LAUSR

Reliability-based design optimization (RBDO) utilizing computer simulations can lead to a highly reliable optimum design. However, conventional RBDO methods require full statistical information of input variables to estimate reliabilities of engineering systems or components, which is not easy to obtain in most engineering applications. In this paper, an uncertainty quantification method with mean-correlated simulations is proposed to estimate the statistical information of input variables from corresponding system response distributions obtained using large-scale test database. The proposed approach employs an error-lumped inverse method and kernel density estimation (KDE) for the uncertainty quantification. All possible errors such as measurement error, simulation error, and error by input variable difference are lumped into one to minimize residual errors of responses. Because quantified uncertainties using the error-lumped inversed method could be too scattered, distribution correction is proposed to reduce effective range of input variables while maintaining response distributions. Numerical and engineering examples show that the proposed approach can well estimate uncertainties of input variables using mean-correlated simulation and system response distributions obtained from test results.