Efficient estimation of structural probability of failure with adaptive kriging and multi-layered hyperball-based importance sampling

Abstract

ABSTRACT The need for more resilient infrastructures entails an accurate and efficient structural reliability analysis. In recent years, Kriging-based meta-modeling has constantly been adopted to reduce the computational cost in estimating the probability of failure. Active learning models focus on most informative samples to increase computational efficiency. Nevertheless, the efficiency gained from the models may diminish when dealing with a rare event with a relatively small probability of failure. To resolve the difficulty, this study proposes a new method, Adaptive Kriging with Multi-layered Hyperball-based Importance Sampling (AK-MHIS), to estimate the structural probability of failure. AK-MHIS uses an adaptive multi-layered hyperball as its candidate sample pool with a new sample filtering mechanism and a more robust stopping condition for the active-learning phase. The performance of AK-MHIS method is validated in benchmark cases. The verification results confirm the superior performance of AK-MHIS to previous methods.