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dc.contributor.authorBbaale, Francis
dc.date.accessioned2022-03-10T12:25:21Z
dc.date.available2022-03-10T12:25:21Z
dc.date.issued2021-06
dc.identifier.citationBbaale, F. (2021). A risk based approach for pipe renewal and rehabilitation decision making in water distributions systems. (Unpublished master's dissertation). Makerere University, Kampala, Ugandaen_US
dc.identifier.urihttp://hdl.handle.net/10570/9475
dc.description.abstractUtilities globally suffer financial losses due to pipe failures caused by a number of aggressive physical and internal factors (Raziyeh, 2016). Despite these challenges, it is not clear which exact factors are more significant for informing prediction of pipe failures (Konstantinos, 2019) (P. Rajeev et al., 2013) (Rajeev, 2014; Rajeev, 2014) . Furthermore, there is lack of approaches for assessing and quantifying pipe failure risks particularly in data scarce scenarios. This study therefore addresses the aforementioned research gap and (i) develops a risk assessment approach for pipe renewal and rehabilitation in water distribution systems, (ii) identifies the most critical physical factors associated with high pipe failure rates and (iii) develops a failure prediction model. Risk analysis was based on water pipe failure consequences such as revenue loss, repair costs, customer criticality, number of customers served, reinstatement costs and traffic loading. Pipe failure data for a period of 1964 to 2017 was obtained from available databases of National Water and Sewerage Corporation (NWSC). Additional Data regarding pipe characteristics was obtained from GIS department (geographical information systems, GIS) on factors such as average pressure, soil types, depth from the ground and pipe material. Statistical analysis was then performed at univariate level with the aid of bar graphs, bivariate level with the aid of Spearman’s rank correlation and Analysis of Variance (ANOVA) as well as multivariate level (MANOVA) using binary logistic regression and Cox proportional hazards. Results showed that system pressures were insignificant and had no association with pipe failures unlike other factors (pipe depth, pipe material, pipe age and pipe distance from the road). The Kaplan Meier Survival estimates showed that after forty years of service metallic pipes possessed a higher hazard compared to uPVC pipes (HR = 2.532914). Further, the study indicated that pipes placed closer to the road experience more failures compared to those placed far away. Pipes laid more than a foot deep underground last longer than shallow/exposed pipes. Irrespective of the pipe material, pipes laid in gravel soils registered less failures than pipes laid in water logged soils and clay soils. Nonetheless, unplasticized Polyvinyl chloride (uPVC) pipes fail more in clay soils while metallic soils fail more in waterlogged soils. Metallic pipes being susceptible to corrosion effects stand a high risk of failing with age while uPVC pipes are not so much affected by aggressive environmental factors. xiii This study found out that adopting a risk-based model is of utmost importance to the water utilities to guide pipe renewal decision making. Decisions based on risk quantification result in lower operational costs, increased growth, improved service reliability, water losses and enables optimum allocation of resources. Of the six factors, revenue loss was the most severe to water utilities followed by number of customers on a pipeline, customer criticality, population density, repair costs and revenue loss. The risk quantification model shows that pipe failures lead to high figures of Non-Revenue Water (NRW) through physical losses hence pausing a very big loss to utilities.en_US
dc.language.isoenen_US
dc.publisherMakerere Universityen_US
dc.subjectRisk based approachen_US
dc.subjectPipe renewalen_US
dc.subjectPipe rehabilitationen_US
dc.subjectPipe failureen_US
dc.subjectRisk quantificationen_US
dc.subjectNon-Revenue Wateren_US
dc.titleA risk based approach for pipe renewal and rehabilitation decision making in water distributions systemsen_US
dc.typeThesisen_US


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