Structural Reliability

Structural reliability analysis aims to quantify the probability of structural failure under uncertainty, focusing on accurately predicting the likelihood of various failure modes. Current research emphasizes developing efficient and robust methods for handling complex scenarios, including multiple failure criteria and time-dependent behavior, employing techniques like sequential adaptive importance sampling, PC-Kriging surrogate models for active learning, and Koopman operator-based approaches leveraging deep learning for nonlinear systems. These advancements improve the accuracy and efficiency of reliability assessments, leading to safer and more cost-effective designs across various engineering disciplines.