An efficient reliability method combining adaptive Support Vector Machine and Monte Carlo Simulation

To enhance computational efficiency in reliability analysis, metamodeling has been widely adopted for reliability assessment. This work develops an efficient reliability method which takes advantage of the Adaptive Support Vector Machine (ASVM) and the Monte Carlo Simulation (MCS). A pool-based ASVM is employed for metamodel construction with the minimum number of training samples, for which a learning function is proposed to sequentially select informative training samples. Then MCS is employed to compute the failure probability based on the SVM classifier obtained. The proposed method is applied to four representative examples, which shows great effectiveness and efficiency of ASVM-MCS, leading to accurate estimation of failure probability with rather low computational cost. ASVM-MCS is a powerful and promising approach for reliability computation, especially for nonlinear and high-dimensional problems.