Efficient reliability-based optimization of linear dynamic systems with random structural parameters

This paper proposes a novel method to address the challenges associated with reliability-based design optimization (RBDO) of a class of problems, namely design of a linear system with random structural parameters subject to stochastic excitation. This method effectively estimates the failure probability as an explicit function of the design variables by representing the failure probability function (FPF) as a weighted average of sample values, which are generated by means of a single reliability analysis. The resulting estimation of the FPF is then used to decouple the target RBDO problem into a deterministic optimization problem, which can be solved by any appropriate deterministic optimization algorithm. In addition, a sequential approximate optimization framework is adopted to iteratively seek the solution of the RBDO problem. Several examples are provided to demonstrate the high accuracy and efficiency of the proposed method.

Automated summary

This paper proposes a novel method for reliability-based design optimization by estimating the failure probability as a function of design variables through a weighted average of sample values generated by a single reliability analysis, decoupling the RBDO problem into a deterministic optimization problem, and iteratively seeking the solution using a sequential approximate optimization framework. Several examples demonstrate the high accuracy and efficiency of this method.