Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations

Stochastic nonlinear dependencies have been reported extensively between different uncertain parameters or in their time spatial variance. However, the description of dependency is commonly not provided except a linear correlation. The structural reliability incorporating thus needs to be addressed based on correlations. This paper first demonstrates capture by fitting various bivariate non-Gaussian copulas limited data samples material properties. vine copula model used enable flexible modeling multiple mapping correlations into parameters. A sequential search strategy applied achieve estimate numerous parameters, and simplified algorithm further designed for involving stationary stochastic processes. subset simulation then adopted efficiently generate random variables from corresponding distribution high evaluation. Two examples including frame structure with properties cantilever beam spatially variable modulus are investigated discuss possible effects reliability. Since can determined qualitatively data, proposed method provides feasible way evaluation prescriptions correlated