Abstract
The spatiotemporal variability exists in almost all engineering problems, since the environmental actions and material properties may change both spatially and temporally in a stochastic manner. Therefore, reliability analysis is inherently space–time-dependent. In this study, an innovative space–time-dependent outcrossing rate (STOR) method is proposed for dealing with such reliability problems. The failure probability of a structure is determined based on outcrossing rate which is theoretically derived for the first time, considering the spatiotemporal variability of all basic variables. Then, an efficient numerical algorithm for computing the failure probability is proposed based on Gauss-Legendre quadrature. The application of STOR method is investigated through three examples, including non-Gaussian random processes, non-Gaussian random fields, strong nonlinear limit state functions and small failure probability problems. The proposed STOR method is found to be efficient for space–time-dependent reliability analysis with enough accuracy.
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