Performance Modeling for Sewer Networks Khalid Kaddoura A Thesis In the Department Of Building, Civil, and Environmental Engineering Presented in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy (Building Engineering) at Concordia University Montreal, Quebec, Canada June 2018 © Khalid Kaddoura, 2018 CONCORDIA UNIVERSITY SCHOOL OF GRADUATE STUDIES This is to certify that the thesis prepared By: Khalid Kaddoura Entitled: Performance Modeling for Sewer Networks and submitted in partial fulfillment of the requirements for the degree of Doctor Of Philosophy (Building Engineering) complies with the regulations of the University and meets the accepted standards with respect to originality and quality. Signed by the final examining committee: Chair Dr. M. Kahrizi External Examiner Dr. Sunil Sinha External to Program Dr. Amr Youssef Examiner Dr. Fuzhan Nasiri Examiner Dr. Ashutosh Bagchi Admin. Supervisor Dr. Fariborz Haghighat Approved by Dr. Fariborz Haghighat, Graduate Program Director Monday, July 16, 2018 Dr. Amir Asif, Dean Faculty of Engineering and Computer Science ABSTRACT Performance Modeling for Sewer Networks Khalid Kaddoura, Ph.D. Concordia University, 2018 In spite of the pressing need to preserve sewer networks, sewer pipelines and manholes are prone to deterioration and hence to collapse. According to the American Society of Civil Engineers (ASCE) (2017), the sewer network’s grade of the United States (US) is grade “D+”, making it one of the worst infrastructure assets in the US. In addition, the Canadian Infrastructure Report Card (CIRC) (2016) states that more than half of their linear wastewater assets’ physical condition were ranked between very poor to good states, with a total replacement value of $47- billion. Despite the enormous studies conducted in this field, many of the efforts lack a comprehensive assessment of sewer components, leading to misjudged rehabilitation decision plans and continued asset deterioration. Improved cost-effective models that optimize sewer rehabilitation plans, given the scarcity of resources, are clearly needed. Accordingly, the paramount objective of this research is to design a decision-support system that optimizes the maintenance, rehabilitation and replacement (MRR) decisions of sewer pipelines and manholes. The first phase of the research is to identify several defects that impact the condition of sewer components and to model the erosion void defect utilizing fuzzy expert system. The model provided accuracy, true positive rate and precision values of 83%, 76%, and 80%, respectfully. The identified defects were then grouped into several robust models to study their cause and effect relationship through the application of the Decision-Making Trial Evaluation Laboratory (DEMATEL). The overall condition of the sewer pipeline is then found by integrating the DEMATEL method with the Quality Function iii Deployment (QFD), while the manhole condition is calculated using the aforementioned two techniques along with the Analytic Network Process (ANP). After validating the two models with the Royal Gardens neighbourhood’s sewer network in Edmonton, the average validity percentage (AVP) for the pipeline and manhole assessment models were 58.68% and 76.24%, respectively. Subsequently, Weibull distribution analysis is adopted to predict the future calculated conditions of sewer manholes and pipelines by modelling the deterioration of each. The research establishes an approach to aggregate the condition indexes of all pipelines and manholes in the network through a criticality model to supply the overall network performance index. Accordingly, the economic factors are deemed the most important ones compared to environmental and public factors. An informative optimized model that integrates the outputs of the previously developed models is designed through the Particle Swarm Optimization (PSO) approach to maximize the sewer network performance and minimize the total costs. Different trade-off solutions are then established by varying the weights of the objective functions and considering the defined constraints. The best network performance improvement attained is 1.47 with a total cost of $1.39- million. The comprehensive sewer network assessment performed in this research will improve current practices in sewer networks management, thereby reducing sewer network failures and avoiding catastrophic sinkholes. iv ACKNOWLEDGEMENTS I would like to express my sincere appreciation to Professor Tarek Zayed. Professor Zayed provided patience, knowledge, and assistance during my studies. His trust and encouragement played as incentives to accomplish the work. His experience in several topics facilitated the work and made it possible. Also, I would like to thank the Faculty of Engineering and the Department of Building, Civil and Environmental Engineering for the continuous assistance. I am also grateful to my friends, the committees, and people who provided scholarships during my studies. I would like also to thank Mr. Sam Kagan and Mr. Luke Kurach from the city of Edmonton and Dr. Khaled Shehata from the city of London for providing comprehensive information and data regarding the cities’ sewer networks. Special Thanks to PipeTech team for giving a free licence to run manhole inspections. It is my pleasure to express my gratitude to my beloved parents, my father Mahmoud Kaddoura and my mother Insaf Khalaf. Their continuous encouragement and support were always surrounding me. Although they are thousands of miles away, their care, advice, support, emotions, and respect were close to me. Special thanks to my sisters Julia, Manal, Zenat and, Aalaa. I am fortunate as I have loving sisters. I would like to thank them for their support offered prior commencing and during my studies. I would like to express my sincere appreciation and respect to my wife, Aya Hejjo, for her encouragement and PATIENCE. My lovely and adorable kid, Zayd, I owe you a lot of the time I allocated to accomplish this work. v Dedicated to My Parents Mahmoud & Insaf My Wife Aya & My Adorable Kid Zayd vi TABLE OF CONTENTS LIST OF FIGURES .................................................................................................... xii LIST OF TABLES .................................................................................................... xvii LIST OF ABBREVIATIONS ................................................................................... xxi 1 Chapter One: Introduction .................................................................................. 1 1.1 Overview .......................................................................................................................... 1 1.2 Problem Statement ........................................................................................................... 3 1.3 Research Objectives ......................................................................................................... 4 1.4 Document Organization ................................................................................................... 4 2 Chapter Two: Literature Review ........................................................................ 6 2.1 Overview .......................................................................................................................... 6 2.2 Sewer Inspection Techniques ........................................................................................... 6 2.3 Condition Assessment Models ....................................................................................... 10 2.4 Deterioration Models...................................................................................................... 16 2.5 Sewer Network Performance ......................................................................................... 20 2.6 Decision-Making Models in Sewer Infrastructure ......................................................... 24 2.7 Erosion Voids in Buried Infrastructure .......................................................................... 26 2.8 Quality Function Deployment (QFD) ............................................................................ 28 2.9 Causality vs. Correlation ................................................................................................ 30 2.10 Decision-Making Trial Evaluation Laboratory (DEMATEL) .................................... 32 2.11 Analytic Network Process (ANP)............................................................................... 33 2.12 Fuzzy Set Theory ........................................................................................................ 36 2.12.1 Fuzzy Set Shapes .................................................................................................... 37 2.12.2 Fuzzification and Defuzzification ........................................................................... 38 2.13 Weibull Analysis ........................................................................................................ 41 2.14 Particle Swarm Optimization (PSO)........................................................................... 42 2.14.1 PSO Algorithm Parameters ..................................................................................... 47 2.14.2 Swarm Size ............................................................................................................. 47 vii 2.14.3 Number of Iterations ..............................................................................................