Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost

Abstract

Steel bridges are widely used in bridge engineering. In the structural design of steel bridge deck pavement systems, engineers focus on obtaining mechanical properties by calculating design parameters and are keen to establish a quick and accurate solution method. Because of the complex knowledge system involved in the numerical calculation method, it is difficult for the general engineering designer to master it. Researchers have started using artificial intelligence algorithms to solve problems in civil engineering. This study developed an XGBoost-based mechanical performance prediction model for steel bridge deck pavement systems. First, numerical simulation tests are conducted at unfavorable load locations using a finite element model to establish a dataset. Then, an XGBoost model is built using this dataset, and its parameters are optimized and compared with traditional machine learning models. Finally, an explanatory analysis of the model is performed using SHAP, an interpretable machine learning framework. The results indicate that the developed XGBoost model accurately predicts the mechanical properties of steel bridge deck pavement systems.

Keywords

• steel bridge deck pavement
• artificial intelligence
• ensemble learning
• mechanical property prediction
• feature importance