Prioritising Water Pipes for Condition Assessment with Data Analytics

This work proposes a method for prioritising critical water mains (CWMs) with high risk, in terms of both failure probability and consequence costs. The proposed method can improve the prioritisation of high-risk CWMs requiring condition assessment in the near future and will further optimise the operational maintenance of water utilities. Due to the improved prioritisation, water utilities can reduce economic loses by predicting more CWM failures with high consequences and renew only mains of high risk. Our contributions mainly lie in two data analytics techniques: 1) an efficient and effective method of CWM failure prediction based on Bayesian nonparametric modelling called hierarchical beta process (HBP), and 2) a riskaversion method of CWM selection for condition assessment based on constrained binary integer programming (BIP). Test results on a dataset from the water utility show that the proposed method outperforms previous methods in both pipe failure prediction and consequence cost savings by doubling the failure prediction performance.