Machine Learning Synthesis of Fire-Following-Earthquake Fragility Surfaces for Steel Moment-Resisting Frames

This paper presents a probabilistic methodology for generating fragility surfaces for low- to mid-rise steel moment-resisting frames (MRFs) under fire-following-earthquake (FFE). The framework integrates nonlinear dynamic seismic analysis, residual deformation transfer, and temperature-dependent fire simulations within a Monte Carlo environment, while explicitly accounting for uncertainties in structural properties, ground motions, and fire simulation. A fiber-based modeling strategy is employed, combining temperature-sensitive steel materials with fatigue and fracture wrappers to capture cyclic deterioration and abrupt failure. This formulation yields earthquake-only and fire-only fragility curves along the surface boundaries, while interior points quantify the joint fragility response under sequential hazards. The methodology is benchmarked against a machine learning (ML) synthesis framework originally developed for earthquake–tsunami applications and extended here to FFE. Numerical results for a three-story steel MRF show excellent agreement (R2 > 0.95, RMSE < 0.02) between simulated and ML-generated surfaces, demonstrating both the efficiency and hazard-neutral adaptability of the ML framework for multi-hazard resilience assessment.