Research on Global Seismic Reliability Analysis of Steel Frames Based on Machine Learning

Seismic reliability assessment of steel frame structures using nonlinear finite element analysis is often hindered by implicit limit state functions and high computational cost. To address these challenges, this study proposes a machine learning-based framework for global seismic reliability analysis. A nine-story steel frame model is established and validated through modal and pushover analysis. Global sensitivity analysis using the Sobol’ method is performed to identify key parameters governing the maximum inter-story drift ratio. Three machine learning models—PSO-SVR, PSO-XGBoost, and PSO-BPNN—are trained with the selected features and integrated into Monte Carlo simulation (MCS) for reliability calculation. The results show that the PSO-BPNN model achieves the highest accuracy with the maximum error of 1.0259% relative to direct MCS, outperforming the conventional MLE-based approach, which yields errors up to 11.9383% due to the non-standard distribution of the structural response. The impact of training sample size on model performance is also examined, with 1000 samples identified as a practical threshold for acceptable prediction accuracy. Existing code design methods require modifications based on the total probability approach for global reliability analysis. This study offers an efficient and precise methodology for seismic reliability design of steel frame structures, particularly when structural responses deviate from standard parametric distributions.