## 1 Introduction  
In structural systems, damage may be defined as a change that negatively affects the structure's original performance. In most cases, even though the presence of damage may not represent an immediate issue, it may cause the system to no longer operate under ideal conditions. However, a relevant increase in damage levels can provoke a progressive loss in the structure's functionality. Such a situation can exceed safety threshold levels, causing structural collapse and failure. Hence, the development of strategies capable of identifying structural novelties is considered crucial and is a topic of interest for several researchers, as shown in the studies of [PERSON] and [PERSON] (2021) [1], [PERSON] et al. (2021) [2], and [PERSON] et al. (2021) [3]. Structural Health Monitoring (SHM) is a field of research dedicated to developing techniques and equipment to investigate damage or alterations in structures. It is known that degradation processes directly influence structural dynamic behaviors, since it alters the structures' physical properties, such as mass and stiffness. Therefore, most damage detection and health monitoring methods were mainly developed taking into account vibration signals monitored over time (i.g., time histories of accelerations, displacements, and velocities), as seen in the classic reference work of [PERSON] (1998) [4] and the present-day literature review made by [PERSON] et al. (2021) [5]. One of the best-known traditional methods to characterize a signal coming from structural monitoring is through modal identification, in which natural frequencies, vibration modes, and damping ratios are extracted from vibration data, so much so that several approaches based on modal properties have been developed lately, as reported in the works of [PERSON] and [PERSON] (2017) [6], [PERSON] et al. (2017) [7], and [PERSON] et al. (2019) [8]. Although it is possible to detect damage from deviations in natural frequencies and mode shapes, such a methodology has some difficulties, especially when dealing with actual structures due to the influence of environmental and operational factors on the modal behavior [9; 10; 11; 12]. Temperature variations and uncertainties about loadings may lead to changes in dynamic parameters that can be mistakenly attributed to structural damage. In addition to the procedures used to mitigate the ambient effects [13; 14; 15], many contributions have been proposed to circumvent this problem, including different signal processing techniques for analyzing dynamic data based on metrics or parameters extracted from the signals in the time domain or any other transformed domain [16]. In the last few decades, the evolution of computational technologies has brought remarkable advances in data acquisition systems, making it necessary to improve SHM tools and techniques to deal with large amounts of vibration data. Thus, what was done in an incipient way with a reduced number of variables through modal analysis, probability, and statistical techniques is now automatically identified with machine learning methods, such as the popular Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Tress, Clustering, among others. The works of [PERSON] and [PERSON] (2018) [17], [PERSON] et al. (2019) [18], [PERSON] et al. (2019a) [19], [PERSON] et al. (2019) [20], [PERSON] et al. (2021) [21], [PERSON] et al. (2019) [22] are good examples associated with this kind of SHM strategy. In such approaches, relevant features are extracted from dynamic responses by signal processing techniques and are transferred as input parameters to computational intelligence algorithms. These, in turn, try to recognize similar patterns among the data and assign them to distinct structural conditions or damage states. Generally, machine learning methods play an important role in SHM strategies.