Enhanced soft Monte Carlo simulation coupled with support vector regression for structural reliability analysis
In structural reliability analysis, it is a major challenge to develop a general method that can ensure high computational accuracy and low computational cost for low failure probability and high-dimensional problems. In this study, a novel enhanced simulation method termed enhanced soft Monte Carlo simulation coupled with support vector regression (EMCS-SVR) is proposed. First, a generalized enhanced simulation (ES) scaling formula is proposed as an improved scheme. Furthermore, the soft Monte Carlo simulation is combined with generalized ES scaling formula and SVR model for evaluating the failure probability. The efficiency and accuracy of the ESMCS-SVR are verified by comparing with an existing popular method in four numerical examples and three engineering examples.
Authors
- Debiao Meng (ORCID: https://orcid.org/0000-0002-8306-0046)
- Changqi Luo (ORCID: https://orcid.org/0000-0002-9559-3720)
- Shiyuan Yang (ORCID: https://orcid.org/0000-0003-4255-440X)
- Xiaoyan Su (ORCID: https://orcid.org/0000-0001-6132-6969)
- Hengfei Yang
Institutions
- University of Electronic Science and Technology of China (CN)
- Universidade do Porto (PT)
- Shanghai University of Electric Power (CN)
- Institute of Mechanical Engineering and Industrial Mangement (PT)
Publication Details
- Journal
- Proceedings of the Institution of Civil Engineers - Transport
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1680/jtran.24.00128
- Citations
- 43
- Primary Topic
- Probabilistic and Robust Engineering Design
- Type
- article
- Field-Weighted Citation Impact
- 19.83