## 5 Conclusions  
This paper presents the theoretical framework of the PRE (numerical simulation-based vehicle and bridge parameter and road roughness estimation) approach, which uses the vehicle's vibration and position data to simultaneously estimate the characteristics of the vehicle and the bridge systems and the road unevenness. Additionally, it quantitatively confirms the PRE method's suitability for estimating vehicle and bridge parameters. In the numerical simulation, a half-car model and an Euler-Bernoulli beam are used as the vehicle and bridge models. Several stiffness patterns are prepared for the bridge model, and it is suggested that stiffness reduction of bridges can be detected in all scenarios. It is also demonstrated that accurately determining the extent of stiffness reduction is challenging. The PRE's uniqueness compared to existing methods [26; 27; 28; 29] is the applicability of bridge identification. The purpose of previous schemes [26; 27; 28] is to estimate road profiles, while this study focuses on a new drive-by bridge monitoring technology, which can estimate the spatial distribution of bridge flexural rigidity and other characteristics as current performance. It is of value both academically and practically. The noise in the observed data is not considered in this investigation. The PRE approach is probably not robust because it uses the process of numerical integration. It is necessary to enhance the model to account for noise. The vehicle adopted in this study is only 10 tons, while the traffic-induced bridge vibration is not relatively dominant in the input profile. As a result, while modifying the vehicle weight and bridge span, it is also essential to consider a suitable vehicle-bridge combination for the PRE method. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Scenario** \\ **1** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **2** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **3** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **4** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **5** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **6** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **7** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **8** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **9** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **10** \\ \end{tabular}  & \ \begin{tabular}{c} **Scenario** \\ **11** \\ \end{tabular}  & \begin{tabular}{c} **Scenario** \\ **12** \\ \end{tabular}  \\ \hline \(m_{s1}\) & 0.95 & 0.93 & 0.98 & 0.95 & 0.92 & 0.94 & 0.99 & 0.94 & 0.90 & 0.99 & 0.98 & 0.97 \\ \(m_{s2}\) & 0.86 & 0.83 & 0.95 & 0.87 & 0.78 & 0.86  
The obtained accuracy for bridge damage identification is limited. The MCMC method is inefficient and overestimates the reduced stiffness of the bridge, which can lead to dangerous accidents. The reason for low estimation accuracy lies in the low efficiency of the optimization method. Therefore, the efficiency of the optimization process must be improved. The model accuracy, such as the 3D vehicle with engine vibration and 3D bridge interaction model, should also be examined. In this study area, a field experiment is still a technical issue. When doing PRE-based bridge monitoring, the sensor installation is only required on a vehicle. However, while testing the PRE method, vibration sensors should also be installed on bridges to examine the PRE accuracy. The accuracy can be evaluated by comparing the bridge responses estimated by the PRE method with the measured data. In addition, these practical challenges have not yet been overcome. According to the previous study performing the field test [32] of the PRE method, many of the causes come from the low robustness due to observation noises.