A Bayesian Regularization Neural Network Model for Fatigue Life Prediction of Concrete
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Détails bibliographiques
| Auteur(s): |
Zhenyu Sun
(Beijing University of Technology, Beijing, China)
Huating Chen (Beijing University of Technology, Beijing, China) Zefeng Zhong (Beijing University of Technology, Beijing, 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): | 1959-1968 | ||||
| Nombre total de pages (du PDF): | 10 | ||||
| DOI: | 10.2749/nanjing.2022.1959 | ||||
| Abstrait: |
The fatigue life of concrete is affected by many interwoven factors whose effect is nonlinear. Because of its unique self-learning ability and strong generalization capability, a neural network model is proposed to predict concrete behavior in tensile fatigue. Firstly, the average relative impact value was constructed to analyze the importance of parameters affecting fatigue life, such as the maximum stress level Smax, stress ratio R, failure probability P, and static strength f. Then, using the backpropagation neural network improved by Bayesian regularization, S-N curves were obtained for the combinations of R=0,1, 0,2, 0,5; f=5, 6, 7MPa; P=5%, 50%, 95%. Finally, the tensile fatigue results obtained from different testing conditions were compared for compatibility. Besides utilizing the valuable fatigue test data scattered in the literature, insights gained from this work could provide a reference for subsequent fatigue test program design and fatigue evaluation. |
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| Mots-clé: |
béton
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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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