Abstract For addressing the Time-Dependent Failure Possibility (TDFP) under fuzzy uncertainty, the Kriging surrogate models combing with fuzzy simulation (FS) have achieved promising results. However, existing learning functions for Kriging do not comprehensively account for the predictive sign misclassification probability and the contribution of newly selected samples for the failure possibility with fuzzy uncertainty described by membership functions (MF). To further enhance the computational efficiency, this study proposes a new adaptive Kriging model based method for solving TDFP. This method first estimates the misclassification probability for critical performance sign via multivariate Gaussian distributions, then selects samples based on both their failure possibility contribution and misclassification probability. This strategy directs new samples toward critical failure regions with maximum joint membership functions, accelerating Kriging convergence. Three test examples and an engineering application of a simplified turbine blade validate the advantages of the proposed method, which can reduce the number of performance function calls while preserve accurate estimates of TDFP.
Wang et al. (Wed,) studied this question.