Seismic damage assessment for concrete bridge columns using maximum and residual displacement data synthesized via generative AI

Seismic damage index for concrete bridges often quantifies only peak deformation and neglects residual response, which reflects cumulative damage and governs postearthquake recovery. Meanwhile, their use in risk-based fragility assessments renders the results highly sensitive to data scarcity, particularly for rare high-damage states under extreme earthquakes. This study develops an integrated probabilistic framework that couples damage quantification with data augmentation. A new damage index is proposed to combine maximum and residual displacements into a single interpretable measure. Optimized limit states are calibrated accordingly. To address data scarcity, a fragility-informed conditional generative adversarial network (cGAN) is introduced. The model incorporates adversarial, physics-based, and fragility-based losses to generate realistic combinations of intensity measures and engineering demand parameters for calibration and uncertainty quantification. By enriching the dataset with cGAN-generated samples, the method produces fragility curves with reduced dispersion and improved reliability in severe-damage regimes for the bridge demonstration. The integration of enhanced damage index and statistically consistent data augmentation offers a robust basis for seismic performance evaluation and supports informed decisions on inspection, repair, retrofit, and resilience planning.