## 3 Application of Drive-by Methodologies on the Bridge Subsystem
### Bridge Damage Identification  
#### 3.2.1 Overview  
The identification of bridge damage is an indispensable task that demands a comprehensive approach to guarantee the safety and operational efficiency of bridges. The criticality of this procedure lies in its ability to determine necessary maintenance or repair actions, which can prevent catastrophic failures from occurring [52]. Furthermore, early detection of any structural abnormalities enables stakeholders to implement measures that will help mitigate potential economic losses and physical harm. The process of bridge damage detection involves the meticulous examination and analysis of various indicators that could reveal potential defects or structural impairments. This comprehensive inspection entails not only visual inspections, but also non-destructive testing techniques such as ultrasound technology, infrared thermography and acoustic emission monitoring to scrutinize different components including beams, columns and decks for shreds of evidence of deterioration [53]. The gathered data from these examinations are then evaluated using advanced analytical tools which provide insights into possible weaknesses in the structure's integrity, allowing engineers to determine appropriate measures necessary for addressing any detected issues. Over the years, technological advancements have played a significant role in enhancing the accuracy and reliability of bridge health monitoring systems [54; 55; 56; 57], making it easier than ever before for experts to detect even minor damages with precision [58]. By facilitating prompt interventions and effective maintenance activities through sophisticated strategies that include a comprehensive sensor network and smart algorithms, optimal functionality throughout the lifespan of any given bridge structure can be ensured. Several researchers have developed many bridge damage detection strategies for SHM in recent decades which include vibration-based approaches, model update strategies, and neural network applications [59; 60; 61; 62; 63; 64; 65; 66]. In addition to the secondary studies discussed in Section 1 (Table 1) of this paper, which are focused on drive-by applications, [PERSON] and [PERSON] [66] carried out a comprehensive review of the literature on damage identification in bridge structures. The authors critically evaluated different methods that have been proposed to detect damages in different bridges and concluded that there is still a long way to go in the field of bridge damage detection, and this requires the construction of a comprehensive information model for bridges. Aligned with the main topic of this paper, the latest research studies have focused on monitoring bridge health using sensors mounted on moving vehicles, a field of study often known as indirect bridge health monitoring. The first work based on this type of monitoring was published by [PERSON] et al. [32]. In their study, the equation of motion combining the dynamic properties of both the vehicle and the bridge was analyzed before representing the passing vehicle's acceleration as a function of the bridge's dynamic qualities. By applying Fourier transform on the acceleration of the vehicle, the bridge's frequencies were detected. Afterwards, indirect health monitoring became an active research field, and the developed approaches that led to breakthrough improvements in this area are discussed in the following subsections. Figure 11 depicts the topics covered, which focus on the main approaches regarding bridge damage identification. Firstly, Section 3.2.2 presents several signal processing techniques that are used to extract features from response measurements. These features are defined as damage indexes that allow assessing the structural condition of bridges, and their extraction and selection is a crucial step in signal-based damage detection. Frequency-domain methods extract features from the frequency-domain signals such as natural frequencies, mode shapes, damping ratios, modal strain, and modal curvature.