Health Index-Based Maintenance of Prestressed Concrete Bridges Considering Building Information Modeling Application

Bridge maintenance faces challenges regarding data management and decision-making efficiency, primarily owing to the manual processing of extensive inspection data and the absence of integrated digital solutions. This study addresses these challenges by proposing a health index (HI)-based bridge evaluation framework for prestressed concrete bridges, based on building information modeling (BIM) for inspection data integration and long short_term memory (LSTM) models for accurate deterioration prediction. The proposed framework categorizes and analyzes bridge deterioration data from inspection reports and develops a predictive LSTM model that allows quantitative bridge evaluation based on accumulated historical data. The results demonstrate that this approach enhances the efficiency and accuracy of bridge condition evaluation while enabling long-term prediction of deterioration trends. In a case study of a bridge, the bridge-level HI value decreased by 17% over 16 years, while the condition grade remained unchanged. The findings suggest that integrating BIM with HI-based bridge evaluation can support sustainable bridge maintenance strategies.

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