Line sampling-based local and global reliability sensitivity analysis

Local reliability sensitivity (RS) and global RS can provide useful information in reliability-based design optimization, but the algorithm for solving them is still a challenge, especially in case of small failure probability and high dimensionality. In this paper, a novel method by combining Monte Carlo simulation (MCS) with line sampling (LS), an efficient method for estimating small failure probability in case of the high dimensionality, is proposed to evaluate local RS and global RS simultaneously. Since the proposed method employs LS samples to approximately screen out the failure samples from the MCS sample set, the proposed method possesses both the efficiency of the LS and the accuracy of the MCS. One numerical example and two engineering examples illustrate the accuracy and the efficiency of the proposed method.