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• W4311148602 abstract "Artificial intelligence (AI), often known as machine learning, is a powerful tool for solving engineering problems. The evaluation of the network reliability of a flow network is a NP-hard problem, with computational effort growing exponentially with the number of nodes and arcs in the network. Also, the components assignment issue is NP-hard, and the computational effort increases with the number of available components. Many candidate solutions are typically examined during optimal components or optimal capacity assignment, each requiring reliability calculation. Consequently, this paper proposes an artificial neural network (ANN) predictive model to evaluate the flow network reliability. The neural network is one of the artificial intelligence tools constructed, trained, and validated using the maximum capacity of each component input and the network reliability as the target. The proposed ANN model provides empirical proof that neural networks can accurately estimate reliability by modeling the connection between the maximum capacities of network components and the reliability value." @default.
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• W4311148602 date "2023-08-01" @default.
• W4311148602 modified "2023-09-25" @default.
• W4311148602 title "Artificial intelligent applications for estimating flow network reliability" @default.
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• W4311148602 doi "https://doi.org/10.1016/j.asej.2022.102055" @default.
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