A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis

The reliability analysis system is currently evolving, and reliability analysis efforts are also focusing more on correctness and efficiency. The effectiveness of the active learning Kriging metamodel for the investigation of structural system reliability has been demonstrated. In order to effectively predict failure probability, a semi-parallel active learning method based on Kriging (SPAK) is developed in this study. The process creates a novel learning function called U-A, which takes the correlation between training points and samples into account. The U-A function has been developed from the U function but is distinct from it. The U-A function improves the original U function, which pays too much attention to the area near the threshold and the accuracy of the surrogate model is improved. The semi-parallel learning method is then put forth, and it works since U-A and U functions are correlated. One or two training points will be added sparingly during the model learning iteration. It effectively lowers the required training points and iteration durations and increases the effectiveness of model building. Finally, three numerical examples and one engineering application are carried out to show the precision and effectiveness of the suggested method. In application, evaluation efficiency is increased by at least 14.5% and iteration efficiency increased by 35.7%. It can be found that the proposed algorithm is valuable for engineering applications.

Automated summary

The reliability analysis system is currently evolving, with a focus on correctness and efficiency. The active learning Kriging metamodel has been shown to be effective for investigating structural system reliability. In this study, a semi-parallel active learning method based on Kriging (SPAK) is developed to predict failure probability. It introduces a novel learning function, U-A, which considers the correlation between training points and samples. The suggested method is demonstrated to be valuable for engineering applications, improving evaluation efficiency and iteration efficiency.