A Novel Support-Vector-Machine-Based Grasshopper Optimization Algorithm for Structural Reliability Analysis
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Aiming at the characteristics of high computational cost, implicit expression and high nonlinearity of performance functions corresponding to large and complex structures, this paper proposes a support-vector-machine- (SVM) based grasshopper optimization algorithm (GOA) for structural reliability analysis. With this method, the reliability problem is transformed into an optimization problem. On the basis of using the finite element method (FEM) to generate a small number of samples, the SVM model is used to construct a surrogate model of the performance function, and an explicit expression of the implicit nonlinear performance function under the condition of small samples is realized. Then, the GOA is used to search for the most probable point (MPP), and a reasonable iterative method is constructed. The MPP information of each iteration step is used to dynamically improve the reconstruction accuracy of the surrogate model in the region that contributes most to the failure probability. Finally, with the MPP after the iteration as the sampling center, the importance sampling method (ISM) is used to further infer the structural failure probability. The feasibility of the method is verified by four numerical cases. Then, the method is applied to a long-span bridge. The results show that the method has significant advantages in computational accuracy and computational efficiency and is suitable for solving structural reliability problems of complex engineering.
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SVM is beneficial for pattern recognition and can be used for regression analysis.
“The technique is beneficial for pattern recognition and can be used for regression analysis [242-244].”
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- Authors
- Yang, Y., Sun, W., & Su, G.
- Affiliations
- Science and Engineering Teaching and Research Office, Zhaoqing Open University, Zhaoqing 526000, China
- Guangxi University (CN)
- Corresponding author
- Sun, Weizhe
- Venue
- Buildings
- Publisher
- MDPI AG
- Published
- 2022-06-19
- Volume
- 12
- Issue
- 6
- Pages
- 855
- ISSN
- 2075-5309 (ISSN-L)
- OpenAlex
- W4283124025
- Cited by
- 11
- References
- 33
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 1
- FWCI
- 1.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Buildings (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China for financial support (52169021, 51869003, 202006)
- High Level Innovation Team and Outstanding Scholar Program of Universities in Guangxi province (52169021, 51869003, 202006)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Probabilistic and Robust Engineering Design
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Geotechnical Engineering and Analysis
- Keywords
- structural reliability analysis, grasshopper optimization algorithm, support vector machine, surrogate models, limit state function, most probable point, importance sampling, failure probability, long-span bridges
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