Support vector machine in structural reliability analysis: A review

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