Predicting crack evolution for small- and medium-span beam bridges using XGBoost–SHAP model

Abstract Accurate prediction of bridge crack evolution is essential for infrastructure safety assurance and maintenance optimization. This study develops an interpretable machine learning framework to predict the expansion of cracks on the main beam in small- and medium-span highway beam bridges and identify the underlying mechanisms of structural deterioration. A comprehensive database was constructed from inspection and monitoring records of over 100 bridges, featuring critical degradation indicators, including crack density (CD) and maximum crack width (MCW). Following data preprocessing and feature selection through correlation analysis, three machine learning algorithms, that is, support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost), were implemented and evaluated using statistical metrics (R 2 , RMSE, and MAE). The XGBoost model demonstrated superior predictive performance with R 2 values of 0.9433 and 0.9413 for MCW and CD, respectively, reducing RMSE by up to 66.8% and MAE by up to 72% compared to alternative models. SHAP (SHapley Additive exPlanations) analysis revealed that four factors, namely, vehicle load (VL), annual average daily truck traffic (ADTT), bridge age (BA), and annual average daily traffic (ADT), collectively contributed 61.45 ± 2.35% to crack development, with VL (19.7%) being the most influential factor. These findings identify excessive traffic loading and aging as the dominant drivers of crack propagation in beam bridges, providing valuable insights for targeted maintenance strategies and bridge management.