Support vector machine in structural reliability analysis: A review

By Atin Roy, Subrata Chakraborty. Reliability Engineering & System Safety, 2023

457 citations recorded by the source corpus.

5 Support vector machine (SVM) is a powerful machine learning technique relying on the 6 structural risk minimization principle. The applications of SVM in structural reliability 7 analysis (SRA) are enormous in the recent past. There are review articles on machine 8 learning-based methods that partly discussed the development of SVM for SRA applications 9 along with other machine learning methods. However, there is no dedicated review on SVM 10 for SRA applications. Thus, a review article on the implementation of various SVM 11 approaches for SRA applications will be useful. The present article provides a synthesis and 12 roadmap to the growing and diverse literature, specifically the classification and regression-13 based support vector algorithms in SRA applications. In doing so, different advanced variants 14 of SVM in SRA applications and hyperparameter tuning algorithms are also briefly 15 discussed. Following the detailed review studies, future opportunities and challenges in the 16 area of applications are also summarized. The review in general reveals that the SVM in SRA 17 is getting thrust as it has an excellent capability of handling high-dimensional problems 18 utilizing relatively lesser training data. The review article is expected to enhance the state-of-19 the-art developments of support vector algorithms for SRA applications. 20

Subfields: Civil and Structural Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a295ef741e41c56547343b210827a0c6f4ce341c