A Superposed Model for the Pipe Failure Assessment of Water Distribution Networks and Uncertainty Analysis: A Case Study

Qiang Xu, Zhimin Qiang, Qiuwen Chen (), Kuo Liu and Nan Cao
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Qiang Xu: Chinese Academy of Sciences
Zhimin Qiang: Chinese Academy of Sciences
Qiuwen Chen: Nanjing Hydraulics Research Institute
Kuo Liu: Beijing Waterworks Group
Nan Cao: Beijing Waterworks Group

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 5, No 11, 1713-1723

Abstract: Abstract Pipe failure often occurs in water distribution networks (WDNs) and results in high levels of water loss and socio-economic damage. Physical-based, statistical and data-driven models have been developed to estimate pipe failure rates (failures per km of pipe per year) to efficiently manage water losses from WDNs and to ensure safe operations. Due to the complexities of pipe failure patterns, we develop a superposed statistical model to depict the relationship between pipe failure rate and pipe age. The model’s level of uncertainty was then quantified by simulating pipe failures as Poisson numbers. Part of Beijing’s WDN is taken as a study case, and pipe failure data for a 4-year period, as well as pipe properties, are collected to develop the pipe failure model. The case study results show that the pipe failure rates vary with time in a non-monotonic manner and that the proposed model captures pipe failure behaviour with an R2 value of 0.95. A 95% confidence interval of modelled pipe failures for each pipe age group is used to describe the uncertainty level of the model. We find that 88% of the observations fall under the 95% confidence interval. The established model could be applied to prioritize pipes with higher failure rates to optimize pipe replacement/rehabilitation strategies. Our uncertainty analysis of this model can help utility managers understand the model’s reliability and formulate reasonable WDN management plans.

Keywords: Pipe failure model; Water distribution network; Superposed statistical model; Uncertainty analysis (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11269-017-1899-8

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