###### Abstract  
Aiming at the problems of large capacity, narrow transverse width, large excitation, high safety level, and difficulty in accurately grasping the working state of the cable-stayed bridge for the long-span track, this research obtains the structural response data in real time by establishing a health monitoring system. The adaptive filtering method was employed to separate the train load response and the temperature load response. Then, a train load effect analysis method based on the influence line and a temperature load effect analysis method based on the correlation were proposed to assess the operational status of the bridge in real time and objectively. The Chongqing Nanjimen Railway Track Bridge (hereinafter Chongqing Nanjimen track bridge) project was utilized as a case study to demonstrate the application of these methods. The results show that the adaptive filtering method can effectively separate the response of train and temperature loads. The normalized cross-correlation (NCC) results of the measured train load response and the influence line's finite element calculation show a high degree of fit between the measured values and the theory, proving that no significant anomalies are found in the bridge. There is a strong correlation between the ambient temperature difference and the Pearson correlation coefficient of structural response, which indicates that the Chongqing Nanjimen track bridge is currently in normal working condition. special cable-stayed bridge for track; adaptive filtering method; train effect; temperature effect; Pearson correlation coefficient +
Footnote †: journal:separating temperature effects, and quickly and accurately evaluate the working status of large-span bridges, ensuring the safety of the bridge during its service stage. It is an urgent and difficult issue that the engineering community needs to overcome [4]. Scholars at home and abroad have made useful explorations in analyzing bridge structural performance and load effect. [PERSON] et al. [5] proposed a web-based and design-oriented structural health assessment system (SHES) for long-span bridges equipped with long-term structural health monitoring systems. [PERSON] et al. [6] investigated using output-only structural health monitoring (SHM) to evaluate the reliability and safety of highway steel plate beams of Yuanxiao Bridge. Displacement, strain, and acceleration measurements are used to assess the behavior of the bridge under designed static, semi-static, and dynamic truck load effects. [PERSON] et al. [7] designed and installed a long-term structural health monitoring system to monitor the behavior of the bridge during the huge fluctuations in temperature between summer and winter. [PERSON] et al. [8] analyzed the data recorded by the suspension bridge structural health monitoring system (SHM) and developed a series of indicators for evaluating future changes in the structure's health status. [PERSON] et al. [9] proposed a data-based damage detection method for steel bridges, which obtained data from real bridges under different structural conditions and applied an unsupervised machine learning algorithm to evaluate the most detectable damage types. [PERSON] et al. [10] proposed a calculation method for vehicle-mounted impact parameters based on the deflection influence line. A correlation model of vehicle-mounted impact parameters, ambient temperature, and bridge deflection was established based on the GRU neural network. [PERSON] et al. [11] analyzed domestic and foreign norms and standards and proposed evaluation criteria for the service performance of long-span track bridges. [PERSON] et al. [12] introduced an FE(finite element)-based adaptive enhanced Kalman filter (AAKF) B-WIM framework and adopted a new adaptive noise filter optimized by a genetic algorithm to realize accurate load recognition by combining finite element (FE) model update and bridge motion weighing (B-WIM). [PERSON] et al.