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Optimizing cluster selections for the replacement planning of water distribution systems
Author(s) -
Chen Thomas YingJeh,
Man Chung,
Daly Craig Michael
Publication year - 2021
Publication title -
awwa water science
Language(s) - English
Resource type - Journals
ISSN - 2577-8161
DOI - 10.1002/aws2.1230
Subject(s) - computer science , selection (genetic algorithm) , cluster (spacecraft) , integer programming , process (computing) , plan (archaeology) , operations research , graph , engineering , geography , artificial intelligence , archaeology , algorithm , theoretical computer science , programming language , operating system
Risk assessment is an effective tool for revealing criticalities across an entire water system. However, standard outputs that report risk at the individual pipe segment level can have limited use for decision support since utilities typically plan for the replacement of aggregated areas to minimize disruption and cost. This research presents a process for aggregating individual pipes into high‐risk clusters. Each cluster is a group of contiguous pipe which, when targeted, will reduce mobilization cost and community disruption. A graph search is first used to locate potential clusters, and then the optimal selection, which maximizes risk capture, is identified. An integer programming model is presented, and the process is implemented on a real system. Empirical trials suggest that the ability to prevent future breaks is not significantly reduced when prioritizing clusters rather than individual pipes. This shows that the proposed method can guide more cost‐efficient planning for pipe replacements. Article Impact Statement A method is presented to identify contiguous clusters of high‐risk individual pipes and optimize their selection for maximum risk reduction.