LAUSR

For the reliability analysis with imprecise probability distributions, the failure probability and its sensitivity are always denoted as the functions of distribution parameters. The computation of those functional relationships remains a challenging task due to the intensive computational burden. To further improve the computational efficiency, this work proposes a new computational method based on the classical active learning Kriging model with U learning function. The Kriging model is constructed with a group of initial point and updated by the best contributing samples selected from different sample spaces corresponding to discrete distribution parameters, which guarantees the prediction ability in the variation range of distribution parameters. Consequently, the functions of failure probability and sensitivity can be estimated by the same Kriging model, which avoids re-constructing Kriging models corresponding to different distribution parameters. Several examples including two numerical examples and three engineering practices are investigated to validate the reasonability and superiority of the proposed method. Finally, the proposed method is applied to the fatigue life reliability and sensitivity analyses of a turbine disk structure.