AK-SYS: An adaptation of the AK-MCS method for system reliability

A lot of research work has been proposed over the last two decades to evaluate the probability of failure of a structure involving a very time-consuming mechanical model. Surrogate model approaches based on Kriging, such as the Efficient Global Reliability Analysis (EGRA) or the Active learning and Kriging-based Monte-Carlo Simulation (AK-MCS) methods, are very efficient and each has advantages of its own. EGRA is well suited to evaluating small probabilities, as the surrogate can be used to classify any population. AK-MCS is built in relation to a given population and requires no optimization program for the active learning procedure to be performed. It is therefore easier to implement and more likely to spend computational effort on areas with a significant probability content. When assessing system reliability, analytical approaches and first-order approximation are widely used in the literature. However, in the present paper we rather focus on sampling techniques and, considering the recent adaptation of the EGRA method for systems, a strategy is presented to adapt the AK-MCS method for system reliability. The AK-SYS method, “Active learning and Kriging-based SYStem reliability method”, is presented. Its high efficiency and accuracy are illustrated via various examples.