements. Multiple PBCs can be stitched together and optionally front-ended with a user interface to create a composable application. These modular apps can then be packaged to establish a business capabilities portfolio. APIs or Event Channels can be the “glue” that holds these PBCs together (Fig. 3). Application programming interfaces (APIs) enable programs to exchange data with one another
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Fig. 3. Packaged business capability (PBC) [21]
without needing to understand how they are implemented [23]. In essence, API acts as a bridge between two different apps, allowing them to communicate and exchange data. Application Program Interfaces have been around for some time. They have been successfully bridging the gap between different software programs for decades. Historically, application programming interfaces (APIs) were an afterthought or a requirement, not the primary way to interact with software. However, the composable ERP architecture requires quite different APIs. These APIs aren’t an afterthought and don’t just cover a small fraction of features. Instead, they make all of the application’s features available and supply a mechanism for creating loosely decoupled systems, i.e., composable ERP. An API is created before a composable application is built, and the channels through which the API will be exposed are defined. In order to begin the development process, developers first have conversations with possible customers about the API. They create use cases and prototypes of the API and begin work on the actual application. The term “API-First development” has come to describe this method of building applications. API-First Development is a fundamental paradigm change in the API design process in which APIs are established before applications and reflects the company’s aims and ambitions [24]. The reference architecture for composable ERP systems shown in Fig. 2 integrates all existing ERP and related solutions in the enterprise. It is feasible to gradually upgrade old ERP systems and provide a controlled and gradual transfer to new technologies by using well-designed APIs and the regulated integration of new PBCs. Organizations, in particular, are not ready to reject the solutions they have been using for years, both for economic reasons (investment in equipment, SW, and employee training) and because of distrust in new, poorly tested technology. By integrating existing and new solutions (internal and external) through a layer of a unique composition platform and an integrated data layer (Fig. 2), composable ERP architecture allows organizations to capitalize on prior ERP investments while also benefiting from the introduction of new solutions. The software development, release, and design processes must be governed for the composable enterprise to succeed. Whether built in-house or sourced outside, all components and APIs must adhere to the same minimum standards of safety, legality, quality, and other attributes. Although modularity, or composability, is not a revolutionary method in software development or ERP, it is a new way of structuring and linking its components (APIs, PBCs) and technologies such as cloud, SaaS, iPaaS, and AI. A distinguishing feature of the composable enterprise is the use of multidisciplinary “fusion” teams comprised of
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business and technology experts to execute application design and redesign. Historically, most design models and engineering tools were made for either IT specialists or businesspeople (citizen developers). The need for flexibility in the face of rapid corporate change is something neither party can handle alone. Leaders in applications and software engineering must make it possible for interdisciplinary fusion teams to take on the role of “composers” for their organizations to adopt the composable enterprise model [25]. The democratized composable enterprise is largely propelled by this change in software engineering’s center of attention from individual IT specialists to interdisciplinary business IT fusion teams. To make the most of their combined skillsets, business, and IT workers can use the same building blocks and composition tools. Gartner’s [22] ERP system reference architecture for composable enterprise provides a high-level overview that can serve as a jumping-off point for further theoretical and practical development.
5 AI-Driven ERP ERP has traditionally been in charge of knowledge organization, report preparation, and dashboard analysis of actual real-time results. On the other hand, companies nowadays want innovative technology to recognize trends, recommend actions, evaluate numerous interrelationships, and simplify dynamic operations [26]. The degrees of flexibility available on-site and cloud models on current ERP systems may contribute to the growth of this market. ERP accounts for the system’s existing requirements to achieve similar process adjustments, which is a precondition for implementing intelligent technology [27]. Artificial intelligence (AI) was the missing instrument decision-makers were yearning for at the time. Artificial intelligence, a term coined by computer scientist John McCarthy in 1955, has undergone a massive transformation in the last decade. Defining AI is challenging due to two main issues: (1) what constitutes human intelligence and (2) whether aspects of human intellect might be amenable to computer simulation. Due to the complexity of the subject matter, there are numerous competing definitions encompassing diverse areas [28]. The OECD [29] suggests that an “AI system is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy”. In the academic world, one of the most cited definitions is from Li and Du [30], saying that “AI combines a variety of intelligent behaviors and various kinds of mental labor, known as mental activities, … [to] include perception, memory, emotion, judgment, reasoning, proving, identification, understanding, communication, designing, thinking and learning, etc.” The authors [31] state that AI can now discover relevant information extract it from documents, and make it accessible for human auditors, allowing them to devote more time to areas that require more human judgment. That is in line with the findings of [32], who argued that managers spend too much time on administrative tasks when they could be focusing on higher-level tasks like human judgment, which computers can’t replicate. Artificial intelligence is a relatively new technological invention that has given business leaders a new tool to boost organizational effectiveness [33]. There is currently little
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research into the best ways to integrate AI into management practices, notably in ERP systems [34]. Businesses have altered their workflows as a result of digital disruptions. AI has significantly changed how software operates and functions within enterprises. ERP software available today is complicated compared to predecessors, even those used only five years ago [27]. The modifications that ERP systems have undergone since their beginnings till today were discussed in the section on ERP system evolution. Gartner [13] refers to ERP systems of the newest fourth generation as composable ERP systems to underline the importance of their compatibility with composable enterprises. However, because of the increasing integration of AI solutions within the architecture of these systems, these modern ERP systems are frequently referred to as intelligent ERP systems. The architecture of Gartner’s composable (intelligent) ERP system is depicted in Fig. 2, with the essential components being packaged business capability (PBC). Gartner [21] utilizes the term Analytics PBC rather than intelligent or AI-driven PBC, which might represent an umbrella term incorporating AI solutions for analysis (Fig. 2). APIs ensure connectivity with other ERP components (internal or external). In addition, one of the functions of Data PBC (Fig. 2) should be data preparation for AI-driven – Analytics PBC. To underline the integration of AI within the framework of Gartner’s architecture [21], the authors enhanced that architecture with the new types of PBC to demonstrate the capability of integrating AI as clearly as possible in the design. Figure 4 depicts the enhanced Gartner design, which includes an AI-driven (intelligent) PBC that enables the integration of AI into the ERP architecture and an AI-driven (intelligent) Data PBC that prepares and transforms data for the AI-driven PBC.
Fig. 4. The architecture of AI-Driven ERP – the authors’ enhancement of [21]
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Although the major ERP manufacturers (Oracle, Microsoft, SAP, etc.) have been working hard in recent years to integrate AI into their ERP systems, it is still a very new and under-researched topic. However, companies that were early adopters of AI integration with ERP systems saw significant benefits, which fueled further interest and investment in the technology. The use of artificial intelligence in businesses has tripled in the last two years, necessitating a reevaluation of fundamental infrastructures and optimization for AI efficiency [35]. Artificial intelligence has the potential to enable profound shifts in the products, services, innovation processes, business models, and even the character of business activities in industrial ecosystems [36, 37]. To guarantee value delivery, capture, and competitiveness, the new business model necessitates a revised logic concerning the underlying principles of how AI technology is incorporated into value offerings and how it interacts with the work of individuals, organizational functions, and the diverse processes across the business [36]. Businesses want to reap substantial, quantifiable benefits from AI implementation. Here are some of the benefits of ERP systems powered by artificial intelligence: • Improvement of the decision-making process. One of the primary benefits of ERP systems is the ability to improve and streamline workflows and define anything from output to policy [38]. AI may improve these capabilities by processing larger data volumes than was previously possible. • Easy integration of multiple divisions and simpler management of all parts of a firm. AI-powered systems can combine data from numerous divisions into a single database and handle massive amounts of data. • Developing modern human resource management activities and altering how an organization manages its human resources [39]. AI enables better human resource management at three levels: assisted knowledge, enhanced intelligence, and self-intelligence [40]. • Providing many business models benefits to clients, such as cost savings, improved service quality, increased coordination and efficiency, and increased delivery efficiencies [41]. AI-driven business models can utilize new ways of producing, delivering, and capturing value to drive competitiveness by expanding the scale, breadth, and learning opportunities [36]. • Transforming and unleashing the next phase of digital transition in industries that have grown exponentially over the last decade [42]. However, success in reaping the benefits mentioned above is far from automatic, and many organizations fail to realize more significant value generation and capture from AI when developing business models [43]. Despite its benefits, artificial intelligence has limitations and obstacles. Some of the challenges associated with AI integration into ERP are as follows: • Artificial intelligence’s lack of objectivity and cautions tend to reflect the biases of the humans who design it [44]. According to [31], there is data-driven bias, which results in biased outcomes due to flaws in the data itself; a bias that occurs when the machine learns the biases of the people who train them, emergent bias, which occurs when
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the machine shields humans from conflicting points of view while providing them with information that confirms their preferences, and conflicting-goal bias, which is an unforeseen bias that occurs as a result of stereotype-driven bias. Investing in data storage, computing power, and other digital assets is not enough. Value creation through the use of AI technology necessitates the adoption of new habits, skills, operational processes, and business models. The results of AI algorithms are sometimes less specific than initially anticipated, with many businesses realizing that human interpretation, reasoning, and action are still required to deliver tangible, valued outputs [45]. Scaling AI services beyond proof of concept to bigger client groups via AI business models and demonstrated solutions [46]. As a result, there is a need to understand better the principles that underpin AI-enabled business model innovation, in which AI capabilities are integrated into business operations such as value generation, delivery, and capture to assure scalable growth. Although internet platforms make it easier for businesses to benefit from links to intelligent technology, they are not ready-made industrial solutions. Working with a supplier who knows the nuances of business and the semantics of ERP information is crucial. The domain experience of an ERP supplier for particular industrial needs or functional areas is critical for intelligent systems to optimize processes efficiently and quickly [47]. Because of the resources required to work with such technologies, implementation is complicated. The organization needed a long time to prepare them and explain what and how to utilize these tools [48]. Data security. Concern for data privacy is paramount when developing a machinelearning model using sensitive data [49]. Businesses and academic endeavors in a specific field that want to succeed need access to top-secret information before they can make any headway. A tremendous barrier to privacy and agreement exists in the AI privacy space, which is still expanding even as understanding among businesses and individuals remains divided.
Most intelligent ERP installations suggest that integrating AI with ERP may boost business processes. Intelligent systems can analyze historical data and recommend improving internal processes that have been shown to produce the most remarkable results. AI may take the next step and continually offer all the data needed to complete an activity after reading a predetermined process [50]. The ability of an ERP system to access several smart function technologies distinguishes it as “intelligent” rather than any single technology. The first step in this manner is to automate monotonous but vital processes that are the foundation of most businesses. In order to reap the benefits of new technologies, businesses need to update their present competitive technology and be ready to respond to and adapt to changes in the industry. AI will surely modify and influence the future of ERP systems and significantly impact industries of any size. As a fundamental part of the ERP system, AI would alter the whole nature of regular business. Therefore, the necessity to lower operational expenses is a driving factor in introducing new technologies, which in turn encourages employee workflows and boosts the overall quality of corporate operations [51]. However, corporations can’t afford to fall
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behind the competition. Therefore, they need to invest in AI advancements to succeed in the long run. The proposed AI-Driven ERP architecture (Fig. 4) retains the comprehensive and composable vision of modern ERP developed by Garner, with a higher emphasis on integrating AI into ERP systems, and can thus serve as a starting point for further theoretical and practical research.
6 Conclusion ERP systems based on a composable architecture provide a fresh perspective on how individual parts function together. Instead of dissolving digital companies, businesses that embrace composable architecture are built to be flexible in real time. However, it is essential to remember that PBCs and APIs are the binding forces that hold everything together and that one must consider the business implications of the components. It’s crystal evident that the end goal is assembling parts to give a novel user experience based on reusable components. Collaboration, a thirst for knowledge sharing, the ability to foresee and respond swiftly to shifts in customer needs, and the creation of systems that minimize waste and maximize efficiency will all be necessary for organizations to successfully adopt the composable way of thinking and ERP design mindset. The combined effect of these factors is resilience by design. The concept of composable ERP may appear novel and ground-breaking at first look. Still, a more thorough examination reveals that its most important contribution is incorporating AI into ERP. The modular, or composable, approach to software development, the cloud, SaaS, integration platforms, and even AI are just some concepts and technologies that have been around for a while and are referenced by composable ERP. What is novel, though, is how all of these technologies are combined and utilized. The significance of user interaction in designing and developing ERP components (PBCs) is again underlined, which is also not new in itself. What is different now is the availability of various tools that make it easier for users to get involved in software development and the increasing number of users of younger generations (Millennials, Z generation) who either came into contact with technology at a very young age or are technological natives, i.e., they grew up with technology. It is much easier for them to accept more advanced technical solutions, including AI. However, all of the benefits of composable ERP (PBC, cloud, APIs, SaaS, etc.) simultaneously provide the most significant challenges to its growth and widespread adoption. The ultimate success in developing composable ERP may depend on the formation of fusion teams, consisting of people who know the business context well and those who know the technology, and on their mutual understanding, as well as the understanding of the process of developing PBCs and APIs. Since the precise method for determining which level of granularity to employ and how even to define a business capability that should be packaged in PBC depends on the specifics of the business environment, neither can be determined with any degree of certainty. That is precisely the biggest challenge for this ERP architecture and can determine its future.
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Building a Recommendation System for E-Commerce Using Machine Learning and Big Data Technologies Naida Fati´c , Mirza Krupi´c(B)
, and Samed Juki´c
Faculty of Engineering, Natural and Medical Sciences, International Burch University, Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. The popularization of today’s e-commerce sites made a big impact on IT technologies. Since e-commerce companies want the best experience for their customers, many technologies are involved to create that experience. One way to gain loyal customers is to build recommendations based on the other opinions. Since the best recommendations are based on the customers opinions and experience we can use that knowledge to build a recommendation system. In this research paper we have combined big data technologies with machine learning in order to create the best model for recommending products in e-commerce sites. Using the Apache Spark technologies with the Python language and PySpark module which connected Apache Spark and Python program and Amazon reviews dataset, we have managed to create a model with the Root-mean-square deviation of 0.823. We achieved such a result by utilizing two different approaches for normalization and performing cross validation on them. Keywords: Recommender System · Amazon Review Dataset · ALS · Collaborative Filtering · Apache Spark · PySpark · Python
1 Introduction The world has seen an astounding increase in online marketplaces, also known as ecommerce, over the past 20 years. Not only has this development been significant, but it has also been accompanied by numerous advancements and innovations. Offering their customers the finest experience possible is one of the constant goals of large e-commerce websites and businesses. They do this, among other things, by utilizing recommender systems to offer personalized recommendations to their customers, enhancing their experience and increasing revenue for the company. Nowadays, every one of us is creating new data. We are here working with Amazon data [1], which is data from one of the biggest if not the biggest e-commerce type of website so data generated here is considered big data since we are working with millions of records. On Amazon, there are about 75,000 reviews left daily. This calculation was done knowing that there are about 750,000 purchases a day and about 10% of users actually return and leave reviews for their purchases [2]. Recommender systems are generally divided into three main categories: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 376–387, 2023. https://doi.org/10.1007/978-3-031-43056-5_27
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– Content based – Collaborative filtering – Hybrid [3, 4] All of the data items are gathered into various item profiles in content-based recommender systems according to their features or descriptions. For instance, the features of a car manufacturer, engine strength, type of fuel etc., are examples of features in the case of a motion picture. When a user gives an item a positive rating, all of the other things in that item’s profile are combined to create the user profile. This user profile aggregates all of the item profiles whose products have received high user ratings [5]. Using other people’s opinions to filter or evaluate items is a method known as collaborative filtering. Even though the concept of collaborative filtering (CF) has only been around for a little over a decade, it has its roots in something that people have been doing for centuries: exchanging thoughts. Again, there are two categories for collaborative approaches: memory-based approaches and model-based ways. Memory-based collaborative techniques make suggestions for new products while taking the neighborhood’s preferences into account. For prediction, they employ the utility matrix directly. Building a model is the initial stage in this method. The utility matrix serves as the input for the function that represents the model. User-based collaborative filtering and item-based collaborative filtering are the two subtypes of memory-based collaborative techniques. The user rating of a new item is determined using the user-based technique by locating other users from the user neighborhood who have already given the same item a rating. A new item is recommended to the user if the user neighborhood gives it positive reviews [6]. Recommender systems that combine various types of recommender systems are known as hybrid recommender systems. There are many more subcategories, such as demographic filtering, contextual filtering, knowledge-based filtering, etc., even though we only mentioned three types here. Hybrid recommender systems combine two or more types of recommender systems. Big data makes workloads heavier, which makes recommender systems more difficult to utilize. Scalability, where the system must be able to handle a larger dataset without experiencing performance degradation, is one of these problems. With the recommender system methodologies currently in use, as the number of users and products causes a rise in calculation time. Another issue is to deliver high-quality recommendations quickly in order to satisfy and keep their users. The third difficulty was brought on by the sparseness of the data, where each user had only given ratings to a small portion of the accessible objects also known as cold start issue [7–9]. This makes it more difficult to identify user similarities because there are few, if any, things that are frequently rated [10].
2 Literature Review With today’s technologies like big data and machine learning the analysis can be done to create the powerful recommendation systems. An example of one of them is the paper done by Gosh et. al. [11] analysis. In this paper the Alternating Least Square (ALS) was used as a machine learning model together with Apache Spark. The Apache Spark was used for preprocessing big data while ALS
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was used to build collaborative filtering recommender systems. In order to store large amounts of data and distribute them the author used Hadoop Distributed File System (HDFS), achieving RMSE of 0.870. A notable example of a movie recommender system created using Apache Spark and the ALS machine learning model is the research described by Aljunid and Manjaiah in [7]. The authors were able to optimize their model by using cross-validation techniques, and the findings were impressive, with a root mean squared error (RMSE) of 0.916. Large quantities of data could be processed effectively thanks to the use of Apache Spark, which is necessary for the successful application of recommender systems. The study’s overall findings emphasize the potential of collaborative filtering methods for the creation of precise and effective recommender systems in the field of movie recommendations. The study done by Panigrahi et. al. Detailed in [12] presents a novel approach for improving the effectiveness of collaborative filtering through the implementation of a hybrid approach utilizing both ALS and K-Means algorithms in conjunction with Apache Spark. The authors sought to address the challenge of the cold start problem by correlating users and products with relevant features or tags. Through this approach, they were able to achieve impressive results, with a root mean squared error (RMSE) of 0.88. This study demonstrates the potential of utilizing hybrid algorithms and incorporating featurebased correlations to improve the performance of recommender systems. Additionally, the use of Apache Spark highlights the importance of efficient data processing in the implementation of such systems, particularly when dealing with large datasets. Overall, this research provides valuable insights into the development of more accurate and effective recommender systems. Alexopoulos et. al. Paper [13] elaborates Apache Sparks parameters. The work was based around the Rank, maxIter and minIter parameters of the ALS machine learning model. From the experiment we can conclude that higher the parameter values are, the smaller the MSE, RMSE and MAE evaluation values are. Also we should take into consideration time, since these operations require proportionally long executing time. This paper is helpful in analyzing our data using the same approach and then comparing our results. The Woo and Mishra work [14] revolves around Azure and Apache Spark performance. The author proved that using a machine learning model and big dataset like Amazon recommendation dataset, the Apache Spark performed better than Azure. The Apache Spark could take a large amount of data for execution compared to Azure data samples. Using this knowledge we can be sure that we are working with the right technologies.
3 Methods and Materials In this experiment we will work with the Amazon Review dataset and Alternating Least Square as a machine learning model using PySpark as interface for Python in Apache Spark system.
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3.1 Alternating Least Squares (ALS) Algorithm One of the more often used algorithms for recommender systems is Alternating Least Squares, or ALS for short. It is used to factorize matrix R (in our case it is a matrix that consists of unique reviewer id, item ids and as values we have item overall rate). ALS algorithm together with Apache Spark is considered of the industry standards for big data recommender systems nowadays and it is widely used especially for the benefit of its flexibility because we can add additional workers on the go with almost no downtime. We also have a lot of flexibility due to the ability to change configuration (amount of memory used by workers and master, heap memory, number of workers, etc.). Matrix factorization is used by the ASL method. Matrix factorization is a method for determining the relationship between the entities of items and users. Thanks to matrix factorization, we are able to take a matrix and decompose it and find out the hidden structure behind our matrix [15]. By using vectors of factors determined from item rating patterns, matrix factorization classifies both items and users. A recommendation is made when item and user factors are highly compatible [16]. The matrix R is factored into the U and V factors using the ALS method. The R≈UT V rule must be observed in this situation [17]. Latent factor, often known as row dimension or rank, is unknown to us. Rank represents the number of possible feature vectors in the approximate low rank matrix [17]. The better, but it will affect the amount of memory needed for model training, is the number of potential characteristics. Since we are leveraging the user/customer and item relationships, it is also crucial to understand that U matrice stands for user matrix and V matrice for item matrix. (ri,j − uiT vj )2 + λ( nui ||ui ||2 + nvj ||vj ||2 ) argmin U ,V
{i,j|ri,j =0}
i
j
As a regularization parameter, it is employed. The solution of the upgraded problem will lead to a monotonic reduction of the total cost function. By doing this procedure alternately on the matrices U and V, we can incrementally improve the matrix factorization [18]. 3.2 Apache Spark The beginning of Big Data technologies was encountered by the fast growth of the Internet and its data. Big Data technologies are designed to withstand processing of large amounts of the data across multiple machines and deliver solutions much faster than previous technologies. This solution led to a bloom in the machine learning and artificial intelligence fields. The technologies like Internet of Things, recommender systems, and face recognition were getting more popular and easy to implement. Since in this research paper we are working with the dataset that contains 2,875,917 real reviews, Big Data technologies will be used. Apache Spark is a popular framework in Big Data technologies used for data processing. It works on the distributed system where executions of each workers’ jobs are done concurrently. This is done using driver – executer architecture. The Apache Spark architecture also helps in the distribution of the large amount of computing power needed for the processing data. It consists of Spark core and many Spark libraries for data processing. Spark libraries are Spark’s MLlib for machine learning, GraphX for
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graph analysis, Spark Streaming for stream processing and Spark SQL for structured data processing. Resilient Distributed Dataset is one of the cors data abstractions of the Apache Spark. It is used in data distribution and partitioning across different executors. For compatibility with other programming languages like Scala, Java, Python the Apache Spark has integrated rich APIs. The Apache Spark is the key to the data preprocessing and iterative algorithms that works on the big datasets. It helps with data distribution across clusters and the parallelization [19]. 3.3 Proposed Methods In this experiment we will work with the Amazon Review dataset. Since it consists of 2 million review data samples we will use Big Data technologies to process data and build recommender system. For building machine learning algorithm we will use the ALS model written in Python language. As seen from Fig. 1. in this process we will compare two approaches for preprocessing the data using normalization. In the first approach we will apply min-max normalization and in the second approach we will apply mean-standard deviation normalization. We will also compare model performances by evaluating different model’s hyperparameters. After each normalization method the cross-validation of the different model’s hyperparameters is performed. In total we will have 12 different results to compare, 6 of them are made using min-max normalization and other 6 by mean-standard deviation normalization. The hyperparameters that we will use for cross-validation are Rank and regParam. Rank hyperparameter in the ALS model is the number of latent factors, in our case we will test Rank values 1–6. The regParam in ALS model represents a regularization parameter and in our case we will test 0.1–0.3 values. Since this is a large dataset we will be using Apache Spark’s interface for Python, PySpark. Through the Apache Spark configuration, we will connect multiple machines in master-slave architecture in order to gain more computing power.
Fig. 1. Method flowchart.
Before we apply normalization, we will preprocess the data. The dataset that we are going to use for this experiment is the Amazon Review dataset for the Arts Crafts and
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Sewing [1]. Dataset contains 2,875,917 real reviews from Amazon products which are perfect to use for big data analysis. It is in json format and it contains columns: – – – – – – – – – – – –
‘reviewerID’ – id of the user that reviewed ‘asin’ – id of the product that is reviewed ‘reviewerName’ – name of the user that reviewed ‘image’ – image of the reviewed product ‘overall’ – rate of the product in the number format ‘reviewText’ – text of the product review ‘reviewTime’ – time when the review was written ‘style’ – is consisted of many sub columns that describe the product ‘summary’ – summary of the review text ‘unixReviewTime’ – review time in unix format ‘verified’ – information about reviewer verification ‘vote’ Table 1. Final dataset sample. UserID
ItemID
rating
A3U4E9PIZ8OWH1
0449819906
5.0
A3V21ZT37Y242L
0449819906
2.0
A1Q7YJ1NPE6E0W
0449819906
5.0
A3945D2TJ0PI86
0449819906
5.0
Since not all of the columns contain meaningful information to our problem, we will drop all columns except ‘overall’, ‘reviewerID’, ‘asin’ and we will also rename the columns to have better readability as seen in Table 1. – ‘overall’ → ’rating’, – ‘reviewerID’ → ’ItemID’, – ‘reviewerID’ → ’ItemID’. In order to acknowledge the skewed class in our dataset we have represented our dataset in the histogram as seen in Fig. 2. As shown in Fig. 2. a significantly large number of reviews are rated 5.0 rather than 2.0 and 1.0. This skewed class problem can represent a potential performance issue when it comes to big data technologies. It can affect the speed of big data performance. In order to avoid this problem we have dropped some of the data points, as seen in Fig. 3. Dataset now contains 120,994 reviews for each rating class and overall 604,970 reviews. In the dataset we have 466,582 distinct reviewers and 80,219 distinct reviewed items. UserID is the feature of data type string whereas the rating is of type double. Rating column has range from 1.0 (minimum rating) to 5.0 (maximum rating) and mean value of 3.0 as seen in Table 2. Because UserId and ItemId features are of string type, they need to be transformed into numeric values. It is done using PySpark’s built-in function called StringIndexer.
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Fig. 2. Class distribution.
Fig. 3. Class distribution.
Table 2. Summary of rating column. summary
rating
mean
3.0
min
1.0
max
5.0
StringIndexer is a label indexer that converts labels from a string column to label indices. It converts a numeric input column to a string and index the string values if it is numeric. The indexes are in the format [0, numLabels]. The most common label receives index 0, as this is by default arranged by label frequencies. StringOrderType can be set to control the ordering behavior [20]. Also, in order to maximize the accuracy of the model we have performed the data normalization before applying the machine learning model. As we discussed before we will apply two approaches for the normalization, min–max and mean-standard deviation normalization. In Table 3. The final dataset is presented with columns UserId and ItemId but also UserId_index and ItemId_index as result of the StringIndexing process previously explained. Rating_Normalized represents a column that was created as a result of the process from the mean and standard deviation normalization.
4 Results We utilized the Apache Spark master-slave architecture in order to make the data preprocessing and training more convenient and reliable. Apache sparks is based on the concept of one master node and multiple worker nodes. Worker nodes are tied to the corresponding master node which broadcasts work based on the computational needs. This way, we will increase our performance and speed the process of learning and training. Using this technique, we can run cross validation using the ALS model and compare the results from the values we got. For both approaches the same Apache Spark configuration is used. The configuration is set up through the PySpark as seen in Table 4. For our architecture we have used 8
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Table 3. Dataset after indexing and normalization. UserID
ItemID
rating
UserID_index
A3V21ZT37Y242L
0449819906
2.0
373432.0
ItemID_index 2192.0
rating_Normalized
A1GWGUBP1CUKGM
0449819906
2.0
131106.0
2625.0
−0.7071061967715571
A3473JQJTGQ7TD
0449819906
2.0
297952.0
2625.0
−0.7071061967715571
−0.7071061967715571
A3OWKFSSAWSOOP
0449819906
2.0
355928.0
10815.0
−0.7071061967715571
AHZ1JD7R6B6HK
0486417700
2.0
416210.0
10815.0
−0.7071061967715571
AC0J24YW9X2FO
0486473082
2.0
22833.0
9174.0
−0.7071061967715571
A3NB7TBEBO5BW7
0848724666
2.0
351507.0
1013.0
−0.7071061967715571
worker machines of 6.7GB RAM memory and one master (driver) machine of 10.6GB RAM memory. Each machine’s processor had 4 cores, in total 32 cores and total 50GB of RAM memory. Table 4. PySpark configuration. Configuration parameter
value
spark.executor.memory
6gb
spark.driver.memoryOverhead
50gb
spark.executor.cores
4
spark.executor.instances
8
spark.driver.memory
49gb
For the execution of our model we have divided this stage into two experiments. On each experiment we will run cross validation with the same hyperparameters just using a different normalization method for the final dataset. As already mentioned, the model that we are going to use to build the recommended system is the ALS model. For both experiments the model coldStartStrategy hyperparameter is set to drop and maxIter to 15. ALS hyperparameters rank and regularization parameter are used in cross validation as seen in Table 5. In the first experiment, the normalization is realized using mean and standard deviation values (1), while in the second experiment, the minimum and maximum values (2). Before applying the final dataset to ALS model the dataset is split into 80/20 split with randomSplit function. For evaluation of our experiments we will use the Root-Mean-Square Error evaluation metric. Generally, this is one of the most, if not the most, used evaluation metric that is used when working with the ALS algorithm. The smaller the RMSE is the better our model performs. It estimates the error of the model and calculates the error rate that a user assigns to the system [21]. rating[value] − min , max − min
(1)
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rating[value] − mean (2) sttdev From the results in Table 5, we see that we have executed 18 tests for each experiment. In total, 36 executions were executed in 8.1h across 8 different machines. The best result from the first experiment is proven to be an RMSE of 0.823 using hyperparameters rand 6 and regularization parameter of 0.2. The best result from the second experiment is the RMSE of 0.873 that was obtained using hyperparameters rand 6 and regularization parameter of 0.2. We see that optimal hyperparameters for our ALS model is rand of 6 and regularization parameter of 0.2 since both experiment #1 and experiment #2 have given best results using these hyperparameters. The smallest RMSE of 0.823 is obtained using mean and standard deviation for the normalization formula. Table 5. Experiments results. Experiment
Rank
regParam
RMSE
Experiment
Rank
regParam
RMSE
#1
1
0.1
1.081
#2
1
0.1
1.005
0.2
1.006
0.2
0.976
0.3
0.977
0.3
0.957
0.1
0.989
0.1
0.970
0.2
0.941
0.2
0.946
0.3
0.977
0.3
0.936
0.1
0.919
0.1
0.926
0.2
0.889
0.2
0.946
0.3
0.876
0.3
0.915
0.1
0.870
0.1
0.890
0.2
0.855
0.2
0.890
0.3
0.851
0.3
0.900
0.1
0.859
0.1
0.886
0.2
0.846
0.2
0.891
0.3
0.845
0.3
0.902
0.1
0.829
0.1
0.867
0.2
0.823
0.2
0.873
0.3
0.828
0.3
0.896
2
3
4
5
6
2
3
4
5
6
5 Discussion If we compare the RMSE values obtained in this research with those presented in the literature review section, it is evident that we managed to achieve better performance and result with this approach. The best performing scenario mentioned in the literature
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review is Gosh et. al. Research [11] with RMSE value of 0.870 which is performing worse than our best value of RMSE which we managed to achieve to be 0.823. It is observed that this [11] project used a number of maximum iterations to be 5, which is smaller compared to our project maximum iteration number, which is set to 15. This may contribute to the lower RMSE score of the project [11]. Also, in the experiment [11] the author only performed the ALS algorithm with one set of parameters, regularization parameter of value 0.01, while we tested two approaches with different parameters and got twelve different results. Compared to the other projects that were mentioned in the literature review, research done by Aljunid and Manjaiah [7] with RMSE value of 0.916 and Panigrahi et. al. Research [12] with RMSE value of 0.88, we can say that our experiment performed better in both approaches. The result of our first approach, using mean and standard deviation regularization achieved RMSE value of 0.823 and the result of our second approach using minimum and maxim regularization achieved RMSE value of 0.873, which both have better results compared to the values of the projects mentioned. All of the projects mentioned above have a similar approach and utilize the same exact algorithm (ALS algorithm), but our approach implements two different normalization techniques with cross validation on multiple parameters. Result of this experiment is twelve different RMSE values. Also a major approach that helped us speed up computation time and also reduce computation power needed to perform those executions is fixing skewed classes issues.
6 Conclusions The contribution of the work done in this research compared to the other researches is in testing multiple scenarios using different values of latent factors and regularization parameters. Tests were performed on smaller and larger values of both latent factors and regularization parameters with the goal to see which ones are performing better for this specific dataset. The approach we have taken in this research is the exhaustive testing using different parameters, only with this approach can we prove that we discovered the best solution. Other than just testing out regularization parameters and ranks, we also tested two different approaches when it comes to normalization of rating/review value. We used the mean value and standard deviation regularization approach in one scenario and minimum and maximum values of ratings in the second regularization approach and the goal was to compare all of these scenarios. Normalization approach with mean and standard deviation values showed better performance by decreasing the RMSE value by 0.05 compared to the minimum and maximum regularization approach. This decrease, even though it seems small on the first look, can be crucial, especially with e-commerce businesses where every customer counts. Comparing these two experiments, we can see that the best performing ones for both cases are with rank value of 6 and regularization parameter of value 0.2. Comparing all obtained values from both approaches we can see that normalization of review values done with mean value and standard deviation approach is performing significantly better. That approach is working better for almost all combinations in this cross validation except
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for a rank value of 2. In the real world system, based on the results presented in this paper, standard deviation and mean value regularization approaches with normalization are showing much better results and are more desirable to implement since this approach is outperforming other approaches in every scenario. In conclusion we prove that using our method for Arts Crafts and Sewing Amazon dataset we can obtain excellent recommendations and that it can be used in similar recommendation problems. Approach that we have experimented in this paper can be tested in the real world scenario. The plan for future work of this research is to prove or disprove our hypothesis on more precise examples, implementing it on the ecommerce website. By comparing the recommendations that our system predicted and items that users bought we can measure the accuracy of our recommender system.
References 1. McAuley, J.: Amazon review data. https://jmcauley.ucsd.edu/data/amazon/. 10 Jan 2023 2. Product Management Exercises: How many reviews are left on Amazon on a given Monday? https://www.productmanagementexercises.com/4604/how-many-reviews-are-lefton-amazon-on-a-given-monday. 8 Jan 2023 3. Miryala, G., Gomes, R., Dayananda, K.: Comparative analysis of movie recommendation system using collaborative filtering in spark engine. J. Global Res. Comput. Sci. 8(10), 10–14 (2017) 4. Biswas, P.K., Liu, S.: A hybrid recommender system for recommending smartphones to prospective customers. Expert Sys. Appl. 208, 118058 (2022) 5. Roy, D., Dutta, M.: A systematic review and research perspective on recommender systems. J. Big Data 9(1), 1 (2022) 6. ResearchGate. Collaborative filtering recommender systems. https://www.researchgate.net/ publication/200121027_Collaborative_Filtering_Recommender_Systems 7. Aljunid, M.F., Manjaiah, D.H.: Movie recommender system based on collaborative filtering using Apache Spark. In: Balas, V.E., Sharma, N., Chakrabarti, A. (eds.) Data Management, Analytics and Innovation. AISC, vol. 839, pp. 283–295. Springer, Singapore (2019). https:// doi.org/10.1007/978-981-13-1274-8_22 8. Cheuque, G., Guzmán, J., Parra, D.: Recommender systems for online video game platforms: the case of STEAM. In: Companion Proceedings of the 2019 World Wide Web Conference, pp. 763–771. ACM Press, San Francisco, USA (2019) 9. Ouassini, M.: Development a recommendation system for an e-commerce based on alternating least squares. (Unpublished Master’s Thesis). Altınba¸s University, Graduate School of Education, Istanbul (2022) 10. Almohsen, K.A., Al-Jobori, H.: Recommender systems in light of big data. Int. J. Electr. Comput. Eng. 5(6), 1553–1563 (2015). https://doi.org/10.11591/ijece.v5i6.pp1553-1563 11. Gosh, S., Nahar, N., Wahab, M.A., Biswas, M., Hossain, M.S., Andersson, K.: Recommendation system for e-commerce using alternating least squares (ALS) on Apache Spark. In: Vasant, P., Zelinka, I., Weber, G.W. (eds.) ICO 2020. AISC, vol. 1324, pp. 880–893. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68154-8_75 12. Panigrahi, S., Lenka, R.K., Stitipragyan, A.: A hybrid distributed collaborative filtering recommender engine using Apache Spark. Procedia Comput. Sci. 83, 1000–1006 (2016). https:// doi.org/10.1016/j.procs.2016.04.214 13. Alexopoulos, A., Drakopoulos, G., Kanavos, A., Sioutas, S., Vonitsanos, G.: Parametric evaluation of collaborative filtering over Apache Spark. In: 2020 5th South-East Europe
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Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNISM), pp. 1–13 (2020). https://doi.org/10.1109/seeda-cecnsm49515. 2020.9221836 Woo, J., Mishra, M.: Predicting the ratings of Amazon products using Big Data. WIREs Data Min. Knowl. Discovery 11(3), 99–110 (2021). https://doi.org/10.1002/widm.1400 Dheeraj, B., Sheetal, G., Debajyoti, M.: Matrix factorization model in collaborative filtering algorithms: a survey. Procedia Comput. Sci. 49, 136–146 (2015) Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009) Li, J.-B., Lin, S.-Y., Hsu, Y.-H., Huang, Y.-C.: Implementation of an alternating least square model based collaborative filtering movie recommendation system on hadoop and spark platforms. In: Advances on Broadband and Wireless Computing, Communication and Applications, pp. 237–249 (2019) Alternating Least Squares: https://nightlies.apache.org/flink/flink-docs-release-1.2/dev/libs/ ml/als.html. Last accessed 02 Jan 2023 Salloum, S., Dautov, R., Chen, X., Peng, P.X., Huang, J.Z.: Big data analytics on Apache Spark. Int. J. Data Sci. Anal. 1(3), 145–164 (2016) StringIndexer — PySpark 3.3.1 documentation. https://spark.apache.org/docs/latest/api/pyt hon/reference/api/pyspark.ml.feature.StringIndexer.html. Last accessed 11 Jan 2023 Awan, M.J., et al.: A recommendation engine for predicting movie ratings using a big data approach. Electronics 10(10), 1215 (2021)
Implementation and Evaluation of a Deep Neural Network for Spam Detection: An Empirical Study of Accuracy and Efficiency Luka Varga, Časlav Livada(B) , Alfonzo Baumgartner, and Robert Šojo Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University in Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia [email protected] Abstract. The problem of spam emails is a widespread issue that creates a lot of inconvenience for individuals and organizations. According to statistics, approximately 84% of emails received on a daily basis are recognized as spam. This paper aims to present a solution to this problem by proposing the use of a neural network capable of identifying and classifying potential spam emails. The neural network was developed using Python, TensorFlow, Keras, Google Colaboratory, and Jupyter. These tools were chosen because they are widely used and well-suited for the task of creating a deep learning model. The results of the network were found to be satisfactory, with an accuracy rate of approximately 99%. This is comparable to the results achieved by large companies such as Google and Yahoo! who are known to use similar methods to combat spam. Overall, this paper demonstrates that neural networks can be a powerful tool for addressing the problem of spam emails and that the proposed solution has the potential to improve the efficiency and effectiveness of spam filtering for individuals and organizations. Keywords: Classification · Machine learning · Neural networks · Spam
1
Introduction
As technology advances, a significant portion of our daily lives takes place online. We submit personal information to companies, such as bank card details and photos from social networks. Spam emails, which according to statistics from last year account for 84% of all email traffic [4], are sent to a large number of people and can sometimes have malicious intentions. An average worker using an official email account for communication receives over 144 emails per day, of which over 122 are unsolicited [4]. This is a major problem for users. Large companies such as Google, Yahoo and others have effectively addressed this problem by using artificial intelligence. They use a “huge database of malicious links” [11] to filter emails by analyzing not only the content, but also the c The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademović et al. (Eds.): IAT 2023, LNNS 644, pp. 388–402, 2023. https://doi.org/10.1007/978-3-031-43056-5_28
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IP address, the sender’s domain, the protocols used for sending, and the authentication of the user’s account. In addition, they have developed a system called whitelist that recognizes users based on their reputation and links them to their personal account. This system uses pre-built algorithms with names like Bayes, Porter and others. 1.1
Related Work
One study proposes a new semi-supervised graph embedding model based on a graph attention network for spam bot detection in social networks [26]. Another study aims to develop a new method for detecting phishing attacks and determining how to protect against such threats using LSTM deep learning. This paper presents a technique for classifying emails as phish or not phish using machine and deep learning algorithms. The dataset was preprocessed and converted into suitable features using regular expression and NLP in Python programming. SVM, NB, and LSTM classifiers were used to detect phishing attacks [6]. A semantic-based classification approach is proposed in another study, which shows that the proposed method enables better spam detection compared to existing methods based on Bag-of-Words (BoW) and semantic content [17]. A convolutional neural network demonstrates high accuracy in detecting spam in SMS text messages after comparing various algorithms [18]. The effectiveness of conventional neural network design with two boosting methods in detecting Twitter spam was examined in a proposed methodology. The algorithms were evaluated in different scenarios by increasing the volume of training data while keeping the spam-to-non-spam ratio constant. The study found that the stochastic gradient boosting approach was optimal in terms of all performance metrics [8]. The most important question we should ask is: “Can the proposed neural network model be used to improve the efficiency and effectiveness of spam filtering for individuals and organizations compared to other methods used by large companies such as Google and Yahoo?” The purpose of this question is to investigate whether the neural network model proposed in this paper can be used to improve spam filtering. In this paper, we address the solution of the spam email problem in a noncommercial application using a manually developed neural network model. First, we will explain the theoretical basis of the solution, followed by a detailed description of the programming environment used. We will also define the types, models, and optimization of the network itself, and finally present test results showing the effectiveness of the various approaches to the problem at hand.
2
Neural Networks
A neural network consists of basic units of neurons divided into layers. Depending on how the model was created and what kind of network is used, these neurons are connected to each other in a certain way. There are different types
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of neural networks, from single-layer to multilayer, from those with one hidden layer to those with multiple hidden layers, recurrent or feedforward. In the basic architecture, there are three types of neural layers: Input, hidden, and output layers. Signal flow in feedforward networks is strictly in the forward direction, from input to output units and there are no feedback loops [2]. Feedback connections are seen in recurrent networks, Fig. 1. In contrast to feedforward networks, the dynamic properties of the network are crucial here. Under certain circumstances, the activation values of the units undergo a relaxation process that causes the network to evolve to a steady state where the activations do not differ. In some applications, the changes in the activation values of the output neurons are so significant that the performance of the network is determined by its dynamic behavior [24].
Fig. 1. Neural network architecture [24].
There are several other architectures of neural networks such as symmetrically connected networks, perceptrons, convolutional neural networks, longterm/short-term memory networks, etc. In this paper, the most important are the recurrent networks and the long-term/short-term memory networks, because they were used to create the final model networks. 2.1
Recurrent Neural Networks
Recurrent neural networks (RNNs) are a type of neural network well suited for processing sequential data. They are designed to process inputs of varying lengths, such as time series data, speech signals, and text. RNNs are able to maintain an internal state that allows them to remember information from previous time steps and use it for their current output. One of the most important properties of RNNs is their ability to process data with long-term dependencies. Traditional feedforward neural networks struggle with this task because they process inputs independently without considering the context of previous inputs. RNNs, on the other hand, can use their internal state to track previous inputs and use that information to inform their current
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output. This makes them particularly useful for tasks such as language modeling and speech recognition. One popular type of RNN is the Long Short-Term Memory (LSTM) network. LSTMs were introduced in 1997 by Hochreiter and Schmidhuber in [12]. LSTMs were designed to overcome the vanishing and exploding gradient problem that can occur with traditional RNNs. They accomplish this by introducing a series of gates that control the flow of information through the network. This allows LSTMs to selectively choose which information to remember and which to forget, making them particularly well suited for tasks that require the ability to retain long-term memory. In recent years, RNNs have also been used in combination with other neural network architectures to improve performance on a variety of tasks. For example, the attention mechanism presented in [5] has been used to improve the performance of RNNs in machine translation tasks by allowing them to selectively focus on specific parts of the input.
3
Designing and Building a Neural Network
For the solution, it was necessary to study the optimal design of the layers of the neural network itself, which data sets are given to the network as input data, and how to determine whether the conditions are satisfied or not [7]. The basic workflow of the experiment conducted in this paper is described on Fig. 2.
Fig. 2. Flowchart of the experiment.
3.1
Designing the Network
In order for the network to be as efficient as possible, it is necessary to figure out how many layers the artificial neural network will consist of, how many neurons each layer will contain, and what activation functions will be used to determine
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the output results. It is very important that the network is not overtrained. The network could become overadaptive due to overtraining [9] when learning patterns from the training set and thus be unable to effectively identify data outside of the training set. The basic principle of how artificial neural networks work is to provide a large enough amount of suitable data to allow the network to create patterns that it uses to categorize solutions and determine the threshold that meets the initially established criteria for a successful solution. Selecting the number of neurons - depicts how successfully the network can separate data depends on how many hidden neurons there are. The network can correctly predict the data it has been trained on, and a large number of hidden neurons ensures accurate learning, but its performance on new data and its ability to generalize will be compromised. The network may not be able to understand the connections between data if there are not enough hidden neurons, and the error cannot be reduced. Therefore, the choice of the number of hidden neurons is an important decision. Initial weights - the learning algorithm uses a gradient descent procedure that continuously descends in the space until it reaches the first valley. Therefore, it is crucial to choose a starting point in the multidimensional space. However, other than experimenting with different initial weight values to see if the results of the network improve, there are no proposed criteria for this selection. Thus, the model uses random weights. Learning Rate - As each weight is updated, the learning rate effectively controls the number of steps taken in the multidimensional weight space. If the learning rate is set too high, the local minimum may be constantly exceeded, leading to oscillations and slow convergence to a lower error state. If the learning rate is too low, a large number of iterations may result in slow performance. The rate used is the default value of the learning rate when using the Adam optimization algorithm [15] and is 0.001. Layers - Layers are the basic building blocks of neural networks in Keras. A layer consists of calls to layer methods and some states held in TensorFlow variables (layer weights). Types of layers used: – Embedding layer - Primarily used in applications related to natural language processing, such as language modeling, but can also be used in other neural network tasks [14,22]. – LSTM layer - One of the main features of the LSTM is to remember and recognize information and to discard information that the network does not need in order to learn data and make predictions [1,20,23] – Dropout layer - Solves the problem of overtraining deep neural networks with a large number of parameters so that they do not over adapt to the combination when predicting during testing [19] The activation function in a neural network defines how the weighted sum of the inputs is converted into an output by a node or nodes in the network layer.
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There are different types of activation functions [13]. Explanation of the more frequently used ones: – Rectified Linear Unit (ReLu) - the most used hidden layer feature. It’s common because it’s easy to implement and effective in overcoming the limitations of other activation features like Sigmoid and Tanh. In particular, it is less sensitive to vanishing gradients that prevent deep models from being trained, although it can suffer from other problems such as saturated or “dead” units. The ReLU function is shown on Fig. 3. – Sigmoid - function used in the logistic regression classification algorithm. The function takes any real value as input and returns values in the range 0 to 1. The larger the input (more positive), the closer the output value will be to 1, while the smaller the input (more negative), the closer the output will be to 0 (Fig. 4). – Hyperbolic tangent - Tanh - it is very similar to the sigmoid activation function and even has the same S-shape. The function takes any real value as input and outputs values in the range −1 to 1. The larger the input (more positive), the closer the output value is to 1, while the smaller the input (more negative), the closer the output is to −1.
Fig. 3. Re-Lu activation function [16].
3.2
Creating the Model
Since we are working with text data, we need to convert it into numbers before feeding it into a machine learning model, including neural networks. To identify words with numbers, each word from the dataset must be associated with a specific value. This is what Keras’ Tokenizer preprocessing class is for. It allows vectorization of a structured text record by converting each text into a series of integers (sequences) (each integer is an index of a tag in the dictionary) or into a vector. By default, all punctuation marks are removed from the texts, leaving only spaces between words, some of which may contain a ’ character. Then, lists of tokens are created from these sequences, which are vectorized or indexed. The coefficient for each token can be binary, based on the word count Listing 1.1.
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Fig. 4. Sigmoid activation function [3].
Listing 1.1. Adding a numerical value to each word. tokenizer = Tokenizer(num_words = vocabulary_size) tokenizer.fit_on_texts(train_data) word_index = tokenizer.word_index print(word_index) {’i’:1, ’to’:2, ’you’:3, ’a’:4, ’the’:5, ’u’:6, ’and’:7}
To achieve that each sentence entering the embedding layer contains the same number of words, the padding function is used, which works by padding the difference between the sentences with a certain value, in this case (Listing 1.2) with zeros. Listing 1.2. Alignment of sentence lengths. sequences = tokenizer.texts_to_sequences(train_data) test_sequences = tokenizer.texts_to_sequences(test_data) print(sequences[0]) padded = pad_sequences(sequences, maxlen=sentence, padding=’post’) test_padded = pad_sequences(test_sequences, maxlen=sentence, padding=’post’) print(padded[0]) print(len(sequences[0], len(sequences[1])) print(len(padded[0], len(padded[1])) [52, 424, 3837, 812, 813, 572, 67, 9, 1260, 84, 128, 331, 1436, 143, 2587, 1123, 65, 59, 3838, 141] [52 424 3837 812 813 572 67 9 1260 84 128 331 1436 143 2587 1123 65 59 3838 141 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0] 20 6 40 40
The embedding layer allows us to convert each input word into a fixed-length vector of a given size. For simplicity, the sentences can be compared with categorical variables. For this purpose, categorical features are converted into numbers by a unique encoding. Since this work is about spam classification, this means that regular emails are labelled with the number 0, while spam is labelled with the number 1. The resulting vector is dense and contains not only 0 and 1, but
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also real values. The fixed length of the word vector and the reduced dimensions allow us to express words more efficiently: (1, 2) [[[ 0.04502351 0.00151128 0.01764284 -0.0089057] [-0.04007018 0.02874336 0.02772436 0.00842067]]]
When creating the Embedding layer, there are three parameters, namely the input size, i.e. the size of the dictionary, the output size, i.e. the length of each vector, and the maximum length of the string to be processed, i.e. the length of the sentence. After the search, the first parameter is set to 20,000, since it is a dataset with 5 thousand sentences. The second parameter is set to 32, since a smaller number does not provide enough information, while larger numbers provide unnecessary information and can confuse the network. The third parameter is set to 40 because according to the research conducted [10], the desired average length of a sentence in an email is between 28 and 50 words. The LSTM architecture enables the network to find correlations between words and sentences because it is able to analyze data and learn long-term dependencies, especially in sequence prediction problems. It works by eliminating unused information and remembering the sequence of information using its cells. The input gate evaluates the accuracy and importance of the data to make predictions, and decides the importance of the information by updating the state of the cells. Forgetting gates determine whether data can travel through different layers of the network. Information that is not necessary to learn about predictions is discarded. The Dropout layer is used to prevent the network from overfitting to the data during training and producing poor results on test examples. Each neuron in the Dense layer receives input from each neuron in the layer before it, which is called a “deep connection”. It multiplies matrices with vectors, and the values of the matrix are parameters used to train and improve the network using backpropagation. At the output, there is another layer consisting only of a neuron and a sigmoid activation function, which is used to present a binary result of 1 or 0, i.e. whether the message is detected as spam or not. In the construction of the network, the Adam optimization algorithm [25] and the binary cross entropy were set, which compares the actual results with the predicted ones and shows how far apart the result and the actual value are. The creation of the model ends with the transfer of the parameters and the determination of the weights, Listing 1.3. Listing 1.3. Assigning the weights. weights=model.get_weights() print(weights) [array([[-0.04436097, -0.01054129, 0.01997608, ..., -0.00912695, -0.00591241, -0.03568421] [-0.06112846, -0.08427896, 0.06286675, ..., -0.03732788, -0.05644974, -0.07379684] ... ,
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Results
The output data used for the study is a collection of spam “Spam Text Message Classification” [21], and there are two categories of results obtained by pulling said data through the network. The data consists of 5797 unique values having the following distribution - not-spam 67% and spam 33%. These are the results of the first part of the network example where the network learns, and the results of the actual examples given. The accuracy or precision of the results is compared with the established threshold and the optimal solutions that can be found in large companies dealing with a similar problem. Hyperparameters such as the number of epochs and the number of data samples in an epoch, i.e., one pass through the data, are determined using experience and the keras.callbacks.EarlyStopping() class, which does not allow the difference between the actual and predicted results to increase, but stops the model when it detects deterioration. Ultimately, the optimal hyperparameters are as follows: – – – – –
vocabulary size = 20000 sentence = 40 embedding vector dimension = 512 number of epochs = 15 batch size = 512
The results will be seen later in the paper (Table 2). The model was originally created as a network with the GlobalAveragePooling1D layer, which pooled the values from the Embedding layer and used the similarity of the vector values to try to find the correlation between the words (Table 1). Table 1. Layers of the model. Layer (type)
Output Shape Parameters
Embedding GlobalAveragePooling1D Dropout Dense Dense
(None, (None, (None, (None, (None,
40, 32) 32) 32) 6) 1)
640000 0 0 198 7
Total params: 640,205 Trainable params: 640,205 Non-trainable params: 0
The tests were performed with both manually entered regular mails and spam mails, which can be seen in one of the examples in Listing 1.4.
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Listing 1.4. Test example and result. text= [ Guaranteed CASH MONEY only if you call us at - 0800 555 7475!! , Did you see what happened yesterday with Melissa?, You just won FREE holiday in our best hotel on shore!, Can you help me with moving to place today?, Book your favorite holiday places for discount price in July!] CheckRandomInput(text) Guaranteed CASH MONEY only if you call us at - 0800 555 7475!! [0.25199008] Did you see what happened yesterday with Melissa?, [0.223349] You just won FREE holiday in our best hotel on shore!, [0.25929904] Can you help me with moving to place today?, [0.23130766] Book your favorite holiday places for discount price in July!] [0.25112814]
Although the network was able to distinguish between regular and spam emails, the percentage of almost 25% with which it detected errors was low and therefore unacceptable. The Table 2 shows some other results of the validation dataset and the estimation accuracy of the same model, but with the manipulation of hyperparameters. Table 2. First test results. Number of epochs Number of samples Training accuracy Validation accuracy Testing accuracy 12
256
0.9562
0.9561
0.1664
15
256
0.9742
0.9695
0.1286
20
256
0.9670
0.9578
0.0574
15
512
0.8811
0.8834
0.1150
20
512
0.945
0.9327
0.1193
Although the results of training and validation were correct, they were not sufficiently pronounced, and many regular emails were detected as spam when classified. Table 3. Further tests with improved model. Number of epochs Number of samples Training accuracy Validation accuracy Testing accuracy 12 20 15 20
256 256 256 512
0.9998 1.0000 0.9989 1.0000
0.9857 0.9865 0.9883 0.9874
0.8922 0.5847 0.9941 0.9962
0.9991
0.9848
0.9980
with predefined dropout 15
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Fig. 5. Accuracy graph of the training data and validation data.
