Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers

2020/06/05 by Dilusha Weeraddana, Bin Liang, Weeraddana, Dilusha +13
Engineering · #Computers and Society (cs.CY) #FOS: Computer and information sciences #Geotechnical Engineering and Underground Structures #Machine Learning (cs.LG) #Oil and Gas Production Techniques #Water Systems and Optimization

paper · pdf · doi:10.48550/arxiv.2006.03385

openalex publication_date 2020/06/05 · openalex created_date 2020/06/12 · openalex updated_date 2026/07/28

Abstract

Data61 and Western Water worked collaboratively to apply engineering expertise and Machine Learning tools to find a cost-effective solution to the pipe failure problem in the region west of Melbourne, where on average 400 water main failures occur per year. To achieve this objective, we constructed a detailed picture and understanding of the behaviour of the water pipe network by 1) discovering the underlying drivers of water main breaks, and 2) developing a Machine Learning system to assess and predict the failure likelihood of water main breaking using historical failure records, descriptors of pipes, and other environmental factors. The ensuing results open up an avenue for Western Water to identify the priority of pipe renewals

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