## 3 Application of Drive-by Methodologies on the Bridge Subsystem
### Bridge Damage Identification
#### 3.2.3 Machine Learning Methodologies  
The main challenge in applying drive-by methodologies for damage identification is that the signals collected on drive-by vehicles also include signatures from the vehicle and the rail profile; in addition, they are easily influenced by environmental and operational effects such as vehicle speed, traffic, temperature, or surface roughness [77]. Therefore, for drive-by health monitoring to be feasible, the variabilities in dynamic measurements caused by environmental and operational variations (EOVs) need to be considered and the algorithms implemented should be able to remove or reduce their influence. Furthermore, most current drive-by damage identification systems rely on prior knowledge of the vehicle or bridge dynamic characteristics, which has led to the limited application of the concept in practice so far. Since the works mentioned in Section 3.2.2 are still in the preliminary stage of research, these conditions were not considered, therefore the feature extraction algorithms were not tested in the most realistic environment. On the other hand, feature extraction as an isolated approach does not allow automatic damage identification (without user intervention). In line with this, to allow a robust, efficient and automatic drive-by damage identification on bridges, the path seems to be the implementation of a complete methodology with several steps that group different techniques. Due to the scarcity of works that follow approaches for drive-by condition assessment of railway bridges, this research was also extended to damage identification based on dynamic responses measured on the bridge. However, because vehicles and bridges create a coupled system, their application in a drive-by approach is very promising. [PERSON] et al. [57; 78] and [PERSON] et al. [79] showed that damage identification methodologies must resort to four main operations after data acquisition: (i) feature extraction, (ii) feature normalization, (iii) data fusion and (iv) feature classification. Feature extraction is often required as raw data are not informative enough about the occurrence of damage. As mentioned in Section 3.2.2, Hilbert Transform and Continuous Wavelet Transform are examples of methods effectively implemented as extractors of damage-sensitive features in drive-by applications. In applications based on acceleration measurements, time series alone provide little information regarding the structural behavior whose appropriate characterization requires the extraction of damage-sensitive features. In this context, autoregressive (AR) models and autoregressive exogenous (ARX) models have been widely reported [57; 78; 80]. [PERSON] et al. [78] implemented an unsupervised data-driven SHM based on a continuous online procedure for damage identification using train-induced dynamic bridge responses. For extract features, the authors applied AR models. Figure 14 shows 30 AR features calculated with the measurements from an accelerometer located at the second mid-span of the concrete slab (Ac1) for baseline and damaged conditions. Baseline scenarios include the variability of temperature actions, train type, speed, and loads. Damaged scenarios were defined based on simulated stiffness reductions (5%, 10% and 20%) in the diaphragm, concrete slab, and arches, as well as friction increases in the movements of the bearing devices. Comparing undamaged (Figure 14a) and damaged features (Figure 14b), the AR parameters present a higher variability in the presence of a range of environmental and operational variations. This behavior reveals the difficulty in distinguishing damaged and undamaged scenarios as variations in EOVs result in similar or greater changes in the parameters. Feature normalization is key to prevent false alarm detections since, as mentioned above, environmental variations (e.g., temperature) or operational actions (e.g., trains crossing at different speeds) may inflict greater changes than those occurring due to damage.