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Title:Probability based scheduling to optimize sewer maintenance
Author(s):Ma, Xiaoxuan
Advisor(s):Schmidt, Arthur R
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Sanitary sewer overflows
Sewer failure modeling
Inspection program
Abstract:With municipal administrations and EPA (Environment Protection Agency) concentrating more on the issue of SSOs (Sanitary Sewer Overflows), sewer failures have been studied much in recent years. This thesis focuses on the blockages of sewer lines, which cause nearly half of the SSOs. A simulation model is developed to analysis efficiency of different inspection programs. A combined factor, which affects the interval time between blockages, is described by two-parameter distribution. Each pipe in the sewer system has characteristic parameters and distribution that is also utilized to simulate the operation of sewer system in the model. Fitting the parameters from historical database, estimated parameters are used to predict blockages. Two methods (Birnbaum-Saunders Distribution estimation and Median estimation) to estimate the parameters are compared from the accuracy and operation time aspects. Meanwhile, failure probability in certain period is calculated from the distribution to support the maintenance schedule, which leads to a probability-based inspection strategy. To ensure the effect of this strategy, a line-by-line inspection strategy in which inspected pipes are selected randomly is also studied. The results show that the strategy with highest inspection efficiency is the probability-based one with parameters estimated from BSD estimation method. Moreover, economic analysis of the strategies is studied to optimize the capital investment of maintenance and the civil penalties regulated by EPA.
Issue Date:2017-04-27
Type:Text
URI:http://hdl.handle.net/2142/97775
Rights Information:Copyright 2017 Xiaoxuan Ma
Date Available in IDEALS:2017-08-10
Date Deposited:2017-05


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