The bridge deck is more vulnerable to deterioration than other structural components. This is due to its direct exposure to environmental factors such as vehicular loads, chloride ingress, and freeze–thaw cycles. The resulting accelerated degradation often results in a serviceability life that is shorter than the intended design life. However, the absence of standardized condition assessment methods coupled with clear definitions of remaining service life has limited the establishment of rational guidelines for repair and strengthening. In a bid to address this lack, this study focuses on PSC-I type bridges in South Korea, utilizing long-term field inspection data to analyze environmental, structural, and material factors—including reinforcement corrosion, chloride diffusion, and freeze–thaw actions. Environmental zoning was applied based on regional conditions, while structural zoning was performed according to load characteristics, thereby allowing the classification of deck regions into moment zones and cantilever sections. Machine learning models were employed to identify dominant deterioration mechanisms, with the validity of the zoning classification being evaluated via model accuracy and SHAP value analysis. Additionally, a regression-based approach was proposed to estimate the remaining service life of the bridge deck for each corrosion phase, thereby providing a quantitative framework for durability assessment and maintenance planning.

Bridge slabs are critical structural components that directly sustain vehicle loads and generally have the shortest service life among bridge elements, leading to increased maintenance needs and costs. In many countries, damage and repair histories have been systematically recorded for over four decades. In this study, a digital twin framework for predicting the future performance of bridge slabs by integrating long-term inspection data was proposed. Historical 2D damage drawings were digitized using the YOLOv7 deep-learning model to extract the spatial coordinates of the damaged locations. Based on this data, eight representative damage states were defined to support the prediction of the service life. The damage and repair history was embedded into the 3D bridge models using a unique coding system to enable temporal and spatial tracking. As the corrosion of the reinforcement cannot be directly observed by visual inspection, its development and progression is estimated using empirical models. The digital twin concept is validated using historical inspection records to demonstrate its applicability to existing bridge slabs. The integration of cumulative deterioration data significantly improves the accuracy of the performance predictions and facilitates data-driven maintenance and rehabilitation strategies.

The bridge deck is more vulnerable to deterioration than other structural components due to its direct exposure to environmental factors such as vehicular loads, chloride ingress, and freeze–thaw cycles. This accelerated degradation often results in a serviceability life that is shorter than the intended design life. However, the absence of standardized condition assessment methods and clear definitions of residual service life has limited the establishment of rational guidelines for repair and strengthening. This study focuses on PSC-I type bridges in South Korea, utilizing long-term field inspection data to analyze environmental, structural, and material factors—including reinforcement corrosion, chloride diffusion, and freeze–thaw actions. Environmental zoning was applied based on regional conditions, while structural zoning was per-formed according to load characteristics, allowing classification of deck regions into moment zones and cantilever sections. Machine learning models were employed to identify dominant deterioration mechanisms, and the validity of the zoning classification was evaluated through model accuracy and SHAP value analysis. Additionally, a regression-based approach was proposed to estimate the residual service life of the bridge deck for each corrosion phase, providing a quantitative framework for durability assessment and maintenance planning.