Reliability-Based Design Optimization under Imprecise Uncertainty

Abstract

Many reliability-based design optimization (RBDO) techniques have been developed to obtain a reliable design accurately and efficiently in the presence of uncertainties. Probability theory has primarily been used for uncertainty analysis in RBDO. However, in a situation in which information of uncertain variables is imprecise and incomplete, probabilistic techniques are not appropriate to describe the propagation of uncertainty. In this work, evidence theory, also called Dempster-Shafer theory, is employed to handle imprecise uncertain variables as an alternative to classical probability theory. Since the uncertainty quantification method of evidence theory is not compatible with prevailing probabilistic RBDO techniques, we propose an efficient optimization strategy by defining a plausibility function in a trustregion. The procedure for handling imprecise uncertain variables is presented on a practical large-scale structural RBDO problem.

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