Abstract
Deterioration of urban Water Distribution Networks (WDNs) is one of the primary cases of water supply losses, leading to the huge expenditures on the replacement and rehabilitation of elements WDNs. An accurate prediction of pipes failure rate play a substantial role in the management of WDNs. In this study, a field study was conducted to register pipes break and relevant causes in the WDN of Yazd City, Iran. In this way, 851 water pipes were incepted and localized by the Global Positioning System (GPS) apparatus. Then, 1033 failure cases were reported in the eight zones of under study WDN during March-December 2014. Pipes break rate (BRP) was calculated using the depth of pipe installation (hP), number of failure (NP), pressure of water pipes in operation (P), and age of pipe (AP). After completing a pipe break database, robust Artificial Intelligence models, namely Multivariate Adaptive Regression Spline (MARS), Gene-Expression Programming (GEP), and M5 Model Tree were employed to extract precise formulation for the pipes break rate estimation. Results of the proposed relationships demonstrated that MARS model with Coefficient of Correlation (R) of 0.981 and Root Mean Square Error (RMSE) of 0.544 provided more satisfying efficiency than M5 model (R = 0.888 and RMSE = 1.096). Furthermore, statistical results indicated that MARS and GEP models had comparatively at the same accuracy level. Explicit equations by AI models were satisfactorily comparable with those obtained by literature review in terms of various conditions: physical, operational, and environmental factors and complexity of AI models. Through a probabilistic framework for the pipes break rate, the results of first-order reliability analysis that MARS technique had highly satisfying performance when MARS-extracted-equation was assigned as a limit state function.
Human-centric sensing
