Adaptive surrogate-assisted sampling pool reduction strategy for low failure probability estimation

To improve the efficiency and accuracy of rare event reliability analysis of complex structures, an advanced adaptive Kriging-based candidate sample reduction strategy (AK-CSR) method is proposed by integrating candidate sample reduction strategy and advanced adaptive Kriging method. Through the improved first-order reliability method and the updated Kriging model, the accurate most probable failure point can be obtained with the update of Kriging. By domain constraint and distance constraint functions, the CSR strategy can ceaselessly find desired samples to update the Kriging model. The proposed method is verified by three numerical examples and two engineering examples. The analysis results demonstrate that AK-CSR method can be used to perform the rare event reliability analysis of complex structures and improves the computational efficiency while maintaining a good accuracy. Moreover, this study offers a useful insight for the reliability-based design optimization of complex structures, and enriches the field of structural reliability theory as well.