Nonlinear Behavior Identification of HDR-S Bearing Using Neural Network for Seismic Structural Design
|
|
|||||||||||
Détails bibliographiques
| Auteur(s): |
Katrina Montes
(Graduate Student, Saitama University, Japan)
Ji Dang (Associate Professor, Saitama University, Japan) Akira Igarashi (Professor, Kyoto University, Japan) Yuqing Tan (Graduate Student, Kyoto University, Japan ) Takehiko Himeno (Kawakin Core Tech Co. Ltd.) |
||||
|---|---|---|---|---|---|
| Médium: | papier de conférence | ||||
| Langue(s): | anglais | ||||
| Conférence: | IABSE Symposium: Challenges for Existing and Oncoming Structures, Prague, Czech Republic, 25-27 May 2022 | ||||
| Publié dans: | IABSE Symposium Prague 2022 | ||||
|
|||||
| Page(s): | 1551-1558 | ||||
| Nombre total de pages (du PDF): | 8 | ||||
| DOI: | 10.2749/prague.2022.1551 | ||||
| Abstrait: |
The initial parameter selection is the bottleneck of optimization method in determining the nonlinear parameter of seismic isolators during seismic isolation design. Bilinear model is easy to understand physically but more complicated nonlinear models are hard to explain due to hidden characteristics. Therefore, this study used a machine learning based approach which aims to classify the suitable nonlinear model and predict the nonlinear parameters of an HDR-S experiment data at both low and room temperature. The trained neural network model A shows that at low amplitude, Bilinear Model was classified, however at higher amplitude, Modified Park-Wen model governs. On the other hand, neural network model B successfully predicted the five parameters of Modified Park-Wen model and solve the initial parameter assumption problem of KH Method. The proposed inverse approach can be used to train an ANN model using more complicated nonlinear models. |
||||
| 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. |
||||