Watermain's failure index modeling via Monte Carlo simulation and fuzzy inference system

The aging and degradation of water supply systems are deemed serious problems that cause pipeline failure and breakages. The risk of watermains failure assessment is one of the key strategies that can pinpoint pipes at risk and maintain their sustainability. This research paper showcases a novel method for deterioration modeling in conjunction with quantifying the water network's failure index (FI). The methodology builds on various algorithms, computational intelligence, and interactions between numerous factors. It involves developing two intelligent models; the first is the Monte Carlo Simulation Model (MCSM) that is designed to estimate the deterioration indices (DIs) of watermains through intricate iterative simulations. The produced indices are then streamlined and channeled to the fuzzy engine to develop the second model, namely, the fuzzy inference system model (FISM). After designing the model's configuration, the DIs are mapped to 84 fuzzy-if-then-rules that are in turn mapped to output values; finally, the fuzzy consolidator generates one crisp number that represents the watermain's FI. The developed method is implemented on the water system of the City of El Pedregal in Peru. The results indicate a moderate risk of failure status, which corresponds to 62.3 FI. Moreover, the efficacy of this method is verified against the multiple linear regression (MLR) method and proved to be sound. This research provides insights for infrastructure managers in the aspects of when to intervene, what to maintain, replace, or rehabilitate, and how to focus their constrained funding on the most deserving assets.