A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis

REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis

https://open.library.ubc.ca/media/download/pdf/53032/1.0076230/1

Adaptive surrogate model with active refinement combining Kriging and a trust region method

Machine learning for reliability engineering and safety applications: Review of current status and future opportunities

This paper proposes an efficient Kriging-based subset simulation (KSS) method for hybrid reliability analysis under random and interval variables (HRA-RI) with small failure probability. In this method, Kriging metamodel is employed to replace the true performance function, and it is smartly updated based on the samples in the first and last levels of subset simulation (SS). To achieve the smart update, a new update strategy is developed to search out samples located around the projection outlines on the limit-state surface. Meanwhile, the number of samples in each level of SS is adaptively adjusted according to the coefficients of variation of estimated failure probabilities. Besides, to quantify the Kriging metamodel uncertainty in the estimation of the upper and lower bounds of the small failure probability, two uncertainty functions are defined and the corresponding termination conditions are developed to control Kriging update. The performance of KSS is tested by four examples. Results indicate that KSS is accurate and efficient for HRA-RI with small failure probability.

Zhili Sun

Papers

LIF: A new Kriging based learning function and its application to structural reliability analysis