## 4 Validation with Field Test
### Data Analysis and Discussion
#### 4.3.2 Transfer Reconstruction from High-Frequency to Low-Frequency Strain  
The ResNet, which had been previously trained on high-frequency response data, was subsequently employed to estimate low-frequency strain without the need for additional training. It is imperative to emphasize that no new ResNet models were trained for this specific task. The low-frequency strain measurements at cross-sections 04-S02 and 02-S02 were input into the existing ResNet model, with the expectation that the output would reconstruct the low-frequency strain at the intermediate cross-section, 03-S02. It is important to note that temporal factors, such as traffic conditions or air temperature, were not explicitly captured during the monitoring period; hence, these variables were not directly represented in the input data. However, it is posited that the traffic conditions, including vehicle count, mass, and velocity, were inherently embedded within the input information, as suggested by Equations (8) and (9).  
Figure 19: Frequency spectrums of high-frequency strain in reconstruction results and monitoring data: (**a**) reconstruction results; (**b**) measurement results.  
Figure 20: Comparison of amplitudes of the first (**a**) and second (**b**) natural frequencies between the reconstruction results (solid line) and the direct measurement data (dashed line): (**a**) first frequency; (**b**) second frequency.  
Figure 21 presents a comparative analysis between the reconstructed and measured low-frequency strains over time. The good agreement between the two datasets substantiates two fundamental conclusions. Firstly, it reaffirms that the transfer matrices for both low- and high-frequency bridge responses are indeed identical, as evidenced by Equation (15). Secondly, the close match between the predicted and actual low-frequency strains suggests that the ResNet trained on high-frequency strain data effectively simulated the transfer matrix governing the bridge's response.  
The implication of these findings is that once a transfer matrix has been established using high-frequency responses, there is no necessity to conduct a separate training process for low-frequency strain. This realization implies the potential to significantly expedite the engineering process by reducing the need for extensive data collection and model training specific to low-frequency strains. Such an optimization could lead to more efficient structural health assessments and a streamlined approach to bridge monitoring and maintenance.  
Furthermore, we conducted an evaluation to ascertain the versatility of the trained ResNet under varying traffic conditions and air temperatures. The ResNet, which was originally trained on high-frequency strain data from the second and third days, was provided with low-frequency strain measurements taken from 04-S02 and 02-S02 on the first day as input. This test was designed to assess the model's capacity to generalize across different datasets. The comparison between the strain reconstructed by the ResNet and the actual measurements from 03-S02 on the first day is depicted in Figure 22.  
Upon reviewing Figure 22, it is clear that the reconstructed low-frequency strain closely matched the direct measurements, attesting to the model's precision. This also implies that the transfer matrix was consisted for strains across different cross-sections,  
Figure 21: Comparison between reconstructed and measured low–frequency strain at 03–S02.  
Figure 22: Comparison between reconstructed and measured low–frequency strain at 03–S02 on the first day. (Solid line: reconstruction result; dashed line: measured strain).  
even under fluctuating traffic conditions and air temperatures. Furthermore, similar to Figure 21, Figure 22 also shows that the ResNet was able to accurately capture the time-invariant characteristics of strain responses across various cross-sections under unknown and varying traffic conditions.