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• W4200440966 abstract "Globally, the loss of potable water through buried and service water distribution networks (WDNs) is one of the persistent challenges confronted by water utilities. Most of the WDNs inspection methods are ad-hoc and typically provide the current status of the pipes. Despite the significant research efforts, a reliable prediction method of the water leak of buried and service water distribution pipes is rare. To overcome this challenge, gene expression programming (GEP) models – that uses extracted features of acoustic signals collected from noise loggers attached to pipeline valves at the chamber – are developed and validated in the current study. To develop these models, extensive fieldwork hitherto unavailable was undertaken in this study to record the flow-induced acoustic signal of WDNs. The GEP models are developed using a step function – that returns a binary output – which is leveraged to exhibit the proposed detection and prediction methods using the acoustic signal collected from the buried WDNs. The models only consider the highly correlated features with the leakage. The models are found to be able to predict leakages on metal and nonmetal pipes with about 95% accuracy. These models were developed to reduce the time and cost of deployment of equipment for leakage detection of pipes. The proposed method presented in this study highlights the prospect and advantage of using the GEP in infrastructural management given the amazing capability of the machine intelligence technique to recognize multi-dimensional circumstances with ease, high prediction accuracy, and potential for unceasing improvement." @default.
• W4200440966 created "2021-12-31" @default.
• W4200440966 creator A5006678325 @default.
• W4200440966 creator A5091382230 @default.
• W4200440966 date "2022-01-01" @default.
• W4200440966 modified "2023-10-18" @default.
• W4200440966 title "Gene expression programming based mathematical modeling for leak detection of water distribution networks" @default.
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• W4200440966 doi "https://doi.org/10.1016/j.measurement.2021.110611" @default.
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