Abstract

This contribution presents an approach for solving reliability-based optimization problems involving structural systems under stochastic loading. The associated reliability problems to be solved during the optimization process are high-dimensional (1000 or more random variables). A standard gradient-based algorithm with line search is used in this work. Subset simulation is adopted for the purpose of estimating the corresponding failure probabilities. The gradients of the failure probability functions are estimated by an approach based on the local behavior of the performance functions that define the failure domains. Numerical results show that only a moderate number of reliability estimates has to be performed during the entire design process. Two numerical examples showing the effectiveness of the approach reported herein are presented. © 2009 Elsevier B.V. All rights reserved.