To investigate the stress behavior and load-transfer mechanism at the steel-concrete joint segment of a flat arch bridge, this study takes a flat arch bridge in Guangdong with a calculated span of 196 m as the engineering background. Based on the Abaqus platform, a refined finite element model of the steel-concrete composite arch abutment was established to analyze the stress level and distribution characteristics of the structure under six unfavorable load cases. In addition, a 1∶8 scaled patial model was fabricated for multi-condition loading tests to study the mechanical behavior of the steel-concrete joint segment under different scenarios. The results indicate that, under all loading cases, the stress in the bottom plate of the steel arch box is most significant, making it the primary load-bearing component, with a maximum stress of -116.875 MPa. In the stress distribution of the bottom plate, the stiffening ribs play a major role, exhibiting the most prominent stress levels compared to other areas. The web area adjacent to the bottom plate of the steel arch box experience the next highest stresses, with a maximum value of-32.16 MPa across all conditions. The stress level in the top plate of the steel arch box is relatively low. The measured stress values in the concrete pile cap are generally small, with a maximum stress not exceeding -0.73 MPa.The axial compressive stress in the steel arch box gradually decreases from the loading end to the concrete pile cap end. When subjected to loads, the steel arch box primarily transfers the applied loads to the concrete pile cap through the bottom plate and the adjacent web regions. Under all test conditions, the stress level at the steelconcrete joint segment is relatively low, as most of the stress from the bottom plate of the steel arch box is dispersed into the concrete pile cap via components such as PBL shear connectors, bearing plates, densely arranged stiffening ribs, and penetrating reinforcement bars.

To investigate the temperature patterns of steel box girders in long-span cable-stayed bridges on high-speed railways, this study utilized measured temperature data from the Yuxi River Bridge on the Shangqiu-Hefei-Hangzhou High-Speed Railway, along with database resources. By employing machine learning techniques, the research explored the influence of various meteorological factors on the temperature behavior of steel box girders, as well as the temporal and spatial distribution characteristics of the temperature field. By establishing machine learning models that map various meteorological factors to the uniform temperature of the steel box girder, the superiority, inferiority, and applicability of each model were analyzed, and the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder was obtained. A comprehensive study on the vertical distribution pattern of the temperature of the steel box girder was conducted using machine learning methods and exponential fitting. The results show that the importance ranking of meteorological factors affecting the uniform temperature of the steel box girder from high to low is: air temperature, cumulative radiation, air pressure, humidity, radiation intensity, wind direction, horizontal visibility, wind speed, and precipitation, with the temperature importance far exceeding other meteorological factors. Among them, the atmospheric temperature 2 to 3 hours ago has the greatest impact on the uniform temperature of the steel box girder, reflecting a lag of 2 to 3 hours in the impact of atmospheric temperature changes on the uniform temperature of the steel box girder. Neural networks, random forests, and XGBoost models can all accurately predict the uniform temperature of the steel box girder, with the neural network model performing better overall. The negative temperature gradient in the steel box girder exhibits lower sensitivity to meteorological factors and is more strongly correlated with the internal heat transfer characteristics of the structure itself. The exponential function can accurately fit the vertical distribution of the maximum positive temperature gradient in steel box girders, with its parameters determinable through machine learning methods. Each parameter holds distinct physical significance. The research findings provide valuable reference for predicting temperature fields and understanding distribution patterns in the steel box girders of long-span cable-stayed bridges on high-speed railways.