The next step was to apply dropout and the LSTM architecture, which showed great improvements. Some of the results can be seen in Table 3. Figure 5 shows how the measurement accuracy increases during the run through the training dataset (blue line - accuracy) and does not decrease at the endpoint, indicating that the network is not overtrained, which is not the case during the run through the validation dataset (orange line - val_accuracy), where overtraining occurred. However, it was pointed out that using accuracy as the only index of success is not sufficient. In addition to the accuracy of the model in the test examples, some of the parameters that are important in assessing the success of the model are the loss function and the false positives and false negatives. If a spam message is misclassified as a regular email, this is a rather trivial problem, as the user only has to delete such a message. In contrast, it is annoying when non-spam messages are incorrectly classified as spam, as this increases the risk of losing important data due to filter misclassification. Therefore, it is insufficient to rely solely on classification accuracy when evaluating the effectiveness of algorithms as spam filters. To make classification as reliable as possible, parameters are included to indicate loss and detected false-negative messages in addition to accuracy (Fig. 6). False-positive samples, while showing the quality of the network, are not a serious problem and are therefore not shown. As can be seen in the Fig. 6, the blue line representing the training set rises and does not fall, indicating that no overtraining occurs, and the yellow line representing the validation set also does not break at the end of the validation. We also see a dramatic improvement in the loss function across epochs, along
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Table 4. Loss, false negative and false positive results. Number of epochs Loss
False negatives Accuracy
1/15
0.5160 145
0.8700
4/15
0.2478 145
0.8700
8/15
0.0746
34
0.9677
11/15
0.0531
9
0.9874
13/15
0.0602
8
0.9883
15/15
0.0683
11
0.9857
with false-negative detected patterns. The loss of the most efficient model, the false-negative estimate, and the accuracy of the estimate can be seen in Table 4. Of the 1000 samples provided for model validation, the number of 8–11 samples found to be false negatives corresponds to a percentage of only 0.01% error, making the network quite reliable for use.
5
Conclusion
In this paper, we proposed a deep neural network architecture for spam detection in various online communication channels. By leveraging the power of deep learning, our model was able to accurately classify spam and non-spam messages with a high level of precision. Our results demonstrate the effectiveness of machine learning techniques, such as deep learning and convolutional neural networks, in detecting and preventing spam. However, we acknowledge that the dynamic nature of spamming tactics requires continuous adaptation and improvement of spam detection methods. While our model showed promising results, further research could explore hyperparameter optimization and feature development to enhance its performance on specific datasets. We also recognize that the performance of our proposed method may vary depending on the dataset and feature selection. Therefore, future research should focus on developing more robust and adaptive neural network models to effectively detect evolving spamming strategies. Our study contributes to the ongoing efforts to combat spam and promote a more secure and trusted online environment. The use of machine learning techniques has shown promising results in improving the accuracy of spam detection. However, challenges still need to be addressed, such as the emergence of new types of spam and finding ways to prevent spammers from circumventing existing security measures. In conclusion, we believe that the development of effective methods to detect and prevent spam and phishing attacks is crucial in today’s digital age. We hope that our approach inspires future research in the field of machine learning and spam detection, leading to more robust and adaptive models that can effectively
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Fig. 6. Graphic display of network quality indicators.
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detect evolving spamming strategies. The field of artificial intelligence is vast and constantly evolving, and we encourage researchers to continue exploring new approaches to combat spam and other online threats.
References 1. A Complete Guide to LSTM Architecture and its Use in Text Classification. https://analyticsindiamag.com/a-complete-guide-to-lstm-architecture-andits-use-in-text-classification/. Accessed 20 Oct 2022 2. Abraham, A.: Artificial Neural Networks, vol. 17 (2005) 3. Activation Functions in Neural Networks. https://towardsdatascience.com/ activation-functions-neural-networks-1cbd9f8d91d6. Accessed 8 Dec 2022 4. Average daily spam volume worldwide from October 2020 to September 2021. https://www.statista.com/statistics/1270424/daily-spam-volume-global/. Accessed 16 Oct 2022 5. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014) 6. Butt, U.A., Amin, R., Aldabbas, H., Mohan, S., Alouffi, B., Ahmadian, A.: Cloudbased email phishing attack using machine and deep learning algorithm. Complex Intell. Syst., 1–28 (2022) 7. Dada, E.G., Bassi, J.S., Chiroma, H., Abdulhamid, S.M., Adetunmbi, A.O., Ajibuwa, O.E.: Machine learning for email spam filtering: review, approaches and open research problems. Heliyon 5(6), e01802 (2019). https://doi.org/10. 1016/j.heliyon.2019.e01802. https://www.sciencedirect.com/science/article/pii/ S2405844018353404 8. Devi, K.K., Kumar, G.: Stochastic gradient boosting model for twitter spam detection. Comput. Syst. Sci. Eng. 41(2), 849–859 (2022) 9. Dietterich, T.: Overfitting and undercomputing in machine learning. ACM Comput. Surv. 27(3), 326–327 (1995). https://doi.org/10.1145/212094.212114 10. Email Length Best Practices for Email Marketers and Email Newbies. https:// www.campaignmonitor.com/blog/email-marketing/email-length-best-practicesfor-email-marketers-and-email-newbies/. Accessed 28 Oct 2022 11. Gmail Spam Filter: When It Is Not Enough to Stop Spam. https://clean.email/ gmail-spam-filter. Accessed 18 Oct 2022 12. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997) 13. How to Choose an Activation Function for Deep Learning. https:// machinelearningmastery.com/choose-an-activation-function-for-deep-learning/. Accessed 19 Nov 2022 14. Hrinchuk, O., Khrulkov, V., Mirvakhabova, L., Orlova, E., Oseledets, I.: Tensorized embedding layers. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4847–4860 (2020) 15. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). https:// doi.org/10.48550/ARXIV.1412.6980. https://arxiv.org/abs/1412.6980 16. Mohamed, I.S.: Detection and tracking of pallets using a laser rangefinder and machine learning techniques. Ph.D. thesis, European Master on Advanced Robotics+(EMARO+), University of Genova, Italy (2017) 17. Saidani, N., Adi, K., Allili, M.S.: A semantic-based classification approach for an enhanced spam detection. Comput. Secur. 94, 101716 (2020)
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Gradual Increase of Natural Disaster Occurrences in the Context of Climate Change Amela Mudželet Vatreš(B)
, Aldin Kovaˇcevi´c , and Samed Juki´c
Faculty of Engineering, Natural and Medical Sciences, International Burch Univesity, Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. Natural disasters represent an act of nature which may lead to disruption of normal societal activities, significant financial damages and loss of human life. Annually, natural disasters account for the death of over 90,000 individuals and accumulate several-hundred-billion dollars in property damage. Moreover, recent trends show that climate change has had the greatest impact on increased frequency of natural disasters. The purpose of this research is to showcase the correlation between the rising number of extreme weather events and ongoing climate change, using the data provided by the National Weather Service of the United States. Using Python tools for data mining, analysis and visualization an indepth study into the rate of natural disaster occurrences, their associated monetary damages and overall human fatalities was performed, with the primary focus on tornado and flood events. In the end, the comprehensive research results validated the initial hypothesis – that there has been a significant increase in the number and severity of natural disasters in recent years, which can be understood as an effect of climate change. The results of this research were compared to those previously conducted in the field, which served to further corroborate the conclusion made in this study. Keywords: natural disasters · climate change · data analysis · tornado events · flood events · disaster aftermath
1 Introduction Natural disasters can be defined as an act of nature of such magnitude as to create a catastrophic situation in which the day-to-day patterns of life are suddenly disrupted, and people are plunged into helplessness and suffering [1]. According to statistics, natural disasters of different types are the cause of death of over 90,000 people every year. Natural disasters include tsunamis, floods, heat, droughts, thunderstorms, etc. Consequences and damage caused by different kinds of events are various and can be separately examined for different areas. Globally, 0.1% of all deaths are caused by natural disasters, while the annual damage caused by disasters exceeds several-hundred-billion dollars. Research has shown that the number of natural disasters has been increasing over time, likely as a result of ongoing climate change [2]. Climate © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 403–419, 2023. https://doi.org/10.1007/978-3-031-43056-5_29
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change refers to a change in average weather patterns and conditions in a region, over a long period of time [3]. It primarily manifests through higher temperatures, storms of higher intensity, increased droughts, a rise in ocean levels, etc. [4]. Climate change represents a continuous problem for nature and society, and as such a lot of research has been dedicated towards that topic. The amount of data and its diversity were the main motivating factors to start analyzing natural disasters. The primary focus of this research were the natural disasters and their consequences in the United States of America [5]. The main contributions of this paper can be summarized as follows (1) developing a standard method for performing a set of exploratory and descriptive data analyses on the weather events dataset, with the goal of extracting useful weather information and trends, and (2) providing a decent way of comparing the obtained results with previous research in this field, to corroborate similarities and compare differences in analysis.
2 Literature Review Climate change is one of the major challenges facing the world today, and its impacts are being felt across the globe in the form of extreme weather events such as hurricanes, droughts, and heatwaves. The gradual increase of natural disasters in the context of climate change is a significant concern for both developed and developing nations, as the frequency and intensity of these events are likely to increase in the coming years. In recent years, there has been a growing body of literature that highlights the relationship between climate change and the increased frequency and intensity of natural disasters. A study by the Intergovernmental Panel on Climate Change (IPCC) found that the frequency of natural disasters such as hurricanes, droughts, and heatwaves has increased over the past century, and that this trend is likely to continue. This is due to a combination of factors, including rising temperatures, sea-level rise, and changes in precipitation patterns [6]. Research has also shown that climate change is exacerbating the impacts of natural disasters, making them more destructive and longer-lasting. For example, a study by the World Meteorological Organization (WMO) found that rising temperatures have been linked to an increase in the frequency and intensity of heatwaves, which can lead to widespread crop failures and famine [7]. Additionally, a report by the United Nations Framework Convention on Climate Change (UNFCCC) found that sea-level rise is contributing to the increased risk of coastal flooding and erosion, which can have devastating impacts on coastal communities [8]. When it comes to the United States, according to data from the Federal Emergency Management Agency (FEMA), the number of presidential disaster declarations in the US has steadily increased since the 1950s, with a particularly sharp increase since the 1990s [9]. Similarly, data from the National Oceanic and Atmospheric Administration (NOAA) shows an increase in the frequency of extreme weather events, such as heat waves, droughts, and heavy precipitation events, in the US over the past century [10]. Moreover, the US Global Change Research Program (USGCRP) has noted that rising global temperatures and changes in precipitation patterns are increasing the frequency and severity of heat waves, droughts, and heavy precipitation events, which in turn increase the risk of wildfires, flooding, and landslides [11].
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Additionally, 2017 and 2018 were both notable for the number and severity of major hurricanes, including Harvey, Irma, and Maria, which caused widespread damage and displacement across Texas, Florida, and Puerto Rico [9]. The western US has experienced an increase in the frequency and severity of wildfires in recent years, with the 2018 and 2020 fire seasons both breaking records for the number of acres burned and the cost of firefighting efforts [12, 13]. The central and eastern US have also experienced an increase in the frequency and severity of floods, driven in part by heavy rainfall events associated with changing climate patterns [10]. In conclusion, the gradual increase of natural disaster occurrences represents a major concern for the entire world. This increase is likely to continue into the future, and the impacts of these events are likely to become more severe and longer-lasting.
3 Materials and Methods Throughout this research, a variety of “big data” and data analysis tools have been utilized to perform numerous exploratory and statistical analyses on the input weather event dataset. The following section describes the data cleaning and preprocessing aspect of the research paper. 3.1 Dataset Overview The dataset used for the purposes of this analysis is provided by the National Weather Service (NWS) and contains data about disasters that occurred within the United States of America from January 1950 to October 2022. The data is in the form of chronological listing of different kinds of disasters, such as winds, hail, floods, thunderstorms, tornadoes, hurricanes, etc., with their description and other relevant information. The first four years (1950–1954) represented in the dataset contain only tornado events. The following period (1955–1992) also contains the representation of hail, tornado and thunderstorm events, while the rest of the dataset (1993–2022) contains information about 48 different types of disasters. A timeline of recorded disasters can be seen in Fig. 1. The lack of information for the initial years is due to the missing technological and processing features of that time. By observing the dataset, it can be noticed that data is grouped into 3 separate units: locations, fatalities, and details. Each group has a total number of 72 files, where each file contains data for one year. Therefore, the Storm Events Database consists of 216 different CSV files [14]. Data Cleaning and Preprocessing. As mentioned earlier, the dataset that was used is not a unique file containing all the data. Instead, data was grouped into disaster details, fatalities and locations. Hence, before the process of cleaning the data, all files were combined using Apache Hadoop and Pig. Afterwards, the data had to be cleaned, which included deleting duplicate rows, filling null and N/A values with appropriate replacements, finding average values for specific aggregated fields, etc. The final dataset consists of 1,781,287 records. Figure 2 represents a brief description of all the steps involved in data cleaning and preprocessing.
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Fig. 1. Timeline from 1950 to 2022 with event types measured and available in the data set.
Fig. 2. Flow graph representing the process of data cleaning and preparation for further analysis.
3.2 Utilized Technologies Technologies used during data preprocessing and analysis are Apache Hadoop, Apache Pig, Pandas, Matplotlib, Basemap, NumPy, SciKit-learn. The Apache Hadoop project is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models [15]. Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs [16]. Pandas is an open-source Python library that is widely used in data munging and wrangling [17]. Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms [18]. Building on top of Matplotlib, Basemap is a graphical extension for creating maps [19]. NumPy is a powerful Python library for scientific computing, namely linear algebra, Fourier transformations and random number capabilities [20].
4 Results of Analysis Data analysis is defined as the process of transforming and modeling data to discover useful information for decision-making or arriving at conclusions [21]. The entire process of data preparation and analysis in this case was directed towards getting statistics about disasters and damage, so that they could be compared to other sources.
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4.1 Exploratory Data Analysis According to the database description, starting from 1992, the initial dataset contained information about 48 different event types. However, the analysis showed that the total number of event types was 69. After examination, it was noticed that there exist subgroups of event types (for example, both “high snow” and “heavy snow” can be categorized as “snow”). Among all disaster types, the USA was most affected by thunderstorm winds, with the total number of 500,343 events of such type. They are followed by hail, whose number of occurrences was 388,685 and flash floods with a much smaller total number of 96,340. Even though tornado events are on the sixth place based on frequency, they were the most devastating disaster type in the USA by total damage. The value of total damage caused by tornado events is over $60 billion. Not only were tornadoes the most devastating natural disaster type by damage cost, but also by total number of deaths. Namely, tornadoes were the cause of death for 5,910 people in the USA since 1950. However, it should be noted that tornado events were the only events measured throughout the entire recording period, with other disaster types being added in later years. Therefore, some statistics may be skewed towards tornados. When all fatality data was taken in consideration, the male/female death ratio was around 2.08. This leads to the conclusion that twice as many men died by the consequences of natural disasters, in comparison to women. The most common cause of death for both males and females was heat (consequences of high temperature), followed by tornadoes and flash floods. An interesting fact is that one of the most common causes of male deaths were lightning and rip currents, which was not the case for females – among which another common death cause was winter weather.
Fig. 3. Victim age distribution.
The average age of deceased populace is 45.63. Figure 3 represents the age distribution of the deceased population. The graph shows that the most affected demographic was aged between 20 and 70 years.
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Figure 4 represents the average mortality age over time. Starting from 1995, it can be noticed the highest average mortality age peaks occurred around the years 2000 and 2011. Most of the time, average mortality age is below 50, meaning that the highest number of victims were able-bodied people.
Fig. 4. Timeseries of average mortality age.
For the majority of the US states, the most present disaster type was thunderstorm wind. This matches with the previously analyzed fact that thunderstorm winds are the most common disaster type in the US. The geographical distribution of different types of disasters can be seen in Fig. 5, where red color is used for the representation of thunderstorms, green for high wind, dark blue represents hail, light blue represents high surf, purple color represents heavy snows, yellow is used for strong wind and black is used for winter storms.
Fig. 5. Most common event types by state (Color figure online).
Figure 6 represents property damage distribution over USA states. According to numerical statistics, Texas was the most affected state with a total amount of almost $38 billion racked up in property damage. The same is represented on the map, where
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Texas is the only state with black notation. It is followed by Louisiana ($33.4 billion) and Florida ($22.5 billion).
Fig. 6. Property damage distribution over states.
During the first couple of years, only tornado events were recorded, being the most common disaster type in that period. However, looking at the general situation, thunderstorm winds prevailed in most years. The year with the most disasters in the period 1950–2022 was 2011. The closest year according to event occurrences was 2008, which can be seen in Fig. 7.
Fig. 7. Timeseries of weather events.
Similarly, the year with the most fatalities in the period 1950–2022 was 2011, as shown in Fig. 8. Due to characteristics of each season, certain disaster types occur more often in specific time periods. Hence, the winter season is full of winter storms. Thunderstorm winds prevail during summer and fall, while hail prevails during spring.
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Fig. 8. Timeseries of fatalities.
4.2 Analysis by Disaster Type In the following section, focus was given to the most prevalent disaster types occurring in the US. The total number of tornadoes in the past 72 years in the US was 74,816. Texas was the most affected state with a total number of 9,640 events of this type, followed by Kansas (4,775) and Oklahoma (4,595). Tornadoes mostly occurred in May and were the most present in the last 10 years. The total damage cost caused by them is over $60 billion. The two following graphs are also tornado-related and look much alike. Figure 9 represents a comparison between tornado occurrences and property damage, whereas Fig. 10 represents the comparison between tornado occurrences and the number of fatalities.
Fig. 9. Property damage vs. number of occurrences.
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Fig. 10. Fatalities vs. number of occurrences.
Both figures show that the F0 tornado type was the most frequent one, but not the most devastating. The most devastating tornado type by both fatalities and damages is F4, as F4 is the second most intensive tornado type (according to Fujita scale) [22]. The second type of disasters the research focused on was flooding events. The total number of flood related events in the period 1950–2022 in the US was 162,060. Texas was the most affected state with a total number of 13,898 events of this type, followed by Missouri (9,649) and Kentucky (7,536). Floods mostly occurred in June, with the highest number of occurrences being in the last 10 years (similar to tornado events). The total property damage caused by flooding was higher than $76 billion. The highest number of flood events occurred in the past 8 years (Fig. 11). The highest property damage caused by floods was in 2017, as can be seen in Fig. 12.
Fig. 11. Flood activity over time.
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Fig. 12. Flood damage over time.
The last event type to be analyzed was hurricanes. The total number of hurricanes in the period 1950–2022 in the US was 2000. North Carolina was the most affected state with a total number of 418 events of this type, followed by Florida (338) and Georgia (318). Hurricanes primarily occurred in September and were mostly present in the period 1996–1999. Total property damage caused by hurricanes is estimated at $49.5 billion. The year most affected by hurricanes was 2005 (Fig. 13), as the highest number of property damage and occurrences is recorded in it (Fig. 14).
Fig. 13. Hurricane activity over time.
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Fig. 14. Hurricane damage over time.
5 Discussion According to Statista, from where the Fig. 15 was taken, the most frequent disaster types in the period 1990–2016 were convective storms, floods and tropical cyclones. They are followed by riverine floods, forest fires, earthquakes, heat waves, flash floods, land fires, droughts, cold waves and landslides. Having in mind that Statista’s time interval does not completely match the one used in the dataset, it can be said that the analysis shows similar results. Namely, according to the results obtained in the research, thunderstorm winds - which are equivalent to convective storms - were the most frequent disaster type in the US. They are followed by hail and floods [23]. According to this research, floods are not only the third most frequent weather event in US history, but also the third most devastating disaster type by number of deaths. Other research conducted on a larger scale shows that floods are the most devastating natural disaster worldwide [24]. According to the results of this study, tornadoes are the deadliest natural events in the US. In terms of numbers, Texas saw the highest number of tornadoes in the period 1991– 2010, per a NOAA analysis. It is followed by Kansas, Florida, Oklahoma, Nebraska, Illinois, etc. Ultimately, this research corroborates the information by analyzing the distribution of tornadoes within US states. The results that were produced closely match the ones taken from Axios, as can be seen on Fig. 16 [25]. It cannot go unnoticed how the map above resembles the map of total property damage that was discussed in the previous section (Fig. 5). Since tornadoes are the most devastating type of disasters by property damage and they are the most frequent in Texas, it can be inferred that Texas is the territory with highest property damage. This assumption has been confirmed by later analysis, where Texas was the state with the highest total property damages of around $38 billion. Figure 17 represents the fatality rate in the United States in the period 1950–2022 and is taken from Our World in Data website [26].
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Fig. 15. Number of natural disasters in the USA from 1900 to 2016.
Fig. 16. Most affected states from tornadoes.
Fig. 17. Deaths from natural disasters.
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On the other hand, Fig. 18 is generated during this analysis and represents a timeseries of fatalities taken from the initial dataset. Even though the format of graphs is not the same, some useful conclusions can be drawn after comparing these two figures.
Fig. 18. Timeseries of fatalities.
Observing the period 1950–2022 (Fig. 17), a huge rise in fatalities can be seen in 2005. The second highest peak was in 1980, followed by 2011 and 2021. On the graph obtained from this research (Fig. 18), 2011 features the highest number of fatalities, followed by peaks around 2021 and 1998. This discrepancy can potentially be explained by the lack of data in the examined dataset. 2005 can be declared as the year of natural disasters in the US. There were different events that occurred, among which were some of the deadliest in the history of the US. The most important one was Hurricane Katrina, which triggered immense flooding. Hurricane Katrina, a tropical cyclone that struck the southeastern US in late August claimed more than 1,800 lives and was ranked as the costliest natural disaster in US history [27]. The storm itself did a great deal of damage, but its aftermath was catastrophic. Massive flooding occurred and the total estimated damage caused by Katrina was around $100 billion. The most affected area from this flooding was New Orleans, where 80% of the city was flooded. After a separate analysis of hurricanes, it could be noticed that 2005 was the year that specially stood out in the hurricane history of the US. September and August were the months most affected by hurricanes, and among others, Texas was one of the most flooded states. Figures 13 and 14 corroborate this information, as they represent hurricane activities throughout US history. Both graphs peak in 2005, which is expected given the Hurricane Katrina occurrence. Another year that stood out on Fig. 17 was 2011, which brought about many extreme weather and natural disasters. The most noticeable one occurred in the US Southeast on April 27. It was a tornado outbreak, which killed a total of 321 people and caused $7.3 billion insured losses. Many people describe it as the most violent tornado outbreak, producing 343 tornadoes, four of which became EF-5’s [28].
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Floods were discussed in a previous section as the most devastating disaster type in the world. Total number of recorded flood related events in the period 1950–2022 was 162,060, and they were mostly caused by heavy rains. Interestingly, 2018 was a year with the most floods, and 2011 was in fourth place. This can be explained by the influence of Hurricane Katrina. Sciencing’s article tells that floods in the US mostly occur from spring to fall, which is reasonable due to rains and storms that occur in that period. The claim was supported by this research, given that the analysis showed that the most affected months were June, May, July, August and September [29]. Figures 11 and 12 are another two graphs created as a product of this analysis. They show flood occurrence and the damage caused by floods over time. An interesting peculiarity obtained from observing these graphs is the increase in flood occurrence in the past 5 years (2017–2022). Figure 12 shows that the highest cost ever caused by floods was in 2017. 2017 started with torrential rainfall in California, which brought a dry spell to an end as floods inundated hundreds of homes, landslides buried roads and high-water levels threatened to burst dams. Flooding across Missouri and Arkansas in the spring claimed 20 lives and the total damage of 2017 floods was around $2 billion. Floods were also present in other areas, which were mostly caused by hurricanes – Hurricane Harvey, Hurricane Irma and Hurricane Maria. Going back to Fig. 14, a slight increase in total property damage from hurricanes in 2017 can be seen, which is expected after knowing this information [30]. Floods, in both their amount and rate of occurrence, represent one of the strongest signals of climate change. Warmer air increases the evaporation rate of water and for every degree Celsius increase in temperature, a parcel of air can hold 7 percent more water. Past couple of years were full of wildfires as well. Even though they can be caused by the action of human thoughtlessness, their cause has been mostly rainfalls as of late [6].
Fig. 19. Timeseries of property damage.
Based on Fig. 19, it can be seen that the costliest period in the history of US weather events was 2017. This is proven by information from various research efforts, which
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is due to previously mentioned disasters, including hurricanes, floods and wildfires. According to the Government Accountability Office, the property damage is continuing to increase. Natural disasters not only take hundreds of lives directly, but also have a long-lasting health effect. According to the analysis – excessive heat is the number one most common cause of deaths, mostly because it can be a factor to stimulate heart attacks, strokes and respiratory arrests. Moreover, high temperatures have a huge effect on ozone, which might be linked to illnesses such as asthma [31]. To conclude, the results of this paper can be an interesting subject of research. Most of the claims from the analysis can be supported by empirical evidence and statistical analysis.
6 Conclusion Throughout the analysis of this topic, we have reached a lot of conclusions that can be corroborated by previous studies, as well as used as a basis for further research. The dataset used is extremely high quality and detailed, so we were able to find descriptions of each individual event. In the end, the most important conclusions we derived can be summarized with the following statements. The total number of disasters recorded in the data set is 1,782,287. The total number of disaster types recorded is 69 – including smaller groups and subgroups of more general event types. Thunderstorm winds were the most frequent disaster type by the number of occurrences (500,343 occurrences), while tornadoes were the most prominent disaster type by total damage ($60.6 billion). Tornadoes were the most devastating disaster type, with a total number of 5,910 deaths. The male fatality rate caused by disasters is twice as high as female fatality rate. The most common cause of both male and female deaths is excessive heat. Texas is the state most affected by natural disasters by both damage and fatalities ($37.8 billion in damages, and 2,075 fatalities), while 2011 was the deadliest year in the interval 1950–2022. Moreover, there has been a noticeable increase in the number of natural disasters in the last 10 years. The last conclusion, that the number of weather and natural disasters increases each year, might correlate with the effects of ongoing climate change. The existing research shows that weather changes and global warming strongly affect the occurrence rate of all weather events and their intensities, which can also be inferred by examining the results of this study. This problem is a subject of research for numerous science groups and governmental teams, as they try to create programs for damage prevention. As can be seen from the previous findings, the methods used in this research performed well in the process of disaster data analysis and conclusion making. Therefore, this type of analysis can be used for further improvements and data-driven research in this field. To sum up, this topic represents an important step for future analysis and improvements in the damage prevention systems. We believe that the analysis of previous events can enable more accurate detection and prediction of future occurrences. Hence, the governments and people would be able to better prepare for disasters and react in the best possible way.
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References 1. WHO. Natural events - WHO. [Online]. Available: https://www.who.int/environmental_h ealth_emergencies/natural_events/en/ (2020). Accessed 07 Jun 2022 2. World Meteorological Organization (WMO) (n.d.). Weather-related disasters increase over past 50 years causing more damage, fewer. [Online]. Available: https://public.wmo.int/en/ media/press-release/weather-related-disasters-increase-over-past-50-years-causing-moredamage-fewer. Accessed 02 Feb 2023 3. NASA Climate Kids (n.d.). Climate change: What it is, how it works, why it’s happening. [Online]. Available: https://climatekids.nasa.gov/climate-change-meaning/. Accessed 02 Feb 2023 4. United Nations (n.d.). Causes and effects of climate change. [Online]. Available: https://www. un.org/en/climatechange/science/causes-effects-climate-change. Accessed 02 Feb 2023] 5. WHO. Natural events. [Online]. Available: https://www.who.int/environmental_health_eme rgencies/natural_events/en/ (2012, August 24). Accessed 07 Jun 2022 6. IPCC. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge University Press (2012). Accessed 02 Feb 2023 7. WMO. Climate and the World’s Most Vulnerable Populations. World Meteorological Organization (2017). Accessed 05 Feb 2023 8. UNFCCC. Climate Change and Its Impacts on Coastal Areas. United Nations Framework Convention on Climate Change (2018). Accessed 05 Feb 2023 9. FEMA. Natural Hazard Statistics. [Online]. Available: https://www.fema.gov/natural-hazardstatistics (2020). Accessed 10 Jan 2023 10. NOAA. Trends in Natural Disasters in the United States. [Online]. Available: https://www.cli mate.gov/news-features/understanding-climate/trends-natural-disasters-united-states (2020). Accessed 10 Jan 2023 11. USGCRP. The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment. [Online]. Available: https://health2016.globalchange.gov/ (2019). Accessed 10 Jan 2023 12. National Interagency Fire Center. National Interagency Fire Center 2018 Wildfire Briefing. [Online]. Available: https://www.nifc.gov/fireInfo/nfn.htm (2018). Accessed 05 Feb 2023 13. USDA. 2020 Wildfires: A Year in Review. [Online]. Available: https://www.usda.gov/media/ blog/2020/12/18/2020-wildfires-year-review. (2020). Accessed 05 Feb 2023 14. NCEI (n.d.). Storm Events Database. [Online]. Available: https://www.ncdc.noaa.gov/storme vents/details.jsp. Accessed 01 Jun 2022] 15. Apache Hadoop (n.d.). Apache Hadoop. [Online]. Available: https://hadoop.apache.org/. Accessed 07 Jun 2022 16. Apache Pig (n.d.). Welcome to Apache Pig! [Online]. Available: https://pig.apache.org/. Accessed 07 Jun 2022 17. Bronshtein, A.: A Quick Introduction to the ‘Pandas’ Python Library. Medium. [Online]. Available: https://towardsdatascience.com/a-quick-introduction-to-the-pandas-pyt hon-library-f1b678f34673 (2019, October 29). Accessed 07 Jun 2022 18. Matplotlib. (n.d.). Visualization with Python. [Online]. Available: https://matplotlib.org/. Accessed 07 Jun 2022 19. Basemap tutorial. (n.d.). Basemap tutorial - Basemap tutorial 0.1 documentation. [Online]. Available: https://basemaptutorial.readthedocs.io/en/latest/. Accessed 07 Jun 2022 20. NumPy. (n.d.). ECOSYSTEM. [Online]. Available: https://numpy.org/. Accessed 07 Jun 2022 21. Guru99. (n.d.). What is Data Analysis? Types, Process, Methods, Techniques. [Online]. Available: https://www.guru99.com/what-is-data-analysis.html. Accessed 07 Jun 2022
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Information and Communication Technologies
A Comparative Performance Analysis of Various Antivirus Software Una Drakuli´c(B)
and Edin Mujˇci´c
Technical Faculty of Biha´c, The University of Biha´c, Biha´c, Bosnia and Herzegovina [email protected]
Abstract. With rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of computer systems. The threats and damages from malicious software are alarming and for that antivirus vendors tend to combat by designing more efficient antivirus software. Antivirus programmers have implemented new techniques such as emulation techniques and heuristic scanning. These methods are helpful in detecting encrypted polymorphic viruses. Some of the modern antivirus software is programmed for static and dynamic heuristics, rootkit heuristics, learning, neural networks, data mining, hidden Markov models, and many other methods to remove almost every virus hidden anywhere in the computer. In this paper, the focus is on the comparative performance analysis of various antivirus software (Avast, Avira, AVG, Eset Nod32, Panda, Malwarebytes). Testing was performed on the following computer configurations: Windows 10 operating system, 8 GB RAM, 64-bit operating system type, CPU Intel (R) Core (TM) i7-2670QM 2.20GHz. A clean, reinstalled operating system was prepared for testing. Antivirus software have been tested in the same way, that is, the same environment was prepared for each. The goal of testing is ranking antivirus software according to its functionalities and choosing the most effective one. Based on the comparative performance analysis of various antivirus software, the parameters that offer the utmost performance considering malware detection, removal rate, the memory usage of the installed antivirus, and the interface launch time is recommended as the best. Keywords: Antivirus Software · Malicious Software · Comparative Performance Analysis
1 Introduction The interest in antivirus software development is growing in importance due to the increasing reliance on computer systems in most societies. Computer systems now include a very wide variety of smart devices. It includes smartphones, televisions, and tiny devices as part of the Internet of Things, and networks include not only the Internet and private data networks, but also Bluetooth, Wi-Fi, and other wireless networks [1–3]. All antivirus vendors including well-known names like McAfee, Symantec, TrendMicro, VBA32, Panda, PC Tools, CA eTrust, ZoneAlarm, AVG, BitDefender, Avast, Kaspersky, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 423–430, 2023. https://doi.org/10.1007/978-3-031-43056-5_30
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Sophos, etc. have vulnerabilities that virus programmers saw as opportunities for creating viruses. BusinessWire, an American newspaper company, mentioned that about 800 vulnerabilities including system access, DoS, privilege escalation, security bypassing, etc. were discovered in various antivirus products, by writing “The conclusion: contrary to their actual function, the products open the door to attackers, enable them to penetrate company networks and infect them with destructive code.” [4]. Research paper [5] writes about the antivirus software of the next generation. The organization testing the antivirus is ICSA Certification, Westcoast Labs Checkmark, University of Hamburg VTC malware tests, etc. Viruses that are network aware, demonstrate that virus-specific techniques are insufficient to prevent the spread of new viruses. The inherent backlog of post-infection generic methods and pre-infection heuristics make virus detection a more powerful way to prevent and remove viruses. Antivirus products that consist of a hybrid approach are likely to evolve. The performance study of different antivirus is described in detail for computers in research paper [6]. The classification is based on three groups of antiviruses i.e., AV Test, VB, AV comparative. According to the research, no antivirus engine consistently holds the top place each year across all testing organizations, hence every year new antivirus software are launched, or modified into better versions. Authors of the research paper [4] have performed an analysis of various antivirus software tools based on different effective parameters for the year 2016, and based on the results they performed the Kaspersky software is the one they recommend. In this paper are presented the best-claimed antivirus software for the year 2022. The main motivation for writing this paper is to use and to recommend the end user’s most effective, userfriendly, cost-efficient antivirus software based on a detailed analysis that were performed on the six most recommended antivirus software. A large number of antivirus software are available or launched in the market every year, each one of them offering new features for detecting and eradicating viruses and malware [7–9]. Therefore, people have a choice of different types of antiviruses i.e., both in the form of freeware software or licensed software. People frequently change their antivirus software according to their liking and needs without evaluating the performance and capabilities of the various antivirus software available [10, 11]. Hence there is a need to find the most reliable antivirus software.
2 Testing and Analysis of Antivirus Software When looking for the best antivirus software the statistics from testing on how well it detects malware programs should be reviewed. In this paper, the six antivirus software were tested. Chosen antivirus software for testing were Avast, Avira, AVG, Eset Nod32, Panda, Malwarebytes. Tests were performed with emphasis on the speed of the computer while scanning and the number of detected files. There are a large number of antivirus software on the market, free or paid versions. Prices are variable, ranging from 10 to over 100 euros. Regardless of the price, no antivirus software can provide 100% protection.
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For a comparative performance analysis of antivirus software are used the installation files are shown in Fig. 1.
Fig. 1. The installation files used for a comparative performance analysis.
The first installation file in Fig. 1 is an add-on for Minecraft, one of the most popular games in the world. The OptifineInstaller does not change the basic game mechanics but improves the graphics showing during the game and downloading it worldwide. The second installation file in Fig. 1 represents the installation of Bandicam. Bandicam is helpful a screen capture and imaging program originally developed by Bandisoft and later by The Bandicam company, which can take screenshots or record screen changes. The third installation file in Fig. 1 is the installation file of fake antivirus software Malwarebytes, which is intended to harm a computer or trick a user into switching to a paid version that supposedly offers better protection measures. The fourth installation file in Fig. 1 represents the installation of Elbot, an add-on in the Tibia game, which is provided information such as the name of the player, the group to which the player belongs, his level and strength, and on in this way, an advantage over other players in the game was achieved. Before downloading these installation files to the computer, an online check was performed using the VirusTotal antivirus scanner. The check showed that the installation files from Fig. 1 contain malicious software. In this paper, testing was also performed on real malicious software spread in computer systems networks, and the number of malicious software that antivirus software managed to detect is observed. Used malicious software are shown in Fig. 2.
Fig. 2. The malicious software used for a comparative performance analysis.
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Backdoor Tyupkin is a piece of malicious software that allows cybercriminals to drain ATMs and especially affects the ATMs of the main ATM manufacturer that is running 32bit Microsoft Windows. The Brain is the industry standard name for computer malicious software that was first published in 1986 and is considered the first computer malicious software for MS-DOS. It infects a part of the boot sector which is in charge of storing DOS files. The Cascade virus is a prominent computer virus that was prevalent in the 1980s and early 1990s. It infected.COM files and cascaded text onto the screen. Infected files are increased their size by 1701 or 170 bytes. The Form malicious software belongs to the group of boot sector malicious malware, it infects the boot sector instead of the Master Boot Record. The only noticeable characteristic is the clicking sound associated with some infections. The infections can result in more serious data corruption. The Friday_the_13th is the Jerusalem virus that belongs to the group of logic bombs. After infection, it becomes a memory resident using 2 KB and then infects all running files except COMMAND.COM. The Keylogger Ardamax is a compact, affordable, but also extremely powerful and flexible keylogger intended for comprehensive monitoring of user activities on any computer to which it is installed. It runs in the background and records every keystroke on the user’s system and prepares access to encrypted files that are accessible only to the administrator. The Petya ransomware was first discovered in 2016. It targets Microsoft Windows systems, infects master boot record to prevent Windows from starting, and then requires that the user make a payment in Bitcoin to regain access to the system. The NewLove and the NewLetter belong to a group of computer worms that are sent as attached files with the title “ILOVEYOU”. They attack the Windows operating system and other operating systems that are not endangered. The GreenBug is a Trojan virus that uses Bug as a malware symbol. It’s hacking and theft of personal data. The malicious software for testing was found on the website: https://git hub.com/ytisf/theZoo. After scanning the hard disk on which the unpacked files with malicious software are located, a scan of packed files with the same content was also performed. Testing was performed on the following computer configurations: Windows 10 operating system, 8 GB RAM, 64-bit operating system type, CPU Intel (R) Core (TM) i7-2670QM 2.20 GHz. A clean, reinstalled operating system was prepared for testing. After finishing testing with one antivirus software, that antivirus software is uninstalled. This procedure was repeated for testing each of the six antivirus software. The emphasis of testing antivirus software was on: • The speed of computer testing, quick and full scan, • The number of detected installation files, and • The number of real malicious software detected. The antivirus software testing was performed on free versions of Avast, Avira, AVG, Eset Nod32, Panda, Malwarebytes antivirus software.
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2.1 Analysis of the Quick and Full Scan A quick scan is very important during for the important files, for computer functionality, which includes Windows folders, memory, and search for rootkit programs. Overview of antivirus software depending on the availability of quick scan and test results are shown in Fig. 3.
Fig. 3. Test results of a quick scan.
Based on test results shown in Fig. 3, can be concluded that antivirus software AVG, Eset Nod32 and Panda don’t provide quick scan. The Malwarebytes require least amount of time 00:33 s, the second least time require Avira 01:03 s, and Avast require the 08:13 s. During the full scan, the antivirus software completely and thoroughly scan data on the hard disk. The time required for each of the six antivirus software for full scan is shown in Fig. 4.
Fig. 4. Test results of a full scan.
Based on Fig. 4, can be concluded that the least time the for full scan require Malwarebytes 26:20s, while AVG require the longer time for the full scan 58:56 s. Also, the AVG antivirus software during a full scan slows down of the computer significantly. 2.2 Analysis of the Installation Files Detecting The second analysis performed in this paper is based on the number of detecting installation files. Four installation files (see Fig. 1) were used for testing and the test results are shown in Fig. 5.
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Fig. 5. Installation files detection (of 4 files).
Based on Fig. 5, can be concluded that Malwarebytes antivirus software has detected all 4 of 4 installation files, Avira, Eset Nod32 and Panda antivirus software detected 3 of 4 installation files, Avast antivirus software detected 1 of 4 installation files, and AVG antivirus software did not detect any installation file. 2.3 Analysis of the Antivirus Software Using Real Malicious Software The third analysis performed in this paper is based on the detecting malicious software in packed and unpacked files. Ten real malicious software (see Fig. 2) were used for testing which are widespread in computer networks. The unpacked files were placed on the hard disk for this test. Test results are shown in Fig. 6.
Fig. 6. Unpacked files detection (of 10 files).
Based on results in Fig. 6 can be concluded that the Avira antivirus software detected 8 of 10 unpacked files, Malwarebytes antivirus software detected 4 of 10 unpacked files. The testing of the packed files was also performed, and the results of the testing are shown in Fig. 7.
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Fig. 7. Packed files detection (of 10 files).
Based on results in Fig. 7, can be concluded that the Panda antivirus software did not detect a single malicious software in packed files. Avast, AVG, and ESET Nod32 antivirus software detect 7 of 10 packed files. Avira and Malwarebytes antivirus software detect 1 of 10 packed files.
3 Conclusion Based on the performed tests and conducted analysis, can be concluded which antivirus software (between Avast, Avira, AVG, Eset Nod32, Panda, and Malwarebytes) is the best to use. Based on the time required for the quick and full scan, the best time has Malwarebytes antivirus software, for a quick scan required time is 00:33 s, and for a full scan required time is 26:20 s. Based on the number of detected installation files, Malwarebytes antivirus software is the best antivirus software by detecting 4 of 4 installation files. Based on the number of real malicious software detected in unpacked files, Avira antivirus software is the best by detecting 8 of 10 unpacked files. Based on the number of real malicious software detected in the packed files, Avast, AVG, and Eset Nod32 antivirus software gave the same results by detecting 7 of 10 packed files. The contribution of the proposed paper is proposing the best antivirus software to keep protected from the virus in a large virtual world depending on the end user’s needs. Based on the analysis carried out for the best-proclaimed antivirus software for 2023, it can be seen that different antivirus software are better than others depending on the type of testing and the software needs. It is very difficult to conclude that only one antivirus software is the best because it all depends on the protection module that the end user needs. The directions of future research are the analysis of as much antivirus software as possible on the malicious software that most often appears in computer networks. In this way, the comparative analysis would get more accurate results about antivirus software and eliminate many of them that slow down the computer, take a long scan time, and detect non or a small number of malicious software.
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Acknowledgments.
This research was conducted with the late Professor Edin
Mujˇci´c at the University of Biha´c, Technical faculty. Professor Edin Mujˇci´c died on January 6, 2023. I want to dedicate this research to him as a thank-you for everything he taught me
References 1. Ritwika, S., Raju, K.B.: Malicious Software Detection and Analyzation Using the Various Machine Learning Algorithms. In: 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–7. Kharagpur, India (2022). https:// doi.org/10.1109/ICCCNT54827.2022.9984402 2. Alrubayyi, H., Goteng, G., Jaber, M., Kelly, J.: Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches. In: Journal of Sensor and Actuator Networks, Volume 61, (2021) 3. Sharma, N.A., Kumar, K., Raj, M.A., Ali, A.B.M.S.: A Systematic Review of Modern Malicious Softwares and Applicable Recommendations. In: IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), pp. 1–6. Brisbane, Australia (2021). https:// doi.org/10.1109/CSDE53843.2021.9718392 4. Durga Devi, K., Monah Kumar, K.: An analysis of varius anti-virus software tools based on different effective parameters. Int. J. Comp. Sci. Tren. Technol. IJCST 4(4) (2016) 5. Remya, T., Nachamai, M.: Performance Investigation of Antivirus-A Comparative Analysis. In: Oriental Journal of Computer Science & Technology, An International Open Free Access, Peer Reviwed Research Journal (2017) 6. Arkajit, D., Kakelli, K., Aju, D.: An emerging malware analysis techniques and tools: a comparative analysis. Int. J. Eng. Res. Technol. (IJERT) 10(04) (2021). ISSN: 2278-0181 7. Patil, B.V., Jadhav, R.J.: Computer virus and antivirus software - a brief review. Int. J. Adv. Manage. Econo. 4(2) (2014). ISSN: 2278-3369 8. Matilda, R., Pete, B., Adam, W.: Real-time malware process detection and automated process killing. J. Sec. Commu. Netw. (2021). ID 8933681 9. Abhesa, R.A., Hendrawan, S.J., Ismail, I.: Classification of malware using machine learning based on image processing. In: 2021 15th International Conference on Telecommunication Systems, Services, and Applications (TSSA), pp. 1–4. Bali, Indonesia (2021). https://doi.org/ 10.1109/TSSA52866.2021.9768222 10. Pascal, M., Abdun, N., Mohammad, J.: A study on malicious software behaviour analysis and detection techniques: Taxonomy, current trends and challenges. In: Future Generation Computer Systems, Vol. 130, pp. 1–18 (2022) 11. Pavithra, J., Selvakumara, S.: A comparative study on detection of malware and benign on the internet using machine learning classifiers. In: Mathematical Problems in Engineering, Article ID 4893390 (2022)
Enhancing the Accuracy of Finger-Based Heart Rate Estimation During At-Home Biofeedback Therapy with Smartphone Alma Še´cerbegovi´c(B)
, Asmir Gogi´c , and Aljo Mujˇci´c
Faculty of Electrical Engineering, University of Tuzla, Tuzla, Bosnia and Herzegovina [email protected]
Abstract. Smartphone-based heart rate monitoring is a promising biofeedback therapy tool, but its accuracy is frequently limited by motion artifacts and incorrect finger placement. We present a novel calibration procedure in this study that improves the accuracy of smartphone-based heart rate estimation during biofeedback therapy. Advanced signal processing techniques are used to minimize motion artifacts and optimize finger placement, resulting in a significant reduction in heart rate estimation errors and an increase in correlation coefficients with ground truth measurements. Our findings suggest that, if the calibration procedure is followed correctly, smartphone-based heart rate monitoring can be a reliable and accessible tool for biofeedback therapy in clinical and home-based settings. The proposed method could help improve the effectiveness of biofeedback therapy by allowing patients to more accurately and precisely regulate their physiological states and improve their overall health. As a consequence, an improved calibration procedure has the potential to contribute to the wider adoption of smartphone-based biofeedback therapy, thereby improving access to this effective technique for a broader population. Keywords: mHealth · Biofeedback · Smartphone · Heart rate · Photoplethysmography
1 Introduction Smartphones’ pervasiveness in modern society has resulted in their widespread use as a diagnostic and monitoring tool for healthcare. With the advancement of mobile health technology, smartphones have evolved into a powerful tool for health assessment and monitoring from the comfort of one’s own home [1–3]. Smartphone applications can now track a variety of health metrics, such as heart rate [4, 5], blood pressure [6, 7], and respiration rate [8, 9], thanks to the availability of advanced sensors and machine learning algorithms. This provides users with real-time feedback on their health status, allowing them to take proactive measures to prevent disease or manage chronic conditions. Smartphones have the possibility of making an important impact on healthcare. Users can easily access their health data, track their progress over time, and receive personalized recommendations based on their data. Individuals can take control of their © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 431–440, 2023. https://doi.org/10.1007/978-3-031-43056-5_31
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own health and well-being by becoming capable of tracking their health in real time. As a result, using smartphones for home health assessment has the potential to change the way we approach healthcare. While these applications are typically used for short measurements to provide users with real-time information about their current heart rate, the widespread availability of smartphones has opened up new possibilities for their use as a biofeedback device. Smartphones, with the right software, can provide users with real-time feedback on their physiological states, allowing them to regulate their vital signs through techniques such as breathing exercises and meditation. Biofeedback is a technique that allows patients to control their sympathetic nervous system through the use of feedback from specialized devices [10]. Biofeedback therapies are typically performed in medical settings under the supervision of trained biofeedback experts and with access to a variety of specialized equipment. Patients receive immediate feedback on their physiological responses to specific stimuli, such as changes in heart rate or breathing patterns, during a biofeedback session. Patients can develop the ability to regulate their physiological states and improve their overall well-being by observing and adjusting their responses. Heart rate monitoring is a fundamental tool in biofeedback therapy, allowing patients to regulate their physiological states and improve their overall health. However, accurate heart rate measurement can be challenging, particularly when using smartphone-based monitoring applications [4]. In this paper, we present a novel calibration procedure that leverages advanced signal processing methods to improve heart rate estimation accuracy during biofeedback therapy with a smartphone device. Our procedure focuses on improving finger placement and minimizing motion artifacts, resulting in significantly lower heart rate errors and increased correlation coefficients with ground truth measurements. These results highlight the potential for using smartphones as reliable and accessible tools for heart rate monitoring in clinical and home-based settings.
2 Background Biofeedback therapy is a widely recognized treatment method for a range of health conditions, including stress-related disorders such as anxiety, depression, pain management, and hypertension [10–12]. This therapy aims to improve patients’ awareness and control of their physiological responses to stress by providing real-time feedback on various biological signals, such as heart rate and skin conductance. Traditionally, biofeedback therapy is conducted in medical institutions or specialized clinics, where patients are connected to multiple sensors and monitoring devices that capture physiological signals. However, recent advances in smartphone technology have opened up new possibilities for at-home biofeedback therapy [13]. Smartphones are ubiquitous and easy to use, and many devices now come equipped with sensors that can capture physiological signals, such as heart rate, respiration rate, and heart rate variability [14, 15]. Finger-based smartphone photoplethysmography (PPG) works by using the camera and flash of a smartphone to measure changes in blood volume in the finger. When the subject places their fingertip over the camera lens of the smartphone and applies minimal pressure, the light from the flash passes through the finger and is partially absorbed by the blood vessels. The camera then detects changes in the intensity of the light that has passed through the finger, which corresponds to changes in blood volume.
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The PPG signal is then extracted from the color intensity changes by analyzing the time-varying patterns of the light absorption. This signal reflects the periodic variations in blood volume associated with the cardiac cycle, and it can be used to calculate the subject’s heart rate. Several studies have explored the use of smartphone-based heart rate monitoring for biofeedback therapy. For example, in [16] authors have developed Breeze, a smartphone-based acoustic real-time detection of breathing phases, which is used for gamified biofeedback breathing training. Finger-based heart rate estimation with a smartphone Android device was used as a mobile health biofeedback system for the treatment of panic attacks [17]. On the other hand, numerous research papers have demonstrated that the estimation of heart rate is a viable and efficacious method when utilizing the finger-based smartphone camera [18–20]. However, one limitation of smartphone-based heart rate monitoring is the potential for motion artifacts, which can cause inaccuracies in the measured signal. To address this limitation, recent studies have proposed various techniques to improve the accuracy of smartphone-based heart rate monitoring. These include the selection of optimal region-of-interest (ROI) determination from fingertip videos obtained by the smartphone’s back camera [21, 22], recognition of motion artifacts [23], proper finger placement [24] or combination of different methods into calibration session at the beginning of the recording [25]. In this paper, we propose a novel calibration procedure to improve finger placement and minimize motion artifacts during smartphone-based heart rate monitoring for athome biofeedback therapy by using iPhone 13 smartphone with multiple cameras. We build on previous research in this area by focusing specifically on finger-based measurement of heart rate, which is less susceptible to motion artifacts than other body locations (e.g., the wrist). By improving the accuracy of smartphone-based heart rate monitoring, our proposed method has the potential to enhance the effectiveness and accessibility of at-home biofeedback therapy for a range of health conditions.
3 Calibration Procedure for Heart Rate Estimation Motion artifacts in smartphone-based heart rate monitoring are errors in heart rate measurement caused by the smartphone moving or shaking during the measurement process. This can lead to inaccurate readings, making reliable heart rate measurements with smartphone-based methods difficult. Because the movements of the smartphone can interfere with the sensor’s ability to detect the pulse, motion artifacts can result in incorrect heart rate readings. Movement during measurement can also cause variations in heart rate readings, making consistent and reliable measurements difficult. Motion artifacts can also result in false positives or negatives, which can lead to incorrect diagnoses and potentially harmful outcomes.The need to hold the smartphone steady during measurement can be inconvenient for users, as it may require them to stay still for an extended period, leading to discomfort or even errors due to movement. In order to improve the accuracy of finger-based heart rate estimation during biofeedback therapy with a smartphone device, several approaches can be taken. These include finger placement optimization, motion artifact removal, ambient lighting control and machine learning. A solution to the above-mentioned challenges is to implement a novel calibration procedure. For the initial 15 s of the measurement, the user is required to properly place
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Fig. 1. The proper placement of the finger on the iPhone 13 back camera.
the index finger on the smartphone’s back camera. The camera must be completely covered by the finger, and the top part of the finger should not cover the LED light, as seen in Fig. 1. Four small regions of interest (ROIs), marked numbers from 1 to 4 (64x64 pixels), are defined in four corners of the frame, as demonstrated in Fig. 2. The mean value of green pixels in these ROIs is tracked over time due to the great signal-to-noise ratio and proven resistance to motion artifacts. In other words, the effects of improper finger placement are mitigated.
Fig. 2. Corners in the frame (marked from 1 to 4) a) are used as indicators of incorrect finger position; b) ROI1 and ROI2 areas are used for heart rate estimation.
In Fig. 3, video frames of the correct finger placement are presented. The main frame is mostly red, which corresponds to the transmitted and reflected by the fingertip with each pulse. Figure 4 shows different scenarios where finger is not properly covering all corners of the camera, introducing artifacts and noise in PPG signal. During the calibration process, three regions of interests are defined (ROI A, ROI B and ROI C), as seen in Fig. 2.b. For each ROI, signal to noise ratio is calculated by using Eq. 1 as defined in [25]: SNR = f /2 s
S(fHR )
f =0 S(f ) − S(fHR )
(1)
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Fig. 3. Video frames of the correct finger placement for heart rate estimation.
Fig. 4. Video frames of the incorrect finger placement for heart rate estimation.
where S(fHR ) presents the power spectrum of the frequencies that corresponds to the regular heart rate values (40 – 140 bpm) when seated, while S(f)- S(fHR ) presents the power spectrum of all other frequencies (noise). The ROI that has the highest SNR is selected as the ROI that would be tracked over time. After the definition of ROI, the mean value of all red pixels is calculated and then bandpass filtered for the frequency range [0.6, 2.5], which corresponds to the regular heart rate values. Peak detection algorithm is used for estimation of peaks and calculation of heart rate.
