## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Scope** & **Study Type** & **Methodology** \\ \hline [PERSON] et al. [68] & FE & N & \begin{tabular}{c} A numerical method using ht to detect and locate damage in railway bridges by calculating the IAS from the contact-point response. \\ \end{tabular} \\ \hline [PERSON] et al. [69] & FE & N & \begin{tabular}{c} A numerical method using CWT to detect and locate scour in railway bridges by calculating the difference in average CWT coefficients between healthy and scoured batches of railway crossings. \\ \end{tabular} \\ \hline [PERSON] et al. [70] & FE & N & \begin{tabular}{c} A method utilizing RMS damage diagnosis technique examines \\ spectral components of accelerations from axle box and bogies to detect \\ and locate damage in railway bridges. \\ \end{tabular} \\ \hline [PERSON] et al. [71] & FE & N & \begin{tabular}{c} Direct integration techniques to determine bridge’s apparent profile \\ from vehicle accelerations using BDPD as indicator of damage. \\ \end{tabular} \\ \hline [PERSON] et al. [72] & FE & N & \begin{tabular}{c} Drive-by method using a model of a vehicle equipped with a \\ high-speed laser scanning system to measure the deflection of the \\ bridge and calculate the FR using the Unit Load Method. \\ \end{tabular} \\ \hline [PERSON] et al. [22] & FE & N & \begin{tabular}{c} Two time-frequency algorithms, CWT and HHT, are used to analyze \\ the vibration response of the train bogie to identify the location and \\ severity of damage in a short-span Warren truss bridge. \\ \end{tabular} \\ \hline [PERSON] [73] & FE & N & \begin{tabular}{c} Drive-by inspection approach using vehicle parameter optimization \\ and vehicle displacement as a damage indicator. \\ \end{tabular} \\ \hline [PERSON] et al. [74] & FE & E & Full-scale field study of drive-by bridge monitoring using acceleration \\  & & & measurements on an instrumented train. \\ \hline [PERSON] et al. [75] & FE & N & \begin{tabular}{c} Novel method to determine AP of the track and detect railway bridge \\ condition using sensors on in-service trains. \\ \end{tabular} \\ \hline [PERSON] et al. [76] & FE & N & \begin{tabular}{c} Feasibility of applying MFCC to the indirect damage detection in \\ high-speed railway bridges by onboard sensors. \\ \end{tabular} \\ \hline [PERSON] [77] & ML & N & \begin{tabular}{c} Drive-by health monitoring of railway bridges using machine learning \\ considering environmental and operational variations. \\ \end{tabular} \\ \hline [PERSON] et al. [57] & ML & N & \
\begin{tabular}{c} Unsupervised data-driven SHM based on a continuous online \\ procedure for damage detection identification based on train-induced \\ dynamic bridge responses. AR models were used to extract features. \\ \end{tabular} \\ \hline [PERSON] et al. [78] & ML & N &
\begin{tabular}{c} Application of the Mahalanobis Distance (MD) for \\ damage identification. \\ \end{tabular} \\ \hline \hline \end{tabular} Abbreviations: Feature extraction (FE), machine learning methodologies (ML), numerical (N), and experimental (E).  
\end{table} 
Table 3: _Cont._learning methods can incorporate such external factors into damage detection processes. Therefore, the article highlights the development and application of feature extraction and machine learning techniques for evaluating the condition of railway bridges through drive-by monitoring. Despite the aforementioned advancements, most of the developed work relies on simplified numerical models in which the complexity of the effects of dynamic vehicle-track-structure interaction is not properly included. Thus, the potential of the methods developed so far needs to be further evaluated, either in field experiments or in numerical models that include VTBI models in which, although the need for prior knowledge about the dynamic characteristics of the vehicle and the bridge represents an added difficulty in their practical implementation, it is possible to evaluate the robustness of these methodologies against environmental and operational effects.