Combining Importance Sampling with Machine Learning to Accelerate Structural Reliability Analysis
Résumé
This paper presents an innovative approach combining Importance Sampling (IS) and Machine Learning (ML) to accelerate structural reliability analysis. Reliability analysis is essential for assessing the safety and performance of structures subject to uncertainties and random excitations. However, traditional methods can be computationally expensive, especially when failure events are rare. In this study, we propose a hybrid method that uses Importance Sampling (IS) to generate meaningful samples while integrating machine learning models to predict structural behavior under various loading conditions. This combination reduces the number of required simulations while improving the accuracy of failure probability estimates. Experimental results show that our approach can significantly reduce computational time while maintaining accuracy comparable to conventional methods. This method opens new avenues for reliability analysis, enabling engineers to better assess the risks associated with complex structures.
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