Spatially-Adaptive Calibration for Reliable Uncertainty Quantification in Seismic Response Prediction of RC Frames - Donwoo Lee; Seungjae Lee (Civil Engineering Journal, 2026)
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 Temperature Scaling calibration. While a global scaling parameter (T* = 4.40) reduces calibration error by 91.4%, we discover that the optimal calibration factor varies systematically across structural locations: T* ranges from 1.94 at fixed-base nodes to 5.52 at mid-height floors—a 2.8-fold variation that single-parameter approaches cannot capture. This spatial variation reflects physical differences in prediction uncertainty: boundary-constrained nodes exhibit lower uncertainty requiring less scaling, while mid-height nodes dominated by higher-mode contributions show greater uncertainty underestimation. Building on this finding, we propose floor-adaptive calibration using location-specific scaling factors. Compared to global calibration, this approach reduces average calibration error by an additional 62%, with improvements of 61-70% at ground and top floors, where global calibration performs worst. The method requires no model retraining—only a lookup table mapping floor levels to optimal scaling factors. Validation across 12 RC frames (3-7 stories), 2,400 analysis cases, and 35,000+ node-level predictions confirms that spatially adaptive calibration provides more reliable uncertainty estimates across all structural locations, enabling trustworthy confidence intervals for performance-based post-earthquake assessment.