Surface decomposition method for sensitivity analysis of first-passage dynamic reliability of linear systems

TL;DR

This paper introduces a surface decomposition method for efficiently performing sensitivity analysis of first-passage reliability in linear systems with Gaussian excitations, utilizing closed-form expressions and importance sampling.

Contribution

It presents a novel surface decomposition approach that simplifies sensitivity analysis by breaking it into surface integrals, leveraging linear system properties and importance sampling for efficiency.

Findings

Requires only 10^2 to 10^3 function evaluations

Efficient for large numbers of design parameters

Demonstrated effectiveness through three numerical examples

Abstract

This work presents a novel surface decomposition method for the sensitivity analysis of first-passage dynamic reliability of linear systems subjected to Gaussian random excitations. The method decomposes the sensitivity of first-passage failure probability into a sum of surface integrals over the constrained component limit-state hypersurfaces. The evaluation of these surface integrals can be accomplished, owing to the availability of closed-form linear expressions of both the component limit-state functions and their sensitivities for linear systems. An importance sampling strategy is introduced to further enhance the efficiency for estimating the sum of these surface integrals. The number of function evaluations required for the reliability sensitivity analysis is typically on the order of 10^2 to 10^3. The approach is particularly advantageous when a large number of design parameters are considered, as the results of function evaluations can be reused across different parameters. Three numerical examples are investigated to demonstrate the effectiveness of the proposed method.