Enhanced sequential directional importance sampling for structural reliability analysis

TL;DR

This paper introduces an improved sequential directional importance sampling method for structural reliability analysis, combining subset simulation and Kriging-based active learning to enhance efficiency and robustness in estimating failure probabilities.

Contribution

It proposes an enhanced SDIS method that integrates subset simulation and Kriging-based active learning, improving efficiency and robustness over standard SDIS in complex reliability problems.

Findings

Enhanced SDIS demonstrates higher efficiency on benchmark problems.

The method robustly identifies multiple failure roots.

Performance surpasses traditional SDIS in complex scenarios.

Abstract

Sequential directional importance sampling (SDIS) is an efficient adaptive simulation method for estimating failure probabilities. It expresses the failure probability as the product of a group of integrals that are easy to estimate, wherein the first one is estimated with Monte Carlo simulation (MCS), and all the subsequent ones are estimated with directional importance sampling. In this work, we propose an enhanced SDIS method for structural reliability analysis. We discuss the efficiency of MCS for estimating the first integral in standard SDIS and propose using Subset Simulation as an alternative method. Additionally, we propose a Kriging-based active learning algorithm tailored to identify multiple roots in certain important directions within a specificed search interval. The performance of the enhanced SDIS is demonstrated through various complex benchmark problems. The results show that the enhanced SDIS is a versatile reliability analysis method that can efficiently and robustly solve challenging reliability problems