Reliability Assessment Using Discriminative Sampling and Metamodeling

Abstract

Reliability assessment is the foundation for reliability engineering and reliability-based design optimization. It has been a difficult task, however, to perform both accurate and efficient reliability assessment after decades of research. This work proposes an innovative method that deviates significantly from conventional methods. It applies a discriminative sampling strategy to directly generate more points close to or on the limit state. A sampling guidance function was developed for such a strategy. Due to the dense samples in the neighborhood of the limit state, a kriging model can be built which is especially accurate near the limit state. Based on the kriging model, reliability assessment can be performed. The proposed method is tested by using well-known problems in the literature. It is found that it can efficiently assess the reliability for problems of single failure region and has a good performance for problems of multiple failure regions. The features and limitations of the method are also discussed, along with the comparison with the importance sampling (IS) based assessment methods.