Artificial intelligent applications for estimating flow network reliability
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447922003665 |
| _version_ | 1797826885009276928 |
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| author | Moatamed Refaat Hassan Salem Alkhalaf Ashraf Mohamed Hemeida Mahrous Ahmed Eman Mahmoud |
| author_facet | Moatamed Refaat Hassan Salem Alkhalaf Ashraf Mohamed Hemeida Mahrous Ahmed Eman Mahmoud |
| author_sort | Moatamed Refaat Hassan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T12:39:26Z |
| format | Article |
| id | doaj.art-2521abec05ea4b1c9d2b651681d3a9f7 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-04-09T12:39:26Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-2521abec05ea4b1c9d2b651681d3a9f72023-05-15T04:14:19ZengElsevierAin Shams Engineering Journal2090-44792023-08-01148102055Artificial intelligent applications for estimating flow network reliabilityMoatamed Refaat Hassan0Salem Alkhalaf1Ashraf Mohamed Hemeida2Mahrous Ahmed3Eman Mahmoud4Computer Sciences Branch, Department of Mathematics, Faculty of Sciences, Aswan University, 81528 Aswan, EgyptDepartment of Computer, College of Science and Arts in Ar-Rass, Qassim University, Ar Rass 52571, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Energy Engineering, Aswan University, 81528 Aswan, Egypt; Corresponding author.Electrical Engineering Department, College of Engineering, Taif University, Taif 21944, Saudi ArabiaDepartment of Mathematics, Faculty of Sciences, Aswan University, 81528 Aswan, EgyptArtificial 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.http://www.sciencedirect.com/science/article/pii/S2090447922003665Artificial intelligentFlow networkComponents assignment problemCapacity assignment problemReliability evaluationANN |
| spellingShingle | Moatamed Refaat Hassan Salem Alkhalaf Ashraf Mohamed Hemeida Mahrous Ahmed Eman Mahmoud Artificial intelligent applications for estimating flow network reliability Ain Shams Engineering Journal Artificial intelligent Flow network Components assignment problem Capacity assignment problem Reliability evaluation ANN |
| title | Artificial intelligent applications for estimating flow network reliability |
| title_full | Artificial intelligent applications for estimating flow network reliability |
| title_fullStr | Artificial intelligent applications for estimating flow network reliability |
| title_full_unstemmed | Artificial intelligent applications for estimating flow network reliability |
| title_short | Artificial intelligent applications for estimating flow network reliability |
| title_sort | artificial intelligent applications for estimating flow network reliability |
| topic | Artificial intelligent Flow network Components assignment problem Capacity assignment problem Reliability evaluation ANN |
| url | http://www.sciencedirect.com/science/article/pii/S2090447922003665 |
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