4 Experimental Setup and Results 4.1 Dataset The dataset consists of ten subjects (7 female) with mean age of 34.9 ± 19.3 years. The subjects’ skin color was categorized with Fitzpatrick Scale [26]: eight subjects belonged to the lighter skin categories (I-III) and two subjects had darker skin and were assigned categories V and VI. For each subject, the recording protocol lasted 19 min with the specific scenario, as seen in Fig. 1. Before the recording, the subjects were seated comfortably in a chair and had their index finger placed on the back-camera of the smartphone. Contact-based PPG sensor (Procomp Infiniti device, Thought Technology Ltd., Canada) collected the ground-truth blood volume pulse signals from the other hand. The subjects were instructed to remain seated and still during the recording. For the initial 4 min, subjects were breathing normally. During the relaxed 3-min breathing sessions, subjects followed the breathing pacer with a breathing rate manually selected by the subjects as the relaxing breathing rate. For mental arithmetic, the subjects performed serial subtractions in their head, e.g.,
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serial 13s from 1000. This exercise consists of subtracting the number 13 from 1000, and after calculating the result 987, subtracting that number from 13 (987 – 13 = 964), then subtracting 13 from 964, etc. During the mental stress exercises, the subjects silently thought about a recent stressful event. The study protocol is designed to ignite different stress responses from the participants to generate different values of heart rate (Fig. 5).
Fig. 5. Study protocol.
The video data was obtained with iPhone 13 smartphone device with frame rate of 15 frames per second and 480 × 640 resolution. Ground-truth blood volume pulse signals from Procomp Infiniti device were collected with frame rate of 256 samples per second. 4.2 Results The dataset is analyzed in two different modes. The first mode includes the processing of the whole frame of video and without initial calibration procedure introduced in previous chapter. The second applies the calibration procedure, meaning that initial 15 s are used for smaller ROI selection (ROI A, ROI B or ROI C based on the SNR) and constant tracking of the four corner ROIs for proper finger placement check. Figure 6.b shows artifacts in the signals introduced by the finger movement, which are detected within the four ROIs 1 to 4 as indicated on the Fig. 6.a. For each subject and mode, four metrics are computed: mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and Pearson’s correlation coefficient (CC). The metrics are computed over a 10-s window, with 9 s overlap over entire measurement (approximately 19 min) without initial calibration period of 15 s. Table 1 contains the results for MAE and RMSE for 10 subjects, while Table 2 describes the results for MAPE and CC. The proposed calibration procedure and ROI selections show lower MAE values as opposed to the whole frame processing for 0.4 bpm on average for all ten subjects. Bland-Altman plots in Fig. 7 and Fig. 8 demonstrate subject 10, which clearly indicates that the proposed calibration method leads to lower variance.
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Fig. 6. a) Standard deviation of the green component in 4 small corner ROIs; b) filtered red component of the whole frame.
Table 1. Performance measures of proposed calibration compared to the calibration processing. Subject
MAE without calibration
MAE With calibration
RMSE without calibration
RMSE with calibration
1
0.4037
0.2398
0.5336
0.2969
2
1.171
0.5996
0.4217
1.1477
3
0.441
0.2636
0.5755
0.3151
4
0.8808
0.4035
1.3254
0.5520
5
2.1822
1.6434
0.1686
0.2639
6
1.1084
0.5558
0.0395
0.8144
7
0.8079
0.4515
1.0833
0.5851
8
0.532
0.2906
0.7266
0.3668
9
2.5167
1.8898
1.0949
0.1956
10
0.8196
0.4116
1.1619
0.5321
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Table 2. Mean absolute percentage error (MAPE) and Pearson’s correlation coefficient (CC) of the proposed method compared to no-calibration processing. Subject
MAPE without calibration
MAPE with calibration
CC without calibration
CC with calibration
1
0.4834
0.2857
0.9923
0.9984
2
1.8218
0.8318
0.7049
0.8933
3
0.6085
0.3644
0.9855
0.9962
4
1.2116
0.555
0.9587
0.989
5
3.0967
2.2248
0.5111
0.6802
6
1.5549
0.7817
0.8788
0.9421
7
1.0619
0.5909
0.8966
0.9486
8
0.6397
0.3501
0.9767
0.991
9
3.5565
2.0575
0.2551
0.6023
10
0.8313
0.4142
0.9539
0.9816
Fig. 7. Bland-Altman plot for subject 10 for whole frame ROI and without calibration.
Fig. 8. Bland-Altman plot for subject 10 for selective ROI tracking and applied calibration.
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5 Conclusion The proposed calibration procedure that involves signal processing methods for improving finger placement and motion mitigation has demonstrated promising results in improving heart rate estimation accuracy during biofeedback therapy with a smartphone device. The results show a decrease in heart rate errors and an increase in the correlation coefficient, indicating that the calibration procedure effectively enhances the reliability of the heart rate measurements. Future research can focus on applying machine learning algorithms to further improve heart rate estimation accuracy. By utilizing machine learning techniques, it may be possible to account for inter-individual variability in finger placement and motion during biofeedback therapy and improve the accuracy of heart rate estimation in a personalized manner. Overall, the findings of this study provide valuable insights into the development of more accurate and reliable heart rate monitoring tools for biofeedback therapy using smartphones.
References 1. Mosa, A.S.M., Yoo, I., Sheets, L.: A systematic review of healthcare applications for smartphones. BMC Med. Inform. Decis. Mak. 12(1), 1–31 (2012) 2. Watson, H.A., Tribe, R.M., Shennan, A.H.: The role of medical smartphone apps in clinical decision-support: a literature review. Artif. Intell. Med. 100, 101707 (2019) 3. Baig, M.M., Gholam Hosseini, H., Connolly, M.J.: Mobile healthcare applications: system design review, critical issues and challenges. Australas. Phys. Eng. Sci. Med. 38, 23–38 (2015) 4. De Ridder, B., Van Rompaey, B., Kampen, J.K., Haine, S., Dilles, T.: Smartphone apps using photoplethysmography for heart rate monitoring: meta-analysis. JMIR cardio 2(1), e8802 (2018) 5. Siddiqui, S.A., Zhang, Y., Feng, Z., Kos, A.: A pulse rate estimation algorithm using PPG and smartphone camera. J. Med. Syst. 40, 1–6 (2016) 6. Chandrasekhar, A., Kim, C.S., Naji, M., Natarajan, K., Hahn, J.O., Mukkamala, R.: Smartphone-based blood pressure monitoring via the oscillometric finger-pressing method. Sci. Trans. Medi. 10(431), eaap8674 (2018) 7. Matsumura, K., Rolfe, P., Toda, S., Yamakoshi, T.: Cuffless blood pressure estimation using only a smartphone. Sci. Rep. 8(1), 7298 (2018) 8. Reyes, B.A., Reljin, N., Kong, Y., Nam, Y., Chon, K.H.: Tidal volume and instantaneous respiration rate estimation using a volumetric surrogate signal acquired via a smartphone camera. IEEE J. Biomed. Health Inform. 21(3), 764–777 (2016) 9. Massaroni, C., Nicolo, A., Sacchetti, M., Schena, E.: Contactless methods for measuring respiratory rate: A review. IEEE Sens. J. 21(11), 12821–12839 (2020) 10. Schwartz, M.S., Andrasik, F.: Biofeedback: a practitioner’s guide. Guilford Publications (2017) 11. Siepmann, M., Aykac, V., Unterdörfer, J., Petrowski, K., Mueck-Weymann, M.: A pilot study on the effects of heart rate variability biofeedback in patients with depression and in healthy subjects. Appl. Psychophysiol. Biofeedback 33, 195–201 (2008) 12. Moravec, C.S.: Biofeedback therapy in cardiovascular disease: rationale and research overview. Clevel. Clin. J. Med. 75(2), S35 (2008) 13. Secerbegovic, A., Spahic, M., Hasanbasic, A., Hadzic, H., Mesic, V., Sinanovic, A.: At-home biofeedback therapy with wearable sensor and smartphone application: proof of concept. IEEE TELFOR, pp. 1–4. IEEE (2021)
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14. Bae, S., et al.: Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms. Communications medicine 2(1) (2022) 15. Peng, R.C., Zhou, X.L., Lin, W.H., Zhang, Y.T.: Extraction of heart rate variability from smartphone photoplethysmograms. Computational and mathematical methods in medicine (2015) 16. Shih, C.H., Tomita, N., Lukic, Y.X., Reguera, Á.H., Fleisch, E., Kowatsch, T.: Breeze: smartphone-based acoustic real-time detection of breathing phases for a gamified biofeedback breathing training. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 3(4), 1–30 (2019) 17. McGinnis, R.S., McGinnis, E.W., Petrillo, C.J., Price, M.: Mobile biofeedback therapy for the treatment of panic attacks: a pilot feasibility study. In: 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 1–4 (2019) 18. Poh, M.Z., Poh, Y.C.: Validation of a standalone smartphone application for measuring heart rate using imaging photoplethysmography. Telemedicine and e-Health 23(8), 678–683 (2017) 19. Kwon, S., Kim, H., Park, K.S.: Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone. IEEE EMBC, pp. 2174–2177. IEEE (2012) 20. Nam, Y., Kong, Y., Reyes, B., Reljin, N., Chon, K.H.: Monitoring of heart and breathing rates using dual cameras on a smartphone. PLoS ONE 11(3), e0151013 (2016) 21. Peng, R.C., Yan, W.R., Zhang, N.L., Lin, W.H., Zhou, X.L., Zhang, Y.T.: Investigation of five algorithms for selection of the optimal region of interest in smartphone photoplethysmography. Journal of Sensors (2016) 22. Lee, K., Nam, Y.: Optimal roi determination for obtaining ppg signals from a camera on a smartphone. J. Elect. Eng. Technol. 13(3), 1371–1376 (2018) 23. Tabei, F., Askarian, B., Chong, J.W.: Motion and Noise Artifact Detection in Smartphone Photoplethysmograph Signals Using Personalized Classifier. IEEE Healthcare Innovations and Point of Care Technologies, pp. 5–8 (2019) 24. Karlen, W., Lim, J., Ansermino, J.M., Dumont, G.A., Scheffer, C.: Recognition of correct finger placement for photoplethysmographic imaging. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7480– 7483 (2013) 25. Kurylyak, Y., Lamonaca, F., Grimaldi, D.: Smartphone-based photoplethysmogram measurement. In: Digital image and signal processing for measurement systems, pp. 135–164. River Publishers (2022) 26. Fitzpatrick, T.B.: The validity and practicality of sun reactive skin types i through vi. Archives of Dermatology 124(6), 869–871 (1988)
Effects of Protection Cloud Accounting and Connection with the Frequency of Cyber Attacks Valentina Stipi´c Vinšalek1(B)
, Mile Viˇci´c1
, and Mohammad Al Awamrah2
1 University of Applied Sciences “Nikola Tesla” in Gospi´c, Gospi´c, Croatia
[email protected] 2 University Putra Malaysia, Amman, Jordan
Abstract. The entire world is found in the process of fast information and technological changes. In this context, fast and efficient reactions of IT experts are required for the needs of redefining the work and protection of technological processes. Accounting is one of the vulnerable areas, so the issue of the importance of increased protection for the safety of accounting data is raised. Fast growth of information technology and current digital era requirements require the structure of information systems and forces companies to adopt new strategies that respond to the challenges of information security. One of the answers is the application of the work of accounting in the cloud. This work wants to highlight the effects of work and protection of accounting in the cloud in relation to frequent cyber attacks, which is the goal of this research. The results of an empirical research conducted on a sample of 183 respondents (accounting employee) show the importance of storage and authentication of accounting data with the frequency of cyber attacks. Keywords: cloud accounting · data protection · cyber attacks
1 Introduction Today’s competitive business environment forces fast-growing companies to constantly monitor progress, change business strategy and adapt to the risks of the business environment. Over the past decades, the role of information technology has changed significantly [1]. The advancement of cloud technology represents one of the biggest technological trends at the moment. The cloud is a platform for data availability anytime, anywhere, from almost any device that has an Internet connection [2]. The most famous term for cloud technology is the term cloud computing [3]. Cloud computing is computing resources that companies and users can access from remote locations via the Internet [4]. Such solutions occupy an important place in the optimization and improvement of all business processes of the company. Like other business sectors, accounting has also embraced cloud technology solutions. Cloud accounting partly changes the nature of the accounting business, so the slow adoption of cloud accounting is expected. A significant resistance to working with accounting in the cloud is due to the loss of self-control over the company’s accounting and financial data and the fear of hacker attacks and complete © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 441–452, 2023. https://doi.org/10.1007/978-3-031-43056-5_32
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data loss [5]. The question arises as to the cause of frequent hacker attacks on accounting information systems and is business data sufficiently protected in cloud accounting? The answers to these questions were obtained through empirical research on a sample of 183 respondents, accounting employees in the Republic of Croatia. The aim of this research is to prove that the frequency of hacker attacks in accounting depends to a significant extent on the method of data storage and the method of authentication in accounting software.
2 Cloud Accounting The use of new information technology, such as cloud accounting, is a challenge for users (accountants) and companies, this is due to the constant evolution of technology and the frequent appearance of recent technologies that appear every day. A big challenge is the technical language and its features. Some of the computer programs come in languages that users such as accountants cannot easily understand. Therefore, accountants must raise the level of training before use, to enjoy the benefits of cloud accounting. Another challenge is the difficulty of adapting to such frequent changes [5]. Some workers may lose interest, and this may reduce work morale. Cloud computing has been compared and equated to the industrial revolution. The very nature of cloud accounting and its transformation are directly related to security risks and privacy protection [6]. Cloud accounting gives accountants instant and mobile access to financial information and completely changes the way accountants work. Because Small and Medium Enterprises can quickly respond to changes in technology demand, they can be the fastest adopters of cloud accounting services [7]. Companies whose employees work remotely may also prefer cloud accounting for convenience and availability, companies that cannot ensure data security and companies that want to avoid all potential physical accidents with technology, destruction of hard drives, and thus accounting data [8]. Since our research is primarily based on small and medium enterprises, assumption is that they all use some kind of purchased or rented accounting information system from software vendors. Accounting solutions are mainly not global due to high dependence to legislation, and there are no worldwide known accounting information systems. When using non cloud based accounting information system data security is solely users’ responsibility, but when one rents or purchases cloud based accounting system that responsibility is transferred to software vendors who guarantees data security and availability. From accountants’ point of view, using cloud accounting system is one less worry regarding data security. Cloud accounting system vendor has to choose whether to use their own servers or opt for rented computing resources from another service provider, but that is in no way accountant’s concern. We were not able to research are Croatian cloud accounting system vendors using their own servers or are they renting computing resources from some of the known cloud resource providers like Amazon Web Service, Microsoft Azure, IMB or Google since that information is marked as business secret.
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2.1 Advantages and Disadvantages of Cloud Accounting Traditional accounting programs and cloud accounting solutions have their advantages and disadvantages. The use of cloud accounting in business operations brings many advantages, the most prominent of which are [9]: Lower costs – the price paid for using cloud accounting services is generally lower than traditional accounting programs. The user does not pay installation fees or have to purchase recent updates in case accounting rules or tax regulations change because the monthly or annual subscription costs include the cost of updates. Also, the user doesn’t have the costs of purchasing hardware, licenses for antivirus programs and operating systems, and server maintenance costs, as is the case with traditional accounting programs. All you need is a stable internet connection. Security – by using cloud accounting, there is no risk of destruction or theft of hardware and software. All data is stored away from the company on secure servers that are under the control of the company that manages it. Such companies usually have stronger antivirus programs, which makes financial data relatively safer in the cloud than stored locally, on the user’s computer. Availability – in cloud accounting, data is always available to all authorized users, wherever they are. All that is needed is a stable internet connection and a device from which to access the software. This is especially important in companies that prefer remote work. In addition, it is extremely easy to add new users - by setting up an authorized profile and password, which facilitates collaboration and because everyone can see the relevant financial information at any time. Up to date – the latest and updated versions of the software that the cloud accounting service provider upgrades are immediately available to the user, which is especially important when it comes to changes in accounting rules and tax regulations. In this way, the user saves time and focuses on his business, knowing that these tasks will be done for him by the service provider. Automatic Data Backup and Restore – with cloud accounting, there is no need to manually back up accounting data because the software does it automatically, reducing the possibility of human error and forgetfulness. The advantages of cloud accounting overshadow the disadvantages, however, there are potential risks that users of this software may encounter, some of which are: Internet connection - cloud accounting requires a constant and strong internet connection, cloud accounting software does not work well at low speed. Internet speed depends on the line capabilities of the user’s location and can vary from place to place, which is a big disadvantage. Security – as cloud services becomes more popular, data stored in the cloud becomes the target of attacks, which is why some businesses such as banks benefit more from keeping their sensitive financial data “in house” than with a cloud computing service provider. 2.2 Cloud Accounting Features The greatest impact of IT on accounting is the ability of companies to develop and use computerized systems for up-to-date recording of accounting data. Most popular
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accounting systems can also be tailored to specific industries or businesses [10]. This allows businesses to create individual reports quickly and easily for management and/or decision-making. Second, the advantages of computerized accounting systems can be summarized as follows: increased functionality, improved accuracy, faster processing, better external reporting. However, today’s accountants need to be familiar with software tools that can help them perform accounting functions more efficiently.
3 Cybersecurity in Cloud Accounting Cyberattacks are deliberate attempts to disrupt, steal, alter or destroy data stored in IT systems. Attacks are often motivated by profit and many intruders are technically sophisticated and have a nuanced understanding of system functioning [11]. Cybersecurity has become more urgent as malicious actors develop sophisticated techniques. But quantifying the risk or resilience of institutions to cybersecurity incidents is difficult [12]. The lack of standardized data on such incidents and company control is a challenge for the protection of accounting systems, but above all, companies are responsible for their own security against cyber attacks [13]. 3.1 Forms and Effects of Cybersecurity Incidents in Accounting Like any new technology, cloud computing is subject to security threats and vulnerabilities, including network threats, information threats, and underlying infrastructure threats [14] in the form of cyber-attacks. Attack tactics include finding weaknesses in software to get into IT systems, redirecting to fake email servers to steal passwords, redirecting to fake websites that infect users with malware, and software that wipes users from their own systems. Detailed data on the frequency, tactics and results of cybersecurity incidents are scarce. Data is scarce in part because financial firms avoid reporting incidents due to reputational concerns [15]. Concerns about cyber-attacks are growing [16]. However, an effective review creates a basis for advancing knowledge [17]. A continuous analysis of research in the field of protection of accounting and financial data from cyber security is needed. In the last few years, there has been a significant number of cyber attacks. The costs of the consequences of cyber attacks are enormous; therefore, cyber security risk management is considered extremely important for organizations [18]. While at the same time there is an increase in the intensity of cyber warfare through the Internet revolution [19]. It is noted that the Internet revolution has dramatically changed the way individuals, companies and governments communicate and do business. It is believed that the widespread interconnection of industries has increased the vulnerability of computer systems [20]. Private and public sector enterprises are increasingly dependent on network technologies that require access to the Internet, which requires greater data protection due to increased vulnerability to cyber attacks [21]. Therefore, more and more frequent cyber attacks are recorded, especially on servers of financial services, where attacks are more frequent than on other industrial branches [22]. In this global information society, where information travels through cyberspace, it is crucial to manage it effectively [23]. Effective management, on the other hand, is linked to awareness of increasing vulnerabilities, such as cyber threats and information warfare.
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Effective cyber security management is essential. Investments in cyber security are profitable if the benefits of additional activities for information security exceed its costs [24]. Although cyber security does not only help financial organizations (private and public sector), but it is also one of the main risks that requires continuous control from cyber attacks [25]. Previously conducted research talks about cyber security in the IT sector and the implementation of internal audits [26], as well as the impact of cyber security on information on stock exchanges [27]. Based on the presented literature, it is necessary to continuously conduct research on cyber attacks and adequate development of cyber security. 3.2 Effects of Cyber Security Deficiency in Accounting Cyber security is often used as an analogous term for information security. However, cybersecurity is not just about protecting cyberspace itself. Cyber security is the protection of those who continuously use IT technology in business processes, as well as the protection of their tangible and intangible assets from cyber attacks [28]. Effective cyber security reduces the risk of cyber attacks. Cybersecurity is a key factor for achieving information security [29]. Thus, cyber security includes the protection of information that is evaluated and transmitted through any computer network [24]. While cybersecurity threats impose direct costs on businesses, incidents in cyberspace can also pose a broader risk to financial stability. Financial companies work in complex networks and rely on electronic transactions, often representing rapid management in time. They are digitally connected to other and non-financial entities, including third-party service providers. However, defending decentralized networks against cyber-attacks with many entry points can be extremely difficult [30]. Cloud has already conquered almost every business area, but it seems that the accounting profession is skeptical about this new model. According to some accountants, cloud-based software is an obvious threat [31]. Of course, it can be seen as a threat to those unwilling to adapt and clearly understand the benefits involved. Therefore, it is necessary to pay attention to cybersecurity in accounting. In the absence of cybersecurity in accounting, three effects are possible [32]: Lack of interchangeability – the financial services industry relies on a robust IT infrastructure to complete transactions and move payments. In many financial networks, several companies or central e-services servers serve as hubs. Their services will be difficult to replace if lost or interrupted. Problems at key hubs can increase stability concerns. Policies that encourage the expansion of the financial system can reduce those risks. Regulators should consider such policies. Loss of trust – cyber-attacks often target customer account information and financial assets. Most of these attacks were one-off events, injuring only the victim and their clients. However, the excessive occurrence of cyber theft can cause a wider loss of trust. For example, after the cyber theft of customer names, credit card information and phone numbers of a retail chain, many clients call or visit their banks to ask for information about the presence of their money in their accounts. Then many customers cancel their credit cards, which can cause a banking crisis. Lack of data integrity – financial data integrity is critical. Many financial markets operate in real time. Financial companies need a robust data backup that can be recovered
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shortly after a cybersecurity incident. However, there has been a trade-off between rapid recovery and ensuring that recovered data is secure, accurate and does not spread the cyber risks, especially for markets that process orders quickly. Data corruption can disrupt market activity and can be difficult to recover from.
4 The Aim and Hypothesis of Research The previously presented theoretical approach shows the importance of cybersecurity in accounting. This research emphasizes the development of cybersecurity in accounting, ways of storing data and protecting them from possible cyber-attacks. Therefore, this paper tried to investigate the connection between the frequency of cyber-attacks in accounting with the method of data storage, then with the method of authentication in accounting software, and the intensity of cloud accounting protection against cyber-attacks. Accordingly, the following research hypotheses were set: H1 – The frequency of cyber attacks in accounting is significantly related to the method of data storage. H2 – The frequency of cyber attacks in accounting is significantly related to the authentication method in accounting software. H3 – The frequency of cyber attacks on accounting is significantly related to the method of backup of accounting data. Cybercrime is a global and severe problem that requires strong technical and legal responses. Accounting information is an important asset that needs to be secured and safely used because it provides support for making appropriate strategic and business decisions. Because accounting data is a valuable intangible asset of any company and because of this information is exposed to continuous and virulent attacks. Therefore, it is important and necessary to conduct continuous education of accounting employees.
5 Materials and Methods of Research Data for the implementation of this empirical research were collected through a survey on a sample of 183 accounting employees in the Republic of Croatia. The survey was conducted anonymously through publicly available social and business networks in March 2022 and by invitation again in January 2023. The survey used a Likert measuring scale with five intensities (1 – absolutely agree; 2 – agree; neither agree nor disagree; 4 – disagree; 5 – absolutely disagree), from which the following variables were defined: 1. Dependent variable frequency of cyber-attacks (CyberA) = > this variable was obtained from the survey questionnaire by the weighted average value of the answer to the question that attacks on accounting data occur once or more per year, and satisfaction with data protection is at the gossip level. 2. Independent variables: X1 – data storage mode (DataA) => this variable is determined by answering the questions that the accounting software (accounting database) used is installed locally only on own computer; or on a server located on the premises of the company or on a remote server hosted by third parties; or used as cloud accounting
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X2 – authentication mode (Authent) => this variable is determined by the answer to the questions that a password is used for authentication to work in the accounting software, or a username and password; or an authentication device (memory stick, smart cards, mobile app) or no authentication at all to access the accounting software. X3 – data backup mode (ProtectA) => this variable was obtained by answering the questions that saving the backup copy of accounting data is done locally, on the same computer where the accounting program is used; locally to another computer in the same location (in the company); locally to an external data storage device; remotely to a rented storage space or to a company-owned server. The conducted research showed a correlation of the frequency of attacks on accounting information systems with the methods of storing accounting data, the methods of authentication in accounting software and the method of data backup. To prove the set hypotheses, the statistical methods of correlation and regression analysis were applied using the SPSS 26.0 statistical program. The obtained results of the statistical data processing are shown below.
6 Results of the Research A total of 183 accountants from Croatia took part in the research, of which 86.9% were female, while the majority were 31 to 45 years old (48.6%), followed by 18 to 30 years old, which made up 26.2% of respondents. And the rest (25.1%) to respondents aged 46 to 65. According to the level of education, 40.4% of the respondents have a higher vocational education, followed by a bachelor’s degree in 30.1%, while 26.2% of them have a secondary vocational education and the rest are university specialists or doctors of science. The structure of respondents according to the level of work experience in accounting is shown in Table 1. Table 1. Years of work experience in accounting Work experience in years
Number of respondents
%
until 2
48
26,2
3 to 5
27
14,8
6 to 10
25
13,7
11 to 15
29
15,8
16 and over
54
29,5
Respondents answered several general questions about cloud accounting and the protection of accounting data. The answers to individual questions are shown graphically below. Figure 1 shows that the largest number of respondents are familiar with the term and how it works, 39.3% of them, while 8.2% are not familiar with the topic of accounting in the cloud, however only 24.6% of respondents do accounting in the
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cloud. Figure 2 shows the answer to the question of whether the rights and obligations regarding data protection and confidentiality are regulated between the user and the software manufacturer, from which it is evident that 14.2% of the respondents do not have a data confidentiality agreement.
I'm familiar with the term but I don't know how it works. I am familiar with the term and how it works. I am familiar with the term and how it works, and I use technology. I am not familiar with the term, nor do I use the technology.
Fig. 1. Knowledge of accounting in the cloud.
Yes No
Fig. 2. Regulation of rights and obligations on data protection and confidentiality.
Before starting the regression model, an analysis of the linear correlation of the variable cyber-attacks (CyberA) with the independent variables data storage mode (DataA), authentication mode (Authent), data backup mode (ProtectA) was performed, which is shown in Fig. 3. Figure 3 shows a strong positive linear correlation of the CyberA variable with the DataA variable, while with the other variables from the sample of this research, the linear correlation is positive but significantly weaker.
Fig. 3. Linear correlation of CyberA, DataA, Authent and ProtectA variables.
Analyzing the linear correlation between the observed variables shows a significant linear connection between the frequency of cyber-attacks and the method of data storage
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in accounting. From Table 2, a correlation coefficient of 0.807 is visible, while the F ratio is significantly higher than the theoretical value, with a significance level of 0.05 and the number of degrees of freedom 1.81, it can be concluded that the first hypothesis is confirmed. Also, Durbin-Watson is 2.070 which means no existence of autocorrelation of relationship errors. Table 2. Statistical correlation between the frequency of cyber attacks and the way accounting data is stored. Model Summary Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
,807a
,652
,650
,663
a. Predictors: (Constant), DataA b. Dependent Variable: CyberA
By analyzing the statistical connection between the frequency of cyber-attacks and the method of authentication in the accounting software shown in Table 3. a positive but weak significant correlation is visible (0.199). The coefficient of determination R2 is closer to zero than to one, therefore we cannot talk about good representativeness of the model, with a significance level of 0.05 the second hypothesis is partially confirmed, there is a small statistical connection between the frequency of cyber-attacks and authentication in accounting software. Table 3. Statistical correlation between the frequency of cyber attacks and authentication methods in accounting software. Model Summary Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
,199a
,040
,034
1,101
a. Predictors: (Constant), Authent b. Dependent Variable: CyberA
Below, from Table 4. is visible a positive but weak statistical correlation between the frequency of cyber-attack and the method of backup of accounting data. Durbin-Watson has a value close to 2, which indicates the absence of autocorrelation of relational errors. With a significance level of 0.05 and from the coefficient of determination, it is visible that 40% of the variations of the dependent variable are the result of variations of the independent variable. The obtained results partially confirm the third hypothesis. The collected responses to the conducted survey revealed that 60.9% of respondents use a username and password for authentication to work in accounting software, and 20.7% of respondents use only a password. The fact that 8.7% of respondents do not have any security authentication for accessing accounting data is worrying, which can
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Table 4. Statistical correlation between the frequency of cyber attacks and the backup method of accounting data. Model Summary Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
,199a
,040
,034
1,101
a. Predictors: (Constant), ProtectA b. Dependent Variable: CyberA
be considered inadequate protection of accounting data. It is also worrying that 19.6% of respondents save a backup copy of accounting data locally, that is, on the same computer on which they use the accounting program. This research led to the realization that 20.7% of respondents use only a password for authentication to work in accounting software, while the worrying fact is that 8.7% of respondents do not have any security authentication for accessing accounting data, which can be considered inadequate protection of accounting data. It is also worrying that 19.6% of respondents save a backup copy of accounting data locally, that is, on the same computer on which they use the accounting program.
7 Conclusion By conducting empirical research on a sample of 183 accounting employees in Croatia, we wanted to prove that the frequency of cyber-attacks in accounting depends to a significant extent on the method of data storage and the method of authentication in accounting software. The statistical analysis of the collected data confirmed the set hypotheses, and it can be concluded that the frequency of cyber-attacks is significantly related to the method of data storage (0.807), and partially related to the method of authentication in accounting software and the method of backing up accounting data (0.199). Earlier research showed a significant connection between cyber-attacks and the age structure of accounting employees and their level of education, that is, older accounting employees with a lower level of education are significantly exposed to cyberattacks [33, 34]. This research confirmed that more complex authentication and data storage outside the location of the accounting program itself reduces the possibility of cyber-attacks in accounting. Also, storing accounting data on the cloud significantly reduces the possibility of cyber-attacks.
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Applied Science in Mechanical Engineering Technology
Managing Risks of Renewable Energy Projects in Bosnia and Herzegovina Based ISO31000 and PMBOK Hajrudin Džafo(B) Faculty of Engineering, Natural and Medical Sciences, International Burch University, Francuske Revolucije Bb, 71210 Ilidza, Bosnia and Herzegovina [email protected]
Abstract. Risk identification and analysis for complex renewable energy projects have become one of the main parts of today’s project management process. In this paper, we discuss several methods of project risk measurement, such as risk severity analysis and quantitative and qualitative analysis. Identified risks were analyzed probabilities, impact, seriousness of the project. This paper aims to compare the different results obtained through different types of qualitative and quantitative analysis methods and to know the common highly rated risk factors. Risk assessment and risk management in large investments, such as investments in power plants using renewable energy sources, deserve special attention. This paper presents the basics of risks and how they are managed in the planning, preparation and construction of new energy facilities in Bosnia and Herzegovina, such as wind power plants and solar power plants. in application, due to not giving the necessary importance to the impact of project results, timely identification and management of certain specific risks for each project. Also, the paper presents the results of research on the impact on project management. An explanation of the management of renewable energy projects is given, with a main focus on the impacts on the success of the management of these projects in practice. A comparative analysis of worldwide research on the success of managing these types of projects was taken into consideration. For the case study, the project of a 10 MW solar power plant was analyzed, the risk management methodology was applied and the important indicators were presented. In this paper, we will discuss the risk management of this type of project based on PMBOK and ISO 31000. Keywords: Risk management · Renewable energy project · Risk assessment · ISO 31000 · PMBOK
1 Introduction 1.1 Literature Review In its vision 2020, the International Project Management Association (IPMA) stated “the promotion of project management competencies through all segments of society with the aim of making all projects successful” [1]. Project organization and project © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 455–465, 2023. https://doi.org/10.1007/978-3-031-43056-5_33
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financing in the economic activity of electricity production using renewable energy sources is a proven practice in Europe and beyond. Each project in the stages of planning and implementation has its own limitations: scope, budget, deadlines. In the last few decades, the growing world energy needs have led to an increased interest in the use of electricity from renewable sources. World practice has long recognized the importance of the project manager as one of the indispensable factors of project success [2]. The project also has its own risks, so project management, as well as risk management, has a primary impact on the project. Risk management is an area of knowledge in project management. Project management is defined as a set of knowledge, skills and techniques that directs project activities towards the achievement of project goals [3]. It is a bridge between the idea/need/problem as the cause of project initiation and the project goal determined by satisfying the need or solving the problem or realizing the idea as a measure of the project’s success. The success of management makes a significant contribution to the success of the project, so it is important for practice and research [4–8]. The practice of applying specific risk management techniques in Bosnia and Herzegovina lags behind international trends. The inability to manage risks affects the timely implementation of projects, especially for the reason that project managers do not deal with risks in practice. Renewable energy sources include biomass, wind, hydropower and solar energy. The methodology consists in carrying out quantitative analysis only for the most prioritized risks. It includes a numerical assessment of the probability of occurrence and the cost/time consequences of the risk. Estimates should include the expected minimum and maximum consequences in terms of time and cost for each risk. It is better to use the range of consequences, instead of individual figures [9]. This analysis, which adopts a case study approach, indicates that contemporary risk management practices are lacking in Bosnia and Herzegovina, despite the poor track record of project success in the energy sector. The results of the study analysis show that the level of risk management practice in projects of the energy sector, in the field of renewable energy sources, deviates from international practice. Notable gaps in practice include project management professionals who do not know or have not practiced project risk management. It is suggested that the responsible institutions of the public sector at different levels of administrative organization, as Bosnia and Herzegovina have (municipality, cantons, ministries… and others) should invest and educate more people in the areas of project management. It is necessary to engage professionals with extensive experience in managing the risks of each project in the field of renewable energy sources. Bosnia and Herzegovina have natural potential, renewable energy sources, which today can be put to the function of general development and progress of society as a whole, but also of local communities, on whose premises the construction of a plant for the production of electricity is planned. The question arises whether a higher percentage of realized projects can be expected in the next period, say 10 years. Interested domestic investors, as well as foreign investors, with the support of large foreign financial institutions, have been showing interest and investing in researching the potential of renewable energy sources in BiH, as well as developing projects, for many years, starting from 2000 until today. Delay in projects is one of the biggest concerns of this industry in
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Bosnia and Herzegovina. Obtaining various permits, approvals from the relevant ministries and an insufficiently clear procedure affect the justification of investing in these projects. In Bosnia and Herzegovina, there is still (2022) no clear legal framework and legal compliance, which would enable a higher percentage of realized projects. In this paper, we will discuss the risk management of this type of project based on PMBOK and ISO 31000 [10, 11]. Delaying projects means slowing down development in all other related areas. The main goal of the analysis is the evaluation of different types of delays and the reasons for those delays that currently affect the projects of renewable energy sources in the energy sector in Bosnia and Herzegovina. Measures from previous research to reduce or eliminate these delays to mitigation or acceleration methods are analyzed for the case study considered for this research. There are different types of delays that have been identified. It is important to identify whether the delay is critical or not, as identifying critical delays help in taking appropriate action at the right time. The reasons for the delay is primarily due to the unreasonable scope of the project, inadequate early planning and the absence of a risk management system. The contractor further contributes to the delay due to lack of resources and labor productivity. Over-ambitious estimates, incorrect task estimation, lack of task clarity, design/approval delays, and client interference in the decision-making process contribute to delay. Feedback on lessons learned provides important information related to mitigation measures being considered for implementation. Delay prevention can be achieved by mitigation and/or acceleration. It is important to plan/analyze the request in detail. This is done by identifying risks and mapping resources. Project delays are considered one of the frequent problems in construction in the energy sector of Bosnia and Herzegovina. Delays have a negative impact on the project in terms of performance, time and cost. Therefore, it is essential to identify the types of delays that usually occur in a project. Types of delays can be broadly divided into two categories of delays by the investor and contractor. Delays can be identified as critical or non-critical and whether the delay is simultaneous or no simultaneous. Identifying the types of delays leads to the reasons for the delays. Reasons for delays are identified so that their impact on projects is reduced. The contractor often has delays that are related to overambitious estimates and inaccurate assessment of the task, which in turn lead to delays and affect the project. In case of lack of task clarity, an inexperienced contractor or subcontractor may unknowingly delay the work.
2 Possible Risk Analysis of Renewable Energy Projects Every project is exposed to various risks from the very beginning, but it is essential how much professional and other attention is paid to risk elimination and risk management, so that their impact on the project’s goals is minimal or avoided altogether. Renewable energy sources (wind, water, solar radiation) whose availability is intermittent throughout the year, the primary risk arises due to the uncertainty of the input data of the measured parameters, on which the project is further developed. The unreliability of input parameter data affecting the project is taken as standard uncertainty. The basic goal of analysis in risk management is to reduce the scope of uncertainty affecting the success of the project, to plan and introduce measures to avoid risks or
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reduce their impact, as well as timely treatment of those identified risks. By establishing a quality risk management system, the chances of project success are much higher. This primarily involves the following steps: (1) recognition and identification of risks; (2) risk management; (3) monitoring and reporting on risk management. Identification and analysis of risks, assessment of the importance of impact on project goals, as well as risk management itself are an integral part of the management of every project, even in this area, i.e. projects based on the use of renewable energy sources in Bosnia and Herzegovina. Construction risks include meeting the requirements of legal regulations, both locally and at higher administrative levels (canton, entity ministries…). 2.1 Risks in Project Preparatory Phase A large number of risks can be immediately identified already in the preparatory phase of projects, which include external risks: political, administrative, market, as well as internal risks: technical (project development) and financial (insufficiently known financing, the possibility of financing the entire project, including preparatory activities and all procedures up to contracting and realization of construction). In a large number of projects, the preparatory phase lasts a very long time, even years. Very often, the procedure for obtaining all necessary permits to start construction, including environmental aspects, necessary permits, and the interest of the local community and the relationship with the local population is very often not sufficient. These types of projects are met with resistance from the population, very often for the reason that the interest of the local community and the development of the wider region through the construction of such energy facilities is not offered or clearly recognized. When the project (wind power plant, solar power plant) in the preparatory phase received all the necessary permits after investing in high-quality investment and technical documentation (Justification Study, Environmental Impact Study, Elaboration of connection to the electrical grid, concept and main design) in the construction phase, no they expect more risks that previously had an impact on this project. With a quality. contract for project financing, the construction period ranges from one to two years. 2.2 Risks in Construction Phase In the construction phase, there are risks related to the conditions on the ground (construction works), as well as risks in the delay of delivery and installation of equipment at the installation site. The list of risks in the construction phase itself can be long. When planning of such projects, it is necessary to group risks according to importance and priorities. High priority risks in the construction phase are: a) increase in total construction costs: the analysis showed that most, or almost all, projects have delays in the preparation phase (and for several years), as well as in the construction phase (for several months to a year). In such cases, it is a regular occurrence to exceed the project budget by 15–30%.
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b) The risk of resolving property-legal relations, which regularly lead to prolonging the necessary time and increasing project costs; c) An elaborate and insufficiently clear procedure for applying and obtaining the necessary consents and permits from competent institutions, d) and others, which appear in the final stages of the project, when the profitability of project financing deviates from the indicators with which the financing of the same was entered into. The risks identified and indicated in this way require responsible management by the project manager. Methodological application of treatment of previously identified risks is needed. For risks with the greatest impact on the project’s goals, determine and apply possible measures to treat the risks and determine the persons responsible for the application of those measures. A condition for the implementation of risk management on each project is a multidisciplinary team of experts from the engineering profession, finance, law, ecology and others for whom a justified engagement is demonstrated. 2.3 Project Financing Risks Project financing is a very often applied model for these types of investments. Beforehand, economic feasibility studies are done for known technical parameters, which should include all known external and internal costs. There is a risk in the costs that appear later, and very often they are not taken into account. For large projects of renewable energy sources, various incentive measures are applied, such as subsidizing production at the purchase price of electricity, reducing interest rates for credit loans, and others.
3 Project Risks and Limitations In order to be able to manage a phenomenon that is indicated as a risk, we must first know how to define what a risk is. There are several definitions of project risk in use, which are also accepted in the latest standard ISO 31000, which refers to risk management, risk is defined as the effect of uncertainty on project goals. A risk is an uncertain event or condition that, if it occurs, can have a positive or negative effect on at least one of the project’s components. Uncertainty in these projects is related to lack of data, wrong assumptions and the like. Each project has its own limitations, which are related to resource limitations, time limitations, as well as funding limitations. Lack of knowledge and skills in risk management is a very common phenomenon among project teams, affecting the emergence of project risks, such as: poorly defined project tasks, insufficient resource assessment, and inexperienced project managers. Each project risk is characterized by three elements: (1) a risky event; (2) probability of occurrence; (3) the impact it can have on the project.
4 Project Risk Management In the project planning phase, proactive identification, analysis and resolution of risks is recommended for the project manager. Risks are expected in every project. Recognizing risks and treating them is a process of great benefit for achieving project goals. Although
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there are a large number of different definitions of risk, it is most often defined as the possibility of not achieving the defined goals of the project. Just as it can represent a threat, a risk can also mean a certain chance for a project. In any case, risk represents uncertainty – the more one knows about the risk itself and its impact on the project, the greater the possibility of successfully managing it. The first step represents the risk management planning process, which defines a complete approach to risk management activities. 4.1 Risk Management Planning The risk management planning process should define the method of risk management within the project. It should ensure that the degree and methods of management correspond to the expected risk and the importance of the project for the organization, in order to provide the necessary resources and time for the implementation of risk management activities. The main goal is to define the risk management plan, which represents the basis for understanding the rest of the risk management process and the key the result of the risk planning process. The plan should include: – methodology: describes the way of risk management on the project and includes elements such as methods, techniques, data sources that will be used; – roles and responsibilities: defined by a team of people who will be responsible for managing identified risks and responses; – budget; part of the risk management plan that allocates resources and estimates risk management costs; – risk time plan: determines the time of execution of risk management activities on the project and the frequency of those activities; – risk categories; – risk reporting formats; – risk monitoring, – probability and impact matrix, etc. 4.2 Risk Identification It includes identifying and documenting all risk events (positive and negative) that may affect the project. Iterative process – as the project progresses through the phases of the life cycle, new risks are identified that were not identified during planning. It is necessary to include different groups of people: the project team, stakeholders, experts, users and everyone else who can contribute to the best possible identification. The subject of risk can be: funding sources, time plans, changes in project scope, project plan, management processes, technical processes, human resources, etc. The risk identification process involves the use of the following methods: 1) Review of documentation – review of plans, assumptions, historical information from the perspective of the entire project, its parts and individual project activities; 2) Collection of information – includes several different techniques such as brainstorming, Delphi method, interview, cause technique and analysis of strengths and weaknesses;
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3) Checklists – are made based on historical information and previous experience of the project team; 4) Analysis of assumptions – proving the claims and assumptions that were identified and documented in the project planning process; 5) Diagramming technique – can be used in three forms: cause-effect diagrams, process flow diagrams and influence diagrams. The result of the risk identification process is the risk register. The elements that make up the risk register are: a) List of identified risks – it is desirable to create a database that will contain all identified risks and enable their monitoring; b) List of potential answers – sometimes the identification itself points to an appropriate way of solving or avoiding risks. c) Risk causes – it is necessary to go a step further and examine the causes of risk events and then document them as part of the register. d) Updated risk categories – the results of the identification process may indicate that certain risk categories require certain adjustments or changes.
5 Qualitative and Quantitative Risks Analysis The methodology consists in carrying out quantitative analysis only for the most prioritized risks. It includes a numerical assessment of the probability of occurrence and the cost/time consequences of the risk. Estimates should include the expected minimum and maximum consequences in terms of time and cost for each risk. It is better to use the range of consequences, instead of individual figures, according to [9]. Risks are ranked according to their impact on project goals, in order to determine whether it is necessary to perform a quantitative analysis, or whether it is possible to skip the development of risk response plans. Assessing the probability and impact of risks is a method that assesses the probability of the realization of identified risk events and determines the consequences they can cause on the project goals (time, scope, quality and costs). The main goal is to assign a certain numerical value of the probability of occurrence to each risk event and to assess its impact on the project objectives. Sensitivity analysis analyzes the potential impact of risk events on the project. First, all elements of event uncertainty are examined, and then those that have the greatest potential impact on project goals are determined.
6 Case Study: Risk Analysis and Risk Management of 10 MW Solar Powerplant Project A multi-year database of insolation at a potential location is available to the interested investor. For the territory of Bosnia and Herzegovina, insolation is in the range of 1200– 1600 kWh/m2 . General description of the risk management standard: there are three critical components of the risk management standard: principles, framework and process, all well explained and well connected. The principles, framework and processes in ISO
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31000 are very useful in managing project risk management, it can be said that the risk management process in PMBOK can also be done effectively with the support of ISO 31000 principles. The principles, framework and processes in ISO 31000 are very useful in managing project risk management, it can be said that the risk management process in PMBOK can also be done effectively with the support of ISO 31000 principles and framework as illustrated in Figs. 1 and 2.
Fig. 1. A Complementary Risk Management between ISO 31000 and PMBOK [10].
Fig. 2. Relation between the critical components of ISO 31000, [10].
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A list of high-level risks for the total project risk, the implementation of project risk management PMBOK, which ISO 31000 does not mention, has been established. The identified risks are grouped according to the type and location of occurrence, as follows: A) External sources of risks: 1. Legal risk (local regulations, permits, consents); 2. Economic risk (economic price policy, taxes, financing conditions); 3. Political risk (policy changes, elections, work, agreements); 4. Social risk (education, culture, strikes, turnover of people); 5. Natural risk (climate, soil, fires, earthquakes). B) Internal sources of risks: 6. Management risk (unrealistic goals, poor control, organization); 7. Human factor risk (productivity, diseases, motivation, failures); 8. Contractual risk (types of contracts, short deadlines, unrealistic prices); 9. Technical risk (technology, design speed, technical solution and documentation); 10. Logistics risk (deliveries, availability of equipment, lack of people). C) Special risks for a specific project: 11. Risk of choosing a location for a solar power plant; 12. Risk of the shadow of distant objects influencing the operation; 13. Risk of the shadow of nearby objects affecting the operation; 14. Risk of improperly assembled structure to eliminate shading and reduce risks; 15. Risk in Feasibility study conditions; 16. Land acquisition risk. D) Risks of Equipment Selection: 17. Risk of choosing the appropriate solar panels; 18. Risk of choosing solar panels for this project; 19. Risk of pairing solar panels with an inverter; 20. Risk of checking whether the solar panel is really of the power that is written on the technical sheet; 21. Risk of choosing the appropriate substructure. E) Risks of panel planning and assembly: 22. Risk of incorrect dimensioning of the solar power plant and strings; 23. Risk of irregularly arranged strings; 24. Risk of cell breakage – occurrence of microcracks on the solar panel; 25. Risk of errors in the cabling of solar power plants; 26. Risk of lack of lightning rods; 27. Risk of improperly parameterized inverter; 28. Risk of improper assembly – mechanical and electrical. F) Risks of lack of measurement and documentation: 29. Risk of lack of documentation as a condition of guarantee and proper operation of the power plant; 30. Risk of irregular and extraordinary inspection of the plant; 31. Risk of not measuring working and dark characteristics. G) Risks of poor maintenance of the solar power plant: 32. Risk of improper maintenance of the solar power plant; 33. Risk of elimination of disturbances and omissions; 34. Risk of unfavorable optimization of the solar power plant.
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7 Results The risk management plan, road map, cost and risk schedule, risk ranking, determination of risk owners and risk response strategies according to ISO 31000 and PMBOK were reviewed. To measure the effectiveness of risk management, we performed a risk maturity assessment, based on which the implementation refers to the clauses of PMBOK and ISO 31000 as in Table 1. Table 1. Risk Register Summary. Risk type
Number of risks
% of risks
Inherent %
Comliance
16
32
2
Financial
2
1
2
Governance
8
16
24
HSSE
2
1
5
Operation
8
16
22
Reporting
1
0
0
Strategy and Planning
12
25
46
Total:
49
100
Note: Inherent % – part of the risk of material misstatement related to the risk due to certain characteristics
As a result of the risk assessment, the risk identification phase was followed by a qualitative risk analysis and 34 risk events with a risk composition were obtained 45% in the strategy and planning aspect category, 1% compliance/infrastructure aspect as shown in Table 1. Strategic and planning risk is the dominant contribution to the risk register, although the number of risk events is only 20% of the total number of risk events, but the inherent risk value is 45% of the value of the total inherent risk. What is not specified in ISO 31000, but within the PMBOK for risk management, is the relationship between the project risk management knowledge area and other project management knowledge areas.
8 Conclusion Delay in projects is one of the biggest concerns of this industry in Bosnia and Herzegovina. Obtaining various permits, approvals from the relevant ministries and an insufficiently clear procedure affect the justification of investing in these projects. In Bosnia and Herzegovina (it is now 2023) there is still no clear procedure for the preparatory phases of project implementation and legal compliance, which would enable a higher percentage of realized projects. There are risks with renewable energy projects. At the beginning of the implementation of each project, the type and scope of risky events and processes should be considered. In this paper, the basics of risk management are indicated, with special attention
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being paid to their management in projects in Bosnia and Herzegovina. The methodology and action plan according to which one should work and make decisions in all phases of project implementation are given. Risk assessment and risk management in large investments, such as investments in power plants using renewable energy sources, deserve special attention. For the case study, the project of a 10 MW solar power plant was analyzed, the risk management methodology was applied and the important indicators were presented. In this paper, experience in risk management of this type of project based on PMBOK and ISO 31000 was applied.
References 1. IPMA moving fast forward with new strategy 2020. http://blog.ipma.world/ipma-movingfast-forward-with-new-strategy-2020/ 2. Yang, L.R., Huang, C.F., Wu, K.S.: The association among project manager’s leadership style, teamwork and project success. Int. J. Project Manage. 29(3), 258–267 (2011) 3. PMI: PMBOK® Guide & Standards, 5th edn. Project Management Institute, Philadelphia (2013) 4. Mir, F.A., Pinnington, A.: Exploring the value of project management: Linking Project Management Performance and Project Success. Int. J. Project Manage. 32, 202–217 (2014). https:// doi.org/10.1016/j.ijproman.2013.05.012 5. Machado, F.J., Prá Martens, C.D.: Project Management Success: A Bibliometric Analysis. In: 12th International Conference on Information Systems & Technology Management – CONTECSI, São Paulo, pp. 3154–3173 (2014) 6. Chou, J.S., Ngo, N.T.: Identifying critical project management techniques and skills for construction professionals to achieving project success. In: 2014 International Conference on Industrial Engineering and Engineering Management (IEEE IEEM), Malaysia, pp. 1204–1208 (2014) 7. Nahod, M.-M., Radujkovi´c, M.V.M.: The Impact of ICB 3.0 Competences on Project Management Success. Procedia – Soc. Behav. Sci. 74, 244–254 (2013) 8. Mossalam, A., Arafa, M.: The role of project manager in benefits realization management as a project constraint/driver. Hous. Build. Nat.Res. Center 12, 305–315 (2016) 9. Karzner, H.: Project Management a Systems Approach to Planning, Scheduling and Controlling, 10th edn. John Wiley & Sons, INC., New Jersey (2009) 10. International Standard ISO 31000, Second edition 2018-02; Risk management – Guidelines 11. Project Management Institute: A Guide to the Project Management Body of Knowledge – (PMBOK Guide) –5th edn. (2013)
Exploring the Strength of Wood: Measuring Longitudinal Modulus of Elasticity in Structural Applications Irvina Šahinovi´c1 , Husein Roši´c1 , Leila Fathi2 , Damir Hodži´c1 Aladin Crnki´c1 , and Redžo Hasanagi´c1(B)
,
1 Faculty of Technical Engineering, University of Biha´c, 77000 Biha´c, Bosnia and Herzegovina
[email protected] 2 Department of Natural Resources and Earth Science, Shahrekord University, 64165478
Shahrekord, Iran
Abstract. The aim of this paper is the experimental determination of the longitudinal modulus of elasticity by the four-point bending method according to the BAS EN 408+A1 standard. The samples of wooden beams, on which the experimental research was carried out, were made of three types of wood (beech, oak, and spruce). This paper describes the procedure for determining deflection on samples of different stiffness. For this paper, the samples were tested on the SHIMATZU 50 kN device. This test aims to determine the modulus of elasticity for the tested materials precisely by measuring the induced deflection. We achieve this by recording the corresponding amount of deflection for each load increase and using the formula from the standard to obtain the corresponding value of the modulus of elasticity. In theory, it should have been unique for a particular material, but testing showed that its values vary. Keywords: Modulus of elasticity · Solid wood · Deflection · Simple beam
1 Introduction Wood is a natural, monotonous, and heterogeneous material, whose properties vary significantly within and between different species. It is one of the earliest building materials, which has been used for hundreds of years. The structural use of wood and wood-based composites continues to grow. Moreover, new wood-based materials continue to be developed and successfully introduced to the engineering and construction market [1–4]. Solid wood for structural applications must be strength-rated before use. Grading standards provide strength class systems for assigning wood samples to specific strength classes. To keep the grading simple and economical, the process usually focuses on the most important physical and mechanical properties: density, modulus of elasticity parallel to the grain (MOE), and flexural strength (MOR). In Europe, the classification of structural wood is carried out according to a set of three related standards, test methods for © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 466–475, 2023. https://doi.org/10.1007/978-3-031-43056-5_34
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determining mechanical properties as well as dimensions, moisture content (MC), and wood density of test pieces are specified in EN 408 (2003) [5]. Characteristic values of mechanical properties and density are derived from test data according to EN 384 (2004) [5]. The wood is finally classified according to EN 338 (2003) [6]. Since MOE can be performed by non-destructive tests, it is a very important parameter for machine testing. Most testing machines for grading use MOE as an indicator of wood strength [7, 8]. MOE can be determined either by static bending or dynamic methods (vibration, ultrasound) [9, 10]. In the practical design of wooden structures, MOR and MOE are important parameters, because deformations of structural elements are often decisive in the design process [11]. The compressive strength parallel to the fibers as well as the embedment strength correlated with the density was stated in research [7, 12, 8]. In their studies, [14–16] employed the finite element method (FEM) to experimentally assess the elastic properties of beech and spruce wood. Through their simulations, they were able to obtain detailed insights into the behavior of these materials under various loading conditions and deformation states. These findings offer valuable information for the design and optimization of wood-based structures, as well as for the development of new materials with improved mechanical properties. In recent years, researchers have investigated wood and wooden hybrid structures through various experimental, analytical, and numerical methods [17–21]. These studies have practical applications in modern architecture, where large windows require beams with high bending stiffness, load-bearing capacity, and flexural rigidity. To address this need, hybrid beams reinforced with aluminum were compared to reference wooden beams using a four-point bending test [22]. This study aimed to determine the ratio of bending strength, bending MOE, and maximum force of three different types of wood.
