An adaptive reliability method combining relevance vector machine and importance sampling

In this work, a new reliability method is proposed by combining the relevance vector machine (RVM) and importance sampling in a proper way. A modified Metropolis algorithm is utilized to generate the training data that covers the important area. With the training data, a surrogate model is built with RVM to approximate the limit state surface. Then importance sampling is introduced to make sure that the surrogate model can be used in the area...