# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 1 Introduction  
Steel box girder bridges are widely used in bridge engineering due to their large spans, short construction periods, and ease of transportation. The steel deck pavement system comprises orthotropic anisotropic steel bridge panels and asphalt concrete pavement layers that differ in characteristics from ordinary highway asphalt concrete pavements [1,2]. The asphalt concrete pavement layer is laid directly on the steel bridge deck. Due to the flexibility of the steel deck, the force and deformation of the pavement layer become more complex under various factors such as vehicle load and the climatic environment [3, 4, 5]. In recent years, China's traffic volume has continued to grow along with an increase in heavy traffic. As a result, some steel bridge deck pavements have begun to exhibit transverse and longitudinal cracking, rutting, pushing, congestion, and other issues. Traffic management departments have had to invest significant time and manpower into maintaining these pavements. Delayed maintenance can lead to greater safety hazards and economic losses [6,7]. 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. [PERSON] et al. studied the fatigue cracking of steel deck pavement systems under vehicle loading using finite element software and experiments under static and dynamic effects [8]. [PERSON] et al. conducted numerical simulation tests using the finite element model of a steel deck pavement system to obtain the fatigue cracking of the steel deck pavement system by varying the thickness of the pavement layer. Theyalso obtained variations in the mechanical properties of the steel deck pavement system under vehicle loading by a varying thickness and elastic modulus of the pavement layer [9]. [PERSON] et al. adjusted structural design parameters using a finite element model of the steel deck pavement system to obtain variations in the mechanical properties of the steel deck pavement system under vehicle loading [10]. Due to the complex knowledge system involved in numerical calculation methods and their long learning period, it is difficult for general engineering designers to master them proficiently. The accuracy of numerical calculation results is highly dependent on operator experience. Additionally, numerical calculation methods depend on intrinsic model accuracy and numerical calculation algorithm efficiency. When calculating refined models, classical method modeling and solving will take a longer time. Therefore, classical numerical computational analysis methods have significant room for improvement in terms of parameterization and computational efficiency [11]. Artificial intelligence techniques such as machine learning algorithms are gradually being applied in engineering. Data-driven artificial intelligence techniques can break through artificial empirical perception and eliminate dependence on humans [12; 13]. Integrated learning methods represented by XGBoost models have become increasingly popular among researchers in engineering prediction problems in recent years [14; 15]. [PERSON] et al. used several popular machine learning models for concrete strength prediction. Their study showed that the XGBoost model has a superior performance [16]. [PERSON] et al. established a model based on the XGBoost model for predicting the compressive strength of concrete. Their study showed that the XGBoost model has a higher accuracy compared to other existing machine learning models [17]. [PERSON] et al. used GBDT, XGBoost, and LightGBM models to predict the stability of hard rock pillars. The results showed that all three algorithms have a better performance [18]. [PERSON] et al. used four integrated learning models to predict the shear strength of reinforced concrete beams with and without web reinforcement.