Advanced deterioration models for wastewater inspection prioritization
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Balekelayi, Ngandu
issued: 2019-06-06T18:07:32Z
issued: 2019
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Developing an accurate deterioration model for sewer pipes is the basic step to a successful wastewater infrastructure management. However, it is difficult to predict exactly the change in the condition of sewer pipes since they are buried, and several factors influence their condition changes. These factors are collected through inspection and the deterioration models are built to understand the way they affect the condition of sewer pipes. However, only few pipes are inspected and not all the needed factors are collected. Thus, models are made from uncertain and incomplete data resulting in simplified approaches for quick and easy interpretation of the results. In this research, two semi-parametric models are developed to predict the deterioration of sewer pipes. The first model predicts the expected mean deterioration score of sewer pipes and the second gives the complete distribution of the output response through the analysis of its quantiles. In both models, variables are grouped into categorical where an ordinary least square estimation of parameters is applied and continuous variables where nonlinear smooth functions P-splines are applied. To account for unobserved covariates, models are enhanced through the inclusion of geospatial location of pipes as surrogate covariate that is estimated through a Gaussian Markov Random Field approach. The application of these models to the sewer inspection data of the city of Calgary shows a good agreement between the predictions and the observations. The estimation of the risk of pipes failure passes through the identification of critical areas in the network and their rankings, a metric resulting from the aggregation of multiple graph theoretic centrality measures is calculated. Two frameworks are proposed including the Bayesian approach when the hydraulic data are available to estimate the baseline metric and the Ordered Weighting Averaging that considers the decision maker’s attitude. Finally, the value of the informa…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/70558
note: Advanced deterioration models forwastewater inspection prioritizationbyNgandu BalekelayiM.Sc.,UNESCO-IHE Institute for Water Education,Delft,The Netherlands, 2010B.Sc.A.,University of Mbujimayi, The Democratic Republic of Congo, 2004A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE COLLEGE OF GRADUATE STUDIES(Civil Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)June 2019© Ngandu Balekelayi, 2019The following individuals certify that they have read, and recommendto the College of Graduate Studies for acceptance, a thesis/dissertation en-titled:Advanced deterioration models for wastewater inspection prioritizationsubmitted byNgandu Balekelayi in partial fulfilment of the requirementsof the degree of Doctor of PhilosophySolomon Tesfamariam, School of EngineeringSupervisorZheng Liu, School of EngineeringSupervisory Committee MemberSumi Siddiqua, School of EngineeringSupervisory Committee MemberParamjit Gill, Statistics FacultyUniversity ExaminerRoyce Francis, School of Engineering and Applied Science,George Washington UniversityExternal ExamineriiAbstractDeveloping an accurate deterioration model for sewer pipes is the ba-sic step to a successful wastewater infrastructure management. However,it is difficult to predict exactly the change in the condition of sewer pipessince they are buried, and several factors influence their condition changes.These factors are collected through inspection and the deterioration modelsare built to understand the way they affect the condition of sewer pipes.However, only few pipes are inspected and not all the needed factors arecollected. Thus, models are made from uncertain and incomplete data re-sulting in simplified approaches for quick and easy interpretation of theresults. In this research, two semi-parametric models are developed to pre-dict the deterioration of sewer pipes. The first model predicts the expectedmean deterioration score of sewer pipes and the second gives …
has type: Thesis/Dissertation
date issued: 2019-09
is shown at: 10.14288/1.0379341
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/70558