A new active learning method for reliability analysis based on local optimization and adaptive parallelization strategy

In recent years, an active learning method combining Kriging and Monte Carlo Simulation (AK-MCS), has been developed for calculating the failure probability. However, the original AK-MCS only uses serial computing, which limits its ability to take advantage of distributed computing. Thus, this work introduces a novel adaptive learning approach for reliability analysis by combining local optimization and a parallelization strategy. The new approach identifies the points of greatest uncertainty through local optimization and adds them into the design of experiments. An inner learning loop is implemented, searching for additional best points with a pseudo Kriging model so that the performance function can be evaluated in parallel. Furthermore, an adaptive strategy is proposed to determine the amount of additional points during iteration based on the minimum value of the learning function. We conducted a comparison between the proposed method and the original AK-MCS as well as a few additional methods in order to assess its efficacy and precision. Five examples were considered to assess performance.