# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 6 Conclusions  
This study developed an XGBoost-based mechanical performance prediction model for steel bridge deck pavement systems. The solution can be performed quickly and accurately. The dataset was established by conducting numerical simulation tests at the most unfavorable load locations using a finite element model. The XGBoost model was then built using the dataset, and its parameters were optimized. Next, the established XGBoost model was compared with other conventional machine learning models. Finally, the model was analyzed Interpretively using the SHAP framework. The following conclusions can be drawn from this study:  
1. In the prediction model of the maximum transverse tensile stress on the pavement surface, the prediction results of the XGBoost model on the test set are as follows: MAE is 0.040, RMSE is 0.049, and R\({}^{2}\) is 0.871. The optimal combination of parameters is learning_rate = 0.21; max_depth = 7; min_child_weight = 4; and n_estimators = 129. The most important characteristic variables are the elastic modulus of the upper layer of the pavement \(EI\) and the thickness of the upper layer of the pavement \(HI\), and the relatively more important characteristic variables are the thickness of the lower layer of the pavement \(H2\) and the thickness of the steel bridge panel \(T\). 2. In the prediction model of the maximum longitudinal tensile stress on the pavement surface, the prediction results of the XGBoost model on the test set are as follows: MAE is 0.013, RMSE is 0.015, and R\({}^{2}\) is 0.970. The optimal combination of parameters is learning_rate = 0.09; max_depth = 2; min_child_weight = 4; and n_estimators = 276. The most important characteristic variables are the elastic modulus of the upper layer of the pavement \(EI\), and the relatively more important characteristic variables are the thickness of the steel bridge panel \(T\) and the thickness of the upper layer of the pavement \(H1\). 3. In the prediction model of the maximum transverse shear stress between the pavement and steel bridge panel, the prediction results of the XGBoost model on the test set are as follows: MAE is 0.023, RMSE is 0.027, and R\({}^{2}\) is 0.864. The optimal combination of parameters is learning_rate = 0.4; max_depth = 9; min_child_weight = 3; and n_estimators = 170. The most important characteristic variables are the elastic modulus of lower pavement \(E2\) and the thickness of steel bridge panel \(T\). The relatively important characteristic variables are the elastic modulus of upper pavement \(EI\) and the thickness of lower pavement \(H2\). 4. In the prediction model of the maximum longitudinal shear stress between the pavement and steel bridge panel, the prediction results of the XGBoost model on the test set are as follows: MAE is 0.011, RMSE is 0.013, and R\({}^{2}\) is 0.865. The optimal combination of parameters is learning_rate = 0.15; max_depth = 10; min_child_weight = 4; and n_estimators = 230. The most important characteristic variable is the elastic modulus of lower pavement \(E2\), and the relatively more important characteristic variables are the elastic modulus of upper pavement \(EI\) and the thickness of steel bridge panel \(T\). 5. The prediction results of the XGBoost model on the test set in the maximum vertical displacement prediction model of the pavement layer are as follows: MAE is 0.041, RMSE is 0.052, and R\({}^{2}\) is 0.861. The optimal combination of parameters is learning_rate = 0.05; max_depth = 8; min_child_weight = 2; and n_estimators = 230. The most important characteristic variable is the spacing of the cross-partition D, and the relatively more important characteristic variables are the elastic modulus of the upper layer of the pavement \(EI\), the thickness of the steel bridge panel \(T\), and the thickness of the lower layer of the pavement \(H2\). 6. Compared with other traditional machine learning models, the XGBoost model shows a good prediction performance.