Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition

Abstract

This study developed an ensemble-learning-based bridge deck defect condition prediction model to help bridge managers make more rational and informed steel bridge deck maintenance decisions. Using the latest data from the NBI database for 2021, this study first used ADASYN to solve imbalance problems in the data, then built six ensemble learning models (RandomForest, ExtraTree, AdaBoost, GBDT, XGBoost, and LightGBM) and used a grid search method to determine the hyperparameters of the models. The optimal model was finally analyzed using the interpretable machine learning framework, SHAP. The results show that the optimal model is XGBoost, with an accuracy of 0.9495, an AUC of 0.9026, and an F1-Score of 0.9740. The most important factor affecting the condition of steel bridge deck defects is the condition of the bridge’s superstructure. In contrast, the condition of the bridge substructure and the year of bridge construction are relatively minor factors.