## 4 Monitoring Strategy Considering Noise Ratio and Coupled Vibration  
The precision of the monitoring signal is a key factor determining the accuracy of the monitoring when calculating the general range of dynamic responses of the bridge and the vehicle group based on the theory of coupled bridge-vehicle vibration. Accurate data obtained through health monitoring systems are essential for precise results. Measured signals, received through sensors and data acquisition instruments, can be broadly categorized into three types. The first type is the structural vibration signals generated by traffic loads, dominating in amplitude and fully representing the dynamic characteristics of the structure. The second type is ambient vibration signals caused by natural environment factors such as wind and earthquakes, exhibiting small amplitudes and wide frequency bandwidths with characteristics of both randomness and structural dynamics. Often, such signals need to be suppressed and separated. The third type is electromagnetic interference signals or noise produced by natural sources and electrical equipment. Depending on the circumstances, this signal type has characteristics such as sudden, instantaneous, and periodic, exhibiting sudden, instantaneous, and periodic characteristics. In traditional structural detection and monitoring, performance parameters such as instrument range, sensitivity, resolution, and scope are generally guaranteed to meet specified requirements. Among these properties, resolution refers to the minimum value that a test instrument can effectively distinguish, indicating its ability to discern signals from noise. The minimum decibel of the measured signal to noise ratio is typically specified as \(20\)\(\lg(\text{S}/\text{N})\leq 5\) dB, where S represents the signal level of the measured signal, and N is the noise level. The measured signal amplitude satisfying this equation is called the lowest measured vibration level, at which point the signal level is about 1.77 times the noise level. Considering the current general performance of monitoring instruments and the actual dynamic characteristics of the bridge, the above signal-to-noise ratio requirements are assumed to be conservative, making them fundamental prerequisites for bridge health monitoring. One of the important elements of structural health monitoring is the removal of electromagnetic interference signals or noise signals to enhance vibration signals. Under normal conditions, various filtering techniques can partially suppress electromagnetic interference signals, but it is challenging to completely eliminate noise because electromagnetic interference and vibration signals often overlap in the frequency spectrum. In addition, general filtering techniques struggle to achieve real-time filtering and are computationally intensive. Hence, in actual data monitoring and analysis, it is necessary not only to optimize monitoring efficiency through filtering techniques but also to process massive data and optimize monitoring strategies. The specific signal-to-noise ratio requirements are derived based on the actual situation, monitoring requirements and capabilities of the bridge, combined with the results of the vehicle-bridge coupling dynamic analysis. Based on the above analysis, an optimization strategy for bridge health monitoring is proposed, taking into account both signal-to-noise ratio and vehicle-bridge interactions. The objective of this strategy is to ensure structural safety throughout the entire life cycle. It involves assessing the structural conditions, scale, form, and economic considerations to select suitable monitoring instruments and software systems. This approach aims to seamlessly integrate health monitoring with routine bridge inspections and combine real-time monitoring with timely assessments. In adherence to the performance parameters requirement, particularly the signal-to-noise ratio, the dynamic characteristics of the bridge are estimated through ambient vibration and traffic load data provided by the monitoring system.