Watermain's failure index modeling via Monte Carlo simulation and fuzzy inference system. (April 2022)

• Record Type: Journal Article
• Authors: Dawood, Thikra; Elwakil, Emad; Mayol Novoa, Hector; Fernando Gárate Delgado, José
• Is Part Of: Engineering failure analysis. Volume 134(2022)
• Publication Date: 2022-04
• Subjects: Deterioration -- Modeling -- Pipe Failure -- Water Distribution Networks -- Risk -- Infrastructure; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106100
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: A moderate risk of failure status is corresponded to 62.3 failure index (FI). The developed method is implemented on the water system of the City of El Pedregal in Peru. This research provides insights for infrastructure managers in the aspects of when to intervene. The eight factors that cause the degradation and failure of water infrastructure were characterized to develop the Monte Carlo Simulation Model. Abstract: 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.