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Defect-based Condition Assessment Model and Protocol of Sewer Pipelines

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Defect-based Condition Assessment Model and Protocol of Sewer Pipelines

Daher, Sami (2015) Defect-based Condition Assessment Model and Protocol of Sewer Pipelines. Masters thesis, Concordia University.

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Abstract

Infrastructure serves as the backbone of the city and hence plays a significant role in its urban structure. Therefore, it is of utmost importance to monitor its performance and assure its compliance with the growth in demand. Due to their hidden and passive nature, sewer pipelines are neglected making it essential to assess their conditions and address their associated problems to maintain quality productivity and avoid high social costs. Currently, 30% of the Canadian Infrastructure has been evaluated to be in fair to very poor conditions with a cost of $39 billion for infrastructure repair (Felio et al. 2012).In 2008, it was stated that the capital investment needs in the United States are $15 billion annually for the coming 20 years totaling to $298 billion. Moreover, the pipelines in the U.S represent 3/4th of the total needs marking the largest capital need (ASCE 2013). The current condition assessment protocols are limited to several issues including poor accuracy caused by uncertain human judgments and imprecise assessments due to the consideration of the peak score (worst defect) as the total condition score. Therefore, the development of a sound condition assessment protocol with a unified classification of distress indicators regardless of the inspector’s expertise is needed to ensure safety and quality service to the public.
The objective of this research is to develop a defect-based condition assessment model as well as a protocol for sewer pipelines. This model aims to cover the structural, operational, and installation / rehabilitation defects that are associated with the pipelines, joints, and manholes of each pipe length / segment. This Fuzzy Synthetic Evaluation model consists of the Analytic Network Process (ANP) model which covers the interdependencies between the components and their defects in order to deduce their relative importance weights. The second model utilizes the defects’ severities to develop fuzzy membership functions based on a predefined linguistic condition grading scale that would precisely indicate the degree of distress. This model quantifies the distress indicators and encodes their condition linguistically (states) and numerically (scores). Furthermore, a robust aggregation model based on the Hierarchical Evidential Reasoning (HER) and Dempster-Shafer (D-S) theory is created to integrate the defects’ conditions and to evaluate the overall condition of the sewer pipeline. Also, the main grading scale in this model was developed using the K-Means clustering technique. The final condition grade is represented as a crisp value calculated by the weighted average defuzzification method. The data utilized in this research was obtained from sewer condition classification manuals, previous research, and questionnaires distributed to professionals in Qatar and Canada. Also, a sewer protocol was developed, calibrated, and verified by experts’ feedback. The fruit of this fusion was also presented in a user-friendly automated tool (SPCAT). The developed model was implemented in 29 case studies from Montreal and Qatar. The predicted results of 15 inspected pipelines in Montreal, Canada, resulted in mean absolute error values for structural and operational defects of 0.533 and 0.267 respectively with correlation coefficients of 0.846 and 0.934. The second batch of 14 inspected pipe segments in Qatar, resulted in a mean absolute error of 0.643 and a correlation coefficient of 0.60 between the predicted and real values .The results are justified throughout the research body.This model helps in minimizing the inaccuracy of sewer condition assessment through the application of severity, uncertainty mitigation, and robust aggregation. It also benefits asset managers by providing a precise condition overview for maintenance, rehabilitation, and budget allocation purposes.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Daher, Sami
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:15 July 2015
Thesis Supervisor(s):Zayed, Dr. Tarek
Keywords:Defect Based Sewer Pipeline Condition Assessment and Protocol
ID Code:980210
Deposited By: SAMI DAHER
Deposited On:05 Jul 2016 13:54
Last Modified:18 Jan 2018 17:50

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