AbstractBy taking the advantage of Poisson events, the outcrossing method has been widely applied for time-dependent reliability analysis considering continuous processes. Generally, the outcrossing method can only obtain an upper bound of failure probability, which will overestimate the risk and result in unnecessary maintenance costs. In this study, a general conditional outcrossing (GCO) method for time-dependent reliability analysis was proposed, which can obtain a more accurate probability of failure. The conditional outcrossing rate was defined as the outcrossing rate conditioned on fixing the values of time-invariant random variables, which was introduced to satisfy the assumption of independent outcrossing events. A numerical algorithm for the GCO method was developed with the aid of the Gauss-Legendre quadrature and point estimate method. The application of the GCO method is demonstrated by three examples, including an implicit limit state function with a finite-element model and non-Gaussian nonstationary random processes. The failure probability obtained by the GCO method was found to be in close agreement with that by Monte Carlo simulation, which demonstrates the accuracy of the GCO method.

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