Reliability-based multiobjective optimization of structural dynamical systems: A Markov sampling approach

Abstract

Reliability-based multiobjective optimization formulations give valuable insight for the design of structural dynamical systems under stochastic excitation. Nonetheless, since reliability assessment is often carried out using simulation techniques, the generation of the entire Pareto front may lead to excessive or even prohibitive computational costs. Hence, this contribution presents an approach for exploring specific tradeoff solutions. A compromise programming formulation based on aspiration levels is considered. The resulting min-max optimization problem is solved directly using a Markov sampling-based technique. In addition, an adaptive surrogate model is formulated for improved numerical efficiency. An application example involving a four-story building model equipped with nonlinear devices is presented to assess the capabilities of the proposed method.