Adaptive sampling approach based on Jensen-Shannon divergence for efficient reliability analysis

Abstract

Extensive studies have been carried out for reliability studies on the basis of the surrogate model, which has the advantage of guaranteeing evaluation accuracy while minimizing the need of calling the real yet complicated performance function. Here, one novel adaptive sampling approach is developed for efficiently estimating the failure probability. First, one innovative active learning function integrating with Jensen-Shannon divergence (JSD) is derived to update the Kriging model by selecting the most suitable sampling point. For improving the efficient property, one trust-region method receives the development for reducing computational burden about the evaluation of active learning function without compromising the accuracy. Furthermore, a termination criterion based on uncertainty function is introduced to achieve better robustness in different situations of failure probability. The developed approach shows two main merits: the newly selected sampling points approach to the area of limit state boundary, and these sampling points have large discreteness. Finally, three case analyses receive the conduction for demonstrating the developed approach’s feasibility and performance. Compared with Monte Carlo simulation or other active learning functions, the developed approach has advantages in terms of efficiency, convergence, and accurate when dealing with complex problems.

Keywords

reliability / Monte Carlo / Kriging model / Jensen-Shannon divergence / trust-region

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