2 Materials and Test Methods 2.1 Materials The types of wood that were used for testing are spruce, beech, and oak, and the types of wood that are representatives of soft and hardwood, which are most often used in the production of furniture, building materials, and in general in wooden constructions. All samples were cut from the longitudinal direction, where the curvature of the logs was the smallest. Common spruce (lat. Picea abies) belongs to the soft species of wood, which consists of small and few resin canals. The wood has a simple anatomical structure: the basic wood mass consists of tracheids, the strips are very narrow, the resin channels are limited primarily to the latewood, and they are small and hardly noticeable. Its characteristics are soft and light wood, strong and quite elastic when pressed and bent. Spruce was chosen as a material for the sample because it is a soft wood and because of its good pressure-resistant properties. Table 1 shows the dimensional properties for the bending test of spruce wood. This type of wood is mainly used for construction carpentry, staircases, simple furniture, musical instruments, wooden panels, covering walls, floors, ceilings, etc.
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I. Šahinovi´c et al. Table 1. Dimensions of spruce samples.
Spruce samples (softwood) Values
S1
S2
S3
S4
S5
b [mm]
45.50
48.60
48.60
48.60
48.50
h [mm]
20.60
18.70
20.10
20.50
19.70
l [mm]
391.40
355.30
381.90
389.50
374.30
Oak (lat. Quercus robur L.) samples are classified as softwood, yellowish-brown to light-brown. The pores of early wood are large, arranged in a narrow crown of 1 to 3 rings, and easily visible, the pores of early wood are compact. It is thick, semi-heavy, very strong, and load-bearing, extremely durable in dry conditions and wet conditions, especially underwater. Oak was chosen as the material for the samples because it belongs to the semi-heavy wood species and because of its wide application in the wood industry. All oak samples were measured individually, as with the fir, each sample has its weight, which is shown in Table 2. Table 2. Dimensions of oak samples. Oak samples (semi-heavy wood) Values
H1
H2
H3
H4
H5
b [mm]
50.70
50.80
50.90
50.80
50.60
h [mm]
20.50
20.50
20.50
20.50
20.30
l [mm]
389.50
389.50
389.50
389.50
385.70
Table 3. Dimensions of beech samples. Beech samples (heavy wood) Values b [mm]
B1 48.60
B2 48.60
B3 48.80
B4 50.50
B5 50.70
h [mm]
20.45
20.50
20.50
20.60
20.50
l [mm]
388.55
389.50
389.50
391.40
389.50
Beech (lat. Fagus sylvatica) is one of the most widespread forest tree species in Bosnia and Herzegovina. It grows in hilly and mountainous positions in central, western, and southeastern Europe. It is also very solid and heavy. Due to its strength, it is used for parts exposed to high stresses. It is heavy, dense, resistant to pressure, and bending, and very elastic. Beech as a material was not randomly selected and used for making the samples. It was chosen as a representative of heavy wood and good physical properties for resistance to bending (Table 3).
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The samples were made by the given dimensions, with a permissible deviation of ±1 mm. The moisture content of the samples was measured and was 12 ±1%. When making samples, wood errors were eliminated, which affected the results of the experiment. A minimum of 5 test tubes were required for each type of wood. The following image shows clean samples of spruce, oak, and beech wood without knots, which were used for testing the longitudinal modulus of elasticity. These samples were carefully selected to ensure that they were free of any knots, cracks, or other defects that could affect their mechanical properties. The use of high-quality and defectfree samples is crucial for obtaining accurate and reliable results in any experimental study of wood properties (Fig. 1).
Fig. 1. Test specimens.
2.2 Performing the Experiment A device manufactured by SHIMADZU was used for the experimental testing of bending samples, carried out in the laboratory of the Technical Faculty in Biha´c. The testing machine consists of (Fig. 2): – control device (1), – SHIMAZDU machine (2) and – computer (3) With the control device (1), we control the presser and the profile and confirm the parameters on the device, with which we can start testing. When we confirm the parameters, the device gives a signal to the computer (3) that all prerequisites for the safe execution of the experiment are met. The bending speed was 7 mm/min, which was set by the computer, while the standard defines the maximal force with which the experimental samples could be loaded [15]. So, considering the preparatory-final time and the stretching time, each experiment lasted approximately 15 min for each measurement. The samples were loaded to the point of plasticity of the material. Before the actual test, the cross-sectional dimensions of the sample were measured, and during the test, the bending force and deflection of the test sample were recorded. The distance between the supports was 6 sample thicknesses. The
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Fig. 2. SHIMADZU experiment machine (2), type SIL-50NAG.
diameter of the roller which was used to load the test sample was 10 mm. The setting of tools and bending devices, as well as the measurement of the modulus of elasticity itself, proceeded in the following order: – the sample was placed between the pressers, and the pressure is transferred to the wooden sample. Considering that the wood is supported at the ends, there will be bending, that is, pre-cracking of the experimental sample: – the device for testing samples is starting when the parameters for the start of the test are entered into the controller. The mechanical properties of materials are tested, which determines the main properties that characterize the mechanical resistance of materials, but also their deformability, from materials that have a prescribed shape and dimension. The first group (group A-pitch position 0°) were samples where the load force is parallel to the ply line. The second group (group B - 45° position of the tracks) are samples where the load force is perpendicular to the tracks. The experiment was conducted under laboratory conditions, with a temperature and relative humidity that are typical for such settings. Specifically, the laboratory temperature was maintained at 20 and the relative humidity was kept at 48%. These conditions were carefully controlled throughout the experiment to ensure accurate and reliable results. 2.3 Bending Testing Procedures The procedure for determining the strength of solid wood used in construction involves assessing important mechanical properties such as density, modulus of elasticity (MOE), and bending strength, according to the standard EN 408:2013. In this study, ten samples each of spruce, oak and beach wood were tested for MOE, maximum force, and maximum displacement during bending. The samples were positioned to withstand maximum load up to the limit of elasticity. The test samples, with a minimum length of approximately 19 times the depth of the section, were symmetrically loaded in bending at two points, spanning a distance of approximately 18 times the depth. The maximum force was measured at the loading points illustrated in Fig. 3. Using the measured parameters from the tests, the MOE can be calculated using the following Eq. (1) [23]: MOE =
a · l12 (F2 − F1 ) [N/mm2 ] 16 · I(w2 − w1 )
(1)
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where: F2 , F1 = load in newtons on the regression line with a correlation coefficient of 0.99. w2 , w1 = ( increment of deformation in millimeters corresponding to F2 –F1 ) [mm]. a = distance between a loading position and the nearest support in a bending test in millimeters [mm]. I = second moments of area, in millimetres to the fourth power [mm]. l1 = gauge length for the determination of modulus of elasticity in millimetres [mm].
Fig. 3. Schematic illustration of the four-point bending test arrangement according to the BAS EN 408 standard [24].
3 Results and Discussion In the paper, the magnitude of the bending force, modulus of elasticity, and deflection were determined experimentally. The deflection is shown as the displacement of the loaded element. The modulus of elasticity is one of the most important mechanical properties of wood. It depends on the strength of the bonds between atoms and represents the material’s resistance to elastic deformation. The modulus of elasticity depends a lot on the structure of the material itself, while it is also affected by external conditions such as humidity and temperature. As the modulus of elasticity depends mainly on the structure of the material, it will differ for different types of wood. The value of the modulus of elasticity strongly depends on the symmetry of the microstructure, i.e., on the position of the test sample concerning the three main axes: L, R, and T. Therefore, even within the same type of wood, we can have large scatter in the results. Deflection is one of the most frequently determined mechanical properties of wood. It is most often carried out by the method of bending in three or four points. Based on the results obtained, we can say that for spruce samples, the greater the bending force, the greater the modulus of elasticity. This is confirmed by the results shown in the table, sample number 5, 13, 25 and (Table 4 and Fig. 4.) and the Hooke’s diagram. The deformation of the wooden samples was measured using a four-point bending test, with a measurement accuracy of 0.1 mm. This level of precision was achieved through the use of high-quality testing equipment, including a precision load cell and displacement transducer. To ensure accurate and reliable results, each sample was tested
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multiple times, and the average value was used for further analysis. The resulting data were analyzed using advanced statistical techniques to determine the longitudinal modulus of elasticity with a high degree of accuracy.
Fig. 4. Display of measured tensile break forces (experiment 5, 13 and 25).
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Table 4. Presentation of the obtained results of deflection, maximum bending force, and modulus of elasticity for each tested sample. N
Type of wood
Maximum displacement [mm]
Maximum bending force (N)
Modulus of elasticity (N/mm2 )
1
Spruce (A)
4.23
2929
13181
2
Spruce (A)
4.34
1489
10879
3
Spruce (A)
5.01
2417
10391
4
Spruce (A)
4.20
2521
11269
5
Spruce (A)
4.4
2245
10776
6
Spruce (B)
5.21
3104
13021
7
Spruce (B)
6.02
2579
12016
8
Spruce (B)
5.97
2009
11246
9
Spruce (B)
6.68
3401
11071
10
Spruce (B)
5.23
2962
11776
11
Oak A
7.23
3906
14098
12
Oak (A)
7.89
4437
13491
13
Oak (A)
7.34
3991
15163
14
Oak (A)
7.10
3852
14800
15
Oak (A)
6.94
3971
15755
16
Oak (A)
8.23
4239
14054
17
Oak (B)
7.21
4012
14876
18
Oak (B)
7.05
4679
15983
19
Oak (B)
8.36
3893
15872
20
Oak (B)
8.29
4781
15763
21
Beech (A)
8.59
3398
11108
22
Beech (A)
8.03
3774
10839
23
Beech (A)
8.52
3585
10007
24
Beech (A)
8.04
3872
10242
25
Beech (A)
8.21
3551
9881
26
Beech (B)
8.48
3367
10087
27
Beech (B)
8.12
3975
9754
28
Beech (B)
8.93
3891
9734
29
Beech (B)
8.21
3692
9641
30
Beech (B)
8.40
3925
10760
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The values of the results for the maximum force of the sample, shown in Table 4, in the longitudinal direction are the highest for oak and range from 3991.91 to 4437.97 N, while for spruce is the smallest, ranging from 1489.49 to 2929.19 N. For the first group of fir trees, where the position of the knots concerning the bending force is 0°, the maximum modulus of elasticity is 13181 N/mm2 . Moreover, for the second group of the same species, where the position of the knots with the bending force is 45° is 13021 N/mm2 . The position of the ribs concerning the bending force of 45° in all tested samples that have a higher modulus of elasticity, which precisely indicates a greater homogeneity of the structure than the first group. The analysis confirmed that the mechanical properties of the examined types of wood do not depend only on the density structure of the wood and the effect of force, but also on the position of the logs concerning the bending force.
4 Conclusion In conclusion, this paper presented an experimental study on the determination of the longitudinal modulus of elasticity, deflection, and maximum force of three types of wood (spruce, beech and oak) using the four-point bending method according to the BAS EN 408+A1 standard. The results showed that the modulus of elasticity, deflection, and maximum force are dependent on the type of wood and its structure. The study found that oak had the highest maximum force while spruce had the lowest. The position of the knots and the microstructure symmetry also had an impact on the modulus of elasticity and deflection. These findings highlight the importance of considering the structural properties of wood when selecting materials for construction and design purposes.
References 1. Kržišnik, D., et al.: Durability and mechanical performance of differently treated glulam beams during two years of outdoor exposure. Drvna industrija 71(3) (2020) 2. Hasanagi´c, R., Ganguly, S., Bajramovi´c, E., Hasanagi´c, A.: Mechanical properties changes in fir wood (abies sp.), linden wood (tilia sp.), and beech wood (fagus sp.) subjected to various thermal modification process conditions. IOP Conf. Ser. Mater. Sci. Eng. 1208(1), 012025 (2021) 3. Hasanagi´c, R.: Optimization of thermal modification of wood by genetic algorithm and classical mathematical analysis. J. For. Sci. 68(2), 35–45 (2022). https://doi.org/10.17221/95/202 1-JFS 4. EN 408: Timber structures—structural timber and glued laminated timber—determination of some physical and mechanical properties. Comite´ Europe´en de Normalisation CEN (2003) 5. EN 338: Structural timber–strength classes. Comite´ Europe´en de Normalisation CEN (2003) 6. EN 384: Structural timber-determination of characteristic values of mechanical properties and density. Comite´ Europe´en de Normalisation CEN (2004) 7. Fewell, A.R.: Machine stress grading of timber in the United-Kingdom. Holz Roh Werkst 40(12), 455–459 (1982) 8. Glos, P.: Step A6: Festigkeitssortierung. In: Holzbauwerke nach Eurocode 5, Step 1: Bemessung und Baustoffe, Informationsdienst Holz. Arbeitsgemeinschaft Holz e. V, Du¨sseldorf, Germany (1995)
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9. Hearmon, R.F.S.: Vibration testing of wood. Forest Prod. J. 16(8), 29–40 (1996) 10. Kollmann, F., Krech, H.: Dynamische Messung der elastischen Holzeigenschaften und der Da¨mpfung. Holz Roh-Werkst 18(2), 41–54 (1966) 11. Thelandersson, S.: Step 3/11: Deformations in timber structures. In: Holzbauwerke nach Eurocode 5, Step 3: Grundlagen, Entwicklungen, Ergänzungen, Informationsdienst Holz. Arbeitsgemeinschaft Holz e. V, Düsseldorf (1995) 12. Ehlbeck, J., Werner, H.: Coniferous and deciduous embedding strength for dowel-type fasteners. Paper 25-7-2. In: Proceedings of CIB-W18 Meeting 25, August 1992, A˚ hus, Sweden (1992) 13. Gindl, W., Teischinger, A.: Axial compression strength of Norway spruce related to structural variability and lignin content. Composit. Part a. Appl. Sci. Manufact. 33(12), 1623–1628 (2002) 14. Fajdiga, G., Rajh, D., Neˇcemer, B., Glodež, S., Šraml, M.: Experimental and numerical determination of the mechanical properties of spruce wood. Forests 10, 1140 (2019) 15. Daudeville, L.: Fracture in spruce: experiment and numerical analysis by linear and non linear fracture mechanics. Holz als Roh-und Werkstoff 5, 425–432 (1999) 16. Hasanagi´c, R. et al.: Experimental and numerical determination of the longitudinal modulus of elasticity in wooden structures. Drewno 65(210) (2022). https://doi.org/10.12841/wood. 1644-3985.421.10 17. Andor, K., Lengyel, A., Polgár, R., Fodor, T., Karácsonyi, Z.: Experimental and statistical analysis of spruce timber beams reinforced with CFRP fabric. Constr. Build. Mater 99, 200– 207 (2015) 18. Raftery, G.M., Kelly, F.: Basalt FRP rods for reinforcement and repair of timber. Compos. Part B Eng. 70, 9–19 (2015) 19. Winter, W., Tavoussi, K., Pixner, T., Parada, F.R.: Timber-steel-hybrid beams for multi-storey buildings. In: Proceedings of the World Conference on Timber Engineering 2012 (WCTE 2012), Auckland, New Zealand, 15–19 July 2012 20. Thorhallsson, E.R., Hinriksson, G.I., Snæbjörnsson, J.T.: Strength and stiffness of glulam beams reinforced with glass and basalt fibres. Compos. Part B Eng. 115, 300–307 (2017) 21. Nadir, Y., Nagarajan, P., Ameen, M.: Flexural stiffness and strength enhancement of horizontally glued laminated wood beams with GFRP and CFRP composite sheets. Constr. Build. Mater. 112, 547–555 (2016) 22. Šubic, B., Fajdiga, G., Lopatiˇc, J.: Bending stiffness, load-bearing capacity and flexural rigidity of slender hybrid wood-based beams. Forests 9 (703) (2018) 23. BAS EN 408+A1: Timber structures. Structural timber and glued laminated timber. Determination of some physical and mechanical properties, Institut za standardizaciju BiH (2013) 24. Chu, D., et al.: Application of temperature and process duration as a method for predicting the mechanical properties of thermally modified timber forests 13(2), 217 (2022). https://doi. org/10.3390/f13020217
Contribution to the Diagnostics of the Internal Combustion Engine Timing Mechanism Chain Jasmin Šehovi´c(B) Faculty of Mechanical Engineering, Department for Engines and Vehicles, University of Sarajevo, Vilsonovo šetalište 9, Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. Timing mechanism is one of the key systems on an internal combustion engine. Its correct and precise operation is of great importance for the realization of physical processes in the combustion chamber of the IC engine. Any irregularity in the operation of the timing mechanism leads to a deviation of the engine working parameters from the nominal ones. In this paper, a quick method used to detect operational malfunctions of the IC engine timing mechanism chain is demonstrated. A comparison was made between the vibrations caused by the malfunction of the timing chain and the vibrations of the proper working chain by measuring vibrations in the form of vertical accelerations at the characteristic positions of the IC engine. MEMS accelerometers were used for the measurement. Vibrations were measured via these devices at characteristic positions of the engine before dismantling the chain and again after installing a new chain. Based on the measured results, a clear difference can be seen between a proper working chain and a faulty chain in the timing mechanism of the IC engine. Keywords: IC engine · timing chain · timing mechanism · vibrations · diagnostics
1 Introduction From the beginning of the development of the internal combustion engine until today, one of the systems that has remained an integral part of the engine is the timing mechanism. The basic tasks of the timing mechanism are: – to provide charging of the cylinder with fresh mixture or with air, with an optimal volumetric efficiency; – to enable the removal of exhaust gases as efficiently as possible (low coefficient of residual gases) – to seal the compression chamber during compression, combustion and expansion by using valves [1]. Depending on the characteristics of the engine (displacement, power, purpose, etc.), the performance of the timing mechanism may vary. What is certain, however, is that reliable operation of the timing mechanism must be ensured. Recently, a greater number © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 476–485, 2023. https://doi.org/10.1007/978-3-031-43056-5_35
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of engine manufacturers have been using a chain to drive the timing mechanism instead of the classic toothed belt. The idea is to use a chain to drive the timing mechanism in long-term operation to reduce engine maintenance costs, to subject the engine to greater loads in order to increase power. Figure 1 shows a scheme of several methods of driving the timing mechanism of the IC engine by using gears and chains.
Fig. 1. Schematic representation of different variants of the timing mechanism drive [2].
In Fig. 1, positions 1 and 2 show gear drive, position 3 shows a simple chain drive, position 4 shows a combined gear and chain drive. An automatic chain tensioner with a excenter cam whose role is to tighten the chain with the prescribed force can also be seen. Positions 5 and 6 show the camshaft drive of the timing gear using bevel and cone gears. Position 7 shows the drive of two camshafts using a chain and sprocket. What can be noticed is that the chain is tensioned by a spring tensioner. Nowadays the timing chain mechanisms generally use hydraulic spring tensioners. Their role is to tension the chain using engine oil pressure. Engine manufacturers do not provide precise data on the service life of the chain. Some available research indicate that the chain of the timing mechanism should have a service life, just as the engine itself [3, 4]. According to [3, 4], the chain should withstand the service life of 250000 km traveled by a vehicle, or even more. However, empirical data shows that in most cases, the chain begins to show failure symptoms long before the specified mileage, which leads to the conclusion that it should be replaced. Some of the symptoms that the chain is close to the end of its service life are: the engine skips the ignition of the cylinders, the indication of metal splinters in the engine oil, the inability to start the engine, the Check Engine warning light on, the indication of noise and vibrations when the engine is idling (no load). Most available diagnostic methods require disassembly of the timing chain assembly [5, 6]. Such tests involve disassembling the chain from the IC engine and destroying the chain in order to determine its condition. The fact that changing the length of the chain during long-term operation results in a change in the stiffness of the chain as a connecting element, which significantly changes the vibration conditions of the entire mechanism, should be taken into account. This is an indicator to control the operation of the timing mechanism. Thus, disassembly of the engine and possible damage to the timing chain assembly are avoided.
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The proposed method is based on the measurement of engine vibrations when engine is idling after a cold start by using accelerometers. Accelerometers based on MEMS technology can be used to measure vibrations, which have already proven their justification for use in various measurements on motor vehicles [7, 8].
2 Description of the Experimental Measurement Method For experimental research, a simple device was used to measure vertical accelerations on the housing of the timing chain and on one of the engine mounts. It is a SlamStick X triaxial accelerometer, manufactured by Mide Technologies, USA. The accelerometer and its dimensions are shown in Fig. 2. In Fig. 2, the dimensions of the device are given in inches, while in brackets dimensions are shown in millimeters.
Fig. 2. Accelerometer with its dimensions [9].
The installation of these devices on the IC engine mount and timing chain housing is shown below. The engine on which the tests were performed is a turbocharged otto engine with a displacement of 1197 cm3 , built as an in-line four-cylinder engine with a power of 63 kW. At the time of conducting the experiment, the vehicle that the engine was mounted on, had traveled 70000 km and was 10 years old. It should be noted that on the selected vehicle, the replacement of the timing chain assembly was planned according to the recommendation of the authorized vehicle service. Figure 3 shows the positions of accelerometers for recording engine vibrations. All measurements were carried out at the cold start of an engine at idle speed (minimum number of revolutions). The measurement of vibrations in the form of vertical acceleration at the characteristic positions shown in Fig. 3 was carried out in two segments. The first segment refers to the measurement before replacing the timing chain. After measuring and collecting measurements, the timing chain assembly of the IC engine timing mechanism was disassembled in order to replace the old chain with a new one. Figure 4 shows the timing chain assembly with guides before disassembly.
Contribution to the Diagnostics of the Internal Combustion Engine
Fig. 3. Accelerometers installed on the IC engine.
Fig. 4. Timing chain before disassembly from the engine.
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Figure 5 shows a comparison of the original timing chain from the engine that was dismantled and the new timing chain that was then installed on the engine.
Fig. 5. Old and new engine timing chain.
Figure 5 shows the obvious difference in length between the old and the new timing chain of the IC engine timing mechanism, which leads to the conclusion that the old chain was elongated during its long-term operation. After installing the new chain, sprocket and hydromechanical chain tensioner, the engine vibration measurements were repeated at the same characteristic positions as in Fig. 3. Repeated measurements were performed to confirm the difference in vibration measurement results at characteristic positions before and after replacing the timing chain. The results obtained before and after the replacement were further processed in order to reach a conclusion about the proposed method to check the functionality of the timing chain. The goal is to establish a criterion to evaluate the condition of the timing chain by analyzing the results of the recorded vibrations. The processed results are shown in the next chapter.
3 Analysis of the Measured Results The SlamStick Lab software was used to acquire and process the measurement results from the previous chapter. In order to get a better overview and to observe the differences between a proper working and a faulty timing chain, below are the processed measurement results at the characteristic positions of the engine, before and after replacing the timing chain. Figure 6 shows the comparative measured results of the vertical acceleration on the engine mount before and after replacing the timing chain. Figure 7 shows the comparative results of the measured vertical accelerations at the position of the timing chain housing, before and after the replacement of the timing chain assembly.
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Fig. 6. Measured accelerations at the engine mount before and after chain replacement.
Fig. 7. Measured accelerations at the timing chain housing before and after chain replacement.
By observing the measured data of vertical acceleration on the engine mount (Fig. 6) and the timing chain housing (Fig. 7), it can be concluded that there is an obvious difference in the amplitude and duration of the oscillations at the observed positions after starting the IC engine. At both positions on the engine, the same character of oscillations can be seen, both with the old chain (before replacement) and with the new
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chain (after replacement). This difference is visible approximately for 3 s after starting the engine. After that, the oscillations recorded at these positions almost coincide for both cases of the chain condition (before and after the replacement). For a more detailed analysis, a part of the oscillation in the first four seconds from the diagrams in Figs. 6 and 7 are shown. Figures 8 and 9 show the singled out results of measured vertical acceleration oscillations at characteristic positions on the engine, for the first four seconds of engine operation.
Fig. 8. Singled out data for the first four seconds of engine operation at the engine mount.
Fig. 9. Singled out data for the first four seconds of engine operation at the timing chain housing.
When the singled out results of the measured accelerations on the engine mount and the engine timing chain housing are analyzed, the difference in the amplitude and frequency of the oscillations in these positions is obvious. The segments on the diagram taken with the old chain (black color) that represent the knocking of the chain in
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the housing caused by its elongation beyond the intended length during operation can be clearly seen. For better overview, more detailed diagrams are provided below. The measurement results from the second (2nd ) to the third (3rd ) second from the previous diagrams will be shown. Figures 10 and 11 show the detailed results of acceleration measurements at the characteristic positions of the engine from Fig. 3, for both the old and the new chain. The displayed results are for a period of one second (the time period from the second to the third second shown in previous diagrams).
Fig. 10. Singled out results of measurements for one second on the engine mount.
Fig. 11. Singled out results of measurements for one second on the timing chain housing.
All the measurement results shown so far provide a clear insight into the difference between a new chain that is correctly tensioned at every moment of operation and a worn out chain which has endured elongation due to long-term operation. A more detailed discussion of the results is given in the conclusion of the paper.
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4 Conclusion The paper shows the impact of engine long-term operation (service life) on the presence of vibrations caused by a faulty (worn out) chain of the IC engine timing mechanism. In order to compare a timing chain that has endured elongation during its long-term operation and a timing chain that is new, measurements of vibrations in the form of vertical accelerations were made on the engine mount and the timing chain assembly housing. All measurements were carried out at the cold start of an engine at idle speed (minimum number of revolutions). It is evident that the timing chain which has been worn out and elongated due to long-term operation appears to show knocking after engine start for the next three to four seconds. From the diagrams shown (Figures 6 ÷ 11), a change in the amplitude and the frequency of oscillations between the new and the used (old) chain can be observed. This can be clearly seen if the analysis of the presented results is conducted using the Fast Fourier Transformation. Figure 12 shows the dependence of the amplitude on the oscillation frequency for the two observed cases shown for the engine mount (Fig. 10).
Fig. 12. The results of the Fourier transformation of the measured accelerations at the engine mount.
Figure 12 shows the obvious difference in the amplitude and frequency of the oscillations on the engine mount during the engine operation with a worn out and a new timing chain. In both cases, one dominant oscillation amplitude and frequency are clearly visible. However, with the old (worn out) timing chain, a series of low-amplitude oscillations at different frequencies are noticeable, while with the new chain, this is less noticeable. Another reason for this is that the old timing chain has lost its tension, and because of this, the engine starts with a number of revolutions that is higher than when the chain is properly tensioned, which leads to the knocking of the chain and an increase in the frequency of oscillation. Starting the engine with such an increased number of revolutions is by no means favorable for the exploitation of the engine because it leads to unnecessary mechanical stresses on the engine at the cold start, when the engine has not yet reached its optimal operating regime.
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By measuring the vibrations of the timing chain housing or engine mount on a large number of vehicles of the same type, the service shop can define criteria (eg time) when it is necessary to replace the chain (time t2 , Fig. 6) in relation to the newly installed chain (time t1 , Fig. 6). All the presented results are in favor of the fact that this method of diagnostics can be considered a quick method of checking the condition of the IC engine timing mechanism chain assembly of an engine that is in use in a vehicle. The advantage of this method is that no additional disassembly of the given engine or use of specialized tools is required.
References 1. Filipovi´c, I.: Cestovna vozila, Mašinski fakultet Sarajevo, Sarajevo (2012) 2. Garrett, T.K., et al.: The Motor Vehicle. Reed Educational and Professional Publishing Ltd, Oxford (2001) 3. Mulik, A.V., Gujar, A.J.: Literature review on simulation and analysis of timing chain of an automotive engine. Int. J. Eng. Res. Appl. 6(8), 17–21 (2016) 4. Paulovics, L., et al.: Timing chain wear investigation methods – review. FME Trans. 20, 461– 472 (2022) 5. Sappok, D., Sauer, B.: Wear measurement on chain joint components using a roundness instrument. Periodica Polytechnica Mech. Eng. 59(2), 51–59 (2015) 6. Becker, A., Sauer B.: Wear investigations on timing chains using a chain joint tribometer. 73rd STLE Annual Meeting And Exhibition, May 20–24, 2018 Minneapolis, Minnesota, USA. Society of Tribologists and Lubrication Engineers, Minneapolis (2018) 7. Šehovi´c, J., Filipovi´c, I., Pikula, B.: Experimental Determination of Non-Linear Characteristics of the Passenger Vehicle Suspension System. Transactions of FAMENA XLIV(2), pp. 13–22 (2020) 8. Šehovi´c, J., Filipovi´c, I., Gebert, K., Pikula, B.: Novel Approach to Determine Effective Flow Areas at Shock Absorber Restrictions. SAE Technical Paper 2021-01-5059 (2021) 9. Šehovi´c, J.: Application of MEMS accelerometers in measuring vertical oscillations in motor vehicles. In: Ademovi´c, N., Mujˇci´c, E., Akšamija, Z., Kevri´c, J., Avdakovi´c, S., Voli´c, I. (eds.) IAT 2021. LNNS, vol. 316, pp. 313–320. Springer, Cham (2021). https://doi.org/10.1007/9783-030-90055-7_24
Parametric Optimization of MQCL-Assisted Turning Operation of X5CrNi18–10 Steel Using Definitive Screening Design and Multi-Criteria Decision-Making Approach Adnan Mustafi´c(B) Faculty of Mechanical Engineering, University of Tuzla, Tuzla, Bosnia and Herzegovina [email protected]
Abstract. In this paper an investigation and selection of optimal minimum quantity cooling and lubrication (MQCL) process parameters on the surface roughness parameters and the cutting forces while turning X5CrNi18-10 grade steel has done. The effects of the selected MQCL parameters such as vegetable-based cutting oil- and water flow rates, aerosol impact position to the cutting tool, number and stand-off distance of the selected nozzles, and the physical properties of the cutting oils were investigated using a new class of experimental design – definitive screening design (DSD). Finally, multi-response optimization was performed using the grey relational analysis (GRA) method coupled with criteria importance through-inter criteria correlation (CRITIC). Analysis of variance (ANOVA) results show that the oil- and water flow rates and the used vegetable-based oils significantly affects the weighted grey relational grade (GRG). Further, the results were compared with other existing multi-response optimization methods and induced overall good results. Keywords: Minimum Quantity Cooling and Lubrication (MQCL) · Definitive Screening Design (DSD) · Multi-Criteria Decision-Making (MCDM) method
1 Introduction To improve the machinability of stainless-steel grades, it is common to flood the cutting area with specialized liquid for metal processing. However, the traditional mineral oilbased metalworking fluids pose risks to both the environment and human health, a can significantly increase product costs. As a result, they are considered as a major unsustainable element of the machining process, and alternative cooling and lubrication techniques are increasingly being developed. In this regard, one of the research directions is to investigate and apply alternative cooling and lubrication techniques as a possible environmental-friendly solution in the machining process of stainless-steel grades [1]. The minumum quantity lubrication (MQL) and minimum quantity cooling and lubrication (MQCL) techniques, also known as near-dry machining, delivers extremely small amounts of biodegradable metalworking fluid, measured in millilitres per hour, into the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 486–500, 2023. https://doi.org/10.1007/978-3-031-43056-5_36
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cutting zone instead of using conventional circulating emulsion flood systems [1]. In combination with nano cutting fluids, MQCL have been proven as alternative solution for machining difficult-to cut materials while retaining an environmentally friendly characteristic [2]. The influence of technological parameters in machining processes using MQL has been investigated in many studies. Most studies show that the MQL method gives better results in terms of surface quality and tool life than flood conditions [3, 4]. Smaller cutting forces are reported under the MQL condition when compared to dry and flood machining [5, 6]. Therefore, it seems that the implementation of MQCL machining as an alternative to traditional machining strategies is an important research area. A large number of formulation and processing parameters influence the overall performance of MQCL assisted machining. Thus, it becomes extremely difficult to study the effect of each parameter and interaction among them through the conventional approach. Therefore, one of the most important tasks in the MQCL assisted machining process is to evaluate optimal parameters in order to obtain maximum efficiency in the manufacturing process. Multi-criteria decision making (MCDM) methods combined with weight assessment methods are widely uses and proved to be efficient in multi-objective optimization of machining operations [7]. The most common MCDM methods are the technique for order of preference by similarity to ideal solution (TOPSIS), multi-objective optimization by ratio analysis (MOORA), VIše Kriterijumska Optimizacija i KOmpromisno Rešenje (VIKOR), weighted aggregated sum product assessment (WASPAS), additive ratio assessment (ARAS), complex proportional assessment (COPRAS) and stepwise weight assessment ratio analysis (SWARA), grey relational analysis (GRA) and combinative distance-based assessment (CODAS) [7, 8]. In this study, unlike similar publications in which MQCL parameters were investigated in the same time as the cutting parameters, the scope was widened and only the main cooling/lubrication parameters affecting the efficiency of the system were tested. For these purposes, a new class of experimental designs, the definitive screening design (DSD) method was used for the experimental plan. Definitive screening design is a three-level fractional factorial design, which has been developed recently [9]. It performed better than other traditional experimental designs, such as full factorial design and response surface methodology (RSM), in estimating the main effect, interaction, and quadratic effect [10, 11]. In this study, for the multi-objective optimization of MQCL process parameters in the turning process of X5CrNi18-10 steel grade, the grey relational analysis (GRA) method coupled with criteria importance through inter-criteria correlation (CRITIC) has been utilized and compared with some other multi-criteria decision-making methods.
2 Materials and Methods The turning experiments were performed on a conventional lathe machine Potisje PA 501A. All tests were conducted under constant settings of cutting speed, feed rate and depth of cut, by using an advanced MQCL system, produced by Daido Metal (Japan). This MQCL system has the ability to independently adjust oil and water flow rates which are in the range of 10–50 (ml/h) and 300–1800 (ml/h), respectively. A commercial
488
A. Mustafi´c
Iscar IC807 grade with TiAlN + TiN coated carbide insert of CNMG 120408-WG and PCLNR 2020K-12 tool holder were selected for the turning tests. In order to minimize the influence of cutting tool wear on the experimental results, each set of turning experiments was conducted using a new insert edge. A quenched and tempered X5CrNi18-10 (AISI 304) austenitic stainless-steel bar was used as the workpiece material. All machining tests were carried out on a of 70 (mm) diameter bar with separated 15 (mm) long segments for each cutting test. The cutting forces were measured using a Kistler 8257B dynamometer (Kistler, Wintherthur, Switzerland) connected to an amplifier type 5070A and a computer equipped with manufacturer’s DynoWare software. The experimental setup for the turning experiments is given in Fig. 1. Surface roughness parameters were measured using the Mitutoyo surface tester (Surftest SJ-301, Mitutoyo Corporation, Japan) as shown in Fig. 2.
Fig. 1. Experimental setup for the MQCL assisted machining test.
Fig. 2. Surface roughness measurement of the treated surface using the Mitutoyo SJ-301.
Before the measurement, the device was set for the planned measurement of parameters according to DIN 1990 (DIN EN ISO 4287: 1998) standard. The adopted reference test length for all measurement tests was 4 (mm) for the selected profile filter lc = 0.8 (mm) × 5. A total of five measurements were performed for each processed segment at
Parametric Optimization of MQCL-Assisted Turning Operation
489
different locations, and the arithmetic mean was adopted as a reference value for data processing. Selection of input MQCL parameters and levels was carried out through literature review, preliminary experimental tests, manufacturer recommendations and equipment limitations. Two vegetable based MQL oils, namely Castrol Hyspray V1066 and Menzel INDUOIL HL95 were used because of its biodegradability and good lubrication properties with different kinematic viscosity of 39 mm2 /s and 95 mm2 /s at 40 °C, respectively. They are supplied at 2 bars to the mixing chamber (mixed with tap water) and delivered through one or two nozzles inclined at approximately 30° to the cutting tool/workpiece interface at various nozzle stand-off distances. The cutting parameters for cutting speed 113 (m/min), feed rate 0,195 (mm/rev) and depth of cut of 1 (mm) were set based on the recommendations of the cutting tool manufacturer for medium cutting conditions. These parameters were kept constant in all experiments in order to investigate only the effect of MQCL cooling/lubrication parameters on the machinability of X5CrNi 18-10 (AISI 304) stainless steel. The selected process parameters and their levels are shown in Table 1. Table 1. Experimental design settings. Symbol
MQCL Parameters
Units
Level Values −1
0
1
A
Oil flow rate
ml/h
10
30
50
B
Water flow rate
ml/h
300
1050
1800
C
Stand-off distance
mm
30
75
120
D
Oil type/viscosity
mm2 /s
Castrol (39)
Menzel (95)
E
Aerosol impact position
/
Flank face
Rake face
F
Nozzle number
/
One
Two
All experimental runs were designed based on the definitive screening design (DSD) for three numerical and three categorical parameters. Compared to traditional methods, it reduces the experimental runs and, therefore, reduces experimentation time and cost. For instance, there are 160 experimental runs for a problem having three categorical (two level) and three continues (three level) factors using the central composite design (CCD), or 120 experimental runs using the Box-Behnken, or 36 runs using a L36 Taguchi orthogonal array. However, for DSD, there are a total of 14 experimental runs needed. Experimental data collected though the DSD are presented in Table 2.
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A. Mustafi´c
Table 2. Experimental run based on the definitive screening design and measured responses. Exp. Runs
A
B
C
D
E
F
Fx (N)
Fy (N)
Fz (N)
Ra (μm)
Rz (μm)
Rq (μm)
1
0
1
1
1
1
1
307,8
243,4
567,9
1,68
11,39
2,08
2
0
−1
−1
−1
−1
−1
293,4
230,9
576
1,06
7,86
1,32
3
1
0
−1
1
1
−1
283,5
224,8
562,2
1,16
8,68
1,46
4
−1
0
1
−1
−1
1
298,7
235,4
567,5
1,51
10,80
1,89
5
1
−1
0
−1
1
1
272,5
223,6
538,5
1,13
7,56
1,37
6
−1
1
0
1
−1
−1
306,3
266,5
603
1,34
14,68
1,76
7
1
1
-1
1
-1
1
292,5
224,4
567,4
1,32
8,74
1,6
8
−1
−1
1
−1
1
−1
277
258,5
556,6
1,13
7,56
1,37
9
1
1
1
−1
1
−1
287,8
222,2
565,7
1,31
8,34
1,6
10
−1
−1
−1
1
−1
1
297,5
227,6
568,2
1,32
8,52
1,62
11
1
−1
1
1
−1
1
282
227,4
557,8
1,16
8,86
1,41
12
−1
1
−1
−1
1
−1
296,3
228,2
590,1
1,39
9,66
1,7
13
0
0
0
−1
−1
−1
291,6
227,8
577,4
1,21
8,23
1,51
14
0
0
0
1
1
1
319,9
253,1
592,1
1,08
7,31
1,31
3 Optimization Methodology Simultaneous optimization is highly desirable when conflicting objective function exist. The objective functions in the present study are minimization of surface roughness parameters (Ra, Rz, Rq) and the cutting force components (Fx, Fy, Fz). For multiobjective optimization of the MQCL process parameters in the turning process of X5CrNi18-10 (AISI 304) steel grade, the grey relational analysis (GRA) coupled with criteria importance through inter-criteria correlation (CRITIC) method has been utilized and compared with other multi-criteria decision-making (MCDM) methods. The GRA method is widely applied in complex and multivariable systems, where the relationship among various parameters is usually unclear. Grey relational analysis is an impacting measurement method in so called “grey system theory”, which analyzes uncertain relations between one main factor and all other factors in an investigated system [12]. In this regard, the data to be used in GRA must be preprocessed for further analysis [13]. Preprocessing the raw data is a process of converting an original sequence in decimal sequences between 0–1. For data preprocessing in the grey theory, the lower surface roughness and cutting forces are indication of better performance in the MQCL assisted turning process.
Parametric Optimization of MQCL-Assisted Turning Operation
491
The steps followed for GRA were adopted from the study [12] as follows: Step 1: Construct the decision matrix G having the order n × m of measured responses (m) corresponding to each experimental run (n) having a combination of different levels as given in Table 2 using Eq. (1). ⎤ ⎡ g11 g12 · · · g1n ⎢ g21 g22 · · · g2n ⎥ ⎥ ⎢ (1) G = gij n×m = ⎢ . . . .. ⎥ . . . ⎣ . . . . ⎦ gm1 gm2 · · · gmn
Step 2: For the calculation of normalized values of the surface roughness parameters and the cutting forces, the corresponding characteristic is “lower-is-better”, using Eq. (2): xi (k) =
maxηi (k) − ηi (k) maxηi (k) − minηi (k)
(2)
whilst on the other hand, the “higher-is-better” characteristic can be expressed as: Eq. (3): xi (k) =
ηi (k) − minηi (k) maxηi (k) − minηi (k)
(3)
where xi (k) is the normalized value, minηi (k) is the minimal value of ηi (k) for the kth response and maxηi (k) is the maximal value, respectively. The normalized values are ranged between 0 and 1; the larger values yield better performance and the ideal value should be equal to 1. Step 3: Compute the grey-relational coefficient (GRC) as the relationship between the ideal and actual normalized response using Eq. (4): ξi (k) =
min + ζ max 0i (k) + ζ max
(4)
where 0i (k) is the absolute value of the difference between targeted sequence and the comparative sequence and where min and max take the minimum and maximum obtained values. ζ is the distinguishing coefficient and its values lies between 0 and 1. In this particular case, the value of ζ is taken as 0,5 since it provides a balanced effect on the grey relational coefficient. Step 4: Determine the grey relational grade (GRG) as a weighted sum of the grey relational coefficients using Eq. (5): γi =
n
λj · ξi (k)
(5)
k=1
where λj is the weight of response such that nj=1 λj = 1. Step 5: Finally, rank the experimental run according to the decreasing values of the GRG. The experimental run with the highest GRG represents the best experimental run. However, the optimal MQL processing parameter levels can be obtained by determining the average values of the GRG at each level for each parameter. The highest value of the average GRG among the investigated parameter levels corresponds to optimal levels.
492
A. Mustafi´c
Weights of responses were determined according to the CRITIC method. The steps for CRITIC are as follows: Step 1: Calculate the correlation among the normalized responses (obtained in the 1st step of GRA method) using Eq. (6).
n i=1 μij − μj (μik − μk ) αjk = (6)
2 n n 2 μ − μ − μ (μ ) ij j ik k i=1 i=1 Step 2: Determine the degree of conflict created by response k concerning other responses by applying Eq. (7). υj =
n
1 − αjk
(7)
k=1
Step 3: Determine the degree of contrast (standard deviation) using Eq. (8).
2 n i=1 μij − μj σj = n
(8)
Step 4: Combine both degrees of conflict and contrast to obtain weights of responses using Eq. (9). ζj = υj · σj
(9)
where ζj represents the information emitted (i.e. weights); higher values of ζj represent a higher response weight. Step 5: Finally, weights of responses are normalized using Eq. (10). ζj λj = n
k=1 ζk
(10)
4 Multi-response Optimization of MQCL Parameters As mentioned in the previous sections, the multi-response optimization was performed based on the grey relational analysis coupled with criteria importance through intercriteria correlation (CRITIC) method. Table 3 and Table 4 show the normalized, greyrelational coefficients and ranks determined according to Eqs. (2)–(5). As shown in Table 4, the highest GRG value obtained is 0,928 at experimental run 5, and therefore it is ranked as first. The levels for the investigated parameters can be shortly given as A3-B1-C2-D1-E2-F2. The degree of contrast and conflict, as well as the weights of the individual responses were determined using Eqs. (7)–(10). As shown in Table 5, the highest weight was attributed to the passive force - Fy (0,2372) and therefore ranked as first, followed by the arithmetical mean deviation of the roughness profile – Ra (0,1721), root mean square
Parametric Optimization of MQCL-Assisted Turning Operation
493
Table 3. Normalized response values. Exp. Runs
Fx
Fy
Fz
Ra
Rz
Rq
1
0,255
0,521
0,544
0
0,447
0
2
0,559
0,804
0,419
1
0,926
0,990
3
0,768
0,941
0,633
0,846
0,815
0,814
4
0,447
0,702
0,550
0,268
0,527
0,248
5
1
0,968
1
0,894
0,966
0,932
6
0,287
0
0
0,553
0
0,423
7
0,578
0,950
0,552
0,585
0,806
0,632
8
0,905
0,181
0,719
0,894
0,966
0,932
9
0,677
1
0,578
0,593
0,861
0,632
10
0,473
0,878
0,540
0,581
0,837
0,596
11
0,800
0,883
0,701
0,833
0,790
0,876
12
0,498
0,865
0,200
0,459
0,681
0,502
13
0,597
0,874
0,397
0,756
0,876
0,749
14
0
0,302
0,169
0,976
1
1
Table 4. Grey relational coefficients, weighted grey relational grade and rank. Exp. Runs
Fx
Fy
Fz
Ra
Rz
Rq
GRG
Rank
1
0,402
0,511
0,523
0,333
0,475
0,333
0,433
13
2
0,531
0,718
0,462
1
0,871
0,981
0,761
2
3
0,683
0,895
0,576
0,764
0,730
0,729
0,744
3
4
0,475
0,627
0,527
0,406
0,514
0,399
0,500
12
5
1
0,941
1
0,826
0,936
0,880
0,928
1
6
0,412
0,333
0,333
0,528
0,333
0,464
0,399
14
7
0,542
0,910
0,527
0,547
0,721
0,576
0,656
9
8
0,840
0,379
0,641
0,826
0,936
0,880
0,716
5
9
0,608
1
0,542
0,552
0,782
0,576
0,698
6
10
0,487
0,804
0,521
0,544
0,754
0,553
0,622
10
11
0,714
0,810
0,626
0,750
0,704
0,802
0,742
4
12
0,499
0,787
0,385
0,480
0,611
0,501
0,561
11
13
0,554
0,798
0,453
0,672
0,801
0,666
0,666
7
14
0,333
0,418
0,376
0,953
1
1
0,659
8
494
A. Mustafi´c Table 5. Weight determination of responses based on CRITIC method.
υj
2,539
3,403
2,841
2,808
2,232
2,511
σj
0,26804
0,32407
0,25385
0,28497
0,26895
0,29646
ζj
0,68055
1,10282
0,72119
0,80019
0,6003
0,74442
λj
0,14637
0,23719
0,15511
0,1721
0,12911
0,16011
Rank
5
1
4
2
6
3
deviation of the surface roughness profile – Rq (0,1601), cutting force – Fz (0,1551), feed force – Fx (0,1464), and maximum high of the roughness profile – Rz (0,1291). However, to find the experimental run with optimal parameter levels, the average value of the weighted GRG at each level for each parameter needs to be determined as given in Table 6. Finally, based on the results obtained on the average weighted GRG, optimal levels of the parametric analysis can be presented as A3-B1-C1-D1-E2-F1. These findings are in total agreement with author’s research study observed through regression analysis [14]. The main effect plots of investigated MQCL parameters were generated to define the optimal process conditions that lead to the highest values of the weighted GRG as shown in Fig. 3. Table 6. Optimal levels based on average GRG. Level
A
B
C
D
E
F
1
0,5597
0,754*
0,669 *
0,6902*
0,6211
0,6495*
2
0,6301
0,6425
0,6634
0,6081
0,6772*
0,6488
3
0,7538*
0,5497
0,6179
/
/
/
Delta
0,1941
0,2043
0,0511
0,0822
0,0561
0,0008
Rank
2
1
5
3
4
6
*Optimal level for each parameter
The analysis of variance (ANOVA) is carried out to determine the influence of each parameter on the weighted GRG at 95% confidence level, and the summary of ANOVA is given in Table 7. The results indicate that the highest variation on the weighted GRG comes from the water flow rate, followed by the oil flow rate, oil type and its kinematic viscosity, aerosol impact position, nozzle stand-off distance, and lastly from the number of nozzles. Based on the p-value (p < 0,05), it can be concluded that water and oil flow rates, as well as the oil type have statistically significant effect on the weighted GRG value. The highest contribution on the weighted GRG with a percentage contribution (PC) of 40,88% had the water flow rate followed by 37,69% contribution of oil flow rate.
Parametric Optimization of MQCL-Assisted Turning Operation
495
Fig. 3. Main effect plot of CRITIC weighted GRG.
Hence, analyzing the main effect plot as well as the ANOVA results, it can be seen that the oil and water flow rates have the largest impact on the variation on the weighted GRG. This statement certainly makes sense since these two are the main elements of conventional coolants and lubricants. Although, it turns out that the aerosol with lower kinematic viscosity oil (Castrol Hyspray) applied on the rake face of the tool through a single nozzle, from minimum distance (30 mm) resulted in greater value of the weighted GRG. Table 7. ANOVA for CRITIC weighted GRG. Source
DF
Seq SS
Adj MS
F-Value
PC
P-Value
A
2
0,096235
0,047448
28,77
37,69%
0,004
B
2
0,104399
0,042778
25,94
40,88%
0,005
C
2
0,009178
0,007975
4,84
3,59%
0,086
D
1
0,029644
0,017033
10,33
11,61%
0,032
E
1
0,009299
0,009105
5,52
3,64%
0,079
F
1
0,000015
0,000015
0,01
0,01%
0,929
Error
4
0,006598
0,001649
Total
13
0,255367
2,58% 100,00%
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A. Mustafi´c
Surface plots were created to better visualize the effect of MQCL parameters at experimental run 5 on the weighted grey relational grade and presented in Fig. 4.
Fig. 4. Surface plot for CRITIC weighted GRG versus oil- and water flow rates and nozzle stand-off distances.
5 Comparative Analysis of MCDM Methods The optimized MQCL parameters, shortly given as A3-B1-C1-D1-E2-F1 obtained based on the proposed CRITIC-GRA method were compared with several other methods as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), VIše Kriterijumska Optimizacija i KOmpromisno Rešenje (VIKOR), Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS), and Multi Attributive Ideal-Real Comparative Analysis (MAIRCA). Summary of ranking results according to different multi-criteria decision-making methods are shown in Table 8.
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497
Table 8. Ranking options based on different MCDM methods. Exp. Runs
Multi-criteria decision-making methods GRA-CRITIC
TOPSIS
VIKOR
MARCOS
MAIRCA
1
13
13
13
14
13
2
2
2
6
2
4
3
3
5
3
6
3
4
12
12
11
12
12
5
1
1
1
1
1
6
14
14
14
13
14
7
9
9
5
9
8
8
5
3
9
3
5
9
6
8
4
8
6
10
10
10
7
10
9
11
4
6
2
5
2
12
11
11
10
11
11
13
7
7
8
7
7
14
8
4
12
4
10
The given data from Table 8 revealed that all five methods found that experimental run 5 is the best option among 14 alternatives. However, to find the experimental run with optimal parameter levels, the average value of the multiple-characteristic at each level for each parameter needs to be determined as shown in Table 9. Table 9. Comparison of CRITIC-GRA with other MCDM methods. Methods
Optimized Parameters
CRITIC-GRA
A3-B1-C1-D1-E2-F1
TOPSIS
A3-B1-C1-D1-E2-F1
VIKOR
A3-B1-C1-D1-E2-F2
MARCOS
A3-B1-C1-D1-E2-F1
MAIRCA
A3-B1-C1-D1-E2-F1
Results for the optimized parameter settings given in Table 9 for all MCDM methods allows us to say that determining the best alternative, as well as the optimized levels does not depend on the method (at least with the methods used in this study). The exception is only in the case for VIKOR method for the last parameter (number of used nozzles), which unlike other methods is at level 2.
498
A. Mustafi´c
The last step in the MCDM method analysis is the consistency analysis. The goal of the consistency analysis is to compare the degree of association between ranks given in Table 8 obtained using various MCDM methods. In this step the Spearman’s rank correlation coefficient was used. This coefficient can be determined using Eq. (11) [15]. 2 6• m i=1 Di (11) R=1− m(m2 − 1) where m is the number of alternatives and D is the difference between ranks. Table 10 displays the Spearman’s rank correlation coefficient for rankings obtained using various methods. Table 10. Spearman’s rank correlation coefficient. CRITIC - GRA
TOPSIS
VIKOR
MARCOS
TOPSIS
0,930
–
–
–
VIKOR
0,815
0,604
–
–
MARCOS
0,921
0,991
0,604
–
MAIRCA
0,969
0,846
0,903
0,846
According to Table 10 it can be seen that the highest correlation coefficient is 0,991 between TOPSIS and MARCOS, while the lowest is 0,604 between VIKOR-MARCOS and VIKOR-TOPSIS. The correlations obtained between other methods are generally in acceptable range. Finally, the input MQCL process parameters while turning X5CrNi18–10 (AISI 304) steel grade by using an advanced oil-on-water cooling and lubrication system for simultaneously achieve minimum surface roughness parameters and minimum cutting forces (with constant cutting regimes) obtained through multi-criteria decision-making methods can be drawn as: oil flow rate, QOIL = 50 (ml/h); water flow rate, QH20 = 300 (ml/h); nozzle stand-off distance d = 30 (mm); kinematic oil viscosity, ν = 39 (mm2 /s.); oil-on-water mist impact position on the tool-workpiece interface, rake face (R), though a single nozzle. The limitation of this study is that the results of the problem have not been validated and compared with other types of cutting oils, cutting regimes and workpiece materials. That is also highly recommended for further research to be carried out.
6 Conclusions Based on the experimental results and statistical analysis, the following conclusions can be drawn: • According to ANOVA, it was proved that the oil- and water flow rates, and the type of lubricant in means of its physical properties are the most influential variables on the conditions of the turning process. At the same time these factors are the only statistically significant (p-value less than 0,05) on the response variable.
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• Observing the total amount of the oil-on-water aerosol mixture, it turned out that the effect of the MQCL system does not depend on the total quantity, but on its composition. The effective amount of aerosol formed in terms of better quality of the treated surface and minimum cutting forces is that with a maximum oil to water ratio (16,7% share of MQL oil in the atomized mist). • Other process parameters of the MQCL system are of minor influence and are statistically insignificant on the overall performance characteristic. • Vegetable-based cutting oil with a lower index of kinematic viscosity (Castrol Hysprey) proved to be a more adequate choice. The advantage of oil with lower index of kinematic viscosity from the aspect of lubrication is reflected in better fluidity properties, which can be related to the assisted effect of pulsating aerosol jet on which principle the analyzed MQCL system works. • Optimization has proven that a shorter nozzle stand-off distance (30 mm) due to a series of satisfied compromises (carrier air velocity, velocity and dimensions of formed droplets, targeted jet delivery to the cutting zone, jet propagation angle, etc.) provides the best penetration conditions in the cutting zone and thus the best cooling and lubrication effects. • The MQCL process parameters were simultaneously optimized based on the CRITICGRA and induced same results as the other multi-criteria decision-making methods such as TOPSIS, MARCOS, and MAIRCA. The only difference was observed in the case of the VIKOR method in the number of the used nozzles. • Optimal MQCL process parameters obtained were oil flow rate, QOIL = 50 (ml/h); water flow rate, QH20 = 300 (ml/h); nozzle stand-off distance d = 30 (mm); kinematic oil viscosity, ν = 39 (mm2 /s); aerosol impact position on the rake face (R) of the cutting tool though a single nozzle. • It is highly recommended that further research on MQCL turning processes of stainless steels should be conducted in combination with machining parameters (cutting speed, feed, and depth of cut) in order to consider the complete condition and impact of MQCL systems and point out their advantages as an alternative to conventional coolant and lubrication strategy.
