Physics-Informed Feature Fusion and Structural Metadata Integration for Transferable Post-Earthquake Damage Classification: Experimental Evaluation and Community-Recovery Implications

Earthquake-induced structural damage assessment remains a key challenge for population-based Structural Health Monitoring (PBSHM), where damage representations must generalize across heterogeneous buildings. Existing vibration-based methods often rely on structure-specific damage-sensitive features (DSFs) and uniform drift thresholds, limiting transferability. This study proposes a physics-informed feature fusion and structural metadata integration framework for post-earthquake damage classification and resilience-oriented assessment. A nonlinear simulation dataset is generated for building populations with varied geometrical and material properties under multiple earthquake scenarios. To obtain consistent labels, damage states are defined through nonlinear pushover analysis and capacity-based thresholds rather than fixed drift limits. Using sparse ground and roof acceleration measurements, physics-informed DSFs are compared with Catch22 and MiniRocket representations under group-wise cross-structure validation with multiple machine-learning classifiers. Results show that physics-informed DSFs outperform generic time-series representations under sparse sensing. Structural metadata further improves robustness by providing context for interpreting response-based DSFs. Modal information offers limited gains in simulations but substantially improves out-of-distribution shaking-table validation, helping bridge simulation-experiment discrepancies.