A novel kriging based active learning method for structural reliability analysis

Abstract

In the reliability analysis of engineering structures, there are usually implict and highly nonlinear performance function problems, which leads to the time-consuming computations. In this paper, a novel Kriging based reliability analysis method combined with the improved efficient global optimization (IEGO) and a secondary point selection strategy is proposed. Based on the IEGO algorithm, the expected improvement function is redefining, which will focus on the points both with large variance and near the limit state surface. Moreover, a secondary point selection strategy is raised to find the point with larger expected improvement and closed to the limit state surface, which can further improve the efficiency of the active learning process. Five examples indicates that the raised method has satisfactory global and local search capability, and can evaluate the probability of failure efficiently.