References 1. Šterpin Vali´c, G., Kostadin, T., Cukor, G., Fabi´c, M.: Sustainable machining: MQL technique combined with the vortex tube cooling when turning martensitic stainless steel X20Cr13. Machines 11(3), 336 (2023) 2. Tuan, N.M., Duc, T.M., Long, T.T., Hoang, V.L., Ngoc, T.B.: Investigation of machining performance of MQL and MQCL hard turning using nano cutting fluids. Fluids 7(5), 143 (2022) 3. Yildirim, Ç.V., Kivak, T., Erzincanli, F.: Tool wear and surface roughness analysis in milling with ceramic tools of Waspaloy: a comparison of machining performance with different cooling methods. J. Braz. Soc. Mech. Sci. Eng. 41, 83 (2019) 4. Sarikaya, M., Güllü, A.: Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25. J. Clean. Prod. 91, 347–357 (2015)
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5. Hadad, M., Sadeghi, B.: Thermal analysis of minimum quantity lubrication-MQL grinding process. Int. J. Mach. Tools Manuf 63, 1–15 (2012) 6. Mia, M., et al.: Taguchi S/N based optimization of machining parameters for surface roughness, tool wear and material removal rate in hard turning under MQL cutting condition. Measurement 122, 380–391 (2018) 7. Divya, C., Raju, L.S., Singaravel, B.: Application of MCDM methods for process parameter optimization in turning process - a review. In: Narasimham, G.S.V.L., Veeresh Babu, A., Sreenatha Reddy, S., Dhanasekaran, R., Recent Trends in Mechanical Engineering, pp. 199– 207. Springer, Singapore (2021) 8. Stojˇci´c, M., Zavadskas, E.K., Pamuˇcar, D., Stevi´c, Ž, Mardani, A.: Application of MCDM methods in sustainability engineering: a literature review 2008–2018. Symmetry 11(3), 350 (2019) 9. Jones, B., Nachtsheim, C.J.: A class of screening designs robust to active second-order effects. In: Giovagnoli, A., Atkinson, A., Torsney, B., May, C. (eds) mODa 9 – Advances in ModelOriented Design and Analysis. Contributions to Statistics, vol. 43, pp. 1–15. Physica-Verlag HD (2011) 10. Jones, B., Nachtsheim, C.J.: Effective design-based model selection for definitive screening designs. Technometrics 59(3), 319–329 (2017) 11. Mohamed, O.A., Masood, S.H., Bhowmik, J.L.: Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network. Adv. Manufac. 9, 115–129 (2021) 12. Tosun, N., Pihtili, H.: Gray relational analysis of performance characteristics in MQL milling of 7075 Al alloy. Int. J. Adv. Manufac. Technol. 46, 509–515 (2010) 13. Senthilkumar, N., Tamizharasan, T., Veeramani, A.: Experimental investigation and performance analysis of cemented carbide inserts of different geometries using Taguchi based grey relation analysis. Measurement 58, 520–536 (2014) 14. Mustafi´c, A., Mehmedovi´c, M., Be´cirovi´c, D.: Influence of processing parameters of environmental-friendly cooling/lubrication strategy (MQCL) in turning of X5CrNi18-10 stainless steel. Techn. Technol. Educ. Manage. 15(1), 18–29 (2020) 15. Do, T.: The combination of taguchi – entropy – WASPAS - PIV methods for multi-criteria decision making when external cylindrical grinding of 65G steel. J. Mach. Engi. 21(4), 90–105 (2021)
Influence of Different Cutting Speeds on CNC Milling on Surface Roughness of Objects Made from Steamed and Heat-Treated Beech Wood Alen Ibriševi´c, Ibrahim Busuladži´c(B) , Murˇco Obu´cina, and Seid Hajdarevi´c Faculty of Mechanical Engineering, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. Surface roughness is important in the general assessment of the quality of wooden surfaces. The aesthetic value of wood products is strongly related to its surface topography. In this paper, the aim is to analyze the influence of different cutting speeds of the CNC milling cutter on the roughness and quality of processed objects made of steamed and heat-treated beech wood. The paper will examine the quality of processing, depending on whether the beech wood is heat-treated or steamed. The paper presents an analysis of the influence of the spindle rotation speed of CNC machine on the roughness of the milled surface of beech samples. Tests were performed on steamed and heat-treated samples of beech wood. Spindle rotation speeds are 8400 rpm, 9600 rpm, 10800 rpm, 12000 rpm, 14400 rpm, and the feed speed was 5 m/min. Two tools (milling tools) with a diameter of 8 mm were used. The contact method was used to measure roughness. Keywords: roughness · CNC milling · cutting speed · beech wood
1 Introduction The roughness of the wooden surface depends on many influencing factors and can be related both to the properties of the wood and to the processing conditions. Important properties of wood are the type of wood, its density, moisture content and its anatomical structure. Great differences can exist even in the internal structure of the same type of wood [1, 2]. The type of tools such as process parameters have also a significant effect on the surface roughness. The most important factors that influence it are; geometry of tools [3], cutting angle, cutting speed, displacement per blade [4], bluntness of the blade [5], direction of cutting in relation to the fibers and vibrations of the work piece. Whereas the cutting speed is dominant parameter of milling that influences the roughness of the wooden surface [6], it is mostly examined in this work, beside the type of wood.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 501–508, 2023. https://doi.org/10.1007/978-3-031-43056-5_37
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2 Tools Tool selection on CNC machines plays an extremely important role and the number of tools used for those machines is quite large. With an adequate tool, certain operations can be performed replacing a larger number of tools that were previously required. Milling cutter on CNC machines are widely used. In this work, the tests were carried out using two types of milling cutters, with a diameter of 8 mm. They have in common that they have a smaller diameter of the cutting part and that they almost always work like spindle mills. The small diameter of the cutting circle, with these milling cutters, is due to their use. Double-edged flat milling cutters are used when processing massive and chipped wood panels (Fig. 1). They represent one of the most used milling cutters due to the ease of manufacture (compared to other types of milling cutters), the tool material, and thus the price of the product. They are made of high-speed steel HSS or tungsten carbide [7]. With the development of new technologies, new advanced tools for woodworking appears, known as “spiral routers” (Fig. 1). In their appearance and shape, spiral milling cutters are very similar to standard spiral drills, but one can notice significant differences. With them, one to four cutting edges, are always spiraled around the body. The tip of the milling cutter does not end at the central point, but “runs away” a little from it. The result of this is that spiral cutters cut in a hybrid path, that is a mixture of cutting with classic straight cutters and drilling with spiral drills. The spiral path of the cutting edges is what makes this router special compared to classic straight routers.
Fig. 1. Double edge flat milling and spiral cutters.
Flat milling cutters attack the material in a straight line, frontally. This kind of cutting often causes the material to tear and create ragged edges, which are not very nice to see. On the other hand, the bevel cut with spiral milling cutters gives much less unwanted effects, and the resulting profile is cleaner and less exposed to heating. The next big advantage is that the spiral milling cutters continuously remove the sawdust, so that it is not recutting, and this causes a longer tool life.
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3 Materials Beech (Fagus) is a genus of coniferous trees from the Fagaceae family, native to Europe, Asia and North America. Hardwood wood has more complex structure, better mechanical properties and higher density than coniferous wood. The European species Fagus sylvatica produces a useful wood that is strong but dimensionally unstable and is used to produce numerous objects. Two types of treated beech wood were used in this work: steamed beech wood and heat-treated beech wood. Steaming of wood is a common process of heat treatment, which achieves; stabilization of form, color homogenization and color modification. The goals of steaming can be; to reduce the hygroscopicity of the wood, to equalize the humidity throughout the thickness of the material before artificial drying, to soften the wood for later processing or bending, sterilization or mitigation of breakdown. The results regarding these goals are decisively influenced by the duration of steaming, the applied temperature, as well as the steam pressure. Steaming of beech wood is done in special barrels or facilities at a temperature between 90 and 100 °C, in humid conditions, and the process must be stationary. Beech wood often takes on a characteristic pink or golden color (Fig. 2).
Fig. 2. Steamed beech wood.
Dry heat treatment of beech wood achieves the following properties: an increase in physical properties of beech wood such as stiffness but decrease in mechanical properties (flexural and tensile strength), uniformity of color into darker tones (mostly brown – Fig. 3) throughout the entire section and moisture absorption decreases. This type of wood modification has long been known as potentially the most useful method to improve the dimensional stability of wood and increase its biological resistance. In the modification process, only heat and water vapor are used, without the use of any chemical preparations or additives, which makes it completely environmentally friendly and harmless to the environment. For this purpose, temperatures between 100 °C and 230 °C are used, in specially constructed chambers equipped with fully automated systems for managing the entire process.
Fig. 3. Heat treated beech wood.
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4 Surface Roughness By surface roughness we mean the totality of micro geometrical irregularities. Roughness also represents the irregularity of the surface. These irregularities are reflected in the form of smaller or larger elevations. In general, roughness depends on the micro geometry of the tool cutting edge (tool wear causes higher surface roughness), wood type (soft wood has higher roughness than hard wood), wood humidity (higher humidity - higher roughness) and cutting direction (surfaces processed in the opposite direction to the direction of fiber extension are rougher). To estimate the surface roughness in engineering practice, the arithmetic mean deviation of the profile Ra is most often used (Fig. 4) [8], which is equal to the arithmetic mean value of the absolute values of the unevenness profile heights on the measuring length l. 1 |yi | Ra = n n
(1)
i=1
where: Ra [µm] - arithmetic mean deviation of the profile, l [µm] - length on which the surface roughness is measured, y(x), yi [µm] - the heights of the roughness profile with respect to the middle reference line, n - number of points for assessing the heights of the profile along the measuring length. As a parameter of roughness, one can use the mean height of bumps Rz , which is equal to the sum of arithmetic mean of the heights of the five highest peaks and arithmetic mean of the values of the five largest valley depths, on the length l, on which the surface roughness is measured (Fig. 5). The International ISO system defines Rz as follows [8]: n n 1 ypi − yvi Rz = (2) n i=1
i=1
where is: ypi [µm] - height of the ith highest peak, yvi [µm] - depth of the ith lowest valley.
Fig. 4. Mean arithmetic deviation of the profile Ra .
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Fig. 5. Estimating of mean height of unevenness Rz .
5 Experimental Set-Up and Adjustment of Cutting Speed The samples are fixed with vacuum clamping device on the work table of the CNC machine, and notches are made on it as shown in the picture (Fig. 6).
Fig. 6. Experimental set-up.
During the processing of the samples, the rotation speed of the milling cutter was changed, while the displacement speed was constant at 5 m/min. The spindle speeds are selected randomly within the machine’s operating range: 8400 rpm, 9600 rpm, 10800 rpm, 12000 rpm, and 14400 rpm. The depth of milling is 8 mm. The direct relationship between the cutting speed and the spindle speed (S set in the control panel of the CNC machine) during milling is given by the following relationship: N=
1000 • 60 • v π ∗D
(3)
where is: N – spindle speed (min−1 ). v – cutting speed (m/s). D – tool diameter (mm). The cutting speed on CNC machine is adjusted by the spindle speed and the tests are performed for five spindle speeds, such as mentioned in the results section.
6 Measurement of Surface Roughness We distinguish two types of methods to test surface roughness: quantitative methods (optical and contact) and qualitative methods. The surface roughness, in this work, was measured along the processed surface, using an electromechanical profilometer Mitutoyo SJ-201 (Fig. 7).
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Fig. 7. Profilometer Mitutoyo SJ-201.
Used profilometer Mitutoyo SJ-201 has following characteristics; portable for easy use wherever you need to measure roughness, the drive unit can be separated from the display for easier measurement even when it is difficult to access the measurement position, wide measurement range of 350 µm (−200 µm to + 150 µm), a total of 19 analysis parameters are given, including the frequently used Ra, Rz, Rq and Ry parameters and automatic dynamic calibration function.
7 Results A total of twelve measurements were made for each sample. The results are read on the measuring profilometer. The parameters used to evaluate the roughness in this work are: the mean deviation of the profile Ra and the mean height of the unevenness Rz. The following graphs (Figs. 8, 9, 10 and 11) show the results of the given tests for different rotational speeds of milling tools; 8400, 9600, 10800, 12000 and 14400 rpm. Also, the comparison between flat and spiral cutters are done, milling the steamed and heat-treated beech wood.
Fig. 8. Steamed beech wood, double edged flat milling cutters.
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Fig. 9. Steamed beech wood, spiral router.
Fig. 10. Heat-treated beech wood, double edged flat milling cutters.
Fig. 11. Heat-treated beech wood, spiral router.Discussion
By analyzing measuring results, the roughness of the treated surface samples, obtained by applying two different tools, with different spindle speeds and different types of wood, the following conclusions can be drawn; by increasing the spindle speed, the cutting speed proportionally increases, which also affects the roughness of the treated surface. With the slow penetration of the blade into the wood, the wood fibers in front of it slip away and remain uncut but sheared or broken. At higher spindle speeds, the wood
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fibers do not slip away in front of the blades, and they are cut before their connection with the neighboring wood fibers are damaged, resulting with less roughness processed surface. Poorer processing quality, i.e. greater roughness of the processed surface, is obtained when a lower number of revolutions of the main spindle is used. With the increase of the spindle speed, at the same feed rate, the thickness of the chips decreases, which leads to a lower surface roughness, i.e. a surface with better quality. The surface roughness of steamed beech samples was worse than that of the heattreated beech. The best results, i.e. the lowest roughness of the machined surface were obtained at the number of revolutions of the main spindle equal to 14400 rpm, while the highest roughness was at the number of revolutions of the main spindle of 8400 rpm.
References 1. Malkoço˘glu, A., Özdemir, T.: The machining properties of some hardwoods and softwoods naturally grown in Eastern Black Sea Region of Turkey. J. Mater. Process. Technol. 173(3), 315–320 (2006) 2. Zhong, Z.W., Hiziroglu, S., Chan, C.T.M.: Measurement of the surface roughness of wood based materials used in furniture manufacture. Measurement 46, 1482–1487 (2013) 3. Darmawan, W., Azhari, M., Rahayu, I., Nandika, D.: The chips generated during up-milling and down-milling of pine wood by helical router bits. J. Indian Acad. Wood Sci. 15, 9–12 (2018) 4. Azemovic, E., Horman, I., Busuladzic, I.: Impact of planning treatment regime on solid fir wood surface. Procedia Eng. 69, 1490–1498 (2014) 5. Csanady, E., Kovacs, Z., Magoss, E., Ratnasingam, J.: Optimum Design and Manufacture of Wood Products. Springer, Cham (2019) 6. Barcik, Š, Pivolusková, E., Kminiak, R.: The influence of cutting speed and feed speed on surface quality at plane milling of poplar wood. Drvna Industrija 54(2), 109–115 (2009) 7. Garcia, J., Collado-Cipres, V., Blomqvist, A., Kaplan, B.: Cemented carbide microstracture: a review. Int. J. Refract Metal Hard Mater. 80, 40–68 (2019) 8. Gadelmawla, E.S., Koura, M.M., Maksoud, T.M.A., Elewa, I.M., Soliman, H.H.: Roughness parameters. J. Mater. Process. Tachnol. 123, 133–145 (2002)
Thermal Modification of Wood Izet Horman1(B) , Aleksandra Kosti´c1 , Valentina Timoti´c2 , and Melisa Kustura3 1 Faculty of Mechanical Engineering, University of Sarajevo, 71000 Sarajevo,
Bosnia and Herzegovina [email protected] 2 Faculty of Philosophy, University of East Sarajevo, 71420 Pale, Bosnia and Herzegovina 3 Mechanical Engineering School, 71000 Sarajevo, Bosnia and Herzegovina
Abstract. The paper presents the analysis of temperature and moisture distribution during thermal treatment of wood at high temperatures. As a result, nonstationary fields of temperature and humidity were obtained in the cross-section of the wooden beam. The finite volume method (FVM) was used as a calculation tool. The calculation results were compared with the measurement results and a good agreement was observed. The MKV method can be applied to predict the distribution of temperature and humidity in other capillary-porous materials during heat treatment. Keywords: heating wood · numerical analysis · finite volume method · temperature · moisture content
1 Introduction Exposing wood to high temperatures is one of the methods to extend wood life or durability. Thermal modification includes drying wood from a dried state to a state that enables its processing, i.e., use, and then exposing the product to high temperatures [6, 7]. During the heat treatment process, the gradient of temperature and humidity across the cross-section of the wood causes deformations and stresses, which results in defects in the material in the form of shape deformation or changes in the structure of the material, i.e., the appearance of cracks. To manage the process of thermal modification, it is necessary to know the temperature and moisture distribution in the material that is heated and dried over time. This is possible if partial differential equations of heat and mass transfer are solved with initial and boundary conditions, which include all influencing factors. Given that an analytical solution is only possible in a small number of simple cases [9, 10], the paper presents a numerical method for solving the problem. Some authors considered a one-dimensional problem (1D) [3, 4, 10] or a two-dimensional problem (2D) [4] ignoring the temperature distribution. The paper presents a numerical procedure for calculating the 2D non-stationary profile of temperature and humidity, based on the finite volume method (FVM). The work is organized as follows: Section 2 presents a mathematical model with constitutive relations that describe the behavior of materials. Section 3 presents the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 509–517, 2023. https://doi.org/10.1007/978-3-031-43056-5_38
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numerical model with the discretization method of the equations and the solution algorithm. Section 4 presents the results of the research. Concluding remarks are given in Sect. 5, and reference literature in Sect. 6.
2 Mathematical Model The phenomena and behavior of materials during the thermal treatment of wood are described by partial differential equations of energy and moisture balance with corresponding constitutive relations. These equations together with the initial and boundary conditions represent a mathematical model of the thermal modification process. 2.1 Thermal Modification of Wood The process of heating and drying wood is described by the following equations: – energy balance
– moisture balance
∂qj ∂ ∂ (ρcT ) = − + εr (ρcw w) ∂t ∂xj ∂t
(1)
∂mj ∂ ∂T ∂ kw +δ (ρcw w) = − ∂t ∂xj ∂xj ∂xj
(2)
where are t – time, x j – Cartesian spatial coordinates of the point, ρ – density, c – specific heat, T – temperature, qj – heat flux component, ε – ratio of vapor diffusion coefficient and total moisture diffusion coefficient, r – heat of evaporation, cw – specific humidity, w – moisture content, mj – mass flux component, δ – thermogradient coefficient, k w – moisture conduction coefficient. 2.2 Constitutive Relations In order to close the system of Eqs. (1) and (2), the following constitutive relations are used [5]: – connection between heat flux and temperature gradient, Fourier’s law, qj = −k
∂T ∂xj
(3)
k – heat conduction coefficient, – connection between mass flux and moisture gradient, Fick’s law of diffusion, mj = −kw
∂w ∂xj
(4)
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2.3 Mathematical Model Partial differential Eqs. (1) and (2) with constitutive relations (3) and (4) form a system of 2 equations with 2 unknowns, which represent a mathematical model of the wood thermal treatment process. ∂T ∂ ∂ ∂ k + εr (ρcw w) (ρcT ) = (5) ∂t ∂xj ∂xj ∂t ∂ ∂ ∂w ∂T ∂ kw +δ kw (6) (ρcw w) = ∂t ∂xj ∂xj ∂xj ∂xj Initial and boundary conditions are given for obtaining the mathematical model. At the beginning of the process, temperature and humidity have a uniform distribution over the entire solution domain T xj , 0 = Ti (7) w xj , 0 = wi
(8)
where T i and wi are the initial temperature and humidity. The following boundary conditions apply to the boundary surfaces k
∂T + α(T − Ta ) + (1 − ε)rαw (w − wa ) = 0 ∂n
(9)
∂w ∂T + kw δ + αw (w − wa ) = 0 ∂n ∂n
(10)
kw
T a – air temperature, wa – air humidity, α – heat transfer coefficient, α w – mass transfer coefficient.
3 Numerical Model FVM [2, 11] was used to approximate the system of partial differential equations by a system of nonlinear algebraic equations with temperature and humidity as unknowns at discrete points. The equations of the mathematical model can be written in the following form ∂ψ ∂ ∂ ρBψ − ψ − Sψ = 0 (11) ∂t ∂xj ∂xj where ψ – T or w, a are coefficients. Bψ , ψ and Sψ are given in Table 1.
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ψ
Bψ
ψ
T
cT
k
w
cw w
kw
Sψ ∂ (ρc w) εr ∂t w ∂ ∂T δ ∂x kw ∂x j
j
3.1 Discretization In order to solve the Eqs. (5) and (6), the time domain is discretized into an indefinite number of time intervals δt, and the spatial domain into N control volumes (KV), which are limited by 4 cell surfaces Ak (k = e, w, n, s) if the 2D problem is considered. The calculation points are located in the center of KV. After that, the transport Eq. (11) is integrated over the solution domain and over time
1 ∂ ∂ψ ρBψ dV − ψ dAj − Sψ dV dt = 0, (12) δt t V ∂t ∂xj A V the Gauss theorem was used to convert the volume integral of the diffusion flux into the surface integral. As a consequence of the above approximations, the Eq. (12) can be written in the form of aP ψP = aK ψK + b, (K = W , E, S, N ) (13) K
where K denotes the summation of all 4 cells surrounding cell P, and the coefficients are given by the following expressions: When discretizing in space and time, the coefficients are always non-negative, which causes that aK , K = W , E, S, N . (14) aP ≥ K
As a result of the used discretization procedure, a system of 2xN nonlinear algebraic equations with temperature and humidity at nodal points was obtained, where N is the number of KV. The system of equations was solved by an iterative procedure. 3.2 Problem Solving The dependent variables are given appropriate initial values and boundary conditions are used, corresponding to the first time step, and the systems of equations for both dependent variables are linearized and temporarily decoupled, by calculating the coefficients and source terms using the initial values of the dependent variables. In this way, two independent systems of N linear algebraic equations with N unknowns with a diagonally dominant symmetric matrix of coefficients with 5 non-zero diagonals are
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obtained. Then these systems of equations are solved one after the other until a convergent solution is obtained, i.e., until the desired accuracy is obtained [1]. The procedure is considered convergent if the following convergence conditions are met for both systems of equations: N aK ψK + b − aP ψP pR i=1 K m+1 (15) − ψim q ψim , i = 1, 2, . . . , N ψi where the values of p and q are of the order of magnitude 10–3 , and R is the normalizing factor, while the upper indices m and m + 1 indicate the values in two consecutive iterations. The fully implicit time differentiation scheme used eliminates the limitation related to the stability of the time step. This makes it possible to use any time step size. Table 2. Physical characteristics of a wood beam. ρ
c
ε
k
kg/m3
J/kgK W/mK –
390
2400
0.65
r
cw
kw
δ
α
αw
J/kg
kg/kg° M
kg/ms° M
° M/K
W/m2 K
kg/m2 s° M
0.01
2.2·10–8
2
22.5
2.5·10–6
0.3 2.5·106
4 Thermal Treatment of Wooden Planks The distribution of temperature and humidity during thermal treatment of wooden planks of large thickness is observed in the direction of the axis x and y, that is, the problem is considered as 2D. The physical characteristics of the material are given in Table 2. One quarter of the cross section of the plank was analyzed due to double symmetry. At the initial moment, a uniform distribution of temperature and moisture content was taken, and the following boundary conditions were used: ∂T + α(T − Ta ) + (1 − ε)rαw (w − wa ) = 0, ∂x ∂w ∂T + kw δ + αw (w − wa ) = 0, kw ∂x ∂x ∂w ∂T = 0, = 0, x=0: ∂x ∂x
x=l:k
∂T + α(T − Ta ) + (1 − ε)rαw (w − wa ) = 0, ∂y ∂w ∂T + kw δ + αw (w − wa ) = 0, kw ∂y ∂y
y=h:k
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y=0:
∂T = 0, ∂y
∂w = 0. ∂y
The calculation results are given for one quarter of the section l = 0,15 m and h = 0,015 m and for the mesh 15 × 150 FV .
Fig. 1. Temperature time history at different locations.
Fig. 2. Moisture time history at different locations.
Figures 1 and 2 show the temperature history and the history of moisture content in 3 characteristic points. It can be seen from the pictures that at the beginning of the heating process, the temperature gradient is the largest, and that after approximately 30 h of heat treatment, the temperature field across the cross-section of the plank is almost uniform. At the beginning of the drying process, the moisture content in the inside of the plate is constant, and on the surface it decreases due to the dominant convective mass transfer. In the second phase of the process, the moisture concentration decreases at all cross-section points (Fig. 3). Figures 1 and 2 show the distribution of temperature, i.e. moisture content in the time. Figure 2 show the distribution of temperature at x cross sections at some point in
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Fig. 3. Profile of temperature at an X cross section.
Fig. 4. Profile of moisture concentration at an X cross section.
Fig. 5. Profile of moisture concentration at an Y cross section.
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time of 25 h. The distribution of temperature and moisture is shown in different stages of processing. In the initial phase, the temperature gradient is more pronounced, and in the later phase of the process, the moisture content is more pronounced. In contrast to the drying process, which takes place at significantly lower air temperatures and lasts for a longer period of time, here the dominant distribution of temperature is across the cross-section of the wood. Figures 4 and 5 show profile moisture concentration at the time of 35 h. If the section is closer to the plane of symmetry, the temperature gradients are higher. The same can be said for moisture content. In these images, it can be seen that the moisture gradients are larger in the sections closer to the plane of symmetry. This means that higher voltages occur in those sections as a consequence. At the beginning of the heat treatment, the dominant effect is the pronounced temperature gradient, and later the moisture gradient.
5 Conclusion For the analysis of the process of thermal modification of wood, as a non-stationary process, a numerical method was used, which is based on the discretization of the corresponding energy balance and mass balance equations with finite volumes, where the temperature and moisture of the wood are variable quantities. The calculation results were compared with our own experimental results. Given that we are dealing with high temperature gradients, which consequently cause the appearance of high internal stresses, which can cause the destruction of the thermally processed material, it was necessary to create a model for controlled process management. In contrast to the drying process, where the stresses in the material caused by the moisture gradient are dominant, thermal treatment of wood involves processing at high temperatures, and thermal stresses are dominant there. In this paper, we focused on the analysis of the temperature distribution, that is, the temperature gradient. Here, the moisture levels are relatively low and did not pose a risk for damage to the material. The simulation obtained the expected results. The temperature gradients were within controlled limits and could not cause the appearance of stress above the permitted limit, and thus damage to the material that was thermally processed.
References 1. Bui, X., Choong, E.T., Rudd, W.G.: Numerical methods for solving the equation for diffusion through wood during drying. Wood Sci. 13(2), 117–121 (1980) 2. Demirdži´c, I., Martinovi´c, D.: Finite volume method for thermo-elasto-plastic stress analysis. Comput. Methods Appl. Mech. Eng. 109, 331–349 (1993) 3. Droin-Josserand, A., Taverdet, J.L., Vergnaud, J.M.: Modeling the kinetics of moisture adsorption by wood. Wood Sci. Technol. 22, 11–20 (1988) 4. Droin-Josserand, A., Taverdet, J.L., Vergnaud, J.M.: Modeling of moisture absorption within a section of parallelepipedic sample of wood by considering longitudinal and transversal diffusion. Holzforschung 43, 297–302 (1989) 5. Holman, J.P.: Heat Transfer. Mc Graw Hill (1976)
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6. Horman, I., Busuladzic, I., Azemovic, E.: Temperature influence on wear characteristics and blunting of the tool in continuous wood cutting process, 24th DAAAM international symposium on intelligent manufacturing and automation 2013. Procedia Engineering 69, 133–140 (2014) 7. Horman, I., Azemovic, E., Pandzo. A.: Kinetics of internal stresses of water varnishes. In: International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT) 2021, pp. 348–358 (2014) 8. Kouali, M.E., Vergnaud, J.M.: Modeling the process of absorption and desorption of water above and below the fiber saturation point. Wood Sci. Technol. 25, 327–339 (1991) 9. Liu, J.Y., Cheng, S.: Heat and moisture transfer in wood during drying. In: Proceedings of the 3rd Joint ASCE/ASME Mechanics conference, San Diego, July 9–12, pp. 79–85 (1989) 10. Malmquist, L.: Lumber drying as a diffusion process. Holz als Roh- und Werkstoff 49, 161– 167 (1991) 11. Patankar, S.V.: Numerical Heat Transfer and Fluid Flow. Mc Graw Hill (1980) 12. Thomas, H.R., Lewis, R.W., Morgan, K.: An application of the finite element method to the drying of timber. Wood Fiber 11(4), 237–243 (1980)
The Influence of Mill Loading on the Distribution of Pulverized Coal Particles Izudin Deli´c1(B)
, Amel Meši´c1 , Nedim Ganibegovi´c2 , and Midhat Osmi´c1
1 Faculty of Mechanical Engineering, University of Tuzla, Tuzla, Bosnia and Herzegovina
[email protected] 2 EPBIH Concern, Thermal Power Plant Tuzla, Tuzla, Bosnia and Herzegovina
Abstract. Separation of coal particles is a very important process during combustion in a boiler furnace. In this work, a CDF simulation of the current flow of pulverized coal was carried out for different positions of the regulatory bodies in the air mixture channel. An analysis of the possible regulation of the current flow by changing the position of the vane in the mill separator was performed. The simulation was performed under conditions of different granulation of pulverized coal as well as the amount of fuel per ventilation mill. The aim of the analysis is to achieve the most favorable flow conditions. A good match between experimental and numerical results is shown with a maximum difference of 6.65%. The results of the simulation show that increasing the capacity of the mill affects the increase of pulverized coal in the second exit of the separator by 5,75% and raising the flame. Greater granulation of pulverized coal has the consequence of increasing recirculation in the mill by 16,06%. Changing the amount of coal powder on the burner nozzles must lead to the adjustment of the amount of combustion air, on each burner nozzle separately. Keywords: Multiphase Flow · CFD · Pulverized Fuel · Coal Pulverizer Classifier · Aero Mixture
1 Introduction Coal continues to play a predominant role in the production of electricity in the world, especially when it comes to developing countries. A very large percentage of the total coal is burned in pulverized form. One of the critical power plant components that is relied upon to convert the energy stored in coal into electricity is the coal pulverizer or mill. These types of pulverizing systems are generally used in North America, Germany, Poland, South Eastern Europe and Turkey [1]. After the pulverizer, the separation volume is used to control the size of coal that is fed into the burners. The operation of the mill plant significantly affects the level of efficiency of thermal power plants. The efficiency of the ventilation mill is directly related to the character of the multiphase flow in the ventilation mill and the air mixture channels, behind the mill to the burner. Incomplete and faulty operation of the plant is a consequence of processes such as the formation of deposits on the heating surfaces, wear of vital parts of the plant, poorer combustion of coal powder, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 518–532, 2023. https://doi.org/10.1007/978-3-031-43056-5_39
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etc., which leads to a decrease in the production capacity and ventilation effect of the mill. All this has an impact on the productivity, economy and energy efficiency of the entire plant. In this regard, it is necessary to achieve the most favorable conditions for the ignition and combustion of coal powder in the boiler furnace. Following modern trends in this field, numerical simulation of multiphase flow has proven to be a very reliable method of understanding the complex problems of multiphase flow and its optimization, and at the same time the most economical and fastest method. Because coal pulverizing and separation are important for pulverized coal-fired power plants, to increase coal fineness with adequate classification, numerous studies have been conducted on pulverizing and separation systems. The influence of the design and geometric parameters of the pulverized coal separator on the flow field and the pulverized coal classification performance were also simulated and evaluated using computational fluid dynamics (CFD). Some CFD simulations have shown that the inlet and recirculation chamber should be designed so that the air flows with the primary air flow as much as possible [2]. CFD simulations of a coal atomizer were made for a separator with different blade lengths and at different opening angles [3–5]. The optimal classifier design with the highest coal fineness and separation efficiency was determined. CFD was used to preliminarily analyze the separator design for more efficient separation of pulverized coal [6]. Based on this, an experiment was performed on the best design, which showed a good match with the simulation results. The numerical analysis was preceded after a field study where measurements were carried out in a pulverized coalfired (PC) boiler. The results of the field measurements made it possible to create a CFD distribution base model, which was used for the analysis of a new splitter construction to be used in a PF distributor [7]. Subsequent analysis of the splitter enables precise analysis of its construction, including the efficiency of separation and the prediction of conveying of the coal and biomass particles. CFD simulations of the coal classifier physical model indicate good agreement with the plant data, in terms of internal flow patterns, particle collection efficiency and desired cut size [8]. This type of simulations is very useful in understanding the nature of the coal and gas flow rates in complex aeromixture systems. This approach can help us to avoid complex and difficult measurements in some situations. Special significance of determination of the valid numerical model is reflected in fact that this kind of numerical model allows us applying of the same model on different working conditions, with different working parameters [9]. Also, a study was conducted to determine the influence of particle size distribution on the tendency to burn coal in a pulverized coal burner [10]. The results show that the incorporation of improved classification technology, leading to a finer product, will help reduce the level of unburnt carbon. Effect of coal particle size on the proximate composition and combustion properties of a size-classified bituminous coal was investigated [11]. Combustion experiments in a thermobalance revealed that with increasing particle size, the whole burning profile shifted to higher temperatures, resulting in an increase in characteristic temperatures. It implied that finer coal particles exhibited higher reactivity. To achieve better understanding of distribution of the coal and gas mixture through complicated mill-ducts, various experimental and numerical research have been conducted, see, for example, [13–20].
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1.1 Identification of Problems in Mill Operation Blowing of the pulverized coal powder into the boiler furnace is realized in such a way that the coal powder from the mill with the carrier gas, before distribution to the burners itself, is carried out through a double separation system. The first stage of separation is in the mill separator, where insufficiently pulverized coal particles are circulated back to the mill, while the second stage of separation is in the air mixture channels, where the separation of coal powder is carried out by means of the louvered separator and control valves on the stages of the burner. The distribution of coal powder among the burners is very important for the efficiency of the combustion process. 67% of the gas ventilation of the mill and a minimum of 85% of coal powder for combustion are supplied to the nozzles of the main burner. The coal powder in the main burner has a coarser grind compared to the average value that was achieved at the exit from the mill. A minimum of 33% of gas ventilation and 15% of coal powder from the mill (very fine grinding) is fed into the separate gas nozzle. The working life of the mill wheel is about 2000 h, and as it approaches the end of its resource, its grinding power, i.e., capacity decreases significantly, because the quality of grinding deteriorates, Fig. 1. The diagram shows the detailed analysis of coal dust at the exit from the mill for different operating hours of the same plant. The exponential increase in the representative diameter and the increase in the mass fraction of particles that are larger than the same, detect the deterioration of conditions in the mill plant with an increase in the number of working hours.
Fig. 1. Rosin-Rammler granulation curves for sieve analyzes as a function of the number of working hours of the mill.
Finer coal particles burn quickly and more efficiently, reducing carbon in the fly ash while maintaining low NOx emissions and increasing boiler efficiency. Whereas, in a situation where the quality of the coal deteriorates (lower calorific value), the combustion regulation will tend to increase the amount of fuel to maintain the required power. The situation is especially critical if there is a failure of one of the mills, at that moment the regulation will try to compensate for the lack of that mill by increasing the load on other mills. This can lead to different distributions of pulverized coal in the furnace and affect the geometry of the flame. Therefore, it is necessary to know the distribution of
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pulverized coal in different mill loads, for the purpose of predicting and correcting the setting of the control vanes.
2 Numerical Model The process of numerical modeling of multiphase flow in air mixture channels and burners, in principle, consists of two parts. The first part is the generation of the geometry and mesh, the preparation of the model in the form required by the numerical simulation software. The second part refers to the solver, in which the model is selected and the necessary parameters are defined, such as: definition of initial and boundary conditions, monitoring of solution convergence, post-processing of results. The software used was Siemens Star CCM +, a software package that solves the Navier-Stokes equations that define fluid flow, using the control volume method. It is important to emphasize that in previous reconstructions and modernizations of observed channel and burner of boiler OB-650, this method of determination wasn’t used, even thought, this was proven to be significantly useful method in practice. 2.1 Geometry Modelling and Discretization The 3D model of the geometry of the air mixture channel and the mill separator was created due to the technical documentation of boiler OB-650, Fig. 2. Preliminary research on the observed construction showed a significantly better answer to the requirement of good convergence was provided by the grids for tetrahedral and hexahedral units. However, the other criteria performed much better in the case of the tetrahedral mesh. Therefore, a tetrahedral mesh was chosen for discretization of the fluid space, Fig. 3. In order to obtain a quality computational results, 1,1 × 106 grid cells were used for the geometry meshing and simulation. 2.2 Formulation of Physical and Mathematical Model To define the physical model of the primary and dispersed flow, the following assumptions have been adopted: – For description of flow, continuum concept has been adopted; – One component gas is taken into account; – Gas flow is stationary, tridimensional, isotherm, incompressible, chemically inert and turbulent. In accordance with previous research, and as well with experimental parameters for better and complete defining: – – – – –
All particles are of the same material; The shape of the particles is approximated by a sphere; Mass, temperature and particle density is constant; The effect of the secondary phase on the primary is taken into account; When particles hit the inner walls, they lose a certain part of their kinetic energy and move stochastically.
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Fig. 2. Geometry of the simulated second boiler at the Tuzla power plant.
2.3 Simulation Conditions Table 1 gives an overview of the boundary conditions, from a mathematical and physical point of view, which were used in the numerical simulation. The simulation procedure was done repetitively for all geometry models with different classifier blade opening angles. Table 2 summarizes the numerical settings used in this work. Figure 4 show the default initial conditions at each defined boundary of the simulation model. In statistical approach, a smaller number of computational parcels represents the total population of dispersed phases. Each parcel represents a localized group (cluster) of dispersed phases having the same properties. In effect, parcels are a discretization of the population of dispersed phases in the same way that cells are a discretization of continuous space. As with cells, the number of parcels is not arbitrary; it must be large enough so that the properties of the full population of dispersed phases are represented. The parcels are injected into the flow space uniformly throughout the inlet crosssection, with an inlet velocity equal to the inlet velocity of the aeromixture. The diameter of the particles at each of the locations was determined stochastically, by the generation of random numbers, so that the overall granulometric composition is obtained according to the measured data.
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Fig. 3. Discretization of the calculation domain space (tetrahedral mesh).
Table 1. Boundary conditions of the simulation model. Type of border – physical aspect
Type of boundary condition – mathematical aspect
The value of the boundary condition
Inlet boundary
Dirichlet boundary condition
Speed value, temperature value
Outlet boundary
Dirichlet boundary condition
Pressure value
Wall
Neumann boundary condition
Adiabatic boundary
2.4 Validation of Numerical Model Validation of the numerical model was carried out with experimental data for two different positions of the regulating vanes in the mill separator. In the first case, the lower vane is placed at an angle of 45°, and in the second at an angle of 20° (Table 4) (Fig. 5). As already mentioned, the reference data for model validation in both cases is the pressure value in front of the mill separator, p1 . Table 3 lists the experimental values, the values obtained by simulation and the percentage deviation of the values.
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Input
Parameter
Input
Parameter
Particle injection type
Surface
Particle mean diameter
198 µm
Material
Coal
Spread parameter
2, 39
Distribution type
Rosin-Rammler
Primary air flow rate
26.92 kg/s
Particle flow rate
5–10 kg/s
Temperature
201 °C
Particle minimum diameter
113, 44 µm
Gravity
–9.81 m/s2
Particle maximum diameter
368, 56 µm
Fig. 4. Initial and boundary conditions of the simulation model.
The results of the numerical simulation showed a good agreement with the experimental data, especially in the distribution of the gas mixture by burners. Smaller deviations occurred in the case of distribution of coal powder by burners, Fig. 6. The reality of the obtained results depends on the accuracy of the granulometric composition of the coal powder at the entrance to the coal powder separator (behind the mill), as well as the determination of the granulometric composition of the grinding material in terms of taking a representative sample of the grinding material. On the basis of the data from the sieve analysis, Table 1, the relevant diameters and determined particle distribution coefficients around that diameter were calculated. These data were used to define the composition of the coal powder at the entrance to the mill. In our case, we have five groups of particles whose behavior is observed through simulation.
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Table 3. Part of downloaded data from measurements on the boiler from [1]. R.br
Dimensions
Unit
Case 1
Case 2
1
Barometric pressure
hPa
991
992
2
Position of valves in the separator Upper
°
20
20
Lower
°
45
20
3
Pressure in front of the separator p1
mbar
5, 8
5, 7
4
Mill capacity
t/h
32, 2
31, 1
5
Pressure in front of the burner Lower p6
mbar
–1,1
–1, 9
Middle p7
mbar
–1, 6
–1, 9
Upper p8
mbar
–2, 0
–2, 2
6
Ventilation behind the mill
m3 /h
131860
131700
7
The quantity of coal behind the mill mcoal
t/h
23, 1
23, 0
8
Grinding quality/sieve residue R90
%
61, 9
59, 3
R200
%
37, 1
30, 6
R500
%
12, 2
6, 8
R1000
%
2, 8
1, 3
Table 4. Validation of numerical simulations. Case
Pressure in front of the separator
Angle vanes
Unit
Experiment
Simulation
Deviation [%]
Case 1
Pa
570
532.10
6.65
Case 2
Pa
580
551.26
4.95
3 Results and Discussion 3.1 Impact of Mill Load on the Distribution of Pulverized Coal The flow of pulverized coal that enters the separator is divided into two parts by regulating valves, the part that exits the outlet and is led through the burner to the combustion chamber, and the part that is returned to the mill with recirculation water for re-grinding. When analyzing the influence of the mill load, the flow of pulverized coal through the separator changed from the minimum (5 kg/s) to the maximum (10 kg/s) capacity of the mill. The simulations were carried out at the same inclination of all flaps in the separator, which is 20°. The result is the mass flow of fractions of pulverized coal at the exit from
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Fig. 5. Simulation results for blade tilt in sieve basket by 20° and by 45° angle.
the burner nozzle and the mass flow of fractions of coal powder that is recirculated, Fig. 7. The distribution of phases (gas and solid) on the burner nozzles depending on the capacity of the mill is shown diagrammatically. It can be seen that the changes are mostly linear in nature, Figs. 8, 9 and 10. The change of the solid phase depending on the capacity of the mill has a linear dependence with an accuracy factor of 0.9284 to 0.978. Unlike it, the change in the gas phase has a smaller linear dependence because the accuracy factor ranges from 0.6291 to 0.9182. The only significant deviation of the gas phase is at the third nozzle, where due to the change in the capacity of the mill, there are different flow conditions of the fluid particles, in the same continuum that does not change.
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Fig. 6. Comparison of results of experimental and simulation.
Fig. 7. Distribution of pulverized coal by burner outlets depending on mill capacity.
With increased mill capacity, the percentage of pulverized coal on the third nozzle of the burner increases, as well as on the second nozzle, while the recirculation in the mill decreases. Part of the particles is carried by the flow of the aeromixture because the particles do not have space to be separated from the flow and recirculated to the mill for additional processing. Therefore, the geometry of the channel has a great influence on the extraction of coal for recirculation, as shown in [6, 9, 12, 14]. 3.2 Impact of Particle Size on the Distribution of Pulverized Coal As mentioned earlier, the degradation of impact plates in ventilation mills also causes a change in the granulometric composition of the material being ground. Experimental sieve analyses, which refer to the distribution of particles, clearly indicate the fact that the deterioration of the granulometric composition of pulverized coal with the time of operation of the mill plant is an inevitable phenomenon. Accordingly, the numerical analysis of the influence of the granulometric characteristics of pulverized coal on the
I. Deli´c et al. Distribuon of phase [%]
528 50 40
35.25
35.87 35.97
36.15
35.77
36.5
36.5
27.16
27.62 27.11
25.83
24.76
24.34
22.93
8.00
9.00
10.00
30 20 10 0 4.00
5.00
6.00
7.00
11.00
Solid phase
Mill capacity [kg/s] Gas phase
Linear (Solid phase)
Linear (Gas phase)
Fig. 8. Phase distribution on the first nozzle of the burner depending on the capacity of the mill.
Fig. 9. Phase distribution on the second nozzle of the burner depending on the capacity of the mill.
distribution in the channel sections of the aeromixture is set as primary. Primarily for the reason to determine the critical granulometric composition of the pulverized coal that causes deterioration of the operation of the boiler combustion system. With the aim of determining the influence of the granulometric composition of pulverized coal particles on the distribution in the air mixture channels and burners, numerical simulations were carried out for different granulations. The simulations were carried out at the same tilt of the flap in the separator, which is 20°. It is important to emphasize that all boundary conditions in the mentioned simulations were considered constant,
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Fig. 10. Phase distribution on the third nozzle of the burner depending on the capacity of the mill.
except for the reference diameter and the spreading parameter, which define the RosinRammler curve. All simulations were carried out with the limitation of observing five representative diameters of a certain granulometric composition. A diagrammatic representation of the distribution of pulverized coal at the exits from the burner, depending on the grinding granulation, i.e. on the fineness of the grinding, is given in Fig. 11. Three different sizes of mean diameter of 198 µm, 222 µm and 300 µm were used. We can see that the changes on the first and second burner nozzles for these two diameters are insignificant, while the difference is observed in the case of the third, the largest particle diameter.
Distribuon of pulverized coal [%]
70.00 60.34
60.00
60.45
58.69
50.00 40.00
34.69
38.07 29.14
32.80 30.00
26.90
20.00 12.76
27.26
12.29 12.17
10.00 0.00 Izlaz 4 G1 [198 μm]
Izlaz 3 G2 [222 μm]
Izlaz 2
Izlaz 1 Granulaon [μm]
G3 [300 μm]
Fig. 11. Distribution of pulverized coal depending on the grinding granulation.
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Experimental tests were performed at different process parameters. For this reason, the first two diameters were determined on the basis of the available sieve analyses, while the third one was chosen arbitrarily in order to create the possibility of determining the significance of the change in the grinding fineness. By increasing the diameter of the coal particle, the load on the third nozzle of the burner is reduced because the particles are too heavy and cannot be transported to the second or third channel, and therefore the load on the first nozzle is increased. A higher load on the first nozzle can lead to an increase in the percentage of unburnt in the slag, so it is necessary to take this into account. Also, larger particles need a longer combustion path, so if larger particles are found in the second or third nozzle, additional fire in the furnace is caused to rise towards the heating surfaces. Figure 12 shows the distribution of the gas phase depending on the change in the diameter of the coal particle, which is relevant for the analysis. There are no significant changes in the burner, except for the increase in the gas phase due to the deterioration of the granulation when returning to the mill.
Distribuon of the gas phase [%]
40.00
37.07
35.00 30.00
27.26
37.04 37.51 35.41 35.67
35.44
27.55 27.05
25.00 20.00 15.00 10.00
6.85 6.67
7.32
5.00 0.00 Izlaz 4
Izlaz 3 G1 [198 μm]
G2 [222 μm]
Izlaz 2
Izlaz 1 Granulaon [μm]
G3 [300 μm]
Fig. 12. Distribution of the gas phase depending on the grinding granulation.
There is also a representation of the load of the cross-section of the channel towards the exit from the burner, Fig. 12, where larger particles can be noticed in the lower parts of the channel, as explained earlier. The diameter of the coal particles is an important parameter, the distribution of the concentration of the dispersed phase depends on its value, as well as the distribution of the mass flow of the dispersed phase through the burner nozzles. The importance of coal particle size especially on the amount of unburned carbon is shown in [18] (Fig. 13).
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Fig. 13. View of the load of the cross-section of the channel and burner nozzles.
4 Conclusion The load of the mill affects the amount of pulverized coal in the third channel, but also in the other two channels. At the same time, increasing the capacity of the mill reduces the amount of pulverized coal in the first channel in favor of the other two channels. By increasing the capacity of the mill, the percentage of pulverized coal on the third nozzle of the burner and on the second nozzle increases, while the recirculation in the mill decreases. At the maximum capacity of the mill, the amount of coal on the first nozzle decreases from 27.16 to 22.93%, while on the second nozzle it increases from 59.44 to 62.86% and on the third from 13.14 to 14.21. Some of the larger particles are carried by the air mixture flow because the particles do not have a manipulation space to separate them from the aeromixture flow. Grinding fineness also affects the distribution of pulverized coal in the channels in favor of the lower channels. By increasing the diameter of the coal powder particles, the load on the third nozzle of the burner is reduced, the particles are heavier and most of them cannot be transported to the second or third channel, therefore the load on the first nozzle is increased. Accurate knowledge of correct boiler operating conditions at a nominal load may be very useful for modeling boiler operation at other partial loads, in particular, at minimum load. For a better quality of the combustion process, it is necessary to adjust the amount of air for combustion according to the load of the mill, the distribution of coal powder by burner levels and the number of working hours of the mill.
References 1. Kitto, J.B., Stultz S.C.: Steam: its Generation and Use, ed. 41 The Babcock & Wilcox Company (2005) 2. Johansson, R., Evertson, M.: CFD simulation of a gravitational air classifier. Miner. Eng. 33, 20–26 (2012) 3. Ismail, F.B., Al-Muhsen, N.F.O., Hasini, N., Kuan, E.W.S.: Computational fluid dynamics (CFD) investigation on associated effect of classifier blades lengths and opening angles on
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Influence of Coal Mixing Process on the Performance of the Steam Boiler Izudin Deli´c1(B)
, Midhat Osmi´c1 , Nedim Ganibegovi´c2 , Almir Brˇcaninovi´c2 , and Amel Meši´c1
1 Faculty of Mechanical Engineering, Department of Thermal and Fluid Technique, University
of Tuzla, Tuzla, Bosnia and Herzegovina [email protected] 2 EPBIH Concern, Thermal Power Plant Tuzla, Tuzla, Bosnia and Herzegovina
Abstract. The technological process of electricity production is designed for the known characteristics of coal, and the energy efficiency and economy of the production process depend on those coal characteristics. Coal of lower calorific value requires larger quantities for the technologically necessary amount of heat in the production process, which is limited by the capacity of the mill plant. Therefore, the problem of ensuring the required quality of delivered coal is solved by homogenizing different coals in the function of preparing a mixture of coal for the process of electricity production. Within the research, the influence of coal mixture of different quality from the designed one on the production process and the efficiency of the boiler operation was determined. The boiler efficiency depending on the net calorific value of the coal is calculated and based on the energy characteristic of the boiler. The minimum and maximum limits of quality and net calorific value of used coal are defined and the results of the use of coal with a chemical composition different from the design are presented. The results of the model show that the participation of brown coal in the mixture with 25% increases the efficiency of the boiler by 0.6%, reduces the ash content by 5.27% and reduces the consumption of the coal for 8 kg/s. The set mathematical model was used to approximate the combustion process in the furnace of the steam boiler, process parameters with the greatest influence on efficiency in the analyzed operating modes of the boiler were identified. Keywords: Boiler Efficiency · Energy Efficiency · Coal Mixtures
1 Introduction The trends of the decarbonization policy of the European Union and the strategic development directions of the Energy Community of Southeast Europe impose an increase in the use of renewable energy sources. The transition process of the energy sector of developing countries, such as Bosnia and Herzegovina, is taking place slowly due to insufficient financial potential and lack of awareness among decision-makers for inclusion in the world trends of the “green” transition. Therefore, thermal power plants on © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 533–549, 2023. https://doi.org/10.1007/978-3-031-43056-5_40
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fossil fuels are in significant use, because of the lack of alternative and due to still economically profitable exploitation. In such a situation, in the period of transition from fossil fuels to complete transition to renewable sources, it is necessary to focus attention on the rational use of fossil fuels energy, along with mandatory environmental protection measures. The modern exploitation of coal-burning boilers as fuel is associated with the necessity of applying coal with ever-deteriorating characteristics and with the need for flexible operation in as wide a range of loads as possible. The combustion process in the boiler firebox is one of the key processes on which the efficiency and availability of the entire system depends on adequate regulation. The quality of operation of power boiler plants is evaluated from the aspect of achieving design parameters and from the aspect of safe and reliable operation. Also, it is understood that boiler units work with the highest efficiency, with maximum reduction of combustion products emissions, i.e., due to increased environmental protection requirements, an additional condition of operation according to environmental standards has been set before thermal power plants. The quality of coal during exploitation varies for different reasons, but all reasons can be classified as geological and exploitational. However, the efficient operation of thermal power plants implies coal of qualitative characteristics, which are defined by prescribed intervals. The deviation of the coal characteristics from those required, significantly affects the performance of the production process, which inevitably results in increased losses in the production process, which are a direct consequence of deviations in the quality of the basic fuel (coal). Factors that affect the quality of coal are: • moisture content (reduces the thermal power depending on the degree of charring of the substance), • ash content (sulfates, carbonates, etc.-reduces thermal power), • sulfur content in the form of combustible and non-combustible sulfate (an undesirable substance with a harmful effect on technological plants and the human environment), • net calorific value (the most important property, which shows the energy content of coal), • assortment (piece, cube, nut, powder, unsorted). Previous researches show that the study of the influence of fuel on the energy efficiency of the boiler and the efficiency of the thermal power plant has a significant role. The combustion issue of low-energy coal mixtures with an increased amount of moisture is present, which is shown by a large number of researches, with the aim of achieving better working conditions from the ecological and/or energy side. Scientists like Sarkara et al. [1], analyzed the possibilities for improving the plant degree of utilization through a more detailed analysis of coal quality, as it was shown that two coals with the same net calorific value can have a different effect on the degree of utilization of the boiler. Observations are that conventional analyzes do not reveal the true nature and characteristics of coal. The result of their research is the conclusion that additional coal analyzes are necessary in order to fully determine the behavior of different coals [1–3]. Taole et al. [4] examined the combustion of several different types of coal with a focus on the thermographic analysis of the flame temperature and the physical and chemical properties of the used coals. The implications and applications of the achieved results in terms of efficiency, safety and environmental conditions of the plant and possible recommendations are given for the best choice of coal for the examined boiler [4, 5]. Karthikeyan et al.
