## 1 Introduction  
Small- and medium-span beam bridges possess a straightforward structural system, exhibit evident stress patterns, and demonstrate substantial responsiveness to live loads during their operational lifespan. As a result, live load monitoring data are commonly used in health monitoring and evaluation procedures [1; 2; 3]. The prevailing bridge health monitoring system generally functions with the presumption that the constant frequency data acquired from sensors can faithfully depict the true state of deformation exhibited by the structure. Nevertheless, the structural deformation of the bridge experiences ongoing fluctuations. Equal frequency acquisition denotes a discrete process in which data are continuously deformed. However, this approach is not without its limitations, including issues such as the absence of crucial data points and the distortion of curves. Research has shown that the experimental fundamental frequency has been used to predict the prestress losses in simply supported concrete bridges [4; 5]. This measurement has the potential to provide dependable insights into the current prestress losses experienced by concrete girder-bridges undergoing significant fracture. In the context of small- and medium-span beam bridges, it is noteworthy that the live load component constitutes a significant portion of the overall design load, with estimates indicating that it may even reach approximately 50.0% [6]. Therefore, it is crucial to obtain exhaustive influence data regarding the response of the structure to real-world loads, in addition to meticulously determining the optimal sampling frequency for the monitoring system's sensors. Insufficiently setting the acquisition frequency can result in a decrease in the amount of acquired data, a potential compromise in capturing key points, and even the possibility of missing responses. Setting the acquisition frequency excessively high can result in substantial data accumulation, thereby necessitating greater hardware investment and posing challenges in data analysis [7; 8]. After conducting a thorough examination of the available data on frequency settings for small- and medium-span beam bridges both domestically and internationally, it has been observed that there is a scarcity of research in this area. The analysis of the data can be summarized as follows: [PERSON] et al. [9] tested the structural dynamic characteristics of Gorgopotamos bridge, and calculated that the midspan deflection of the structure caused by heavy freight trains crossing the bridge is about 6 mm and the frequency range of the structure is between 3.18 and 3.63 Hz; [PERSON] [10; 11] used a 100 Hz GPS to dynamically monitor a small concrete bridge, and used a CWT-Morlet filter to provide accurate position transient signal and report the fundamental frequency of the structure at the same time. The filtered data were displayed on the identification scale diagram, and it was found that the confidence level was 95.0% and the frequency range was 0.1 to 50 Hz. [PERSON] et al. [12] put forward the method of frequency conversion acquisition, using radar sensors to identify the height of the car body, and then determine whether to adopt high-frequency acquisition in order to achieve the purpose of the real-time online monitoring of bridge deflection by vehicle control. The analysis involves utilizing a three-span continuous beam bridge with a length of 180 m. The bridge's fundamental frequency is determined to be 1.645 Hz. To capture high-frequency data, a frequency of 20 Hz is chosen for acquisition. Similarly, a frequency of 0.1 Hz is selected for acquiring low-frequency data. By employing these frequencies, the response of the entire vehicle crossing process on the bridge is obtained. While it is possible to observe abnormal deflection and deformation, the reliability of the collected data is still a subject of debate. In addition, [PERSON] et al. [13] studied the influence of acquisition frequency on the accuracy of the Newmark-\(\beta\) method.