Aging water infrastructure is a major concern for water utilities throughout the world. It is challenging to develop an extensive water mains renewal program and predict the performance of the water mains. Uncertainties become an integral part of the repair and replacement (R&R) action program due to incomplete and partial information, integration of data/information from different sources, and the involvement of expert judgment for the data interpretation and so on. Moreover, the uncertainties differ because of the amount and quality of data available for developing or implementing R&R action program varies among utilities.

In this research, a Bayesian framework is developed for the R&R action program of water mains considering these uncertainties. At the beginning of the research, state-of-the-art critical review of existing regression-based, survival analysis and heuristic based failure models and life cycle cost (LCC) studies in the field of water main are performed. To identify the influential covariates and to predict the failure rates of water mains considering model uncertainties with limited failure information, Bayesian model averaging and Bayesian regression based model are developed. In these models, decision maker’s degree of optimism and credibility are integrated using ordered weighted averaging operator. A robust Bayesian updating based framework is proposed to update the performance of water main failure model for medium to large-sized utilities with adequate failure information. A LCC framework is prepared for water main of small to medium-sized utilities. Finally, a Bayesian belief network (BBN) based water main failure risk framework is developed for small to medium sized utilities with no or limited failure information.

The integration of the proposed robust Bayesian models with the geographic information system (GIS) of the water utilities will provide information both at operation level and network level. The proposed tool will help the utility engineers and managers to predict the suitable new installation and rehabilitation programs as well as their corresponding costs for effective and proactive decision-making and thereby avoiding any unexpected and unpleasant surprises.