Reliability Based Design Optimization with Correlated Input Variables

Reliability-based design optimization (RBDO), which includes design optimization in design space and inverse reliability analysis in standard normal space, has been recently developed under the assumption that all input variables are independent because it is difficult to construct a joint probability distribution function (PDF) of input variables with limited data such as the marginal PDF and covariance matrix. However, since in real applications, it is common that some of the input variables are correlated, the RBDO results might contain a significant error if the correlation between input variables for RBDO is not considered. In this paper, Rosenblatt and Nataf transformations, which are the most representative transformation methods and have been widely used in the reliability analysis, have been studied and compared in terms of applicability to RBDO with correlated input variables. It is identified that Nataf transformation is one of copulas and more applicable than Rosenblatt transformation. Using numerical examples, it is also shown that the correlation of input variables significantly affects the RBDO results.