A Novel Active Learning Method Combining Adaptive Support Vector Regression and Monte Carlo Simulation for Structural Reliability Assessment

Structural reliability analysis remains challenging when only a limited number of calls to expensive numerical models, such as finite-element solvers, are acceptable. In recent years, active learning (AL) metamodel methods have attracted considerable attention as they offer an efficient and accurate solution for reliability assessment. A common feature of these methods is that they initially construct a low-accuracy metamodel, which is then iteratively updated by sequentially enriching the training dataset according to specific learning functions. This paper proposes a novel active learning reliability method (ALRM) that combines the advantages of support vector regression (SVR) and Monte Carlo simulation (ASVR-MCS). A learning function based on the penalty function method is developed to identify optimal sampling points. To validate the efficacy and versatility of ASVR-MCS, it is applied to four representative structural reliability problems, which are characterized by multiple design points, disjoint failure regions, and implicit performance functions. The performance of ASVR-MCS is systematically compared with that of two other well-established ALRMs, i.e., AK-MCS (based on Kriging) and ASVM-MCS (based on Support Vector Machine). The numerical results demonstrate that the proposed ASVR-MCS method not only achieves high computational efficiency but also exhibits wider applicability in structural reliability analysis.