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Seismic reliability analysis of building structures using subset simulation coupled with deep learning-based surrogate model
- Source :
- Advances in Structural Engineering. 25:2301-2318
- Publication Year :
- 2022
- Publisher :
- SAGE Publications, 2022.
-
Abstract
- The reliability of building structures subjected to ground motion is a time-consuming, complex, and iterative computation problem involving multidiscipline theories such as probability theory, reliability theory, and dynamic structural analysis. In order to address this challenge, this study proposes a highly efficient approach based on the subset simulation and a deep learning-based surrogate model. The subset simulation is an efficient sampling strategy that significantly diminishes the number of samples to compute when determining the failure probability, especially for very small ones. On the other hand, the surrogate model based on a deep learning algorithm can deliver equivalently accurate structures’ responses compared to the well-known finite element method with markedly smaller time complexity, given appropriate available training data. The efficiency and effectiveness of the proposed approach are demonstrated in detail through three case studies with increasing complexity: a 1-DoF problem, a 2D frame, and a 3D structure based on experimental data showing a reduction up to two orders of magnitude in time complexity compared to the Monte Carlo simulation using finite element method.
- Subjects :
- Building and Construction
Civil and Structural Engineering
Subjects
Details
- ISSN :
- 20484011 and 13694332
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- Advances in Structural Engineering
- Accession number :
- edsair.doi...........17ac8603aa5fdcdeb89525c5b21fa707