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[6] in their work pointed out the importance and influence of moisture on the quality of coal of net calorific value, which implies high storage costs and low thermal efficiency achieved by the combustion of such coals [6]. The small variation of coal ash content in power generation has a great impact on total incurred cost. Therefore, consistency in coal quality is desirable [7]. Case study results deliver a valuable insight into the effect of homogenization and improvement of predicting CV-values of shipped coal to TPP as a function of the operational planning approach [8]. Obtaining the best results from minimum homogenization is vital because homogenization raises operating costs and lowers system efficiency. Factors that affect the quality of coal blending are divided into two factors, namely technical and non-technical factors [9]. Technical factors affect the quality of coal blending results, including the quantity of available coal, the parameters used as the benchmark for blending, adequate blending equipment, good communication between workers, and the availability of production tonnage with the blending plan. The research’s improving the coal-based power plant sustainability indicators by implementation of coal homogenization project (example of the thermal power plants within JP EP BiH), provide a higher level of the sustainability in the future [10]. From the research [11], calculations and analyses we can conclude that combustion of coal with higher value of lower heat value, lower ash contain and lower moisture, enables a high boiler efficiency. Considering the above, the subject of this work is the processes that take place in a steam boiler with the aim of establishing correlations between boiler parameters and coal characteristics that would lead to a more rational use of energy and reduction of negative impacts on the environment. In order to provide the coal with the contracted characteristics, it is necessary to apply methods for equating the coal characteristics in order to achieve the required level. Homogenization is a technical-technological and organizational process of mixing coal from different locations and transported in one integrated system that enables coal quality to be equalized before the transport stage by which the coal is directed to the boiler plant for burning in thermal power plants, according to a given or adopted parameter. Equalization of quality is understood temporally and spatially. Successfully implemented homogenization will enable lower transport costs, will reduce the costs of coal combustion, transport and ash disposal, will improve the environmental protection system from pollution, primarily in the combustion phase, it will contribute to a more efficient and profitable use of available natural resources [12]. 1.1 System Under consideration – Boiler OB 650 The object of the research is Boiler type OB 650 in the Power Plant Tuzla, which belongs to the group of steep tube irradiated boilers with natural water circulation. It is intended for operation in a block with a 200 MW turbine. The construction of the boiler is in the shape of the letter , Fig. 1. The burners are supplied with coal from eight mills. The first gas channel represents a boiler firebox with an octagonal cross-section, which in the lower part turns into a funnel. Partition heaters are suspended in the taper at the top of the firebox, and a wall heater is placed on the walls. In the transitional horizontal channel between the first and second draft and in the second draft itself, there are secondary and primary intermediate
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heaters, a convective superheater with a vertical and horizontal part. Three “Ljungstrom” type regenerative air heaters, with vertical shafts connected in parallel, are provided for air heating. The initial heating of the air takes place in steam heaters placed on the intake ducts of the fresh air fan. The amount of primary air controlled the rate of combustion in the chamber. The amount of secondary air controlled efficiency. Sufficient OFA is added for the complete oxidation of any unburned or partially oxidized species originating in the combustion furnace. After passing through the water heater, the rotary air heater and the electric filter, the flue gases are led into the chimney using three radial fans.
Fig. 1. Schematic of the boiler used to create the simulation model.
The contracted qualitative characteristics of the coals, based on the above factors, which are used in the Thermal Power Plant “Tuzla” are shown in Table 1. Table 1. Contracted qualitative content of coal [13]. Coal type
Moisture (%)
Ash (%)
Combustible substance (%)
Sulphur (%)
Hydrogen (%)
Brown coal M1
27
27
46
2
3
Brown coal M2
20
30
50
2
3,5
Lignite coal
35–45
15–30
35–45
0,6
2,08
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The basic contracted heat classes, which are contracted when concluding the coal supply contract, are determined on the basis of the stated technical requirements of the thermal power units and are given in Table 2 (Table 3). Table 2. Contracted basic heat values of coal [13] Coal type
Hnet,CV [kJ / kg]
Brown coal M1
10.000−13.000
Brown coal M2
14.000−16.200
Lignite coal - pit
8.500−10.300
Lignite coal - mine
10.000−12.250
Table 3. Input and calculated parameters of the boiler energy balance Measured data
Calculated data
Air temperature
Lower heating power of coal
Generator power
Corrected boiler efficiency
Mass flow of superheated steam
Boiler efficiency
Pressure and temperature of superheated steam
Coal consumption
Pressure and temperature of intermediate superheated steam
Air mass flow through the boiler
Temperature and pressure of feed water at the Mass flow of flue gases from the boiler inlet and outlet of the water heater Ultimate and proximate analysis of the coal
Heat loss in flue gases from the boiler
Net calorific value of the coal
Heat introduced into the furnace
Flue gas temperature at the exit from the air heater
Heat absorbed in the furnace
Flue gas temperature at the entrance to the air Heat accepted in the evaporator heater Fresh steam temperature at the boiler exit
Heat accepted in the steam superheater
Temperature of the intermediate superheated steam at the inlet and outlet of the boiler
Heat accepted in the steam reheater
Air temperature at the entrance to the air heater
Heat accepted in the water heater
Air temperature at the outlet of the air heater
Heat accepted in the air heater
O2 content in flue gases
Temperature at the outlet of steam superheater No.1 and No.4
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2 Simulation Model The mathematical model is based on the heat and mass balance of the control volumes of the boiler shown in Fig. 2. The thermal energy introduced by coal is transferred to the working media (water, steam, air) through the exchange surfaces. The input data to the model are the process parameters taken from the distributed control system (DCS), the temperature of the feed water, the temperature of the steam at the entrance to the reheater and the amount of heat exchanged in each control volume. The design values of coal for the OP-650b type boiler were used. Also, data from the last conducted normative tests and data contained in the project documentation were used. A schematic representation of the flow of flue gases through the boiler and parts that exchange heat in the water/steam - flue gas relationship also is given in Fig. 2.
Fig. 2. Arrangement of exchange surfaces on the path of flue gases (red-steam, green-water, yellow-flue gas, blue-air). (Color figure online)
The mass and heat balance of the boiler is calculated according to [14–16], which, due to its volume, is not detailed here. Data from normative tests of the considered boiler [17], as well as data from block monitoring during exploitation, were used as an experimental basis. The calculation was made using the Microsoft Excel VBA program, and Fig. 3 shows the initial screen of the created model [18, 19]. To calculate the thermodynamic states of water and water vapor, the MACRO VBA subsystem of the Microsoft Excel application was used, from where the data needed for the calculation was downloaded. According to the principle scheme of the thermal energy block, it is clear that all energy and mass balances should be determined for the calculation of the steam boiler. Analytical calculation of the boiler efficiency is carried out by the prescribed methodology in accordance with the technical standard EN12952– 15 (DIN 1942:1994), according to which two approaches are possible: direct and indirect method (boiler loss method).
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Fig. 3. Main MENU and FUEL menu of the calculation and analysis application.
The FUEL menu shows the ultimate and proximate analysis of the project coal for the analyzed boiler. In the two sub-menus for the two types of coal used in the working fuel mixture, lignite and M1 brown coal, the ultimate and proximate analysis of the coal for which the net calorific value of the coal was calculated individually are entered, Fig. 3. Coal samples for technical analysis were formed according to the norms of BAS ISO 5069–1. 2.1 Validation of Model To validate the model, parameters from DCS, analysis of the coal mixture used in the boiler and analytical calculation of the net calorific value of the mixture of lignite and brown coal M1 with variable percentage values were used [20]. The verification of the model was carried out based on the comparison of the measured parameters, taken from DCS, and the results based on the input data for the analyzed coal mixtures. Parameters for comparison are: flue gas temperatures at the exit from the boiler behind the AH (regenerative air heater), coal consumption, boiler losses due to un-burnt fuel in slag and ash (L2 and L3 ), L7 – boiler losses in exit gases and boiler efficiency. The thermal calculation determines the temperature of flue gases at the exit from the combustion chamber, which defines the starting point for calculating the heat exchanged on the heating surfaces of the boiler. The regulated parameters are set as: superheated steam temperature, fresh steam production, pressure at the outlet of the steam superheater, feed water temperature, excess air, coal consumption, Hnet,cv (net calorific value of the coal). An iterative calculation procedure generates a solution, through the mass and energy balance. The results in Fig. 4 show that the modeled values of flue gas temperatures at the boiler outlet (Tfg,b,out ) do not deviate by more than 2.5% compared to the temperatures obtained by experiment, which makes the model results acceptable for further analysis. The diagram of flue gas temperature changes at the end of the furnace (Tfg,f,out ), Fig. 5, shows the deviation of modeled parameters from experimental measurements in an average amount of 9.3%, except for two cases where the deviation is around 20%. The location of the flue gas temperature measurement point is directly above the tertiary air opening, which leads to flue gas cooling. In some cases, there is a significant deviation.
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180
178.70
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10.03.
174.34
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174.01
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Fig. 4. Validation of the model with flue gas temperature at the boiler outlet (DCS measurements and model).
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Fig. 5. Validation of the model by the temperature of flue gases from the combustion chamber (DCS measurement and model).
3 Simulation and Experiment Results 3.1 Impact of Coal Homogenization Process on the Boiler Performance The boiler unit, which is the subject of the analysis, is fired with a mixture of brown coal M1 and lignite. The delivered lignite coal is of lower quality, and sometimes below the guaranteed quality (increased humidity, higher content of non-combustible components, etc.). The deterioration of the net calorific value of the coal is a direct consequence of the increased content of ballast (water + ash). The energy quality of the fuel in practical conditions is increased by adding brown coal M1 in a certain percentage and creating a mixture with lignite. The presentation of the technical analysis of the coals used during the experimental tests is given in Fig. 6.
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Fig. 6. Diagram of technical analysis and homogenization of the coals used in the experiment.
Tfg,f,out [°C]
The shaded area represents the guaranteed values of the coal quality. In the simulation model, the ultimate and proximate analysis of the mixture was calculated by using the composition of lignite and M1 coal as a basis, from the analysis for the first half of 2020 used on OP-650b boilers in the Tuzla Power Plant. The percentage of lignite and M1 coal in the mixture was chosen based on the mixture that was fired during the coal sampling period, in order to obtain the lower heating power of the fuel obtained from the samples in the laboratory. The analyzed parameters in the sampling periods of the coal fired in the Tuzla Thermal Power Plant boiler obtained by modeling are shown in the following diagrams. Figure 7 shows the flue gas temperature changes at the end of the combustion chamber and at the boiler outlet as a function of the proportion of brown coal M1 in the coal mixture for the case 1 sample. An increase in the temperature at the end of the furnace leads to greater losses and a decrease in boiler efficiency (Fig. 8). 172.0
1005.0 995.0 985.0 975.0 965.0 955.0 945.0 935.0 925.0
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170.5
SAMPLE SAMPLE
170.0
Flue gas temperature at the boiler outlet
5
10
15
20
25
30
169.5 35 40 45 50 Content coal M1 in a mixture [%]
Fig. 7. Change in the temperature of flue gases from the furnace and the temperature from the boiler depending on the content of coal M1 in the mixture.
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Fig. 8. Change in heat value of the mixture and the boiler efficiency by changing the percentage composition of M1 in the mixture.
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The boiler’s heat exchanger surfaces are designed for specific fuel process parameters. The deviation of the process parameters from the designed ones leads to the impossibility of complete transfer of the heat generated by the fuel combustion process. In this case, the result is a higher temperature at the boiler outlet. In Fig. 9, the value of the energy loss through the flue gases changes with the decrease or rise in the temperature of the flue gases at the boiler outlet, which is a consequence of the change in the composition of the operating fuel mixture.
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9.3 27 32 29 Content coal M1 in a mixture [%] temperature of flue gases at the boiler outlet [°C] L7 [%]
17
23.7
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22
Fig. 9. Changes in the outlet temperature of flue gases and losses in the outlet flue gases for different heat values of the mixture.
Coal of lower thermal quality requires larger quantities of coal for the technologically necessary amount of heat in the production process. The increase in the amount of coal affects the specific consumption by which the qualitative assessment of the technological process is carried out.
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3.2 Influence of Ash and Moisture Content in Coal on the Boiler Performance
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Figure 10 shows the relation between the degree of utilization of the boiler and the ash content in the fuel. When using coal with a lower ash content, the boiler efficiency is higher because with a lower ash content, the coal’s lower thermal power is higher. The ash content in the fuel itself directly affects losses due to unburned slag and ash.
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Figure 11 shows the dependence of the value of the lower thermal power of the operating fuel against the ash concentration in the analyzed coal. Given that ash is a ballast in fuel, it is evident that with the increase in ash, the thermal power of the fuel is significantly reduced and vice versa.
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7000 09.03. 10.03. 11.03. 12.03. 16.03. 17.03. 19.03. 22.03. 24.03. 26.03. Sample date A[%] Hnet,CV [kJ/kg]
Fig. 11. Presentation of the change in the heat value of the mixture and the composition of ash in the sampled mixture.
The deterioration of the thermal power of coal is a direct consequence of the increased content of ballast (water + ash), so in order to achieve the appropriate heat fund, a larger mass of coal is needed. The connection between thermal power and ballast content is not straightforward, as it depends on moisture content, ash content and elemental analysis. Figure 12 shows the relationship between moisture content and lower heat capacity of coal, and thus the efficiency of the boiler.
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Sample date
W[%]
Fig. 12. Dependence of the boiler efficiency on moisture and ash in the coal mixture.
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Figure 13 also shows the relation between coal consumption and boiler utilization. When burning coal with a higher content of M1, less coal consumption is required, and at the same time, a better degree of boiler utilization is achieved.
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Fig. 13. Dependence of the boiler efficiency on coal consumption.
3.3 Influence of Coal M1 Content in Mixture on the Boiler Performance In order to be able to compare the operation of the boiler plant when using different coal mixtures, i.e., mixtures with different proportions of brown coal in the analyzed mixtures, the results of the characteristic parameters obtained based on the calculation by the described model are presented below. It can be seen in Fig. 14 that the thermal power of coal decreases with a decrease in the percentage of M1 coal in the mixture and vice versa. Due to changes in the composition of the coal mixture, i.e. the change in the proportion of M1 in the given mixture, different values of the lower heating power are obtained, as well as different values of the mineral content and moisture in the fuel. The influence of the mixture of coal with different percentages of moisture, as well as ash, on the value of the lower thermal power of coal, and then, consequently, on the degree of utilization of the boiler plant was determined. The amount of moisture in the coal mixture with a proportion of coal M1 in the amount of 5% is W = 28.06%, and for the proportion of
Hnet,CV [kJ/kg]
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10300 10100 9900 9700 9500 9300 9100 8900 8700 8500 8300 8100 7900 7700 7500 83.20 83.40 83.60 83.80 84.00 84.20 84.40 84.60 84.80 85.00 85.20 85.40 85.60 85.80 86.00 ηB [%] 09.03. MODEL 17.03. MODEL EXP 09.03. EXP 17.03.
10.03. MODEL 19.03. MODEL EXP 10.03. EXP 19.03.
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12.03. MODEL 24.03. MODEL EXP 12.03. EXP 24.03.
16.03. MODEL 26.03. MODEL EXP 16.03. EXP 26.03.
Fig. 14. The influence of the coal component M1 in the mixture on the boiler efficiency.
28.5
11025
28.0
10025
27.5
9025
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8025
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7025
26.0 5
10 09.
15
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35
09.03.(W %)
40
Hnet,CV [kJ/kg]
W [%]
coal M1 in the amount of 50%, the amount of moisture was W = 26.52%. In accordance with changes in the moisture value by changing the ratio of coal in the mixture, there is a change in the value of the lower heat capacity, Fig. 15.
6025 45 50 M1 in a mixture [%] W
Fig. 15. Change in heat value of coal and moisture with change in mixture.
As an example, the change in the value of H net,CV from 7689 kJ/kg to 10090 kJ/kg due to the change in the proportion of moisture and ash from the value W = 28.06% to 26.52% and the change in ash from A = 35.02% to 29.75% can be given (Fig. 16). As can be seen from the results presented for the values of ïB , it is evident that there is a “significant” improvement in boiler efficiency when using coal mixtures in which a higher proportion of moisture was present in the initial composition of the mixture
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29.00
86
28.50 85.8
W [%]
ɳB [%]
compared to the other analyzed coal mixtures, taking into account the changes caused by adding the same amount of brown M1 coal in the mixture.
28.00 27.50
85.6
27.00 85.4
26.50 26.00
85.2
25.50 85 5
10
15
20
25
30
40
W[%]
09.03.(ηB)
ηB [%]
35
25.00 45 50 M1 in a mixture [%] 09.03.(W %)
Fig. 16. Dependence of the efficiency of the boiler on the moisture content in the coal mixture with a change in the composition of the mixture - Case 1.
86
9.7
85.8
9.6
L7 [%]
ηB [%]
The reduction of the moisture content in the mixture and the flue gas temperature are important factors that affect the boiler efficiency. As the flue gas temperature decreases, boiler losses due to moisture and hydrogen content decrease. Similarly, as the moisture content of coal decreases, the mass fraction of dry flue gases in the total amount of flue gases increases (Fig. 17).
9.5
85.6
9.4 85.4
9.3
85.2
9.2
85 5
10
15 ηB [%]
20
25 09.03.(ηB)
30
35
40
9.1 45 50 M1 in a mixture [%] 09.03.( )
Fig. 17. Dependence of boiler efficiency on changes in boiler losses U7 (modeled composition of the coal mixture on March 9).
The dependence of boiler efficiency on the change in boiler losses L7 (losses due exit gases temperature) and L2 + L3 (losses due to un-burnt fuel in slag and ash) is given in Fig. 18 for the sampling on March 9. As the amount of M1 increases, the amount of ash in the mixture decreases, which directly affects the efficiency of the boiler.
547 2
86 85.9
1.95
85.8
1.9
85.7
1.85
85.6
1.8
85.5
1.75
85.4
1.7
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1.55
85 5
10
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15
20
25 09.03.(ηB)
30
35
40
L2+L3 [%]
ηB [%]
Influence of Coal Mixing Process on the Performance
1.5 45 50 Contet coal M1 [%] 09.03.( )
Fig. 18. Dependence of boiler efficiency on changes in boiler losses due to unburnt in slag and ash (modeled composition of the coal mixture on March 9).
4 Conclusion The research is focused on modeling combustion in a steam boiler with the aim of selecting and then isolating the influence of the most important process factors on the energy efficiency of the boiler. The paper emphasizes that certain indicators of the quality of operation of the energy steam boiler on dusted coal are often opposed to each other. The conducted research enables the harmonization of current needs and possibilities in real working conditions of the boiler plant. Coal quality affects many aspects of power plant performance, notably, capacity, heat rate, availability, and maintenance. Homogenization of fuel quality with available types of coal was carried out in the work. The behavior of a steam boiler was simulated through changes in the proportion of brown coal M1 in the fuel mixture. The deviation of the results of the set model is 2–10% compared to the experimental data. The analysis of the model results shows that an increase in the proportion of brown M1 coal in the mixture gives higher values of the lower heating power and that with a constant boiler production in this case there is a reduction in coal consumption. The results of the model show that the participation of brown coal in the mixture with 25% increases the efficiency of the boiler by 0.6% and reduces the ash content by 5.27%. Also, this content of brown coal in mixture reduces the total consumption of coal by 8 kg/s. Reducing the amount of coal manifests a lower load and the work of coal feeders, coal mills, consequently reduces the load on fresh air and flue gas fans. The reliability of the plant increases. A boiler fired with a mixture with a higher proportion of brown coal achieves higher flue gas temperatures at the outlet from the combustion chamber. A mode of boiler operation is established in which ash temperatures are higher, which can lead to increased sticking and slagging of the furnace walls, which should be taken into account during exploitation and determination of the mixture composition. The combination of effects caused by the increased proportion of brown coal M1 in the mixture of analyzed coals results in an improved boiler efficiency, primarily due to the reduction of dominant boiler losses. The presented model enables the determination
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of the necessary proportions of brown coal and lignite in the mixture in order to achieve the required quality of coal for the combustion process in the boiler. The results of the set mathematical model and the methodology applied in this paper have their practical value and can serve as a kind of presentation of a possible approach in solving specific practical problems in terms of improving the performance of such systems. In addition, this enables users of modern thermal power plants to make business decisions related to the use of different types of lignite coals. Based on all of the above, it can be concluded that the set mathematical model performed approximation of the combustion process in the steam boiler furnace, on the basis of which the process parameters that have the greatest impact from the aspect of energy utilization in the analyzed working regimes of the real boiler were identified.
References 1. Sarkar, A., Prabhansu, C.A., Sadhukhan, A.K., Chatterjee, P.K.: Establishing correct coal quality for achieving optimum boiler efficiency & performance – a case study in the Indian utility industry. Int. J. Chem. Tech. Res. 10(2), 121–131 (2017) 2. James, C., Gerhard, J.: International Best Practices Regarding Coal Quality. Montpelier, VT: The Regulatory Assistance Project (2013) 3. Harikrishnan, S.P., Seshadri, P.S., Balasubramanian, K.R.: Effect on performance of utility boiler with variation in fuel properties. Int. J. Appl. Eng. Res. 11(6), 3786–3790 (2016) 4. Taole, R.L., Falcon, R.M.S., Bada, S.O.: The impact of coal quality on the efficiency of a spreader stoker boiler. J. South Afr. Inst. Min. Metall. 115(12), 1159–1165 (2015) 5. Samardži´c, M., Milovanovi´c, Z., Begi´c, F., Jeremi´c, D., Dumonji´c-Milovanovi´c, S., Škundri´c, J.: Large steam boiler furnaces issues in burning low calorific value and variable mineral composition coals. Termotehnika 37(1), 103–113 (2011) 6. Karthikeyan, M., Zhonghua, W., Mujumdar, A.S.: Low-rank coal drying technologies—current status and new developments. Drying Technol. 27(3), 403–415 (2009) 7. Mohanta, S., Meikap, B., Chakraborty, S.: Impact of coal quality on thermal power plant savings: a case study for an Indian thermal power plant South African. J. Chem. Eng. 20(2), 56–68 (2015) 8. Bankovi´c, M., Stevanovi´c, D., Peši´c, M., Tomaševi´c, A., Kolonja, L.J.: Improving efficiency of thermal power plants through mine coal quality planning and control. Therm. Sci. 22(1), 721–733 (2018) 9. Sujiman, S., Dwiantoro, M., Hermanto, H.: Coal mixing study in the optimization of coal quality in pt. Alamjaya Bara Pratama Kutai Kartanegara regency East Kalimantan Province. Inkalindo Environ. J. (IEJ), 1(2), 135–143 (2020) 10. Kazagic, A., Begic, F., Tanovic, F., Avdic, H.: Improvement of sustainability indicators of coal fired power plants by coal homogenization. In: II Conference on Sustainable Development of Energy, Water and Environmental Systems - Dubrovnik, At: Dubrovnik, Croatia (2003) 11. Bureska-Joleska, L.: Influence of coal quality to the boiler efficiency and opportunity for its improvement. Termotehnika 43(1–4), 59–65 (2017) 12. Ignjatovi´c, D., Kneževi´c, D., Kolonja, B., Lili´c, N., Stankovi´c, R.: Upravljanje kvalitetom uglja. Rudarsko-geološki fakultet Univerzitet u Beogradu, Beograd (2007) 13. Džananovi´c I. Salki´c S.: Influence of quality purchased coal on technical and economic performance of electricity production in thermal power plants Tuzla, In: Brdarevi´c S., Jašarevi´c S. (eds) 9th Research/Expert Conference With International Participation Quality 2015, pp. 105–110, Zenica, Bosnia and Herzegovina (2015)
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14. Stoši´c, N.: Kotlovi. Faculty of Mechanical Engineering Sarajevo, Sarajevo (1987) 15. Ðuri´c, V.: Parni kotlovi. Teorijske osnove. University of Beograd, Beograd (1969) 16. Guliˇc, M., Brki´c, L.J., Perunovi´c, P.: Parni kotlovi. Faculty of Mechanical Engineering Beograd. Beograd (1986) 17. Determination of specific heat consumption of unit #5 (110 MWe) at TPP TUZLA (Bosnia and Herzegovina), NIV-LTE 550, Vinca (2014) 18. Hodži´c, N., Ekinovi´c, E., Kahriman, A.: Analysis of coal combustion process in block 6 boiler of the thermal power plant Kakanj. In: Brdarevi´c S. Jašarevi´c S. (eds) The 4th Conference Održavanje - Maintenance 2016, pp. 129–136 Zenica, Bosnia and Herzegovina (2016) 19. Musulin N. Matematiˇcki model generatora pare [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture (2017). https://urn. nsk.hr/urn:nbn:hr:235:492221 20. RGH Inspekt d.o.o Sarajevo, PONDER analysis of coal sample for the period 01.01.2020. to 30.06.2020. for TPP Tuzla, Sarajevo (2020)
Exergy Analysis of a Solar Assisted Desiccant Cooling System Haris Luli´c , Sadjit Metovi´c(B)
, and Almira Softi´c
Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovo šetalište 9, Sarajevo, Bosnia and Herzegovina {lulic,metovic}@mef.unsa.ba
Abstract. The use of renewable energy sources, as well as the use of new technologies or the improvement of existing ones, represents a great challenge for humanity with the aim of reducing the use of fossil fuels. In order to valorize the work of complex energy systems, it is necessary, in addition to the energy analysis, to perform an exergy analysis, which provides a qualitative assessment of the work of individual components of the system. In this work, an exergy analysis of the solar air conditioning system with a rotating wheel for air dehumidification, which is used in comfort air conditioning was performed. Exergy and energy analysis were carried out based on the parameters of the state of moist air obtained by simulation of the model in TRNSYS, for stationary conditions. The results of calculations for each individual component of the system, as well as for the entire system, are presented in tabular form. The analysis shows that more than two-thirds of the exergy of the air flow is consumed in the desiccant wheel and the air heating process, while the remaining third of the total exergy consumed is due to other components of the system. By observing the processes that take place in individual components of the system, it is possible to identify the causes of process irreversibility, which lead to a decrease in the exergy of the air flow at the exit. Keywords: Exergy Analysis · Solar Desiccant Cooling · Exergy and Energy Efficiency · Exergy Destruction
1 Introduction One of the cooling techniques that is expanding in the field of comfort air conditioning, especially when it comes to the use of renewable energy sources, is the air cooling technique using regenerative and desiccant rotating wheels. Systems that use this air cooling technique during operation combine several processes of changing the state of moist air, such as: the process of indirect and direct evaporative cooling and the process of air drying with the aim of dehumidification. The development of this technology and its application in practice is mainly related to the prediction and evaluation of the performance of the entire system and its individual components, the improvement of the technology used, and the optimization and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 550–561, 2023. https://doi.org/10.1007/978-3-031-43056-5_41
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development of new materials. Dauo et al. [1] explained in their work the basic principles of the air cooling system using the dehumidification process and emphasized their utility value. Through various examples, they showed the possibility of using these air conditioning systems in different climatic conditions, and highlighted their advantage in terms of energy savings and exploitation costs. Mavroudaki et al. [2] conducted research on the possibility of applying solar assisted desiccant cooling systems for the region of southern Europe. At the same time, Halliday et al. [3], independently of this research, conducted a similar study of the application of these systems in the UK. The results showed that savings were achieved in primary energy, regardless of the fact that the regions are located in different climate zones. Dezfouli et al. [4] showed in their work, through the energy analysis of the solar assisted desiccant cooling system, the justification of using these systems in extremely humid climates. The analysis showed that compared to conventional fan coil units, the solar assisted desiccant cooling system was able to meet the prescribed internal parameters of relative air humidity while simultaneously reducing CO2 emissions into the atmosphere. Jurinak et al. [5] dealt with the same issue earlier in his doctoral dissertation, and later in his papers, in which he states open solar air conditioning systems (open-cycle desiccant air conditioning) as a successful alternative to conventional compression refrigeration devices. Bourdoukan et al. [6] conducted an experimental study of the application of the solar cooling system in different operating conditions with the assumption of using vacuum solar collectors for regeneration purposes. During the research, the performance of individual components was examined, and then the overall performance of the system. The analysis showed that the performance of the desiccant wheel is not only affected by the regeneration temperature and environmental parameters. With the aim of detecting system components in which the largest part of exergy destruction occurs, Rafique et al. [7] in their work made an exergy and energy analysis of the solar air conditioning system using the average values of the system parameters calculated from the theoretical analysis. Many researchers have analyzed and observed the behavior of the solar assisted desiccant cooling system using the principles of the Second law (Second law analysis). Thus, Lavan et al. [8] in their work gave a general analysis of the system based on the II law, without details about the operation of the system and its components, using Carnot’s concept to estimate the COP of reversible processes. A similar study was conducted by Bulck et al. [9] focusing on the dehumidifier rotating wheel with solid desiccant. The paper presents the equations for the calculation of entropy during the adiabatic flow of moist air through a dehumidifier with a solid filling, and identified and quantified sources of irreversibility. Pesaran [10] investigated the effect of desiccant degradation on the performance of an open-cycle desiccant air conditioning system. Dincer et al. [11] published a study that talks about what exergy analysis brings new to the design, analysis, assessment and improvement of energy systems. The energy and exergy analysis of the desiccant cooling system with a photovoltaic/thermal-solar air collector and air-based thermal energy storage was published by Ma at all [12]. The results of the analysis indicate the greatest destruction of energy in the solar system.
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2 Basics of Exergy Analysis of Heat-Driven Systems The principle of efficient use of energy capacities of heat systems is connected with the principles of the first and second laws of thermodynamics. In accordance with the first law of thermodynamics, energy cannot be destroyed in processes, but is transformed from one form to another, and the principle of indestructibility of a certain quantity is a useful fact when analyzing any process. Unlike energy, exergy can be destroyed, and its destruction is proportional to the increase in entropy of the system, which is a concept of the second law of thermodynamics. Entropy is a measure of the irreversibility of a process. In thermodynamics, exergy represents the maximum useful work of an open system that can be achieved during a reversible process, which brings that system into equilibrium with the environment, that is, exergy represents the potential of the system to make changes while reaching equilibrium with its environment. In general, exergy is a part of energy that can be fully transformed from one form to another, i.e. a part of energy that is available for use. Exergy can be considered a measure of the value of energy, because the greater the share of exergy in the total energy, the greater the value of that energy. On the other hand, there is a part of the energy that cannot be transformed into any other form and as such is unusable. That part of energy is called anergy. A graphic representation of the flow of energy in a physical process is given in Fig. 1.
Fig. 1. The flow diagram of the energy in a physical process [13].
It is clearly visible from the diagram that the value of exergy at the end of the process decreases with the increase in entropy of the system, that is, the irreversibility of the process increases and the value of anergy increases. In all this, the value of the total energy remains unchanged, only its usability changes. Based on this, it can be concluded that the total sum of the value of exergy and anergy remains unchanged during the process, that in the real process the value of exergy decreases, and anergy increases precisely by the amount of the increase in entropy, and that anergy can never be converted into exergy. From this, it can be seen that when valorizing the work of complex energy systems, it is necessary, in addition to energy, to perform an exergy analysis, which gives a qualitative assessment of the work of individual components of the system, whose improvements enhance the efficiency of the entire system.
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3 Exergy Analysis Methodology The methodology for determining the exergy efficiency of the individual components of the solar air conditioning model, as well as the overall system, shown in this chapter, were made according to the model scheme given in Fig. 2. It should be noted that the indices that appear in the calculations correspond to the markings on the model diagram and define the exact position of the state of moist air during the process. In order to make it easier to follow the calculations, a brief description of the system, shown schematically in Fig. 2, is given below.
Fig. 2. Scheme of the solar desiccant cooling system.
The outside air at the entrance to the system is mixed with the exhaust air from the room in a ratio of 1:4, which creates state 1. The moist air of state 1 passes through the desiccant rotating wheel (DW), delivers a certain amount of water to the adsorbent, dries and heats up, and with state 2 leaves the dehumidifier. After that, it passes through a regenerative type rotary heat exchanger (HW), and in the process from state 2 to state 3 it transfers a quantity of heat to the pre-cooled regenerative air. After heat removal, the process air with sufficiently low absolute humidity enters the direct evaporative cooler (ECsup-1) where it is cooled and moistened in a process from 3 to 4 to the parameters of the state of entry into the air-conditioned space. On the other hand, the exhaust air from the space (state 5) is mixed with the outside air entering the system as a process in the ratio 3:1 and with the outside air used for the regeneration of the desiccant wheel in the ratio 1:4. Regenerative air is led through the solar circuit air heater (RHE) and heated to state 9, then passes through the air reheater (HE) and exits with state 10, which has the required temperature for the regeneration of the rotating wheel. Both the air heater and reheater are of classic design, with a water-glycol mixture, that is, hot water as the working medium. Heat is supplied to the reheater by a conventional system in which fossil fuels are burned, through hot water, with temperature level 80/60 °C. The air heater is located in the solar circuit, which receives heat from a renewable energy source through flat water-cooled solar collectors with a transparent cover. In order to ensure the required flow of the working medium, a circulation pump is installed in the solar circuit, powered by electrical energy, but of very low power. The pump is controlled and regulated by an automatic system, which prevents the flow of the working fluid
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under conditions when there is not enough intensity of solar radiation on the plane of the collector. From the air heater, regenerative air is led through a dehumidifying, rotating wheel, where in the regeneration process, from state 10 to state 11, it receives moisture from the sorption material and carries it further to the environment. The appropriate software tool – TRNSYS was used for system modeling and operation simulation. The solar air conditioning system shown in Fig. 3 is simulated in operating modes that correspond to the conditions of comfort air conditioning with a total cooling load of 5 kW, with an internal design air condition (t5 = 25 °C, x5 = 10 g/kg), for a location whose external design parameters (tamb = 32 °C, x5 = 11 g/kg). The calculation was done using the method of successive substitution with a time step of 6 min. The difference between the calculated and expected values of the output parameters at the end of the calculation must be less than 0.001, which represents the tolerance threshold at which the iterative procedure is stopped. The second case of calculation termination is after 100 iterations.
Fig. 3. Scheme of the solar desiccant cooling system in TRNSYS [14].
Table 1 provides an overview of all the basic TRNSYS components that were used during the simulation of this model. Figure 4 shows the processes of changing the state of moist air in Mollier’s i-x diagram, for the model shown. It should be emphasized that the states of the moist air presented in the diagram are the result of the simulation of the system operating in stationary regime for certain values of the environmental parameters.
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Table 1. List of basic TRNSYS components used in simulations according to model shown in Figs. 2 and 3. Component description
Component label according to Component label according to the scheme (Fig. 2) the scheme (Fig. 3)
Mixing chamber
–
Type 11c
Desiccant wheel
DW
Type 801
Multidimensional interpolation
–
Type 581b
Heat wheel
HW
Type 667c
Evaporative cooler – supply air
ECsup -1
Type 506c
Evaporative cooler – exhaust ECex -1 air
Type 506c-2
Regenerative heat exchanger
RHE
Type 670-1
Air heater
HE
Type 754d
Solar collectors
COLL
Type 1a
Fig. 4. Mollier i-x diagram for solar air conditioner model.
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The maximum value of the coefficient of performance of heat-driven cooling systems is obtained under the assumption that the entire process is reversible and is determined as: T6 T1 (1) COP Carnot = 1 − T10 T1 − T6 where T1 , T6 i T10 are the temperature of the external process air at the entrance to the air conditioning chamber, the temperature of the external regeneration air and the temperature of the regeneration air at the entrance to the dehumidifier wheel, respectively. For an open solar dehumidification cooling system, the reversible COPrev is determined by: Te,ev Tc,cool , (2) COP rev = 1 − Ts,heat Tc,cool − Te,ev where Ts,heat , Te,ev , i Tc,cool are equivalent temperatures in the processes of heating, evaporation and cooling, which are calculated for the same mass flows of process air and regeneration air according to: Ts,heat = Te,ev = Tc,cool =
i6 − i10 s8 − s10
(3)
i4 − i6 − (x6 − x4 ) · iw s4 − s6 − (x6 − x4 ) · sw
(4)
i11 − i1 − (x7 − x3 ) · iw s11 − s1 − (x7 − x3 ) · sw
(5)
In the above expressions, iw i sw represent the specific enthalpy and specific entropy of water. Two expressions are used to determine the exergy efficiency of the system: ψDCS,1 =
COP COP rev
(6)
ψDCS,2 =
Excool Exheat
(7)
where Excool is the exergy of the process air flow from state 1 to state 4 which is calculated according to: Excool = m ˙ pr · [i1 − i4 − Tamb · (s1 − s4 )]
(8)
Likewise, Exheat represents the increase in the exergy of the air stream in the process of bringing heat from state 8 to state 10 and is calculated according to: Exheat = m ˙ reg · [i10 − i8 − Tamb · (s10 − s8 )]
(9)
Neglecting kinetic and potential energy, the exergy value of the air flow is calculated according to: Ex = m·e ˙ x=m ˙ · [i − i0 − T0 · (s − s0 )]
(10)
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where ex represents the specific exergy flow, and the index “0” represents the equilibrium state, in this case, the state of the environment. The exergy destruction value is obtained from the following expression: Exdest = Tamb · Sgen
(11)
in which the term Sgen represents the entropy change of the system component. Exergy destruction values for each system component, assuming no heat exchange between the component and the environment, are calculated according to: ˙ pr · (s2 + s11 − s1 − s10 ) Exdest,DW = Tamb · m
(12)
˙ pr · (s3 + s8 − s2 − s7 ) Exdest,HW = Tamb · m
(13)
˙ pr s4 − m ˙ pr s3 − m ˙ w,EC−1 sw Exdest,EC−1 = Tamb · m
(14)
Exdest,EC−1 = Tamb · m ˙ reg s7 − m ˙ reg s6 − m ˙ w,EC−2 sw
(15)
The exergy efficiency of the rotating wheels can be expressed in the form of the ratio of the growth of the exergy value of the cold air stream to the decrease of the exergy value of the warm air stream, and is determined as: i2 − i1 − Tamb · (s2 − s1 ) i10 − i11 − Tamb · (s10 − s11 ) i8 − i7 − Tamb · (s8 − s7 ) = i2 − i3 − Tamb · (s2 − s3 )
ψDW =
(16)
ψHW
(17)
Generally, the exergy efficiency of each system component can be expressed as: ψ=
Exout Exdest =1− Exin Exin
(18)
where Exout i Exin are the exergy values of the moist air stream at the outlet and inlet of the system component, respectively. Using this relationship, the efficiencies of evaporative coolers EC-1 and EC-2 can be determined according to: Exdest,EC−1 Ex3 Exdest,EC−2 =1− Ex6
ψEC−1 = 1 −
(19)
ψEC−2
(20)
The exergy destruction value and exergy efficiency of the air heater, assuming a constant temperature of the working medium in the heating system, is determined according to: Qreg (21) ˙ reg s10 − m ˙ reg s8 − Exdest,HE = Tamb · m T10 i10 − i8 − Tamb · (s10 − s8 ) ψHE = (22) 10 −i8 ) i10 − i8 − Tamb ·(i T10
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4 Results and Discussion Exergy and energy analysis were carried out for the air cooling system using the rotating wheel dehumidification technique, which is shown in Fig. 3, and based on the values of the moist air state parameters obtained by simulation of the model for stationary conditions, which are shown in Table 2. Table 2. Values of moist air parameters in the cooling process according to the Fig. 3. Index
T °C
Twb °C
x kg/kg
ϕ
i kJ/kg
s kJ/K kg
amb
33,00
21,22
0,0110
0,348
61,38
0,2175
1
27,00
18,80
0,0103
0,458
53,30
0,1905
2
53,68
23,62
0,0060
0,065
69,63
0,2390
3
26,45
15,03
0,0060
0,279
41,90
0,1504
4
18,47
15,01
0,0093
0,696
42,04
0,1522
5
25,00
17,94
0,0100
0,504
50,61
0,1814
6
31,00
31,46
0,0108
0,381
58,68
0,2085
7
21,60
20,44
0,0147
0,902
59,03
0,2107
8
48,33
27,52
0,0147
0,205
86,69
0,3005
9
64,30
30,94
0,0147
0,096
103,22
0,3507
10
76,53
33,27
0,0147
0,057
115,90
0,3876
11
50,00
30,09
0,0189
0,241
99,41
0,3432
Table 3 shows the results of calculations for the values of energy efficiency, exergy efficiency and exergy destruction, for each individual component of the system, as well as for the entire system. The defined regenerative heat exchanger in this model has high efficiency (85%), which can be achieved in these operating conditions, and was confirmed by the equipment manufacturer. At the same time, it has high exergy efficiency, considering that the lost exergy is only 10% of the total for the entire system. The evaporative cooler in the process air stream has a slightly lower efficiency (70%) than the other evaporative cooler located in the regenerative air stream (89%). This is a consequence of the regulation of the operation of the first evaporative cooler with the aim of achieving the expected values of the parameters of the state of the supply air at the entrance to the room. At the same time, EC-1 has higher exergy efficiency, considering that in the second evaporative cooler EC-2, a larger amount of water is injected into the air stream, which results in greater process irreversibility. When calculating the efficiency of the air heater, as an assumption that did not significantly affect the results of the analysis, it was assumed that there is only one water heater that has a constant temperature of the working medium. The assumption is correct, given that the heaters are of identical construction and that the moist air in the process
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from state 8 to state 10 is always given the same amount of heat, needed to reach the regeneration temperature, regardless of which amount comes from which heater. Table 3. Energy and exergy characteristics of solar air conditioning system components. Index
Energy efficiency ε
Exergy efficiency ψ
Exergy destruction
Exergy destruction
%
%
kW
%
HW
85
61,39
0,31
10,3
EC-1
70
39,23
0,32
10,7
EC-2
89
20,62
0,42
14,0
70,07
0,90
30,1
51,26
1,05
34,9
28,19
2,99
100
HE DW
54 33 41
System
The results of the analysis show that the exergy efficiency of the heater is 70.07%. All three values of the efficiency of the dehumidifier wheel are relatively low, especially the second value which is expressed in relation to the regeneration temperature. As can be seen from Table 3, for this simulation case, the required regeneration temperature is above 70 °C, which certainly leads to a larger amount of energy that needs to be delivered to the system, i.e. a lower COP. The third value of the efficiency of the dehumidifying wheel, which refers to the exchanged amount of moisture in the wheel between the air currents and the sorption material, is not much higher than the second one, and it is directly dependent on the design of the wheel and the type of dehumidifying material. At the same time, the exergy efficiency of the dehumidifying, rotating wheel is low compared to the maximum values, which can be expected to be up to 85%. Exact values for exergy destruction, as well as the percentage share of exergy destruction of each component in the total exergy destruction of the system, are shown in Table 3. It can be seen that more than two thirds of the exergy of the air flow is consumed in the dehumidification wheel and the air heating process, while the remaining third of the total exergy consumed belongs to other components. The trend of such results coincides with the results of research on an experimental model of a similar solar air conditioning system, where it was determined that the dehumidifier wheel and the heater contribute the most to the overall increase in entropy of the system [5]. Observing in particular the processes that take place in individual components of the system, it is possible to identify the causes of process irreversibility, which lead to a decrease in the exergy value of the air flow at the exit. If the dehumidifier wheel is analyzed, the increase in process irreversibility is caused, for the most part, by the heat and mass transfer process with larger temperature differences, as well as a larger difference in partial pressures of water vapor from the air flow and water pressure in the wheel
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filling. The same can be said for evaporative coolers, where in the process of adiabatic humidification, due to the difference in concentrations between the incoming air and the supplied water in the evaporative cooler, a mass transfer process occurs and part of the exergy is permanently lost. In the process of heating the air, the irreversibility of the process is caused by the heat transfer process at larger differences in the temperatures of the regenerative air at the end and at the beginning of the heating process, the maximum value of which, in this case, can be T10 − T8. The calculated exergy efficiency of the system, in relation to COPrev , is less than 30%, which means that there are significant destructions of exergy in certain components of the system. With the aim of increasing the exergy efficiency of the system, and bringing the value of COP closer to the values of reversible COPrev , it is necessary to reduce exergy destruction primarily in the dehumidifier and air heater. These components of the solar dehumidification cooling model carry the largest weight share in the total exergy destruction. The efficiency of a dehumidifier wheel mainly depends on its manufacturing characteristics. This primarily refers to the filling material (adsorption material), internal geometry, wheel rotation speed, as well as the ratio of surfaces through which process and regenerative air flows. The regeneration temperature is also an important factor that determines the characteristics of the wheel. Optimizing these parameters can significantly increase its efficiency. The irreversibility of the process due to the heating of moist air in the heater is something that cannot be avoided during the solar air conditioning process, given that there are always sufficiently large temperature differences between the media that exchange heat. One of the ways to increase the exergy efficiency of the system, that is, to reduce the destruction of exergy in the components in which the process of dehumidification and air heating takes place, is the introduction of multi-stage dehumidification. Namely, by placing two dehumidifier wheels in series, the temperature of regenerative air at the entrance to the dehumidifier rotating wheels is reduced. This results in a smaller temperature difference in the process of heating moist air, assuming a constant temperature of the working medium in the heating system, that is, a smaller increase in entropy, a smaller destruction of exergy and a higher exergy efficiency of the component. The same is true for the dehumidifier, considering that the drying process of the sorption material in contact with the stream of moist regenerative air takes place at smaller temperature differences.
5 Conclusion The developed solar desiccant cooling system model for conformal air conditioning was used to perform energy and exergy analysis. The parameters of the change in the state of the air during the process were obtained by simulating the presented model in TRNSYS under stationary conditions. Research shows that by applying exergy analysis to individual elements of the system, it is possible to determine their effectiveness, that is, their participation in the total destruction of exergy. In this way, weaknesses of the system are detected and attention is focused on its improvement. The results of the analysis show that the exergy efficiency of the system is less than 30%, which means
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that there are components of the system in which, due to the irreversibility of the process, entropy increases significantly. This primarily refers to the desiccant wheel and the air heater, the components of the solar air conditioning system identified by the analysis as having the largest weight share in the total exergy destruction. By optimizing the operating parameters of the dehumidifier wheel, primarily the flow rate and temperature of the regenerative air, as well as by improving the physical performance of the solid desiccant, it is possible to significantly increase the efficiency of the component itself, reduce exergy destruction and increase the overall COP of the system.
References 1. Daou, K., Wang, R.Z., Xia, Z.Z.: Desiccant cooling air conditioning: a review. Renew. Sustain. Energy Rev. 10(2), 55–77 (2006) 2. Mavroudaki, P., Beggs, C.B., Sleigh, P.A., Haliiday, S.P.: The potential for solar powered single-stage desiccant cooling in southern Euro. Appl. Therm. Eng. 22, 1129–1140 (2002) 3. Halliday, S.P., Beggs, C.B., Sleigh, P.A.: The use of solar desiccant cooling in the UK: a feasibility study. Appl. Therm. Eng. 22, 1327–1338 (2002) 4. Dezfouli, M.M.S., Sopian, K., Kushsairy, K.: Energy and performance analysis of solar solid desiccant cooling systems for energy efficient buildings in tropical regions. Energy Convers. Manage. 14 (2022) 5. Jurinak, J.J., Mitchell, J.W., Beckman, W.A.: Open-cycle desiccant air conditioning as an alternative to vapor compression cooling in residential applications. J. Sol. Energy Eng. 106(3), 252–260 (1984) 6. Bourdoukan, P., Wurtz, E., Joubert, P.: Experimental investigation of a solar desiccant cooling installation. Int. Solar Energy Soc. 83, 2059–2073 (2009) 7. Rafique, M.M., Gandhidasan, P., Al-Hadhrami, L.M., Rehman, S.: Energy, exergy and anergy analysis of a solar desiccant cooling system. J. Clean Energy Technol. 4(1) (2016) 8. Lavan, Z., Monnier, J.B., Worek, W.M.: Second law analysis of desiccant cooling systems. J. Solar Energy Eng. 104(3), 229–236 (1982) 9. Van Den Bulck, E., Klein, S.A., Mitchell, J.W.: Second law analysis of solid desiccant rotary dehumidifiers. ASME J. Heat Transf. 110, 2–9 (1988) 10. Pesaran, A.A.: Desiccant degradation in desiccant cooling systems: a system study. J. Solar Energy Eng. 115(4), 237–240 (1993) 11. Dincer, I., Ratlamwala, T.A.H.: Importance of exergy for analysis, improvement, design, and assessment. WIREs Energy Environ. 2(3), 335–349 (2013) 12. Ma, Z., Ren, H., Sun, Z.: Energy and exergy analysis of a desiccant cooling system integrated with thermal energy storage and photovoltaic/thermal-solar air collectors. Sci. Technol. Built Environ. 1–16 (2019) 13. SHC IEA: Task38 – Solar Air-Conditioning and Refrigeration, Exergy Analysis of Solar Cooling Systems. A Technical Report of Subtask C3 (2010) 14. Luli´c, H.: Development of Cooling Process Using Solar Energy. Ph. D Thesis, University of Sarajevo – Mechanical Engineering Faculty (2015)
Investigation of the Concentration of Air Pollutants in the Vertical Profile in the Zenica Valley Mirnes Durakovi´c1(B)
, Husika Azrudin2 , and Sadjit Metovi´c2
1 University of Zenica, Institute “Kemal Kapetanovi´c” in Zenica, Fakultetska 3, 72000 Zenica,
Bosnia and Herzegovina [email protected] 2 University of Sarajevo – Mechanical Engineering Faculty, Vilsonovo šetalište 9, 71000 Sarajevo, Bosnia-Herzegovina
Abstract. The most favorable conditions for living and working are river valleys, which is why settlements and industrial plants are usually located next to each other in a relatively small area. In such areas, there are a large number of sources of air emission of polluting substances of different characteristics with regard to the height of emission, the intensity of emission and the variety of pollutants. Due to the weak airflow in the river valleys, especially in the colder part of the year, there is often an inverse state of the atmosphere in terms of the temperature profile. The combination of high pollutant emissions, terrain topography and specific meteorological conditions (inversions) cause enormous ambient air pollution. The purpose of this research is to determine the vertical profile of temperature, humidity, pressure and concentration of polluting materials (particulate matter and carbon monoxide) in the air in the area of the Zenica valley before the ground and elevated temperature inversions layer occurs. The investigation of the vertical profile of temperature and concentration of pollutants in the air was carried out on 09.23.2022 at the Banlozi location and 06.10.2022. at the locations Perin Han and Kamberovi´ca polje, which are distributed along the Zenica basin and which are suitable for researching the vertical profile and concentration of pollutants in the air. The research results show that there was no ground inversion in the vertical profile of the ground layer of the atmosphere at any place along the Zenica valley and that the concentrations of polluting materials (PM10 , PM2.5 , PM1 and CO) were uniform in the vertical profile at all research locations. Keywords: Vertical atmospheric profile · Ground inversion · Air pollutants · Temperature Inversion
1 Introduction The Zenica valley is orographically very specific, which is why ground and elevated temperature inversions are created in conditions of weak air flow. Due to the creation of temperature inverse layers of the atmosphere, there is a sudden increase in the concentration of pollutants in the air. Strong positive relationship between characteristics of an © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 562–568, 2023. https://doi.org/10.1007/978-3-031-43056-5_42
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inversion layer and ambient air pollution with PM2.5 in the St. Louis metropolitan area in Missuri-Illinois during the winter time is determined in research [1]. Another study [2] conducted in Hanoi showed that during the period of inversions, the concentrations of pollutants in ambient air tended to be higher than those in the normal time. Research [3] showed that surface based inversions deteriorate ambient air quality (PM) more than elevated inversion leyer. In addition, the air pollution in the valleys is affected by the mountain and valley breezes that occur one after the other in the daily cycle. Mountain breeze occurs in the evening when warm air rises from the middle of the valley, and cold air blows down the slopes of the valley. This wind brings pollutants into the valley and due to the inversion layer, which hinders the upward movement of air in the middle of the valley and the concentrations of pollutants gradually increase. During the inversion period, emissions from industrial sources cannot spread vertically. When it reaches the base of the inversion layer, the plume begins to mix downward and descend the slope at certain heights below the base of the inversion layer [4]. On the slopes of the Zenica valley there are a large number of small household fireplaces, which, due to the low height of the chimneys, greatly burdens the ambient air. Pollutants emitted from small households fireplaces and industrial plants are brought down by the mountain breeze into the valley at night, resulting in an increase in pollutant concentrations. Therefore, knowing the characteristics of ground and elevated inversion in the Zenica valley, as well as the occurrence of mountain valley wind, are important for understanding the mechanism of air pollution in urban valleys, and especially in valleys where heavy polluting industries such as in Zenica are located. Determining the characteristics of the inverse layer of the atmosphere in which the air temperature increases with height can be determined in various ways: satellite observations, microwave radiometry, sounding measurements using balloons and drone measurements. Satellite observations of ground temperature inversion [5] with the application of inversion evaluation models can provide useful data on the characteristics of ground inversion. Microwave radiometry [6] is successfully used to determine the characteristics of the ground layer of the atmosphere and its behavior depending on the main atmospheric parameters. Sounding measurements using balloons [7] were used for the analysis of the vertical state of the atmosphere for many years despite low vertical stability. The analysis of the vertical profile of the atmosphere [8] using a drone has a number of advantages compared to other methods, such as lower costs per measurement, fast and easy deployment of drone measurement at any point, great vertical stability, the possibility of measuring the temperature profile in both directions, etc. To investigate the characteristics of the inverse layer of the atmosphere of the Zenica valley, measuring devices placed on a drone were used to simultaneously measure temperature, humidity and air pressure, as well as the concentration of floating particles and carbon monoxide. The urban-industrial area of the city of Zenica (Fig. 2) extends along the valley of the middle course of the Bosna River in an orographically highly developed area. The valley is surrounded by mountains up to a height of about 1000 m (NE) and 1300 m (NNE) and 800 m (SW) above sea level from the center of the city of Zenica. Terrain with a complex configuration, such as the area of the Zenica valley, can significantly modify weather conditions, which, due to air flow disturbances, represent unfavorable
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conditions for the diffusion and transport of pollutants. Air quality measurements in the Zenica valley, which are carried out using fixed air quality monitoring stations, show that the most common direction of air flow in the Zenica valley is SE - NW [9].
2 Research Methods To investigate the vertical profile of temperature, pressure, humidity and concentrations of pollutants in the air (PM10 , PM2.5 , PM1 and CO) an unmanned aerial vehicle - drone, which is actually a helicopter with six propellers shown in Fig. 1 was used.
