A novel enhanced kriging-based method for reliability analysis integrating Bayesian optimisation with ensemble strategy

This paper introduces an innovative and adaptive enhanced kriging-based approach for reliability analysis, combining Bayesian optimisation with posterior probability. The proposed methodology aims to overcome the shortcomings of inaccuracy of traditional kriging methods. The enhanced kriging model offers a more accurate prediction by an ensemble of different individual kriging models. Bayesian optimisation enhances the reliability assessment by providing a probabilistic measure of the model's accuracy. Furthermore, the posterior probability is employed to calculate the optimal weights for each kriging model. The approach improves the accuracy and efficiency of reliability analysis in complex systems. To demonstrate the effectiveness of the proposed approach, two illustrative examples including a high-nonlinear problem and a small failure probability problem are presented, showcasing its ability to provide accurate and robust failure probability estimates. This novel integration of kriging, Bayesian optimisation, and posterior probability offers great instruction in improving product quality and engineering applications.