Spatial Interpolation of Pressure Transient Metrics for Improved Water Distribution Network Asset Management

Abstract The growing recognition within the water industry that cyclic loading from pressure transients could accelerate pipe failures has motivated the development and application of the Cumulative Pressure‐Induced Stress (CPIS) metric. This metric incorporates both mean pressures, derived from extended period simulation hydraulic models, and the more challenging dynamic pressures (DP). Estimating DP requires high‐temporal‐resolution data to count pressure cycles, yet sparse pressure monitoring locations and the computational complexity of transient modeling hinder network‐wide DP estimation. To overcome these limitations, this study investigates various methods to estimate DP across an entire water distribution network using spatial interpolation techniques and simplified transient modeling, which use pressure monitoring data from a limited number of locations. Applying these approaches to an operational system, we found that inverse distance weighting reliably approximates DP for pipes without direct measurements. The estimation accuracy depends on factors such as the magnitude and proximity of transient sources and the density of sensors throughout the network. By integrating the interpolated DP values with mean pressures to calculate CPIS for each pipe, models predicting pipe failures can more accurately assess causality and forecast future breaks. The resulting insights offer a better understanding of how to estimate benefits associated with hydraulically calming water distribution networks.