Bridge Superstructure Component Performance Prediction Model Based on LightGBM

The rapid increase in the number of aging bridges domestically requires efficient maintenance strategies, which may utilize bridge performance prediction models. In this study, performance prediction models were developed for structural components with large amounts of data in the superstructure (concrete decks, PSC I girders, steel box girders, concrete crossbeams, and steel crossbeams) using data managed by a Bridge Management System. The condition rating from previous inspections (previous rating) had the greatest impact on the predictions, and regression analysis was used to train the model with the regression value of the previous rating rather than its representative value. Consequently, the prediction performance of the learning model improved, and the same method was applied to other superstructure components. Finally, the prediction results were analyzed by applying actual bridge data to the model.