TY - JOUR
T1 - Watermain's failure index modeling via Monte Carlo simulation and fuzzy inference system
AU - Dawood, Thikra
AU - Elwakil, Emad
AU - Mayol Novoa, Hector
AU - Fernando Gárate Delgado, José
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - Deterioration
KW - Infrastructure
KW - Modeling
KW - Pipe Failure
KW - Risk
KW - Water Distribution Networks
UR - http://www.scopus.com/inward/record.url?scp=85123855612&partnerID=8YFLogxK
U2 - 10.1016/j.engfailanal.2022.106100
DO - 10.1016/j.engfailanal.2022.106100
M3 - Artículo
AN - SCOPUS:85123855612
VL - 134
JO - Engineering Failure Analysis
JF - Engineering Failure Analysis
SN - 1350-6307
M1 - 106100
ER -