Implementation of Neural Networks for the Calibration of a Macroscopic Model of a Lead-Core Bearing Device
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Détails bibliographiques
| Auteur(s): |
Todor Zhelyazov
(Technical University of Sofia, Sofia, BULGARIA)
Rajesh Rupakhety (University of Iceland, Earthquake Engineering Research Center Austurvegur 2a, 800, Selfoss, ICELAND) Símon Ólafsson (University of Iceland, Earthquake Engineering Research Center Austurvegur 2a, 800, Selfoss, ICELAND) |
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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): | 880-886 | ||||
| Nombre total de pages (du PDF): | 7 | ||||
| DOI: | 10.2749/nanjing.2022.0880 | ||||
| Abstrait: |
The increasing popularity of the lead-core bearing devices motivates the research efforts devoted to a more accurate behavior assessment. The contribution provides details of an accurate finite element model of the bearing device. The geometry is reproduced in great detail. Material models are defined for the rubber layers, steel elements, and lead core. The output of the finite element simulations provides an insight into the bearing response, for example, through the numerically obtained ‘Restoring force- displacements’ relationship. The definition of a less demanding model of the lead-core rubber bearing about an implementation into the finite element analysis of a base-isolated structure might be an attractive option. Some elements of the implementation of a neural network for the identification of the model parameters based on results obtained by finite element analysis are discussed. |
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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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