A novel Nested Stochastic Kriging model for response noise quantification and reliability analysis

Surrogate models and adaptive methods can release the huge computational burden of structural reliability analysis. However, it is very difficult to guarantee the accuracy of uncertainty quantification, especially when noises are contained in samples, which may greatly reduce the confidence of reliability analysis. In this study, we propose a novel Nested Stochastic Kriging (NSK) model method with response noise parameters decoupled from other surrogate model parameters, which can significantly improve the accuracy of uncertainty quantification and modeling efficiency. The proposed NSK is for deterministic data with response noise, aiming at reliability analysis. Various types of uncertainty can be identified by the NSK, including the conditions of known and unknown, constant and variable variances. Moreover, a local sample verification method is established to improve the accuracy of uncertainty quantification. To further improve the accuracy of reliability analysis, an adaptive NSK framework is established with new samples added, and then reliability analysis can be conducted. Through several numerical examples, it can be seen that the NSK always provides the most accurate results with the fewest analysis calls.