Prediction of breaks in municipal drinking water linear assets

Abstract

Improper asset management practices increase the probability of water main failures due to inactive intervention actions. The annual number of breaks of each pipe segment is known as one of the most important criteria for the condition assessment of water pipelines. This metric is also considered one of the major performance measures in levels of service (LoS) studies. In an effort to maximize the benefits of historical data, this research utilized the evolutionary polynomial regression (EPR) method in determining the best mathematical expression for predicting water pipeline failures. The prediction model was trained and tested on the city of Montreal water network. After determining the best independent variables through the best subset regression, pipelines were clustered based on their attributes (length, diameter, age, and material). The majority of the models provided high R2 values, but the highest performing model's R2 was 89.35%. Further, a sensitivity analysis was also performed and showed that the most sensitive parameter was the diameter, and the most sensitive material type to age was ferrous material. The tools and stages performed in this research showed promising results in predicting the expected water main failures using four different asset attributes. Therefore, this research can be implemented in asset management best practices and in LoS performance measures to predict the number of water pipeline failures. To further improve the prediction model, additional explanatory variables could be considered along with leveraging multiple artificial intelligence tools.