###### Abstract
[13] proposed a bridge state assessment technology, which used the vibration data collected by sensors, combined natural excitation technology (NExT) and empirical Fourier decomposition (EFD), and adopted a hybrid method to analyze the environmental vibration data of bridges and identify the bridge state for assessment. To separate vehicle-induced strain components from strain monitoring signals mixed with various loads, [PERSON] et al. [14] proposed an online recursive sliding variational mode decomposition method. [PERSON] et al. [15] proposed a robust end-to-end optical fiber sensor (FOS) monitoring prototype based on deep neural networks to convert FOS strain output into an interactive digital twin (DT) visualization in response to the scale of structural deterioration worldwide and the ineffectiveness of existing non-destructive assessment techniques. [PERSON] et al. [16] proposed a new method of real-time time-varying cable force identification. The method updates the cable vibration signal by sliding the window and uses adaptive chirped mode decomposition (ACMD) to identify the instantaneous frequency of the cable. [PERSON] et al. [17] proposed a probabilistic anomaly detection method considering the uncertainty model, aiming at the multi-stage uncertainty modeling problem in the anomaly detection process of long-span bridges. To provide researchers with a clear perspective on the field of health monitoring, [PERSON] et al. [18] reviewed the methods previously studied in the vibration characterization of SHM bridges. The future potential of the vibration-based approach as a structural characterization solution is significant, providing state-of-the-art data-driven measures for damage detection. The increased robustness of SHM also provides room for progress in the precise quantification and interpretation of vibration-based techniques, providing a way for future research to extend the vibration-based frontier. To sum up, scholars at home and abroad have conducted in-depth studies in the fields of bridge health monitoring systems and bridge condition evaluation, with fruitful results. However, there is little research on the load effect analysis of large-span dedicated track bridges based on health monitoring systems. In view of this, this paper proposes the establishment of a dedicated track bridge health monitoring system with the objective of acquiring structural response data in real time. To address the specific characteristics of high frequency, large response, and periodicity of the train load effect, an adaptive filtering method is put forth to differentiate between train load and temperature load. The proposed system is validated through a real bridge test conducted on the Chongqing Nanjimen track bridge. The vertical displacement of the main beam, the longitudinal displacement of the main tower, and the stress of the main beam under the action of the train are studied and compared with the numerical simulation results. Meanwhile, the influence of ambient temperature on the aforementioned variables is analyzed. A linear regression equation was fitted by the least squares method, and the correlation strength and direction were determined by the Pearson correlation coefficient. Finally, the load effect analysis of the bridge was completed, providing scientific and technical support for the maintenance of the bridge in a healthy manner.