Fig. 1. Unmanned aerial vehicle – drone.
On the top of the helicopter, in a specially designed box, there are sensors for measuring concentrations of pollutants (PM10 , PM2.5 , PM1 and CO) with the following characteristics shown in Table 1: Table 1. Peak loads of 3 models. Pollutant
Measuring technique
Model
Range
Resolution
PM10 , PM2,5 ansd PM1
Light scattering method
PLANTOWER PMS5003
0 – 500 µg/m3
1 µg/m3
Carbon monoxide
Electrochemical sensor
ALPHASENSE CO-A4
2000 ppm
5 ppm
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The unmanned aerial vehicle has sensors for the following atmospheric parameters: – temperature: measurement range from −40 to + 125 °C, sensor accuracy ±0.2 °C, – relative humidity: measurement range from 0 to 100%rH, sensor accuracy ± 2% rH and – atmospheric pressure: measurement range from 300 to 1100 hPa, operating temperature from 40 to 85 °C. Considering the terrain configuration of the Zenica valley, three representative locations were chosen for conducting vertical measurements up to 300 m above ground level. Locations are shown in Fig. 2 and Table 2. Table 2. Overview of pollutant sampling locations in the wider area of the Zenica valley. Location designation
Location name
Zone
Geographical coordinates
Elevation
Date of measurement
1 ((south of the valley)
Perin Han
Rural area
E 44° 10 58 N 17° 57 55
327 m a.s
06.10.2022
2 (middle of the valley)
Kamberovi´c polje
Urban area
E 44° 12 22 N 17° 54 54
312 m a.s.
3 (north of the valley)
Banlozi
Industrial zone
E 44° 14 44 N 17° 53 54
306 m a.s
23.09.2022
3. Banlozi
2. Kamberović polje
1. Perih Han Fig. 2. Pollutant sampling location.
The measurement in the vertical profile at the mentioned locations lasted about 10 min. Measurement of temperature and concentration of pollutants is done every 3 s. After the measurement, results were averaged for every 10 m from the ground.
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3 Discussion of Research Results Figures 3, 4 and 5 show the vertical profiles of temperatures, particulate matter and carbon monoxide at the investigated locations. By analyzing the obtained data, it can be concluded that at the time of measurement, there were no inverse layers of the atmosphere up to 300 m above the ground. The data show that the temperature is fairly uniform up to the mentioned measurement height (300−350 m above the ground). A slight inversion was registered at the Banlozi measuring site at a height of 360 to 400 m above the ground. Concentrations of pollutants are uniform along the entire vertical profile at all locations, except that at the Kamberovi´ca polje location at a height of 230 to 300 m above the ground, an increase in concentrations of suspended particles (PM10 , PM2.5 , PM1 ) and carbon monoxide (CO) was registered, although the inverse layer of the atmosphere was not registered at those altitudes. The appearance of higher dust concentrations at these heights is probably influenced by sources of air pollution with tall chimneys located in the Zenica valley. The lowest concentrations of floating particles were registered at the Banlozi location, which is located on the north side of the industrial facilities and is significantly closer to the industrial facilities compared to the other two locations. At the time of measurement at this location, industrial facilities were not operating, and due to the high air temperature, domestic fireplaces were not active, which caused a lower concentration of particulate matter. The data show that at the Kamberovi´ca polje location, the proportion of particles PM2.5 in PM10 was about 71%, and PM1 in PM10 was about 50%, while at the location Perin Han the proportion of particles PM2.5 in PM10 was about 90% and PM1 in PM10 was about 50%.
Fig. 3. Vertical profile of temperature and air pollutants concentration at the Perin Han site.
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Fig. 4. Vertical profile of temperature and air pollutants concentration at the Kamberovi´ca polje site.
Fig. 5. Vertical profile of temperature and air pollutants concentration at the Banlozi site.
4 Conclusions The method of measuring temperatures and pollutants shown in this paper is a very suitable tool for researching the influence of the temperature inverse layer of the atmosphere on the air pollution of the ground layer of the atmosphere. Temperature measurements in the vertical profile of the ground layer of the atmosphere show that there was no ground inversion at any place along the Zenica valley. Concentrations of pollutants (PM10 , PM2.5 , PM1 and CO) were uniform in the vertical profile at all measurement locations, except that at the location of Kamberovi´ca polje an increase in the concentration of suspended particles and carbon monoxide was
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registered at heights from 230 to 300 m above the ground. These air pollutants probably come from high pollution sources. It is necessary to carry out research during the winter period when temperature inversions are formed, especially at the Banlozi location, which is located in the immediate vicinity of the industrial complex and in the vicinity of which there is a large number of household fireplaces. Obtained results should be compared with the obtained results described in this article.
References 1. Hannah, R.J., Gutowski, W., Lennartson, E.: PM2.5 Pollution and Temperature Inversions: A Case Study in St. Louis, MO Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa (2018) 2. Trinh, T.T., Trinh, T.T., Le, T.T., Nguyen, T.D.H., Tu, B.M.: Temperature inversion and air pollution relationship, and its effects on human health in Hanoi City. Vietnam Environ. Geochem. Health (2018). https://doi.org/10.1007/s10653-018-0190-0 3. Nidzgorska-Lencewicz, J., Czarnecka, M.: Thermal inversion and particulate matter concentration in Wrocław in winter season. Atmosphere 11, 1351 (2020). https://doi.org/10.3390/atm os11121351 4. Prcanovi´c, H., Goletic, Š., Durakovi´c, M., Beganovi´c, S.: Seasonal variations of sulfur dioxide in the air in Zenica city during 11 years period 2006 – 2016. Int. J. Adv. Res. 6(3), 1133–1139. ISSN: 2320–5407 (2018) 5. Boylan, P., Wang, J., Cohn, A.S., Hultberg, T., August, T.: Identification and intercomparison of surfacebased inversions over Antarctica from IASI ERA-Interim, and Concordiasi dropsonde data. J. Geophys. Res. Atmos. 121, 9089–9104 (2016). https://doi.org/10.1002/2015JD024724 6. Pietroni, I., Argentini, S., Petenko, I.: One year of surface-based temperature inversions at Dome C, Antarctica. Bound.-Layer Meteorol. 150, 131–151 (2014). https://doi.org/10.1007/ s10546-013-9861-7 7. Sekuła, P., et al.: Measurement report: effect of wind shear on PM10 concentration vertical structure in the urban boundary layer in a complex terrain. Atmos. Chem. Phys. 21, 12113– 12139 (2021) 8. Masi´c, A., Pikula, B., Bibi´c, D.Ž., Hadžiabdi´c, V., Blaževi´c, A.: Drone measurements of temperature inversion characteristics and particulate matter vertical profiles in urban environments. In: 32nd Daaam International Symposium on Intelligent Manufacturing and Automation (2021). https://doi.org/10.2507/32nd.daaam.proceedings.018 9. Air Quality Protection Action Plan for the Zenica-Doboj Canton, UNZE OJ Institute Kemal Kapetanovic in Zenica (2020)
Advanced Electrical Power Systems
Data Mining Techniques Application for Electricity Consumption Analysis and Characterization Hamza Turˇcalo(B) , Lejla Mari´c, Adelina Muši´c, Erma Heldovac, Edin Kadri´c, Jasmin Azemovi´c, and Tarik Hubana University of Dzemal Bijedic in Mostar, University Campus, 88104 Mostar, Bosnia and Herzegovina [email protected]
Abstract. Electricity is a crucial element in modern life, and advancements in technology have led to the development of smart meters that generate a significant amount of data on electricity consumption. As a result, data mining techniques are being used to analyze and predict electricity consumption patterns. In this study, various data mining techniques such as clustering, association, data cleaning, data visualization, classification, machine learning, and prediction were employed to analyze and predict electricity consumption patterns. The aim was to improve energy efficiency, reduce costs, and support the operation of the electricity grid. The study also sought to classify consumers into groups to develop targeted energy conservation strategies. Two datasets with around 3.5 million samples each were analyzed and compared using clustering and classification analysis in the R studio environment. Multiple machine learning models were tested for classification over clustered data, and the results showed high accuracy. The study contributes to the existing body of knowledge by analyzing different preprocessing approaches for data clustering, comparing multiple machine learning models for classification, and testing the prediction accuracy of the best fitting models. The findings demonstrate the potential of data mining techniques as a tool to support electricity grid operators, suppliers, and consumers, leading to better electricity grid and market operation. Keywords: clustering · data mining · data analysis · electricity consumption · electricity characterization · energy efficiency · smart meter measurements
1 Introduction Electricity is an essential part of modern life, powering everything from homes and businesses to transportation and communication systems. Ensuring a reliable and sustainable supply of electricity is therefore a critical challenge for governments and energy companies around the world. One important aspect of this challenge is understanding how and when electricity is being consumed and being able to characterize future electricity customers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 571–582, 2023. https://doi.org/10.1007/978-3-031-43056-5_43
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Data mining techniques play a valuable role in this process, by providing tools and methods for analyzing and modeling electricity consumption data. By identifying patterns and trends in the data, and using machine learning algorithms to make predictions, data mining can help utilities and energy companies better understand and anticipate the electricity needs of their customers. This, in turn, can inform the planning and operation of the electricity grid, and help to optimize the use of available resources. This scientific work will primarily be based on a data set, which will serve to reach significant conclusions. Graphical representations, assessments, conclusions, and theories are things that will be specifically addressed in this paper. Different techniques can be applied to extract relevant information. The most used techniques are clustering, classification, and association rules. After the selection and processing of data on electricity consumption readings, data mining techniques will be applied to discover patterns in the data set. In this analysis, the clustering technique will be used, that is, a comparative analysis of the clustering of energy consumers will be made based on two available datasets, daily_dataset and hhblock_dataset. The results of the analysis will be presented graphically and tabularly. After that, an analysis will be done whether clustering works better when using half-hourly readings of electricity, or when using daily data. After clustering, a dataset will be created with an additional column joining the rows to the clusters that belong to them. Also, machine learning will be used to test different models in order to choose the best fitting model. This research aims to explore the potential applications of data mining techniques for electricity consumption analysis and characterization, and to provide a review of the existing literature on this topic. By examining the successes and challenges of past research, and considering the opportunities and limitations of different approaches, this research aims to shed light on the potential value of data mining for electricity consumption analysis, and to identify directions for future research.
2 Literature Review A review of the literature shows that there are numerous studies on the relationship among energy consumption and prediction of electricity usage rate. Electricity is one of the most essential needs of human life, especially in the new, modern age. So, it’s no wonder that there are more and more scientific research papers about it. These works focus on testing the efficiency of electricity, and methods of reducing and regulating consumption [1, 2]. What will help in solving this problem are the works about data mining and data mining techniques [3, 4]. Data mining is the process of discovering patterns and knowledge from large amounts of data [5]. It involves the use of techniques such as machine learning, statistical analysis, and database management to extract and analyze information from large datasets [6]. This information can then be used to make predictions, identify trends, and make decisions. Some common uses of data mining include identifying customer segments for targeted marketing, detecting fraud, and optimizing supply chain operations. Data mining is used in a variety of industries, including finance, healthcare, retail, and manufacturing [5]. In the modern age, AI has brought many new possibilities in all spheres of life [7]. Machine learning is a subset of artificial intelligence (AI) that involves the development
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of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so [8, 9]. In supervised learning, the computer is given a set of labeled data, and the goal is to learn a function that maps inputs to outputs. This is the most common type of machine learning and it’s used for tasks such as image and speech recognition, natural language processing, and predictive modeling [10, 11]. Data mining techniques have been widely used in the analysis and forecasting of electricity consumption [12]. Literature review on this topic has shown that these techniques can be applied in various ways to improve our understanding and prediction of electricity consumption patterns [13]. By analyzing the large amounts of data generated by smart meters and other devices on the grid, utilities can improve power system efficiency and reliability while identifying opportunities for energy efficiency and conservation [14, 15]. One application of data mining techniques is in the analysis of historical consumption data. By analyzing past consumption patterns, data mining can uncover underlying trends and patterns that can be used to make predictions about future consumption [5, 16]. Another application of data mining techniques is in the analysis of smart meter data [14, 15]. Smart meters are increasingly being used to collect detailed information about electricity consumption at the household level. For example, data mining techniques can be used to predict electricity demand, which is essential for power system planning and operation. By analyzing patterns in electricity consumption, utilities can detect unusual patterns that may indicate equipment failures or power theft, which can help them take preventative measures. Furthermore, data mining techniques can be used to segment customers based on their electricity consumption patterns, which can help utilities target marketing efforts and improve customer engagement [17]. Today, depending on the time of use of the tariff, different prices are set for each hour. To reduce the load, utilities set higher prices for peak times, usually in the evening. Specifically, changes in usage are needed to reduce peak values. The coefficient of devices used at the same time should be reduced [1]. Overall, literature review suggests that data mining techniques have a wide range of applications in the analysis, forecasting and characterization of electricity consumption and can be effectively used to analyze and predict electricity consumption. These techniques can be used to improve our understanding of consumption patterns, to identify factors that impact consumption, and to develop models for characterization of future customers.
3 Theoretical Background 3.1 Smart Metering Electricity is an essential part of modern life and important for the economy. People use electricity for lighting, heating, cooling, refrigeration and to operate appliances, computers, electronics, machinery, and public transport systems. Smart metering is a technology that uses digital devices to measure and communicate the consumption of electric power in real-time. These devices are typically installed in homes and businesses and can communicate usage information to the utility company
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through a wireless or wired connection. Smart meters typically record energy consumption in short intervals, such as every 15 min, and they allow customers to see their usage patterns in greater detail. This can help customers better understand their energy consumption and make more informed decisions about their energy usage. Additionally, smart meters can also provide the utility company with more accurate information about energy usage, which can help improve grid management and reduce costs. Smart meters are devices that are installed in homes and businesses to measure energy usage. They generate a large amount of data on energy consumption patterns, which can be used to identify trends and inefficiencies. This data is perfect for using data mining techniques, such as clustering and regression analysis, to gain insights into energy usage and to develop strategies for reducing consumption. Additionally, smart meter data can be used to create detailed profiles of energy usage for individual customers, which can be used to create personalized energy management plans. Overall, smart meter data is a valuable resource for understanding and managing energy consumption, and data mining techniques are essential for unlocking its full potential. 3.2 Data Mining and Machine Learning Data mining is defined as a process used to extract useful data from a larger set of any raw data. With the help of one or more software, the analysis of data patterns in large series of data is performed. Data mining has applications in many fields, such as science and research. For example, with the help of data mining, companies can get to know their customers better and based on this, develop more effective strategies, and use resources in a more optimal way. Data mining includes collection, storage and computer processing of data. To segment data and estimate the probability of future events, data mining uses sophisticated mathematical algorithms. Key data mining techniques are clustering, association, data cleaning, data visualization, classification, machine learning, prediction, neural networks. Clustering. Cluster analysis is one of the important techniques in data mining that groups similar data points together into clusters or groups. The goal of cluster analysis is to identify patterns in the data and group similar observations together to gain a better understanding of the underlying structure of the data [2]. Clustering is a complex challenge because it entails many methodological choices that determine the quality of a cluster solution [3]. There are many different algorithms that can be used for cluster analysis, including hierarchical methods, which create nested groups of data points, partitioning methods, which divide the data into a fixed number of clusters, and densitybased methods, which identify clusters as dense regions in the data [4]. In this paper, the K-means clustering technique is used because of its simplicity and effectiveness. K-means and single linkage are two types of clustering algorithms use the spectrum analysis of the affinity matrix, which are effective clustering techniques compared with the traditional algorithms [5]. Cluster analysis can be used for a wide range of applications, such as market segmentation, image processing, gene expression analysis, and social network analysis. Classification. Classification is a method of data analysis that is used to predict the category or class of an observation based on its attributes or features. Classification is a
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process of finding a model that distinguishes and describes data classes. It uses current and historical facts to make predictions about future or unknown events. The goal of classification is to identify patterns in the data that can be used to correctly assign new observations to their appropriate class. There are many different algorithms that can be used for classification, including decision tree, logistic regression, random forest, support vector machine (SVM), and neural network algorithms [6]. The choice of algorithm will depend on the specific problem and the characteristics of the data. Common approach to classification analysis is called supervised learning, which involves training a model on a labeled dataset, where the outcome or class of each observation is known. The model is then used to predict the class of new, unseen observations. It is the problem of finding to which of a set of categories, a new sample belongs to, based on a training set of data containing samples and whose categories membership is known [7]. In energy consumption analysis, classification methods can be used to predict energy consumption patterns, identify energy-efficient buildings, and detect energy waste. Additionally, classification can be used for load forecasting, that is predicting future energy demand, which can help utilities to plan capacity and manage resources efficiently. It’s important to note that for these applications to work, data collection and preprocessing is important for the quality of the data set and the results. Machine learning is a method of data analysis that involves using algorithms to automatically learn from data and make predictions or decisions without being explicitly programmed. The basic idea behind machine learning is to use algorithms to learn the underlying structure of the data, so that the algorithm can generalize from the examples it has seen to new examples that it has not seen before [8]. Examples include classification, regression, and structured prediction [8]. The three machine learning types are supervised, unsupervised, semi-supervised and reinforcement learning y[9]. Machine learning can be used for a variety of tasks related to electricity consumption analysis and forecasting. In load forecasting machine learning algorithms, such as artificial neural networks and decision trees, can be used to model the underlying patterns in historical electricity consumption data and make predictions about future demand. Machine learning algorithms can be used to analyze data from building automation systems, such as temperature and lighting control systems, to identify patterns in energy consumption and identify opportunities for energy savings. Smart meters can provide utilities with a large amount of data on energy consumption, which can be analyzed using machine learning algorithms to identify patterns in energy usage and make predictions about future demand. Machine learning can also be used to predict the energy prices in the future, to make more informed decisions about energy trading and purchases. It is important to note that machine learning requires a significant amount of data to be trained and fine-tuned, it also requires a good understanding of the problem domain and the data, to select the appropriate algorithm and avoid problems in the models [10].
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4 Methodology 4.1 Clustering Analysis and Comparation of Electricity Consumption Datasets The k-Means algorithm [18] is likely the most famous non-hierarchical clustering method. It can be applied when all observed variables are quantitative. It looks for the best partition of n units in k clusters. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. It is known that the k-means algorithm is the oldest and popular partitional method [19]. The k-means clustering has been widely studied with various extensions in the literature and applied in a variety of substantive areas [20]. However, these kmeans clustering algorithms are usually affected by initializations and need to be given a number of clusters a priori. In general, the cluster number is unknown. In this case, validity indices can be used to find a cluster number where they are supposed to be independent of clustering algorithms. The first step was to create a comparative analysis of the clustering of electricity consumers based on two datasets: daily_dataset and hhblock_dataset, using the k-Means method. The Daily_dataset contains daily electricity consumption data for over 5,000 households in London over the course of 2 years, from November 2011 to February 2014. The dataset includes information on the date, energy consumption, and the LCLid (Local Control Centre identifier) of each household. The dataset includes statistics such as: LCLid: A unique identifier for each household, day: The date of the record in YYYYMM-DD format, energy_median: The median energy consumption in kilowatt-hours (kWh) for the day, energy_mean: The mean energy consumption in kWh for the day, energy_max: The maximum energy consumption in kWh for the day, energy_min: The minimum energy consumption in kWh for the day, energy_sum: The total energy consumption in kWh for the day, energy_count: The number of half-hourly readings used to calculate the daily consumption, energy_std, which represents the standard deviation of the energy consumption in kilowatt-hours (kWh). The hblock_dataset contains halfhourly electricity consumption data for a subset of 5000 households in London from November 2011 to February 2014. The dataset includes information on the LCLid: A unique identifier for each household, day: The date of the record in YYYY-MM-DD format, energy consumption HH_0-47. The households in this dataset are located in the same block, hence the name “hblock”. The first thing that was done was the clustering of the daily electricity consumption data. Before any activities, what is necessary is to call certain libraries. After that, it is necessary to load the data frame. This is a data frame that relates to daily electricity consumption. Since the data was not completely clean, it was necessary to clean them. The na.omit function was used to clean the data because there were data with N/A and NULL values. As the obtained dataset was quite large, it was decided that further processes would be performed with 300 000 data points via a certain formula in R studio (segregating 300 000 data points). After that, the extracted data needs to be loaded. Using certain steps or formulas, a graph was created for the extracted and loaded dataset. As a result of all the activities described, a graph with a curve and a certain accuracy is displayed in percentages.
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After the analysis of daily energy consumption, a cluster analysis of half-hourly electricity consumption was approached to analyze whether clustering works better when using half-hourly readings or when using daily data on electricity consumers. The procedure applied to the daily electricity consumption dataset is similar. The first step was to merge different csv files with data and create one that is the sum of all individual csv files. Also, it was necessary to clean the data with the the na.omit function in order for the process in R studio to run smoothly. The cleaned dataset goes into further analysis using functions. In the end, a graph with a curve and a percentage accuracy is created based on which we will come to conclusions. 4.2 Comparation of Machine Learning Models of Electricity Consumption Classification After the cluster analysis, the paper classified the data. To start the classification, it was necessary to load the data that will be used in the classification process. 3000 data from the total number of data were used for classification. After loading, the data were prepared for classification by converting them into the appropriate format and deleting unnecessary columns. In terms of classification, four models were created. CART, kNN, Random Forest and SVM. All models were initially trained using data that had been prepared. After training, the model was validated. At the end, the models were tested and certain model accuracies were obtained on an independent data set. The accuracies of the models were compared and it was concluded that the best result was given by the Random Forest model. CART Model. CART stands for “Classification and Regression Trees.” It is a type of decision tree algorithm that can be used for both classification and regression tasks. The algorithm recursively splits the data into subsets based on the values of input variables, creating a tree of decisions that can be used to predict the outcome for new data. CART is very easy to understand, interpret and implement. It can handle both categorical and numerical input variables and also it can handle missing values. k-Nearest Neighbors. kNN is a supervised learning algorithm that can be used for both classification and regression tasks. kNN works by finding the k-number of nearest data points to a given input, and then using the majority class or mean value of those points to make a prediction for the input. The value of k is a user-specified parameter, and a larger value of k will generally result in a smoother decision boundary, while a smaller value of k will result in a more complex boundary. Support Vector Machines. SVMs is a supervised machine learning algorithm that can be used for both classification and regression tasks. SVMs are a linear model that seeks to find a hyperplane, or a line that separates different classes of data in a high-dimensional space. The goal of an SVM is to find the hyperplane that maximizes the margin, or the distance between the hyperplane and the closest data points from each class, known as support vectors. Random Forest. (RF) is a supervised machine learning algorithm that can be used for both classification and regression tasks. RF is an ensemble method that combines multiple decision trees to improve the predictive performance of the model. It creates
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multiple decision trees by randomly selecting subsets of the data and features, and then averaging the predictions of all the trees to make a final prediction. This helps to reduce overfitting and improve the generalization performance of the model.
5 Results and Discussion Figure 1 shows the graph for daily consumption. For daily readings of electric energy consumption, by observing the graph, we can see that there were eight clusters. This gives a data value of 94,2% accuracy. Based on a rich database, editing, and analysis carried out using the K-means method, the following results were obtained.
Fig. 1. Graphic representation of daily consumption.
The K-value is represented on the x-axis and on the y-axis is WCSS (Within-cluster sum of square). A constant decrease was recorded up to K-value 8. A slight improvement is visible at K-value 9, after which a decrease is recorded again. Oscillations are visible from K-value 12 up to K-value 20. The analysis was performed on the basis of 8 clusters and 300 000 data, that is 10% of the total amount of data. Figure 2 shows a graph for a detailed analysis and presentation of half-hourly consumption. The graph shown is related to the half-hourly consumption of electricity. It has an 52,1% accuracy. As in the previous graph, the K-value is represented on the x-axis and on the y-axis is WCSS (Within-cluster sum of square). A constant decrease was recorded up to Kvalue 17. A slight improvement is visible from K-value 17 to K-value 18, after which a decrease is recorded again. The analysis was performed on the basis of 13 clusters and 300 000 data, that is 10% of the total amount of data (Fig. 3).
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Fig. 2. Graphic representation of half-hourly consumption.
Fig. 3. Comparison of the accuracy of two datasets.
When comparing the obtained values (obtained accuracy) of the daily dataset and the half-hourly dataset, we get values of 94.2% and 52.1%. Due to the much higher accuracy in the daily dataset, it has been decided that further research will be based on the daily dataset (Table 1). Four models were used for classification: Classification and Regression Trees (CART), K-Nearest Neighbors (kNN), Support Vector Machines (SVM) and Random Forest (RF). And in terms of classification, the following results are presented. On Fig. 4, accuracy and kappa are displayed. Two models that give almost the same best results are kNN and RF. In order to know exactly which test gives the best result, numerical values are presented in the following table.
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Accuracy
Daily consumption dataset
94.2%
Half-hourly consumption dataset
52.1%
Fig. 4. Graphical representation of the results obtained through four models.
Table 2. Numerical display of classification accuracy. Accuracy Models
Min
1st Qu
Median
Mean
3rd Qu.
Max
CART
0.784
0.790
0.794
0.833
0.895
0.900
kNN
0.986
0.989
0.995
0.993
0.996
0.996
SVM
0.896
0.910
0.913
0.917
0.928
0.942
RF
0.986
0.993
0.995
0.993
0.996
0.996
Observing the first part of the table (Accuracy), the results are indeed almost the same. The Mean column leads to the conclusion that the best results are still given by the Random Forest model (kNN = 0.9933165 and RF = 0.9936531). The Table 2 with Kappa values also notes approximate results. Namely, in this case, better accuracy is achieved based on the Random Forest model as well (kNN = 0.7764256 and RF = 0.9917090).
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6 Conclusions and Future Research Directions The first task of this work was clustering. Out of roughly 3.5 million data sampes, 300 000 were used for clustering, due to limited hardware resources. After cluster analysis of daily electricity consumption data and cluster analysis of half-hourly electricity consumption data, certain accuracies were obtained. It was found that the data of daily electricity consumption gives better accuracy and it was decided that further research will be based on the daily data set. The second task of this work was classification. Due to limited hardware resources, in terms of classification, 3 000 data were used and four models were compared. The best result is given by Random Forst model. All of the research conducted is supported by graphs and tables. There are many ways of application and further directions of research on this topic. Electricity as the fundamental driver of all actions, both in the future and in the pre-sent, gives everyone the opportunity to try to improve themselves through knowledge and to find new laws and theories. Future research in this area should focus on developing more advanced and accurate methods for predicting electricity consumption, as well as finding new ways to reduce consumption and improve energy efficiency. One promising area for future research is the use of machine learning algorithms, such as deep learning and reinforcement learning, to improve the accuracy of electricity consumption predictions. In addition, research should explore the potential for using smart grid technologies and IoT devices to improve the efficiency of electricity distribution and consumption. Research should also focus on exploring the use of data mining for energy management systems in buildings and smart homes, as well as detecting and predicting anomalies and errors in electricity consumption. This paper contributes to the existing body of knowledge by analyzing different preprocessing approaches for data clustering, comparing multiple machine learning models for classification, and finally tests the prediction accuracy of the best fitting models. Promising results demonstrated the applicability of data mining techniques in analyzed area as a successfully tool to support electricity grid operators, suppliers and consumers and ultimately lead to better electricity grid and market operation.
References 1. Ahn, K.-U., Kim, D.-W., Park, C.-S., Wilde, P.D.: Predictability of occupant presence and performance gap in building energy simulation. Appl. Energy 208, 1639–1652 (2017) 2. https://www.iea.org/reports/electricity-information-overview/electricity-consumption 3. Ketchen, D., Shook, C.: The application of cluster analysis in strategic management research: an analysis and critique. Strateg. Manag. J. 17, 441–458 (1996) 4. Niu, D., Dy, J., Jordan, M.: Iterative discovery of multiple alternative clustering views. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1340–1353 (2014) 5. Cios, K., Pedrycz, W., Swiniarski, R., Kurgan, L.: Data Mining: A Knowledge Discovery Approach. Springer, New York, NY, USA (2007) 6. Badase, S., Deshbhratar, G., Bhagat, A.: Classification and analysis of clustering algorithms for large datasets. In: Proceedings of the International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India (2015) 7. Russel, S., Norvig, P.: Artificial Intelligence – A Modern Approach, 3rd ed. (2012)
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8. Machine Learning Tasks – ML.NET|Microsoft Docs 9. Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning, 2nd ed. (2018) 10. Kotsiantis, S.: Supervised machine learning: a review of classification techniques. Informatica 31, 249–268 (2007) 11. Gmyzin, D.: A Comparison of Supervised Machine Learning Classification Techniques and Theory-Driven Approaches for the Prediction of Subjective Mental Workload. Master’s Thesis, Dublin Institute of Technology, Dublin, Ireland (2017) 12. Koo, B-G., Kim, M-S., Kim, K-H., Lee, H-T.: Short-Term Electric Load Forecasting Using Data Mining Technique (2013) 13. Dent, I., Aickelin, U., Rodden, T.: The Application of a Data Mining Framework to Energy Usage Profiling in Domestic Residences Using UK Data (2013) 14. McLoughlin, F., Duffy, A., Conlon, M.: A clustering approach to domestic electricity load profile characterization using smart metering data (2015) 15. Hubana, T., Begi´c, E.: Smart Meter Based Non-Intrusive Load Disaggregation and Load Monitoring (2021) 16. Maksood, F.: Analysis of Data Mining Techniques and Its Applications (2016) 17. Yang, J., et al.: K-Shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement (2017) 18. Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. J. R. Stat. Soc. C: Appl. 28, 100–108 (1999) 19. Sanga, K.P., Yang, M.-S.: Unsupervised K-Means Clustering Algorithm Department of Applied Mathematics. Chung Yuan Christian University, Taoyuan City 32023, Taiwan (2020) 20. Alhawarat, M., Hegazi, M.: Revisiting K-means and topic modeling, a comparison study to cluster Arabic documents. IEEE Access 6, 42740–42749 (2018)
University Campus as a Positive Energy District – A Case Study Emir Neziri´c1(B)
, Damir Špago1
, Mirza Šari´c2
, Edin Šunje1 , and Mirsad Be´ca1
1 Dzemal Bijedic University of Mostar, 88420 Mostar, Bosnia and Herzegovina
[email protected] 2 International BURCH University, 71000 Sarajevo, Bosnia and Herzegovina
Abstract. European Union is implementing the decarbonization process of European society. This process starts a development of novel approaches for energy production, control and management. Positive energy district (PED) is the concept in which multiple buildings in the same geographical area are merged into a unique energy network. The main goal of PED is to produce more energy than it consumes. University campus Sjeverni logor in Mostar (Bosnia and Herzegovina) is analyzed based on requirements to reach a status of PED. This is achieved by photovoltaic installation on the rooftops and by decreasing heating and cooling energy losses. Results demonstrate that it is possible to achieve positive energy balance with PV installation and as achieve a status of PED for entire campus. Further, it was demonstrated that approximately 40% of the produced energy can be delivered to the grid. Geographical location and flooring of buildings are the largest contributors to the positive energy balance. Keywords: Green campus · Positive energy districts · Photovoltaic
1 Introduction Renewable energy systems, data on global CO2 emissions and carbon footprint remain very important topics worldwide. Energy independence targets and environmental concerns continue to push the agenda for transition from conventional towards renewable energy conversion technologies. European society has identified the problem and continues to promote sustainable and clean energy sources of the future. Energy production from green sources, by default, should reduce the consumption of carbon-based energy production. A European Green Deal (EGD) has defined the path that is focused on creating better and healthier environments for humans through reaching climate neutrality, cutting pollution and clean production through inclusiveness in this transition [1]. It is a promising sign that novel ideas for reaching the goals defined by EGD continue to emerge. For developing new ways of fulfilling the goals which are defined by EGD. Buildings consume approximately 40% of total energy consumption [1]. This share of energy consumption needs to be managed using innovative solutions for preventing unnecessary energy loss and energy gain from on-site available sources, such as solar energy (photovoltaic, thermal collectors), wind energy or groundwater heat energy. In © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 583–594, 2023. https://doi.org/10.1007/978-3-031-43056-5_44
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the 1970s, the concept of “Zero-energy buildings” rises as an idea to reduce the energy consumption of the building with the production of the energy as a parallel process [2]. There are multiple zero energy concepts developed and discussed today, such as Net Zero Energy Buildings, Nearly Zero Energy Buildings, Energy Positive Neighborhoods, Positive Energy Blocks, Energy Neutral Districts, and Positive Energy Districts (PED) [3–5]. Also, there are multiple sub-concepts that were derived from these concepts [4, 6]. 1.1 What is a Positive Energy District? A Positive Energy District can be defined as a district within a city that generates more energy than it consumes on an annual basis [6]. It must contain clear geographical boundaries with or without any energy exchange with the external systems. This concept is currently “on the spot”, as the European Union (EU) has introduced Strategic Energy Technology Plan with a target to establish 100 PEDs by 2025 [7]. This incentive provides an increase in number of active projects in different phases of the PED development. Zhang et al. in [8] have analyzed 60 PEDs projects in Europe and showed that there is an expansion of starting PEDs projects in the period 2014–2020. Most of those projects are implemented in Norway (9) and Italy (8). 16 projects of 60 analyzed PED projects are completed, where 11 are already in operation [8]. A similar set was analyzed in [9], where 61 PED projects were considered. It was concluded that only 8% of PED projects had a single source of energy, but about 50% of PED projects had highly mixed energy sources. Saheb et al. in [10] presented an in-depth analysis of 7 net-zero energy districts, which were analyzed through efficient use of raw materials, eco-friendly mobility, reuse of water, net zero carbon emission and other main specs of the PEDs. They also described 54 other projects related to the concept of PEDs. Although the PEDs concept is not new, it still encounters obstacles in its development and implementation. An analysis of 61 European PED projects has shown that the main challenges and barriers to the implementation of PEDs are funding/business models since PEDs are more expensive than conventional projects [9]. In the same research, the second most common issue was inappropriate legislation and regulations, since the PEDs concept considers more buildings connected to mutually share energy sources. Stakeholders’ involvement was recognized as the most important factor to success, which is directly connected to the main challenges (funding, legislative, regulations). Different locations have different local regulations and different climate conditions, which also leads to the conclusion that location defines most of the complexity of PEDs implementation [11]. For countries with mild climate conditions, it is easier to achieve heating and cooling directly from renewable energy sources, and without connection to the city heating system. Also, some countries have better and easier legal procedures for connecting the building in the mutually exchangeable energy system, so it is easier to achieve the PEDs concept. Besides this, there are some other location-dependent challenges and opportunities in the PEDs, which have less significance. The top seven challenges are identified by the Delphi Method and presented in [12] by S.G. Krangsås et al. Analysis has led to the conclusion that most of the experts (more than 75%) have recognized governance as a main challenge in PEDs implementation
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with 54% of “strongly agree” answer [12, 13]. The importance of governance issues was described through the need for strategic capacity building, support for innovation and research, better interconnection between technical and non-technical stakeholders, and starting processes that tackle sociological and technical challenges for better economic planning and growth [14]. The University campus (UC) is the ground where the university infrastructure is placed. It could contain faculty buildings, administration buildings, sports infrastructure, traffic infrastructure, dormitories, and any other buildings needed for common university activities. Since UC is usually ruled by the university administration, it is easier to join all the buildings in the unique network to share energy resources. It would be quite interesting to discover the possibility of implementing PEDs concepts within the UC boundaries, which would be presented in this paper. 1.2 University Campus as PED University politics on sustainability usually strives to achieve a green campus more than an energy-efficient campus. There is developed a ranking list of universities based on Green Campus perception and sustainability practices [15]. The conducted research has shown that universities which have adopted the criteria defined by the Green Campus initiative have more chance to achieve sustainability through, for example, reducing airconditioning usage or private vehicle use. Achieving a carbon-neutral campus is mainly achieved through increasing PV solar system installation and increasing green areas with new trees [16–19]. Similar politics could be used by accepting and implementing PEDs. Since a UC is usually governed by one entity (university), the interaction between buildings and systems inside the campus is much easier than the interaction between buildings which are governed by different entities. An energy audit could be a first step in the analysis of campus energy capacities and weaknesses. It is a top of the policy for achieving significant improvement in energy efficiency and greenhouse gas emissions reduction [16]. Conducting energy audits on regular basis could track improvements in energy balance on the campus and observe possible advancements towards maximum energy efficiency. Most common input into UC energy balance is photovoltaic (PV) electrical energy production. Shafie et al. have shown in [21] that with the PV implementation in the UC campus in Malaysia, it is possible to reduce energy consumption by 20%. It is also shown that a better option is to sell the excess energy to the national electrical company, which could increase financial savings during the year. Sevik in [22] has shown that PV implementation could increase 25% of energy production of the existing energy system. It was also concluded that the payback time of PV investment is less than 7 years, which also contributes to PV advantages when compared to other methods. Creating and implementing a hybrid energy system could be a better option due to the flexibility and adaptability of hybrid systems to environmental conditions (weather, fuel availability). The combination of PV with gas-fired trigeneration system and solar hydrogen generation was discussed in [22], with a focus on its implementation in the UC. Also, the combination of PV electrical energy production with heat production and battery storage could be implemented in the UC with the same or better reliability
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compared to the traditional energy network [23]. Battery prices are regarded as a main obstacle in this proposed UC energy network. Finally, there are a numerous opportunity for improvement of the UC energy system through renewable energy sources implementation, by combining the energy sources, or improving building energy efficiency. There is a long path towards the PEDs concept at UC, but it should be achievable through implementing similar paths paved by green campus or carbon-neutral campus ideology. This paper presents an analysis of possible implementation of the PED concept on the UC.
2 Case Study: Sjeverni Logor University Campus With its Mediterranean climate conditions and topological characteristics, Mostar has warm summers and moderately cold winters. It is common for office and commercial buildings to have HVAC systems that run on electricity. Buildings generally have a basic insulation layer, such as Styrofoam. Mostar is a city with great solar power potential, as shown in Fig. 1.
Fig. 1. Monthly solar irradiation for Mostar for period 2018–2020 (data source: PVGIS) [24].
“Dzemal Bijedic” University of Mostar is located in Sjeverni logor campus in Mostar (Bosnia and Herzegovina) on the north entrance to the city of Mostar urban area. Sjeverni logor campus is bounded by a street from the east, a road from the north and river Neretva from the west and south. The total area of the campus is 250.000,00 square meters and it consists of total of 26 buildings. Besides University buildings, there are also a sports hall, 3 high school buildings, a high school dormitory, a primary school, a fire brigade building, and student clinic. University-governed buildings are marked in Fig. 2. Some of the preliminary research is done for the faculty of the mechanical engineering building, which is already on its path of achieving net-zero energy building. Šunje et al. in [25] have shown that the HVAC system is the largest consumer of electrical energy in the faculty building and that it is possible to create a hybrid energy system which will produce enough energy from different sources (PV, wind turbine, ground heat pump) to be classified as net-zero energy building.
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Fig. 2. University buildings in Sjeverni logor campus.
Groundwater source heat pump operation analysis was conducted by Špago et al. in [26]. The analysis consisted of outdoor air temperature, heating water temperatures and heat output analysis. It was shown that outdoor air temperature does not have a direct impact on heat output from the heat pump, but it would affect the indoor temperatures
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of the rooms. Also, it was concluded that this system could be improved by adding solar thermal collectors as heating support in the winter months. Beside the main building analysis in the previous research, an analysis of the PV panels powering the parking lots was presented in [27]. It is shown that it is required less than 1 kW of PV installed to cover all requirements of the smart parking system (central computer, sensors, internet network operation), and the rest of the installed capacities could be used for the campus electricity network. Preliminary research have shown that a combination of different energy sources is beneficial for reaching a better energy supply system on campus. The concept of net-zero energy building could be extended to a positive energy district inside the campus, and this is a topic of this case study. 2.1 Materials and Methods First, energy consumption data and energy efficiency data for all buildings governed by university is collected. Since all building’s heating systems were based on electrical energy (heat pumps, electrical heaters), all energy consumption data is collected from electrical grid consumption meters. Then, data on possible rooftop PV production of electrical energy in the University buildings are collected. This analysis was conducted in the web-based software Helioscope for all buildings separately (Fig. 3). Proposed PV layouts were created for all rooftops and annual electricity production was estimated. The third step was to collect all the data regarding the building’s energy efficiency such as insulation and opening types, heating and air conditioning systems. This data was used for the analysis of the possible future investments in heating and air-conditioning energy loss reductions since they are the largest consumers in the system. Finally, a detailed analysis of the collected data was conducted, where annual consumption for each building was compared to annual energy production from the installed rooftop PV panels. Also, an analysis of the possible improvement in the energy efficiency of each building was conducted, where gathered data in the third step shows all the weak spots in university-governed buildings. 2.2 Results and Discussion Energy consumption from all University buildings was combined and displayed in the graph (Fig. 4.). As could be seen, the consumption of electrical energy in 2020 was lower than in 2019 and 2021 since it was the lockdown period caused by COVID-19 and most of the activities were online. Total energy production that could be obtained from eventually installed PV panels is displayed in the same graph (Fig. 4.).
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Fig. 3. Example of PV layout obtainet from Helioscope (e.g. Faculty of economics).
Fig. 4. Energy consumption in 2019–2021 period vs. total PV production in campus.
It could be seen that there are only 4 months where theoretical production would not be greater than consumption. This could be concluded under the preposition that all generated energy is also consumed and that there is no energy loss. During those 4 months it is impossible to cover all energy demands from own production and the energy system must be grid-connected to fill the energy production-consumption gap. Rest of the year (8 months) theoretical production is higher than consumption since there is less energy needed for cooling than heating in winter. Also, there is summer vacation
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where (15 days of July and 15 days of August) the buildings are vacant. It is necessary to determine whether theoretical production of the proposed PV system is enough to cover the annual consumption of all University buildings. Data on the annual production versus consumption are shown in Table 1. Approximately 750 MWh of energy could be produced annually, and the consumption of all University buildings is around 455 MWh annually. This leads to the difference of 295 MWh in the favor of the production from the PV panels. This leads to the conclusion that with only PV installation on the roof of the buildings, it is possible to get to the Positive Energy District on an annual basis with 40% of the excess produced energy on annual basis. Installation of other energy sources on the campus (wind turbines, solar thermal collectors, reversible hydropower plant) was not analyzed in this paper. Their installation would help the system in its diversity and flexibility, but it would be difficult to obtain all legal prerequisites for some of them or to control all the energy sources together. Table 1. Energy consumption and production per building. University buildings
Energy production [kWh]
AVG. Energy consumption 2019–2021 [kWh]
Energy balance [kWh]
Agromediterranian faculty
45.056,1
20.636,7
24.419,4
Agromediterranian institute
42.945,8
8.271,7
34.674,1
Library
65.538,0
6.951,7
58.586,7
Faculty of economy
66.780,4
55.370
11.410,4
Faculty of humanities
108.795,7
31.374,3
77.421,4
Faculty of information technologies (1 + 2)
127.006,5
97.657,2
29.349,3
Faculty of mechanical engineering (main building + institute)
107.364,7
35.306,7
72.058,0
Faculty of education
74.608,9
59.690,8
14.918,1
Faculty of law (old building)
22.696,8
19.405,8
3.291,0
Faculty of law (new building)
44 054
23.498,0
20.556,0
Rectorate
43 478,1
50.084,0
−6.605,9
Civil engineering faculty (main + institute)
0,0
46.908,9
−46.908,9
TOTAL
748.325,0
455.155,8
293.169,2
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Characteristics of the buildings are shown in Table 2. Table 2. Building characteristics. University buildings
Insulation
HVAC system
Windows and doors
Agromediterranian faculty
SF
HP, AC, EH
ALU 2LY
Agromediterranian institute
MT
HP, AC, EH
ALU 2LY
Library
MT
AC, EH
PVC 2LY
Faculty of economy
SF
HP, AC, EH
PVC 2LY
Faculty of humanities
MT
AC, EH
PVC 2LY
Faculty of information technologies (1 + 2)
MT
AC, EH
PVC 2LY
Faculty of mechanical engineering (main building + institute)
SF
HP
PVC 2LY
Faculty of education
MT
AC, EH
ALU 2LY
Faculty of law (old building)
MT
AC, EH
WD 2LY
Faculty of law (new building)
SF
AC, EH
PVC 2LY
Rectorate
SF
AC, EH
PVC 2LY
Civil engineering faculty (main + institute)
MT
AC, EH
ALU 2LY
where: SF – Styrofoam, MT – mortar, AC – aircondition, EH – electrical heater, HP – heat pump, PVC – 5 to 7 chamber PVC window frame, ALU – aluminum window frame, WD – wooden window frame, 2LY – double glass. There are a lot of possibilities to improve the energy efficiency of the buildings, especially in terms of insulation and HVAC systems. 50% of buildings have a basic facade with mortar, which has poor insulating characteristics. Also, more than 90% of buildings use electrical heaters for partial heating during the winter period, which makes great energy consumption share. In these two segments, it is possible to improve the energy efficiency of the buildings. It is out of the scope of this paper to do a detailed analysis of the quantification of the energy efficiency improvement contribution to achieving the PED concept of the campus. From the data and analyses shown in this chapter, it could be concluded that Sjeverni logor campus could reach the state of the Positive Energy District in the annual balancing
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period. Since all the buildings inside the campus are with similar characteristics (purpose, storey, geographical location, heating system), similar conclusions could be made about the other buildings on the campus which are not under university governance.
3 Conclusion The positive energy district concept is relatively new, and it is an important tool for achieving a carbon-neutral society. These concepts transform neighborhoods, campuses, city blocks and similar urban regions into connected carbon neutral, energy independent and sustainable areas. A UC is an urban complex, which is usually governed by one body or more bodies interconnected by the same legal representatives. Interconnectivity between buildings is important for total energy balance. Sjeverni logor campus is located in an area with a Mediterranean climate, so by nature, it does not require a large amount of energy for heating and cooling, which is usually the biggest consumer in the buildings. Great solar potential also contributes to the reachability of the PED goal. The consumption– production analysis has shown that it is possible to achieve a positive energy balance on annual basis from PV production from the installation of PV on the roof only. It is possible for multiple reasons, such as geographical location, the building flooring (3 floors maximum), or the buildings are not being used on most of the weekends and summer vacation. An analysis of buildings’ characteristics has shown that there is a possibility for improvements in this segment since there are energy-inefficient heating systems and poor insulation layers on the buildings. It would be easier to achieve the PED concept with improvements in the building characteristics since there would be less energy wasted on heating and cooling. Acknowledgement. This article is supported by COST (European Cooperation in Science and Technology, www.cost.eu) COST Action CA19126 – Positive Energy Districts European Network (PED-EU-NET).
References 1. Gløersen, E., Furtado, M.M., Gorny, H., Münch, A., Alessandrini, M., Bettini, C.: Implementing the European Green Deal: Handbook for Local and Regional Governments, European Union and the Committee of the Regions (2022). https://cor.europa.eu/en/engage/stu dies/Documents/European%20Green%20Deal%20Handbook.pdf. Accessed 22 Dec 2022 2. Goodier, C.: Zero-energy building. Encyclopedia Britannica, 25 June 2019. https://www.bri tannica.com/technology/zero-energy-building. Accessed 22 Dec 2022 3. JPI Urban Europe/SET Plan Action 3.2: White Paper on PED: Reference Framework for Positive Energy Districts and Neighbourhoods (2020). https://jpi-urbaneurope.eu/wp-content/ uploads/2020/04/White-Paper-PED-Framework-Definition-2020323-final.pdf. Accessed 22 Dec 2022 4. Derkenbaeva, E., Halleck Vega, S., Jan Hofstede, G., van Leeuwen, E.: Positive energy districts: mainstreaming energy transition in urban areas. Renew. Sustain. Energy Rev. 153, 111782 (2022)
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5. Shnapp, S., Paci, D., Bertoldi, P.: Enabling positive energy districts across Europe: energy efficiency couples renewable energy. JRC Technical report, Publications Office of the European Union, Luxemburg (2020). https://publications.jrc.ec.europa.eu/repository/handle/JRC 121405. Accessed 22 Dec 2022 6. Lindholm, O., Rehman, F.: Positioning positive energy districts in European cities. Buildings 11(1), 19 (2021) 7. SET-Plan Temporary Working Group: SET-Plan ACTION n 3.2 Implementation Plan: Europe to become a global role model in integrated, innovative solutions for the planning, deployment, and replication of Positive Energy Districts (2018). https://jpi-urbaneurope.eu/wp-content/upl oads/2021/10/setplan_smartcities_implementationplan-2.pdf. Accessed 22 Dec 2022 8. Zhang, X., Penaka, S.R., Giriraj, S., Sánchez, M.N., Civiero, P., Vandevyvere, H.: Characterizing positive energy district (PED) through a preliminary review of 60 existing projects in Europe. Buildings 11, 318 (2021) 9. Bossi, S., Gollner, C., Theierling, S.: Towards 100 positive energy districts in Europe: preliminary data analysis of 61 European cases. Energies 13, 6083 (2020) 10. Saheb, Y., Shnapp, S., Paci, D.: From nearly-zero energy buildings to net-zero energy districts : lessons learned from existing EU projects, European Commission, Joint Research Centre, Publications Office (2019). https://data.europa.eu/doi/10.2760/693662. Accessed 22 Dec 2022 11. Hedman, A., et al.: IEA EBC Annex83 positive energy districts. Buildings 11, 130 (2021) 12. Krangsås, S.G., et al.: Positive energy districts: identifying challenges and interdependencies. Sustainability 13, 10551 (2021) 13. Steemers, K., et al.: Challenges for a positive energy district framework. Sustain. Energy Build. Res. Adv. 8, 10–19 (2022) 14. Sareen, S., et al.: Ten questions concerning positive energy districts. Build. Environ. 216, 109017 (2022) 15. Tiyarattanachai, R., Hollmann, N.M.: Green Campus initiative and its impacts on quality of life of stakeholders in Green and Non-Green Campus universities. Springerplus 5, 84 (2016) 16. Li, Y., Jia, Q.S.: On the feasibility to achieve carbon neutrality in university campus: a case study. IFAC-PapersOnLine 55(5), 78–83 (2022) 17. Wara, S.T., Okundamiya, M.S.: Performance analysis of a grid-linked microgrid system in a university campus. In: Azubuike, S.I., Asekomeh, A., Gershon, O. (eds.) Decarbonisation Pathways for African Cities. Palgrave Studies in Climate Resilient Societies, pp. 95–113. Palgrave Macmillan, Cham (2022). https://doi.org/10.1007/978-3-031-14006-8_6 18. Varón-Hoyos, M., Osorio-Tejada, J., Morales-Pinzón, T.: Carbon footprint of a university campus from Colombia. Carbon Manag. 12(1), 93–107 (2021) 19. Hiltunen, P., Volkova, A., Latõšov, E., Lepiksaar, K., Syri, S.: Transition towards university campus carbon neutrality by connecting to city district heating network. Energy Rep. 8, 9493–9505 (2022) 20. Boharb, A., Allouhi, A., El-houari, H., El Markhi, H., Jamil, A., Kousksou, T.: Energy audit method applied to tertiary buildings: case study of a university campus. AIMS Energy 10(3), 506–532 (2022) 21. Shafie, S.M., Hassan, M.G., Sharif, K.I.M., Nu’man, A.H., Yusuf, N.N.A.N.: An economic feasibility study on solar installation for university campus: a case of universiti Utara Malaysia. Int. J. Energy Econ. Policy 12(4), 54–60 (2022) 22. Sevik, S.: Techno-economic evaluation of a grid-connected PV-trigeneration-hydrogen production hybrid system on a university campus. Int. J. Hydrog. Energy 47(57), 23935–23956 (2022) 23. Garcia, Y.V., Garzon, O., Andrade, F., Irizarry, A., Rodriguez-Martinez, O.F.: Methodology to implement a microgrid in a university campus. Appl. Sci. 12(9), 4563 (2022)
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24. Huld, T., Müller, R., Gambardella, A.: A new solar radiation database for estimating PV performance in Europe and Africa. Sol. Energy 86, 1803–1815 (2012) 25. Šunje, E., Paši´c, S., Isi´c, S., Neziri´c, E., Džiho, E.: Development of hybrid system for airconditioning of almost zero energy buildings. In: Karabegovi´c, I. (eds.) NT 2020. LNNS, vol. 128, pp. 680–687. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46817-0_78 26. Špago, D., Noži´c, M., Isi´c, S.: Analysis of groundwater source heat pump operation with improvement suggestions. In: Karabegovi´c, I. (ed.) NT 2020. LNNS, vol. 128, pp. 649–656. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46817-0_75 27. Špago, D., Lavi´c, A., Neziri´c, E., Šabanovi´c, A.: Powering the smart parking system with photovoltaic solar panels at campus of “Džemal Bijedi´c” university of mostar. In: Karabegovi´c, I., Kovaˇcevi´c, A., Mandžuka, S. (eds.) NT 2022. LNNS, vol. 472, pp. 733–740. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05230-9_87
State Estimation in Electric Power Systems Using Weighted Least Squares Method Amina Hondo1 , Elma Begi´c1 , and Tarik Hubana2(B) 1 Public Enterprise Elektroprivreda of Bosnia and Herzegovina, Vilsonovo Setaliste 15, 71000
Sarajevo, Bosnia and Herzegovina 2 University of Dzemal Bijedic in Mostar, University Campus, 88104 Mostar, Bosnia and
Herzegovina [email protected]
Abstract. State estimation is a powerful method used in electric power systems, whose results are used for various purposes such as analysis, management and planning of power systems. All advanced functions of today’s SCADA/EMS systems that enable reliable and feasible management of the systems largely depend on the accuracy of the data obtained from the state estimation methods. The state of an electrical power system is characterized by the voltage magnitudes and angles in the system buses, and by increased digitalization of electrical power systems, the data required for state estimation are becoming more available. In this paper, an algorithm based on a weighted least squares (WLS) method is proposed for the electrical power system state estimation. The results demonstrate that the weighted least squares method minimizes the error and provides good accuracy. The algorithm for state estimation tested in MATLAB software package, by performing a simulation on the standardized IEEE 118-bus test system. This paper contributes to the existing knowledge base by developing and testing the optimal estimation process of the state of variables including voltages by module and angle in all nodes of the network, which can be useful for the operation and control of the future digitalized and smart electric power systems. Keywords: state estimation · power systems · load calibration · weighted least squares method (WLS method) · SCADA · EMS · distribution systems
1 Introduction Electric power systems are getting more complex and they are going through a transition towards smart grids. This is a result of the development of electricity markets, fast development and integration of renewable energy sources (RES), and an increase in the consumption of electrical energy [1]. With growing interest in the integration of RES into electric power systems (EPS), especially into the distribution network, stability of the power network is coming to focus since it is more prone to the challenges such as a two-way flow of the power, voltage stability issues, distortion and unstable level of safety levels, multiple sources inside of the faulty regime, etc. These challenges are © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 595–605, 2023. https://doi.org/10.1007/978-3-031-43056-5_45
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introduced with a more active distribution network in the sense of distributed generation, which was not present in the traditional distribution networks that were passive. Planning and control of the distribution network thus can be divided into the following activities [2, 3]: voltage control, reactive power control, balancing, power factor control, electricity quality optimization, technical losses optimization, power supply reliability, and malfunction and cut off the electricity supply control. The efficient way of resolving previous activities is possible only through an adequate and continuous process of distribution network control, which includes adequate observation, optimization, and planning of the network. Power flow and estimation are the basic calculations in the EPS. In the basic power flow calculations, system behavior is affected by the input variables which are determined by the chosen classification of the bus. Based on it, using a deterministic approach, output variables can be calculated, which are modules and angles of phase voltage nodes, and non-nominal relations of transformation of classical and control transformers (phase shifters) [4]. State estimation, in relation to the final calculation, takes all measurements into the consideration. Several studies have been reported in the literature regarding the state estimation problem. In [5], the authors propose a semidefinite programming (SDP) formulation to approach the state estimation problem. In this case, a convex semidefinite relaxation is carried out which renders the state estimation efficiently solvable. In [6], the dynamic state estimation (DSE) problem is approached from a statistical decision theory point of view. Therefore, the initial state of the system is regarded as deterministic and unknown. DSE is also studied in [7]. In this case, the authors discuss the advantages of DSE as compared to the static state cases, as well as implementation differences between the two of them. In [8], the authors propose a data-driven robust state estimation method through off-line learning and on-line matching to solve the estate estimation problem in EPS. On the other hand, robust state estimators have been proposed as an important alternative to reduce the effects of measurement errors. In [9] and [10], a comparison between different robust state estimators is performed. Multiple approaches prove that this is an active are of research, to which this paper will also contribute. In order to perform the estimation, it is necessary to that the total number of measurements M are higher than the total number of variables N, since the each measurement is joined by the measurement error, while on the other hand in the load flow analysis, number of measurements is equal to the number of unknown variables. Another difference between the before mentioned methods is that the load flow analysis doesn’t have assumed errors in the measured values. Therefore, the load flow problem has a unique solution (if it exists). On the other hand, the result of the state estimation method is the estimation of the probable operating regime and therefore it is not unique. Since the state estimation is primarily used to remove the statistical errors in the measurements, it presents a stochastic approach, compared to the load flow method that is rather deterministic [11–14]. The purpose of the state estimation is to calculate variable states in a given moment. State estimator is a mathematical algorithm as described in [15]. Estimators are in general classified to parameters estimators (identification of parameters, time variable or slow variable values) and state estimators (time dependent dynamic state value). Input data for the state estimator is provided by measurements from supervisory
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control and data acquisition and energy management systems (SCADA/EMS systems). These measurements include voltage magnitudes, while the state estimator determines the voltage angles at the system bus bars [16]. SCADA system usually records the data in real time from the remote terminal units (RTUs) that are installed in the substations of the EPS. RTU devices collect data such as: analog measurements (e.g. magnitudes of voltage and current) and discrete states (e.g. states of circuit breakers). Additionally, the data from the phasor measurement units (PMUs) can also be integrated in the system. By having PMUs in the SCADA/EMS system, the larger compliance of the measurements with the state estimation is achieved, therefore having larger measurement accuracy and improved accuracy and reliability of the state estimator [17, 18].
