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Implementation of Neural Networks for the Calibration of a Macroscopic Model of a Lead-Core Bearing Device

Implementation of Neural Networks for the Calibration of a Macroscopic Model of a Lead-Core Bearing Device
Autor(en): , ,
Beitrag für IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022, veröffentlicht in , S. 880-886
DOI: 10.2749/nanjing.2022.0880
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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 ele...
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Bibliografische Angaben

Autor(en): (Technical University of Sofia, Sofia, BULGARIA)
(University of Iceland, Earthquake Engineering Research Center Austurvegur 2a, 800, Selfoss, ICELAND)
(University of Iceland, Earthquake Engineering Research Center Austurvegur 2a, 800, Selfoss, ICELAND)
Medium: Tagungsbeitrag
Sprache(n): Englisch
Tagung: IABSE Congress: Bridges and Structures: Connection, Integration and Harmonisation, Nanjing, People's Republic of China, 21-23 September 2022
Veröffentlicht in:
Seite(n): 880-886 Anzahl der Seiten (im PDF): 7
Seite(n): 880-886
Anzahl der Seiten (im PDF): 7
DOI: 10.2749/nanjing.2022.0880
Abstrakt:

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.
Stichwörter:
Seismische Isolierung
Copyright: © 2022 International Association for Bridge and Structural Engineering (IABSE)
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