Spatially-Adaptive Calibration for Reliable Uncertainty Quantification in Seismic Response Prediction of RC Frames

Data-driven models offer the computational speed needed for rapid post-earthquake assessment, but their uncertainty estimates must be trustworthy to support safety decisions. This study reveals that Monte Carlo dropout uncertainty for RC frame seismic response prediction is severely miscalibrated: 95% prediction intervals capture only 46.6% of actual responses, meaning Immediate Occupancy assessments under ASCE 41-17 would be unconservative in over half of cases. We address this through post-hoc