2 Theoretical Background 2.1 State Estimation in Electric Power Systems Application for the state estimation is a key application in energy management systems (EMS). Several methods have been developed for state estimation in EPSs. All methods can be divided into two basic groups: – First group includes methods for estimating the state based on the modification of the algorithm for estimating the state in transmission networks. – Second group of methods is based on the modification of the algorithm for the calculation of power flows in distribution networks. The execution and quality of advanced EMS system applications depend on the robustness and quality of the state estimator results. The state estimation calculation uses analog and status telemetry to provide a complete voltage solution for the power system model and active and reactive power flows. The whole process of estimating the condition consists of the following steps [2]: – network topology processing: data on the state of switches and switching equipment is collected and a real-time network diagram is formed, – observability analysis: observability analysis determines whether a solution to the estimation of the situation can be obtained on the basis of an available set of measurements, identifying unobservable branches and observable islands if they exist, – state vector estimation: state estimation determines the optimal estimation for the state of the system, which consists of complex values of node voltages throughout the system, obtained from the network model and measurements from the system. It also provides the best estimation for power flows per line, load estimation and generator power output, – detection (identification) of bad data: the process of estimating the state includes detection, identification (localization) and rejection of bad data/errors from the set of measurements. These errors occur due to a fault in the measuring or communication chain, and not due to limited measurement accuracy. With sufficient measurement redundancy, the condition estimator can eliminate the impact of errors and temporary loss of measurement, without significantly affecting the quality of the estimation results.
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State estimation process differs for transmission and distribution electricity networks. Distribution networks have a very limited set of measurements compared to the transmission network. Due to the small number of real-time measurements, it is necessary to predict the load (so-called pseudo-measurements) in order to make the system observable. The system is observable when the number and schedule of measurements allow the determination of the state vector. 2.2 Calibrating Electricity Consumption State estimation in electric power is calculated for the selected point in time t. As a first step, electricity consumption in the distribution power system is calibrated. Calibration of electricity consumption is a procedure in which, based on available data, consumption is estimated at all points of the distribution power systems at the selected time. Electricity consumption is described by one of the pairs of quantities: current modulus and power factor or active and reactive power. The calculated result of calibrated electricity consumption, is a data for state estimation whose quality largely depends on the quality of consumption calibration. The calculated values by the calibration procedure are the initial values for calculating power flows for state estimation in the distribution power systems [2]. The result of this procedure are calibrated values of electricity consumption currents in the nodes of the distribution power system ijkal , j = 1, . . . , ncv (where ncv is number of nodes in the systems), while the electricity consumption values are calculated based on measurements at the beginning of the statement with loss and reactive output power. Monitoring consumption (electricity/power) over time is vital for all actions related to the supervision, management and planning of plants in the distribution network. 2.3 Weighted Least Squares Method Remote measurements in the power electric systems usually have an error due to the assumed interference in the very measurements and the data transmission, consequently causing errors in the calculation of the state estimation. With the application of statistics related to the measurement results, the true values of the data can be estimated. There are a wide range of techniques for solving the problem of calculations of the state estimation, but the most commonly used approach is the weighted least squares method WLS method. The WLS method is used for solving of the nonlinear system of equations. It is based on unsynchronized measurements from the SCADA system. The classic procedure of WLS method for state estimation uses the separation of complex values into real quantities due to the impossibility of deriving nonanalytic complex functions. Therefore, it is not possible to directly use complex magnitudes of voltage and current phasor measurements. When estimating the condition, measurements are assumed with an uncertainty, and the weight factor is determined according to the assumed measurement uncertainty. Determining weight factors that are to be used in state estimation is extremely important for systems state estimation. Wrongly chosen weight factors can introduce a large error into the estimated steady state vector of the systems. Weighting factors are individual for each measurement and change over time.
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3 Methodology The WLS method uses an optimization criterion that minimizes the sum of the squares of the deviations measured from the corresponding estimated values of the variables, or the sum of the squares of the errors [1]. Modules and arguments of the voltage vector are most often taken for the elements of the state vector x (sometimes called state variables), and are sufficient to calculate all other electrical quantities in the systems. The set of measured quantities z can be represented by the following expression: z = h(x) + e
(1)
where are: z – measurement vector (mx1), m –number of measurements; h(x) – vector of nonlinear functions (mx1) which connects state vector and measurements vector; e – error vector (mx1), assumed it has a mean value0 i variance σ 2 , i.e. that errors are 2 independent and have a normal distribution ei ∼ N 0, σi . The state estimator based on the WLS method minimizes the sum of the squares of errors divided by the square of the standard deviation, or a goal function J (x) [1]: m zj − hj (x) 2 = 2(z − h(x))T W (z − h(x)) (2) J (x) = σj j=1
R−1
where are: W = = diag(σi2 ) – diagonal matrix of weighting factors; R - matrix of covariance’s of measurement errors. If there is no mutual connection of errors, R is a diagonal matrix with elements σi2 . If there is no linear connection between the state variables with the power flows in the systems, then it is necessary to apply the iterative technique in the procedure of minimizing the function J (x). Newton’s method is often used as an iterative technique. In the following, the gradient of the function J (x) is formed on the basis of the problem of estimating the state from the relation (1). ⎡ ∂J (x) ⎤ ∂x
1 ⎢ ∂J (x) ⎥ ⎢ ∂x2 ⎥ ⎥ g(x) = ∇x J (x) = ⎢ ⎢ .. ⎥ ⎣ . ⎦
(3)
∂J (x) ∂xm
If the functions hi (x) are represented in vector form h(x) and if Jacobian matrix is determined from the function h(x), the following is obtained: ⎤ ⎡ ∂h ∂hm (x) 1 (x) ∂x1 · · · ∂x1 ⎥ ⎢ . . . . ... ⎥ (4) H T (x) = ⎢ ⎦ ⎣ .. ∂h1 (x) ∂hm (x) ∂xn · · · ∂xn The minimum of the objective function can be obtained by equalization function (x) with zero. To solve equation g(x) = 0, the Newton’s method is applied, and following is obtained:
−1 x = − g (x) g(x) (5)
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Jacobian of g(x) is calculated is calculated by considering the matrix H as a constant matrix: ∂g(x) G(x) = (6) = 2H T (x)WH (x) ∂x where is: G(x) - gain matrix Therefore, the step vector is: −1
H T (x)W (z − h(x)) (7) x = H T (x)WH (x)
Fig. 1. Block diagram of flow state estimation.
If the state vector is observed in the k-th iteration, the following is obtained: −1
x(k) = H T x(k) WH x(k) H T x(k) W z − h(x(k) ) x(k) = x(k+1) − x
(8) (9)
The procedure ends when the maximum change of the state vector becomes less than the predefined tolerance E: (10) maxx(k) < ε The block diagram of flow state estimation by the WLS method is shown in Fig. 1. The algorithm for state estimation in electric power systems by the WLS method was verified on the example of the IEEE 118-bus system. The IEEE 118-bus system consists of 118 busbars, 179 branches and 9 transformers, and it is taken from the reference [19]. The configuration of the 118-bus system is shown in Fig. 2. The number of power measurements used in the calculation of the state estimation is 300, namely 78 measurements of injected powers into the busbars and 222 measurements of power flows. Also, the same number of measurements is sufficient to achieve complete observability of the analyzed system. The redundancy factor for the 118-bus test systems 1.28.
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Fig. 2. Configuration of the 118-bus test system.
4 Results and Discussion The vector of state variables (estimated values of voltage modules and voltage angles on busbars) is shown in Fig. 3 and 4, while Fig. 5 shows a graph of estimated values of active and reactive power injected into busbars.
Fig. 3. Estimated values of voltage modules on busbars.
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From Fig. 3 it can be concluded that the amplitudes of the estimated voltages at all busbars are in the range of 0.95 p.u. up to 1.05 p.m. thus meeting the standard EN 50160 [20]. The maximum voltage value appears on busbars 10, 25, 66 and there amount 1.05 p.u.
Fig. 4. Estimated values of voltage angles on busbars.
Busbars 10, 25, 26 are previously defined as P-V busbars, it is expected that they will take on a higher voltage value, in this test example the maximum voltage value. The minimum value of voltage was obtained on the P-Q type of bus with number 76 and it amounts to 0.943 p.u., which leads to the conclusion that the active load on this bus is the highest. Figure 5 shows a graph of estimated values of active and reactive power injected into busbars. Based on the results of the injected active and reactive powers obtained by the WLS method, the detection of bad data of injection power and power flow will be calculated in 118 bus system. Detection of bad data is shown on Fig. 6.
Fig. 5. Estimated values of injection powers in the busbars.
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Fig. 6. Detection of bad data.
The maximum calculated error between the estimated and measured values of injected powers in the busbars, appears in the injection of reactive power on the bus number 104, and it amounts to 0.03701% (Fig. 7).
Fig. 7. Comparison of estimated and measured values of voltage amplitudes.
In the developed state estimation application, the assumption was used that the initial values of the voltage amplitudes/modules on all busbars are 1 p.u. and voltage angles 0°. Using this assumption satisfactory results were obtained. The calculated error between the estimated and real values of the state variables is less than 1.5%. By analyzing the obtained results, it can be concluded that the largest error appears on the bus number 101 and it amounts to 1.3824% (Fig. 8).
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Fig. 8. Comparison of estimated and real values of voltage angles.
5 Conclusions One of the main requirements from the electricity system is to provide consumers with high quality electricity. In order to achieve this requirement, performing the most accurate estimation of the state of power grids is a necessary step in the process. State estimation is one of the most important analytical functions for various power grids calculations. By installing more devices for monitoring, control, measurement and data collection in electricity grids, state estimation would be significantly simplified, because there would be a better insight into the real situation, i.e. the quality of the analysis would increase significantly. In this paper, an algorithm for estimating the state of power grids by the WLS method is presented. The verification of the algorithm was performed on a test example of the IEEE 118-bus network in the MATLAB software environment. Algorithm outputs are greatly influenced by the state of observability of the network, i.e. information on the available set of measurements needed to obtain the value of the state vector. The results presented in this research demonstrated the accuracy of the WLS algorithm, with an error less than 0.1% in case of power estimation and less than 1.5% in case of voltage estimation. In case of topological changes in the grid where related information has not been logged and/or topological changes in parts of the grid that are not observable, incorrect measurements are recorded, posing serious danger to the power grid management as they can lead to wrong conclusions of the grid operator. The WLS algorithm used in this paper can be upgraded by introducing measurements from PMU devices, i.e. by using voltage and current phasor measurements in the node, whereby the redundancy factor would also increase. Increasing the redundancy factor increases efficiency of the WLS algorithm but also the ability of extracting information about incorrect measurements in the grid through identifying critical measurements. The state estimator in that case would be able to eliminate the impact of poor measurements and temporary loss of measurements, without any impact on the quality of estimation results.
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The new approach to the state estimation problem in EPS opens the door to new applications related to the state estimation problem. For example, error treatment procedure could be incorporated, and studies could be carried out considering different types of errors in the set of measurements. This research contributes to the existing area of state estimation knowledge by developing and testing an algorithm that improves the accuracy of state estimation methods. With more available data from the system, and improved accuracy, EPS operators will be able to improve the planning, operation and control of future, more smart power systems.
References 1. Abur, A., Exposito, A.G.: Power System State Estimation, Theory and Implementation, 1st edn. CRC Press, New York (2004) 2. Zorki´c, M.: Estimacija stanja u distributivnim mrežama. In: Zbornik radova fakulteta tehniˇckih nauka u Novom Sadu, Novi Sad (2010) 3. Chakrabarti, S., Kyriakides, E., Valverde, G., Terzija, V.: State estimation including synchronized measurements. In: IEEE Bucharest Power Tech Conference, Bucharest (2009) 4. Plackett, R. L.: The discovery of the method of least squares. Biometrika, 239–251 (1972) 5. Zhu, H., Giannakis, G.B.: Power system nonlinear state estimation using distributed semidefinite programming. IEEE J. Sel. Top. Signal Process. 8, 1039–1050 (2014) 6. Levinbook, Y., Wong, T.F.: State estimation with initial state uncertainty. IEEE Trans. Inf. Theory 54, 235–354 (2008) 7. Zhao, J., et al.: Roles of dynamic state estimation in power system modeling, monitoring and operation. IEEE Trans. Power Syst. 36, 2462–2472 (2021) 8. Chen, Y., Chen, H., Jiao, Y., Ma, J., Lin, Y.: Data-driven robust state estimation through off-line learning and on-line matching. J. Mod. Power Syst. Clean Energy 9, 897–909 (2021) 9. Caro, E., Conejo, A.: State estimation via mathematical programming: a comparison of different estimation algorithms. IET Gener. Transm. Distrib. 6, 545–553 (2012) 10. Chen, Y., Liu, F., Mei, S., Ma, J.: A robust WLAV state estimation using optimal transformations. IEEE Trans. Power Syst. 30, 2190–2191 (2015) 11. Filho, M., da Silva, A., Falcao, D.: Bibliography on power system state estimation (1968– 1989). IEEE Trans. Power Syst. 5, 950–961 (1990) 12. Schneider, A., Hommel, G., Blettner, M.: Linear Regression Analysis. Dtsch Arztebl Int., Germany (2010) 13. Stagg, G., El-Abiad, A.: Computer Methods in Power System Analysis. Mc Graw Hill, New York (1968) 14. Burk, O.: Least Square. Department of Statistics, Oxford (2010) 15. Krsman, V.: Specijalizovani algortimi za detekciju, identifikaciju i estimaciju loših podataka u elektrodistributivnim mrežama. Univerzitet Novi Sad, Novi Sad (2017) 16. Deng, Y., He, Y., Zhang, B.: A branch-estimation-based state estimation method for radial distribution systems. IEEE Trans. Power Deliv. 17, 1057–1062 (2002) 17. Goldthorpe, J.H.: Current issues in comparative macrosociology. Comp. Soc. Res. 16 (1997) 18. Kirinˇci´c, V., Maruši´c, A., Skok, S.: Estimacija stanja elektroenergetskog sustava sinkroniziranih mjerenja fazora. 10. savjetovanje HRO CIGRE, Cavtat (2011) 19. Jain, A., Shivakumar, N.R.: Impact of PMU in dynamic state estimation of power systems. In: 40th North American Power Symposium, pp. 1–8 (2008) 20. Markiewicz, H., Klajn, A.: Standard EN 50160 - Voltage Characteristics in Public Distribution Systems. Wroclaw University of Technology (2004)
Adaptive Under Frequency Load Shedding Using Center-of-Inertia Frequency Maja Dedovi´c Mufti´c(B) , Samir Avdakovi´c , Ajdin Alihodži´c , Nedis Dautbaši´c , Adin Memi´c, and Adnan Mujezinovi´c Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina [email protected]
Abstract. The quality of the power system’s frequency response following under frequency load shedding triggered by a disturbance is primarily determined by the extent to which the magnitude of the disturbance is matched with the load shedding amount, as well as the locations selected for load shedding. Therefore, an accurate and timely assessment of the imbalance of active power in the system after a disturbance is crucial for the system stability. In this paper, an adaptive under frequency load shedding scheme based on the estimation of the magnitude of the disturbance is presented. In addition, this paper presents the signal processing technique empirical mode decomposition approach to estimate the frequency change of the center of inertia. Based on these values, the imbalance of active power in the system is estimated. The under frequency load shedding is simulated in the IEEE 39 bus test system, and the results of the proposed approach are compared with conventional under frequency load shedding scheme after system disturbances. The results according to the performed simulations show that the application of adaptive load shedding leads to less load shedding of active power on consumer buses and at the same time ensures a higher level of nadir frequencies compared to the conventional load-shedding scheme. Keywords: Rate of change of frequency (RoCoF) · Center of Inertia · Empirical Mode Decomposition · Adaptive Under Frequency Load Shedding (AUFLS)
1 Introduction A power system outage refers to a sudden interruption of electricity supply in some part of the power system, which can affect a wide range of users. Power system outages can be caused by a variety of factors, such as natural disasters (storms, floods, earthquakes), human error, equipment failures, overloads, voltage drops and other factors. The consequences of power system outages can be serious and diverse, depending on the size, duration and impact on users. This may include loss of productivity in industry, disruption of supply to health facilities and other emergency services, disruption of public transport, disruption of information and communication systems, etc. In order to reduce the risks of power system outages, power systems are usually designed with a high degree of reliability and safety, and advanced monitoring and control systems are © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 606–615, 2023. https://doi.org/10.1007/978-3-031-43056-5_46
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used to enable a quick and efficient response to any potential failure in the system. One of the measures to prevent the collapse of the power system in the event of an imbalance of active power is under frequency load shedding. Under frequency load shedding in the power system is the system of automatic load shedding when the frequency drops below a certain level. This is commonly referred to as the Under Frequency Load Shedding (UFLS) system. The UFLS system is used to prevent the frequency from dropping further in the event of a power failure in the grid. When the frequency drops below the set limit, the UFLS system automatically shuts down a certain number of loads on the grid to release additional power and keep the frequency within acceptable limits. The UFLS system is often used in large power systems where too much reduction in frequency could cause instability and interruptions in the power supply. This system is important to maintain power system stability, reduce damage to equipment, and prevent major power outages. There are two main types of UFLS systems: adaptive and conventional. Adaptive under frequency load shedding schemes (AUFLS) are newer generation systems and are an improvement over conventional systems. They use more complex algorithms to predict and adjust load in real time. Adaptive under frequency load shedding systems monitor power system conditions, including voltage and power, frequency, response speed, and other factors, to adjust load shedding in real time. These systems are used in modern power systems where high levels of reliability and flexibility are required. Traditional under frequency load shedding systems typically use predefined parameters and settings to disconnect the load when the frequency drops. These systems are less flexible and require manual adjustment of parameters to ensure optimal performance. However, conventional under frequency load shedding systems are still commonly used in power systems where a lower level of reliability and flexibility is required. Both systems operate on the same principle of automatically disconnecting the load when the frequency drops below a certain level, but they differ in the way the load is relieved. With the advent of new technological solutions, phasor measurement unit (PMU) devices, numerical protection devices using newer protocols, high-speed telecommunication links, and automation at the node level where power generation and distribution occurs, it is possible to implement load shedding and management systems that constantly adapt to power system operating conditions. Such a system appears in the literature under different names, the idea being the same: Wide Area Load Shedding (centralized load shedding) [1, 2], Intelligent Load Shedding - ILS [3] or Wide Area Monitoring, Protection and Control - WAMPC [4]. A two-step UFLS scheme is proposed in [5]. In the first step, the dfi /dt value is estimated using a Newton-type nonrecursive algorithm. In the second step, based on the dfi /dt of each generator and its inertia, the magnitude of the disturbance is estimated using the generator oscillation equation. Such a system requires the estimation of the dfi /dt value at the location of the production units and the transmission of this information to a remote center, where an application is used to estimate the magnitude of the disturbance in real time and to generate the proper load shedding schedule. In [6], it is proposed to use WAMPC and new modern telecommunication devices to augment existing System Integrated Protection Scheme. By using PMU units and applications that evaluate the stability of the system and the extent of disturbances based on real-time measurements,
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it is possible to improve the operation of existing local protection units, including UFLS load shedding systems, by using high-speed telecommunication links. In this way, a complete adaptation of the UFLS load shedding scheme is achieved and the amount of load shedding required is reduced. In [7], it is proposed to use PMU units for the calculation of the frequency change and its gradient, and to estimate the magnitude of the disturbance based on this information. Based on the estimated disturbance magnitude an appropriate UFLS scheme is constructed. Such an integral scheme implies centralized evaluation of the disturbances and issuance of a load shedding order from the same control center. In [8], the use of the wavelet transform is proposed for the detection of the transient stability and the estimation of the magnitude of the disturbance. In this paper, an adaptive under frequency load shedding system is described and the paper is organized as follows. In chapter two, the research methodology is explained. Chapter three presents an illustrative example, while chapter four is devoted to concluding considerations.
2 Applied Methodology Due to the oscillatory nature of the frequency after the disturbance, the Hilbert-Huang transform allows the extraction of the fundamental harmonics of the frequency and its gradient (residual). Based on this information and the information about the inertia and damping of the system, it is possible to estimate the magnitude of the disturbance. Such performance requires the use of PMU devices and high-speed telecommunication links, as well as centralized logic that implements appropriate corrective actions in real time based on the detected disturbance. After analyzing the frequency signals from the buses of all generators due to the loss of generation, COI ROCOF is calculated using the EMD method [9]. Additionally, this paper presents a novel approach for estimating the CoI RoCoF using locally measured data from synchronized phasor measurement units (PMUs) located at all generator terminals. For this purpose, an innovative technique using the EMD approach is applied to estimate the rate of change in the frequency of the center of inertia. Then, an efficient UFLS scheme is proposed to take advantage of the EMD approach to estimate the magnitude of the active power imbalance. Next, the steps of implementing the adaptive scheme are shown: Step 1: Development of an algorithm for estimating df/dt and estimating the total imbalance of active power in the power system with specific simulations and calculations: • In order to perform the first step, it is necessary to ensure the availability of the frequency signal from the WAMS (PMU). • The EMD algorithm separates signals into several IMFs and residuals. The residuals obtained after applying the EMD approach reliably reflect the trend of the signal, and it is shown that the residuals are close to the calculated value of the center of inertia, and are therefore suitable for the estimation of df/dt. • The original signal is represented by the sum of all IMFs and residuals:
Adaptive Under Frequency Load Shedding Using Center-of-Inertia Frequency
rn (t) = ωi (t) −
n j=1
609
cj (t)
(1)
where ωi (t) denotes the angular velocity of the ith generator, cj (t) denotes an IMF, rn (t) denotes a residual or low-frequency trend, and n is the total number of IMFs. • Then it is calculated:
m Hi r n (t) i=1 n=1 m d ωc /dt = i=1 Hi
(2)
• The total active power imbalance is: p =
m
pi =
i=1
2
m
i=1 Hi
ωn
d ωc (t) dt
(3)
Detailed mathematical formulation, as well as all necessary calculations are presented in the paper [10]. Step 2: Based on the obtained results, a new approach for the development of adaptive load shedding is proposed: • After estimating the CoI RoCoF as described in the previous step, the magnitude of the disturbance can be calculated using the generator rotor swing equation assuming the system inertia is known. • It is assumed that N buses are equipped with the proposed UFLS relays. Let dfi /dt denote the locally estimated CoI RoCoF at location i. it is assumed that the system inertia is updated every minute in the UFLS relays. Thus, each relay can individually estimate the size of the active power deficit in MW. • The amount of load to be released by the relay is indicated by Pi at the location i and is calculated as follows: pi
PLi = Pi p
(4)
where PLi is the maximum amount of load allocated for load shedding at location i and p is the magnitude of the largest contingency in the system [11]. • A frequency threshold of 59.5 Hz is set for AUFLS activation. The adaptive load shedding scheme is realized in one step. In this context, the UFLS scheme can be designed to shed the same amount of load equal to the estimated magnitude of the disturbance soon after the disturbance.
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3 Illustrative Example The performance of the proposed CoI ROCOF estimation method and adaptive UFLS scheme has been validated through a large number of simulations on the IEEE 39bus test system with DIgSILENT PowerFactory. Loss of generation of different sizes are simulated and three scenarios are illustrated. PMUs are installed on all generator terminals. Load shedding is performed at all 19 load busses. The simulations of the different generator failures are first performed with the under frequency load shedding switched off. The frequency time series obtained from the simulations are recorded. The proposed algorithm utilizes these signals to assess both the frequency deviation of the center of inertia and the magnitude of the disturbance. The amount of load shedding required at each location is determined and then sent to the DIgSILENT PowerFactory to configure the UFLS relays installed in the test system. The same simulations are performed again with the UFLS switched on. In each case, the CoI frequency is also calculated to demonstrate the frequency response of the system after disturbance and UFLS operation. For performance comparison, a conventional four-stage UFLS scheme is developed and tested under the same disturbances. The settings of the conventional four-stage UFLS scheme are determined based on guidelines and common practice [12, 13]. The following Table 1 gives a summary of the conventional four-stage UFLS scheme. Table 1. Conventional four-stage UFLS scheme [14]. Stage
p (%)
fth (Hz)
td (s)
1st
15
59.4
0.2
2nd
10
59.1
0.2
3rd
10
58.8
0.2
4th
5
58.5
0.2
Furthermore, three major generator outage disturbances are simulated, and the frequency responses of the system after the event are plotted on Fig. 1, Fig. 2 and Fig. 3. In addition, the COI ROCOF is calculated and the amount of active power imbalance in the system is estimated according to scenario. The Table 2 shows the results of the active power imbalance assessment using the EMD approach, as well as the actual disturbance values, expressed in terms of MW. The frequency responses to simulated disturbances after using both the conventional load shedding scheme and the proposed UFLS are shown in Fig. 4, Fig. 5 and Fig. 6. It is important to emphasize that two delay times for UFLS relays are taken for this work. The first delay involves blocking the relay operation for a duration of 0.5 s to prevent any unwanted triggering of the relay. The second delay is the 0.2 s required for the relay to operate and for the circuit breakers in the system to open. The nadir of the system’s frequency response is of particular interest and has been recorded for the purpose of comparison with the conventional scheme. Table 3 presents
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Fig. 1. Generators speed deviation in Hz after loss of G02.
Fig. 2. Generators speed deviation in Hz after loss of G05.
the results after applying the conventional scheme and AUFLS. The nadir frequencies for both schemes are shown in the table. From the table it can be concluded that lower nadir frequency values are achieved by applying AUFLS, which represents a significant advantage of applying AUFLS over the conventional scheme. Also, AUFLS methods lead to better results in terms of frequency response and lower required load shedding. The effectiveness of the proposed scheme becomes increasingly evident as the size of the disturbance grows. This is attributed to the scheme’s ability to accurately detect the magnitude of the disturbance and promptly initiate corrective measures.
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Fig. 3. Generators speed deviation in Hz after loss of G09.
Table 2. Active power imbalance and dfc /dt. Generators
p (MW)
dfc /dt (Hz/s)
pEST (MW)
G2
572
−0,183
560.26
G5
508
−0,162
494,63
G9
830
−0,271
805,84
Fig. 4. System frequency of the proposed and conventional UFLS schemes after loss of G02.
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Fig. 5. System frequency of the proposed and conventional UFLS schemes after loss of G05.
Fig. 6. System frequency of the proposed and conventional UFLS schemes after loss of G09. Table 3. Conventional four-stage UFLS scheme. Generators
Outage (MW)
Nadir frequency (Hz) AUFLS
Conventional
G2
572
59,388
59,378
G5
508
59,425
59,357
G9
830
59,413
59,252
4 Conclusion Adaptive under frequency load shedding is a technique used to maintain power system stability by shedding loads in the event of a frequency drop. This technique involves using the system’s derivative of frequency deviation with respect to time and center of inertia to make real-time adjustments to the load shedding process. By continuously
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monitoring the frequency and load demand, the system can accurately predict when and how much load to shed in order to maintain stable operation. This paper presents the signal processing technique empirical mode decomposition approach to estimate the frequency change of the center of inertia. Based on these values, the imbalance of active power in the system is estimated. The under frequency load shedding is simulated in the IEEE 39 bus test system. To validate the effectiveness of the proposed method, it is compared with the conventional load shedding method. The results revealed that the application of AUFLS led to higher nadir frequency values while shedding a smaller amount of load in power system. Future studies should focus on the development and application of EMD approach for processing frequency measurements within the framework of numerical protections, and accordingly the detection of a corresponding disturbance in the system that requires the operation of an adaptive UFLS scheme. The research should provide the possibility of detecting patterns of frequency response, which would ensure against wrong detection of the frequency gradient when changing the switching state. Furthermore, given the increasing share of alternative energy sources in total system power, it is necessary to conduct research on the impact of renewable energy sources, specifically wind power plants and their inertia and injected power on system behavior, as well as the impact on UFLS protection coordination in parts of the system with dominant aggregates of wind power plants.
References 1. Bevrani, H., Tikdari, A.G., Hiyama, T.: Power system load shedding: key issues and new perspectives. World Acad. Sci. Eng. Technol. 65, 199–204 (2010) 2. Parniani, M., Nasri, A.: SCADA based under frequency load shedding integrated with rate of frequency decline. IEEE Power Eng. Soc. Gen. Meet. 6 (2006) 3. Andersson, D., Elmersson, P., Juntti, A., Gajic, Z., Karlsson, D., Fabiano, L.: Intelligent load shedding to counteract power system instability. In: Explorations in Economic History, pp. 570–574 (2004) 4. Grisby, L.: Power System Stability and Control. Taylor & Francis Group, LLC (2006) 5. Terzija, V.V.: Adaptive underfrequency load shedding based on the magnitude of the disturbance estimation. IEEE Trans. Power Syst. 21(3), 1260–1266 (2006) 6. Madani, V., Novosel, D., King, R.: Technological breakthroughs in system integrity protection schemes. In: Proceedings of 16th Power System Computation Conference (PSCC), Glasgow, Scotland (2008) 7. Hamid, B., Abderrahmane, O., Nadir, G., Farid, M., Nikos, E.M.: A new approach applied to adaptive centralized load shedding scheme. In: Proceedings of the 8th WSEAS International Conference on Circuits, Systems, Electronics, Control and Signal Processing (CSECS 2009), Puerto De La Cruz, Tenerife, Canary Islands, Spain, pp. 28–33 (2009) 8. Avdakovi´c, S., Musi´c, M., Nuhanovi´c, A., Kušljugi´c, M.: An identification of active power imbalance using wavelet transform. In: Proceedings of the Ninth IASTED European Conference on Power and Energy Systems, Palma de Mallorca, Spain (2009) 9. Huang, N., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analyses. In: Proceedings of the Royal Society of London Series A-Mathematical physical and Engineering Sciences (1998)
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10. Dedovi´c, M.M., Avdakovi´c, S.: A new approach for df/dt and active power imbalance in power system estimation using Huang’s Empirical Mode decomposition. Int. J. Electr. Power Energy Syst. 110, 62–71 (2020) 11. Sun, M., Liu, G., Popov, M., Terzija, V.V., Azizi, S.: Underfrequency load shedding using locally estimated RoCoF of the center of inertia. IEEE Trans. Power Syst. 36, 4212–4222 (2021) 12. Lokay, H.E., Burtnyk, V.: Application of underfrequency relays for automatic load shedding. power apparatus and systems. IEEE Trans. Power Appar. Syst. 87(3), 776–783 (1968) 13. Horowitz, S.H., Phadke, A.G.: Power System Relaying, 3rd edn. Wiley, Hoboken (2008) 14. PRC-006-SERC-02: Automatic Underfrequency Load Shedding Requirements - NERCipedia. https://nercipedia.com/active-standards/prc-006-serc-02automaticunderfrequencyload-shedding-requirements/. Accessed 28 Jan 2023
Predictive Maintenance of Induction Motor Using IoT Technology Sanela Užiˇcanin1(B)
, Nerdina Mehinovi´c1 and Admir Užiˇcanin2
ˇ , Edina Cerkezovi´ c1
,
1 Faculty of Electrical Engineering, University of Tuzla, Franjevaˇcka 2, Tuzla, Bosnia and
Herzegovina [email protected] 2 Public Enterprise Electric Utility of Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina
Abstract. Induction motors are widely used in various industries and their failures can cause significant downtime and maintenance costs. Predictive maintenance of induction motors is an important area of research in the field of industrial automation. With the increasing availability of Internet of Things (IoT) technology, it is now possible to collect and analyze large amounts of data from the motors, which can be used to predict their maintenance needs. This paper presents a predictive maintenance system for induction motors using IoT technology. The system is based on a combination of data from sensors, machine learning algorithms, and cloud computing platforms. The sensors collect data on various parameters such as vibration, temperature and electrical parameters (current, voltage, power), which are then fed into the machine learning algorithms for analysis. The algorithms use historical data to build models that can predict the maintenance needs of the motor. The system is implemented using cloud computing platforms, which provide scalable and cost-effective solutions for data storage and analysis. The results of the study show that the proposed system can accurately predict the maintenance needs of induction motors, which can help reduce downtime and increase productivity in industrial environments. Keywords: IoT technology · Sensors · Failures · Induction Motor · Predictive maintenance
1 Introduction There are several types of maintenance, including: corrective, preventive and predictive maintenance. Corrective maintenance is a reactive form of maintenance that occurs after a failure has already happened. It is considered the most basic type of maintenance and often involves the equipment becoming unavailable for use until repairs are made. Corrective maintenance can be expensive, as it often requires urgent repairs and replacement of costly components, which can result in possible long wait times for the repairs to be completed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 616–625, 2023. https://doi.org/10.1007/978-3-031-43056-5_47
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Preventive maintenance is a type of maintenance that is performed proactively to prevent equipment or system failures and minimize the need for corrective maintenance. Preventive maintenance includes important actions such as cleaning motor ventilation system, management of humidity and condensation, inspection of electrical connections for looseness (motor junction box), monitoring of voltage and current imbalances, heating test of the stator sheet package, measurement of stator winding resistance, evaluation of bearing and their lifetime, preventive replacement of bearings, lubrication of bearings with appropriate lubricant and at regular intervals. Predictive maintenance uses data and analytics to predict when equipment or system is likely to fail, allowing maintenance to be performed only when it is necessary. With this type of maintenance, activities can be scheduled at the optimal time, maximizing the useful life of equipment and minimizing downtime and repair costs. Predictive maintenance of the electric motor implies constant supervision and monitoring of the operation of the electric motor (recording electrical parameters, recording of the heating of the electric motor, recording of vibrations, condition of the bearings), all with the aim of early detection of a possible failure.
2 Predictive Maintenance of Induction Motor Electric motors are an important component of many industries, and induction motors are a particularly common type of electric motor, accounting for approximately 90% of all electric motors used in industry. Many types of motor failures can affect the output efficiency and performance of the motor. To minimize the number of unplanned outages, multiple predictive techniques are applied on these motors. To improve the reliability and efficiency of motors, the aim is to reduce the downtime incurred by fault handling. Unexpected motor failures result in costly downtime and are generally quite more expensive, long time and difficult to repair. In addition, replacement motors are not always readily available from the manufacturers, and the storage of spare motors as replacements is costly and not always practical [1]. The most common electrical and mechanical predictive maintenance methods applied to detect faults in an induction motor are: Electrical signature analysis (Motor current signature analysis, Motor voltage signature analysis, Extended park’s vector approach and Instantaneous power signature analysis), Vibration analysis, Acoustic analysis, and IR thermography [2]. All of these methods are non-destructive and minimally invasive. In electrical signature analysis, the motor’s electrical signals, including voltage and current waveforms, are analyzed using advanced signal processing techniques to identify any abnormalities in the waveform, which may indicate the presence of a fault or defect. The technique involves collecting data from measuring devices attached to the motor, which record the electrical signals produced by the motor during operation. Vibration analysis is a technique used to monitor the condition of an induction motor by analyzing the vibration signals produced during the motor’s operation. Vibration analysis is a key element of predictive maintenance for electric motors, as it can help identify potential issues before they cause significant damage or result in unplanned downtime. Vibration analysis is based on three basic measurements: measurement of acceleration,
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measurement of speed- root mean square- RMS speed and displacement measurement. It is recommended to measure vibrations along each of the 3 axes (axial, tangential and radial) on the front and back of the electric motor, specifically on the parts where the bearing housings are located. In vibration analysis, there are some disadvantages such as the influence of the transmission path, the effect of certain resonance frequencies on the original vibration signal and mechanical connection of the transducer [3]. The acoustic predictive method relies on the principle that when a fault occurs within the motor, it generates a unique sound signature that can be detected and analyzed. This method is useful for early detection of motor faults before they develop into major issues, which can result in significant downtime and costly repairs. Infrared thermography is used to capture the heat signature of the heated objects in order to visualize the heat energy distribution of the objects. This method has become an important tool for detecting hot spots and defects in the electrical equipment due to its simplicity and effectiveness. Thermography can identify fault occurrence in real time, and can protect the equipment and system from various problems occurring due to faults. The technique can be used in hazardous zones, provides instant information and it is not affected by electromagnetic waves [4]. By implementing appropriate maintenance practices, it is possible to enhance the operational lifetime of equipment, improve the productivity of the induction motor, and increase its efficiency.
3 Failures in Induction Motor There are several common failures that can occur in induction motors. As illustrated in Fig. 1, the most frequent faults in induction motors are bearing and winding faults caused by insulation degradation.
Fig. 1. Statistics of failure modes in induction motor [5].
Induction motors are often operated in difficult conditions. Bearing failure in induction motors is frequently caused by a variety of factors such as inadequate lubrication, excessive mechanical stress, improper assembly, misalignment, problems with the coupling system, dust, corrosion and other similar issues. Stator faults are usually related to insulation failures. Induction motors can experience shorted coils of the same phase, shorted turns, phase-to-phase faults, open circuits, coil-to-ground faults and single phasing, all as a result of various factors such as overheating, misalignment, and usage of poor quality of materials. Techniques for detecting
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faults in the stator are: measurement of the insulation resistance of the stator winding of the electric motor, measurement of impedance and active resistance of the stator winding and testing with increased voltage industrial frequency. Rotor type faults in an induction motor can cause several issues, such as speed fluctuation, torque pulsation, changes in the frequency component of the motor’s power supply, temperature increases, arcing in the rotor, and machine vibration. However, all of this can be avoided, when the motor is supervised by an appropriate condition monitoring or diagnostic system and the fault is identified early enough [6].
4 Predictive Maintenance of Induction Motor Using IoT Technology The use of IoT technology can turn regular factories into smart factories. The industrial internet of things (IoT) is the use of smart sensors and actuators to enhance manufacturing and industrial processes. Also known as the industrial internet or Industry 4.0. The driving philosophy behind IoT is that smart machines are not only better than humans at capturing and analyzing data in real time, but they’re also better at communicating important information that can be used to drive business decisions faster and more accurately [7]. The applications of Internet of Things are in different areas like automotive industries, embedded devices, environmental monitoring, agriculture, construction, smart grid, health care, etc. A regressive review of the existing systems of the automotive industry, emergency response, and chain management on IoT has been carried out, and it is observed that IoT found its place almost in every field of technology [8]. Smart factories have sensors, software, and robots that collect data of machine’s state and condition, such as operating temperature, voltage, current, and vibration. This data is wirelessly sent in real-time to a central storage platform in the cloud. The data is then analyzed using predictive analytics and machine learning to identify issues quickly and help reduce repair costs. IoT offers several advantages, including: – Enhanced device communication: IoT facilitates machine-to-machine communication, allowing devices to connect to a network for greater control, transparency, and efficiency. – Centralized automation and control: Devices are connected; they can be easily managed in a centralized storage platform using a popular wireless technology called Wi-Fi. – Improved device monitoring: With IoT, tasks can be automated with less human involvement, leading to better decision-making, greater transparency, and faster emergency responses through continuous monitoring. Architecture of IoT technology is shown in Fig. 2. There are several modules or components involved in IoT-based predictive maintenance of induction motors. Some of these modules include: – Sensors: IoT sensors are used to collect data on various parameters of the induction motor, such as temperature, vibration, speed, current and many others. These sensors may be embedded within the motor or attached to its external surface.
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Fig. 2. Architecture of IoT.
– Data processing and analytics: Once the data is collected from the sensors, it is processed and analyzed using various techniques such as machine learning algorithms. This helps to identify patterns and anomalies in the data that can be used to predict maintenance needs. – Cloud platform: The data collected from the sensors is often sent to a cloud-based platform for storage and further processing. This allows easy access to the data from anywhere and provides scalability for large-scale deployments. – Predictive maintenance software: This software uses the data collected from the sensors and analyzed by the analytics module to predict when maintenance is required. This helps to reduce downtime and prevent unplanned breakdowns. – Notification and alert system: The predictive maintenance software can send notifications and alerts to maintenance staff when maintenance is required. This helps to ensure that maintenance is performed in a timely manner, reducing the risk of equipment failure. Basics modules of IoT technology are shown in Fig. 3.
Fig. 3. Modules of IoT technology.
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5 Example of Predictive Maintenance of Induction Motor Using IoT Technology The ABB Smart Sensor converts traditional motors into smart, wirelessly connected devices. It enables to monitor the health of motors, optimize efficiency and improve reliability and safety. Measured parameters of the motor are vibration (radial, axial, tangential), skin temperature, speed, operating hours, numbers of starts, frequency and power. Sensor has a temperature measuring range of −40 °C to +85 °C with a resolution of 0.05 °C and a vibration measurement range of 0.04 to 700 mm/s [9]. The device is made of stainless steel material which is IP 66 protected and is externally mounted on the motor’s frame, parallel to motor shaft. The battery has a designed life of 5 years and cannot be replaced. The collected data is stored in its cloud storage. Wireless networks like WLAN and Bluetooth are used for data transmission. Mandatory information about motor commissioning are: motor mounting type, position of terminal box, orientation of sensor, nominal motor speed, type of speed control, nominal motor voltage, nominal motor current, nominal motor power, frequency, information about bearing, motor load and shaft height. The data collected from the sensors helps identify causes and anomalies that can be used to predict maintenance needs. The overall condition of induction motor can be represented by “donut” which is calculated from separate conditions indexes over a period of time. The green line on the Fig. 4 shows the development of the daily total number of alarms and alerts combined. The blue line shows the number of acknowledge alarms and alerts.
Fig. 4. Overall condition of induction motor.
There are three types of events which are shown on Fig. 5. Alarms and alerts are triggered automatically when trespassing thresholds occur. Alarms and alerts can be acknowledged and closed. Maintenance is entered manually in the application. The following section describes the electric motor drive, which consists of a motor that drives the pump (Figs. 6 and 7). Nominal data of induction motor which is part of electric motor drive is given in Table 1.
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Fig. 5. Types of events.
Fig. 6. Example of electric motor drive.
Fig. 7. Sensor mounting position.
Table 1. Nominal data of the motor. Type of motor
Three Phase squirrel cage induction
Nominal power
75 kW
Nominal voltage
415 V
Nominal current
127 A
Nominal speed
2965 rpm
Power factor
0,91
Nominal frequency
50 Hz
Connection type
Delta
DE bearing
6317
NDE bearing
6314
The diagrams in the following figures were generated from sensor data collected over three days on the cloud platform, with a sampling interval of one hour. In Fig. 8, the green line shows tangential vibration, the blue line shows radial vibration, and the black line shows axial vibration. The values are displayed for a period of three days, and the measuring unit is mm/s. Vibrations can also be shown for a longer period. These vibration values can be compared with the standard values for a motor of this nominal power, providing us with more information about the motor’s state.
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Fig. 8. Vibration of induction motor.
Figure 9 displays the output power of the induction motor, measured in kW over a period of three days. The induction motor is currently operating at 60% of its maximum capacity.
Fig. 9. Output power of induction motor.
Speed of induction motor is shown in Fig. 10. The motor runs at a nearly constant speed. The graphic would make more sense if it were a motor working with a frequency converter.
Fig. 10. Speed of induction motor.
The bearing condition of the motor is shown in Fig. 11, where the limits for alerts and alarms could be displayed on the graph. Bearing condition monitoring refers to the process of continuously monitoring the health and performance of bearings. The condition of the bearings is rated from 1 to 10.
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Fig. 11. Bearing condition of induction motor with alarm and alert.
Black line shows total running time and blue line shows number of starts between measurements in Fig. 12. The number of motor starts is important information, especially if it is a high-power motor. The total running time of motor is important due to regular motor maintenance.
Fig. 12. Total running time and Number of Starts Between Measurement.
The cloud platform offers multiple ways to display data, including the ability to show one or multiple parameters on a diagram and to adjust the time period for analyzing the data. This provides us with more information about a given motor state.
6 Conclusion Using IoT technology for predictive maintenance of induction motors offers significant advantages. Using sensors and data analytics, it is possible to monitor motor performance and predict when maintenance is needed before failure occurs. This proactive approach can save time and money by avoiding unexpected downtime and reducing repair costs. In addition, IoT technology enables remote monitoring, which allows maintenance teams to access motor data from anywhere, improving their ability to quickly respond to potential problems. Overall, the use of IoT technology for predictive maintenance of induction motors is a promising strategy that can help businesses increase efficiency, reduce costs, and improve overall equipment reliability. The sensor that was used in this example can be placed on any other motor by pre-setting the information about the given motor. It enables better monitoring of motor operation and reduces the possibility of failure. Further research can focus on forecasting and projecting possible failures in the future. This can be done using neural networks and artificial intelligence.
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References 1. Soergel, S., Rastgoufard, P.: An analysis of induction motor predictive maintenance techniques. In: Proceedings of 28th Southeastern Symposium on System Theory, Baton Rouge, LA, USA, 31 March–2 April 1996 (1996) 2. Venkatesh, V., Krishna, D.V., Kalyani, K.V., Panda, D.K.: Predictive maintenance practices of induction motor. Int. J. Adv. Eng. Manag. (IJAEM) 4 (2022) 3. Bate, A.H.: Vibration Diagnostics for Industrial Electric Motor Drives. Bruel & Kjaer Application Note (1987) 4. Dekhandji, F.Z., Halledj, S.E., Zaboub, O.: Predictive maintenance applied to three phase induction motor. Alger. J. Signals Syst. (2019) 5. Rodrguez, P.V.J., Arkkio, A.: Detection of stator winding fault in induction motor using fuzzy logic. Appl. Soft Comput. 8(2), 1112–1120 (2008) 6. Vaimann, T.: Diagnostics of induction machine rotor faults using analysis of stator signals. Ph.D. thesis, faculty of power engineering Department of Electrical Engineering (2014) 7. Lectures, “Industrial Internet of Things”, School of Electrical and Electronics, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology (2021) 8. Kumar, P.M., et al.: Industrial Internet of Things and its application in industry 4.0. Comput. Commun. 166, 125–139 (2021) 9. ABB Ability Smart Sensor, User guide for Smart Sensor Platform app and web portal (2020)
Author Index
A Ademovi´c, Naida 27, 64 Al Awamrah, Mohammad 441 Albinovi´c, Sanjin 3, 53, 64 Alihodži´c, Ajdin 606 Ambrožiˇc, Tomaž 132 Avdakovi´c, Samir 606 Azemovi´c, Jasmin 571 Azrudin, Husika 562 B Baˇci´c, Željko 186 Baumgartner, Alfonzo 388 Be´ca, Mirsad 583 Begi´c, Elma 595 Beriša, Aldin 329 Berkovi´c, Mirza 92 Bidževi´c, Irfan 75 Bijedi´c, Kerim 53 Bjelak, Abdullah 263 Brˇcaninovi´c, Almir 533 Busuladži´c, Ibrahim 501 C Crnki´c, Aladin 466 ˇ Cerkezovi´ c, Edina 616 ˇ Congo, Naida 145 ˇ Custovi´ c, Hamid 206 ´ c, Ivica 359 Cori´ D Dautbaši´c, Nedis 606 Dedovi´c Mufti´c, Maja 606 Deli´c, Izudin 518, 533 Drakuli´c, Una 423 Duboveˇcak, Fran 157 Durakovi´c, Mirnes 562 Durmi´c, Nermina 233 Džaferovi´c, Samir 92
Džafo, Hajrudin 455 Džananovi´c, Ajdin 92 Džidi´c, Sanin 14, 75 Ðidelija, Muamer 105, 132 E El Sayed, Ahmed
14, 75
F Fathi, Leila 466 Fati´c, Naida 376 Frangeš, Stanislav 157 G Ganibegovi´c, Nedim 518, 533 Gašpar, Dražena 359 Gogi´c, Asmir 431 H Hadži´c, Emina 44 Hadžimustafi´c, Edin 171 Hajdarevi´c, Seid 501 Hamzi´c, Adis 115 Hasanagi´c, Redžo 466 Heldovac, Erma 571 Hodži´c, Damir 466 Hondo, Amina 595 Horman, Izet 509 Hubana, Tarik 571, 595 Husejinovi´c, Admel 233, 251 Husuki´c, Erna 280 I Ibriševi´c, Alen 501 Isakovi´c, Be´cir 300, 329 J Jašarevi´c, Hamza 171 Juki´c, Samed 233, 300, 329, 341, 376, 403
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Ademovi´c et al. (Eds.): IAT 2023, LNNS 644, pp. 627–628, 2023. https://doi.org/10.1007/978-3-031-43056-5
628
K Kadri´c, Edin 571 Karabegovi´c, Almir 145, 206 Keˇco, Dino 300, 329 Kevri´c, Jasmin 233 Kljaji´c, Ivka 186 Kljuˇcanin, Slobodanka 44 Kosti´c, Aleksandra 509 Kovaˇcevi´c, Aldin 341, 403 Krupi´c, Mirza 376 Kulo, Nedim 132 Kustura, Melisa 509 L ˇ Livada, Caslav 388 Ljevo, Žanesa 3, 44, 53, 64 Ljusa, Melisa 206 Luli´c, Haris 550 M Mabi´c, Mirela 359 Mari´c, Lejla 571 Mašeti´c, Zerina 251, 280 Mehanovi´c, Dželila 280 Mehinovi´c, Nerdina 616 Memi´c, Adin 606 Mešanovi´c, Dževad 171 Meši´c, Amel 518, 533 Metovi´c, Sadjit 550, 562 Mudželet Vatreš, Amela 341, 403 Mujˇci´c, Aljo 431 Mujˇci´c, Edin 423 Mujezinovi´c, Adnan 606 Mulahusi´c, Admir 132 Muratagi´c, Adnan 27 Muši´c, Adelina 571 Mustafi´c, Adnan 486 N Neziri´c, Emir 583 Novali´c, Adnan 14 O Obu´cina, Murˇco 501 Omeragi´c, Dinko 300, 329 Omerhodži´c, Adnan 92 Osmi´c, Midhat 518, 533
Author Index
P Pelja Tabori, Nataša 213 Ponjavi´c, Mirza 206 Poslonˇcec-Petri´c, Vesna 186 Pozder, Mirza 3, 53, 64 R Racetin, Ivana 157 Roši´c, Husein 466 S Salkovi´c, Adna 195 Selimovi´c, Ahmed 263 Softi´c, Almira 550 Stipi´c Vinšalek, Valentina 441 Sulejmanovi´c, Suada 3, 44, 53, 64 Šahinovi´c, Irvina 466 Šari´c, Ammar 3, 44, 53, 64 Šari´c, Mirza 583 Še´cerbegovi´c, Alma 431 Šehovi´c, Jasmin 476 Šojo, Robert 388 Špago, Damir 583 Šunje, Edin 583 T Taletovi´c, Jasmin 213 Telalovi´c Hasi´c, Jasminka Timoti´c, Valentina 509 Topoljak, Jusuf 132 Tuno, Nedim 132 Turˇcalo, Hamza 571 U Užiˇcanin, Admir 616 Užiˇcanin, Sanela 616 V Varga, Luka 388 Viˇci´c, Mile 441 Vrce, Esad 105 Vukovi´c, Valentina
186
Z Zejnilovi´c, Emina 280 Župan, Robert 157
195