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Seismic damage assessment for concrete bridge columns using maximum and residual displacement data synthesized via generative AI
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DOI：10.1177/10567895261460981.png)
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Abstract
En 中文
<jats:p>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.</jats:p>
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