A Kriging-assisted adaptive improved cross-entropy importance sampling method for random-interval hybrid reliability analysis

This paper proposes an efficient Kriging-assisted improved cross-entropy (ICE) method for random-interval hybrid reliability analysis. This method employs Kriging to substitute for the projection outlines of limit state surface and incrementally updates the Kriging model within the final layer samples of ICE. A novel learning function that combines both the lower and upper confidence bounds for extremum searching is proposed to identify samples on the projection outlines. Additionally, an error-based stopping criterion (EBSC) is proposed to avoid unnecessary updates to the Kriging model. The efficiency and effectiveness of the proposed method are demonstrated through four benchmark examples. Furthermore, the method is applied to two practical engineering scenarios: the strength reliability analysis of a bogie and the resonance reliability analysis of an axially functionally graded material (FGM) pipeline. The results indicate that the proposed method achieves high levels of efficiency and accuracy.