Prediction of Aerodynamic Coefficients using Artificial Neural Network in Shape Optimization of Centrally-Slotted Box Deck Bridge
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Détails bibliographiques
| Auteur(s): |
Mohammed Elhassan
(Department of Bridge Engineering / State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China)
Ledong Zhu (Department of Bridge Engineering / State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China) Zhongxu Tan (Department of Bridge Engineering / State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, China) Wael Alhaddad (Department of Structural Engineering, Tongji University, Shanghai, China) |
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| Médium: | papier de conférence | ||||
| Langue(s): | anglais | ||||
| Conférence: | IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022 | ||||
| Publié dans: | IABSE Congress Nanjing 2022 | ||||
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| Page(s): | 444-451 | ||||
| Nombre total de pages (du PDF): | 8 | ||||
| DOI: | 10.2749/nanjing.2022.0444 | ||||
| Abstrait: |
Aerodynamic shape optimization of bridge deck is a very important task in the wind-resistant design of long-span bridges and often carried out via wind tunnel tests of sectional model or CFD simulation, both of which commonly need heavy workload, thus are time-consuming and costly. In this paper, an artificial neural network (ANN) model was developed to predict aerodynamic coefficients of a central-slotted box deck of a 1600m main span cable-stayed bridge during the aerodynamic shape optimization to enhance its performance of wind-induced static stability. The ANN model was built and trained with a data set of aerodynamic coefficients obtained from limited cases of wind tunnel tests. The effect of neuron numbers in the hidden layer on prediction accuracy was discussed. The results show that the built ANN model can accurately predict the aerodynamic coefficients and significantly reduce the workload of wind tunnel tests. |
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| Copyright: | © 2022 International Association for Bridge and Structural Engineering (IABSE) | ||||
| License: | Cette oeuvre ne peut être utilisée sans la permission de l'auteur ou détenteur des droits. |
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