Field Data–Based Methodology for Estimating the Expected Pipe Break Rates of Water Distribution Systems

Presented in this paper is a field data–based probabilistic approach to quantifying the expected pipe break rates of water distribution systems. Uncertain demands and variations in the roughness of pipes during their service lives are described as random variables. Sample values of these random variables are generated and input to a distribution system model to determine the resulting minimum and maximum pressures in Monte Carlo simulations. Based on an estimated break rate–maximum pressure relationship, the sample maximum pressures obtained from a Monte Carlo simulation are transformed into a sample of break rates, and the expected pipe break rate can subsequently be determined. The sample minimum pressures are used to gain a better understanding of the distribution network. This probabilistic approach is used for a part of the City of Hamilton’s network in Ontario, Canada. The results show that the frequency of low-pressure events is very small, but a higher minimum pressure criterion would inevitably increase expected pipe break rates. Local field data collection is necessary to use the proposed methodology. Savings resulting from reduced pipe break rates justify costs associated with data collection.