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Article

A Novel Support-Vector-Machine-Based Grasshopper Optimization Algorithm for Structural Reliability Analysis

1
Science and Engineering Teaching and Research Office, Zhaoqing Open University, Zhaoqing 526000, China
2
Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
3
Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning 530004, China
*
Author to whom correspondence should be addressed.
Buildings 2022, 12(6), 855; https://doi.org/10.3390/buildings12060855
Submission received: 28 May 2022 / Revised: 15 June 2022 / Accepted: 15 June 2022 / Published: 19 June 2022
(This article belongs to the Section Building Structures)

Abstract

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.
Keywords: structural reliability; failure probability; machine learning; support vector machine; grasshopper optimization structural reliability; failure probability; machine learning; support vector machine; grasshopper optimization

Share and Cite

MDPI and ACS Style

Yang, Y.; Sun, W.; Su, G. A Novel Support-Vector-Machine-Based Grasshopper Optimization Algorithm for Structural Reliability Analysis. Buildings 2022, 12, 855. https://doi.org/10.3390/buildings12060855

AMA Style

Yang Y, Sun W, Su G. A Novel Support-Vector-Machine-Based Grasshopper Optimization Algorithm for Structural Reliability Analysis. Buildings. 2022; 12(6):855. https://doi.org/10.3390/buildings12060855

Chicago/Turabian Style

Yang, Yutai, Weizhe Sun, and Guoshao Su. 2022. "A Novel Support-Vector-Machine-Based Grasshopper Optimization Algorithm for Structural Reliability Analysis" Buildings 12, no. 6: 855. https://doi.org/10.3390/buildings12060855

APA Style

Yang, Y., Sun, W., & Su, G. (2022). A Novel Support-Vector-Machine-Based Grasshopper Optimization Algorithm for Structural Reliability Analysis. Buildings, 12(6), 855. https://doi.org/10.3390/buildings12060855

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