Prediction of Water Mains Failure - A Bayesian Approach

To develop an effective preventive or proactive repair and replacement action plan, water utilities often rely on water main failure prediction models. However, in the prediction modeling water mains failure, uncertainty is inherent regardless of quality and quantity of data used in model-data fusion. To improve the understanding of water main failure processes, a new and effective Bayesian framework is developed for the failure prediction of water mains. To accredit the proposed framework, it is implemented to predict the failure of CI and DI pipes of the water distribution network of the City of Calgary. In this study, Bayesian model averaging method is presented to identify the influential pipedependent and time-dependent covariates whereas Bayesian Weibull proportional hazard model is applied to develop the survival curves and to predict the failure rates of CI and DI pipes.