###### Abstract
More recent advances focus on integration with model reduction, finite element updating, and specific application domains. [PERSON] et al. [36; 37] enhanced damage detection accuracy by coupling TFs with system equivalent reduction expansion processes and proposing a weighted transmissibility assurance criterion. [PERSON] et al. [38] applied TFs for debonding detection and localization in concrete pavements. [PERSON] et al. [39] offered a comprehensive review of TF-based diagnostics for beam-like structures. [PERSON] et al. [40] combined time-domain TFs with empirical mode decomposition for finite element model updating and damage localization. [PERSON] et al. [41] introduced a wavelet-based TF approach for detecting abrupt damage in buildings. [PERSON] and [PERSON] [42] developed a damage indicator based on response difference transmissibility. [PERSON] et al. [43] and [PERSON] and [PERSON] [44] used TFs with finite element model updating to improve detection accuracy. Most recently, [PERSON] and [PERSON] [45] explored structural invariance concepts that enable TFs to locate damage in complex structures. In the broader context of structural health monitoring, data-driven approaches play a pivotal role in advancing automated damage detection systems. By leveraging the selected damage-detecting features, these systems enable the shift from \"on-schedule\" to \"on-demand\" maintenance, allowing for more efficient and responsive decision-making. According to [PERSON]'s [46] classification of the damage identification problem, the first step in this process is detecting the presence of damage. At this initial level, the task typically involves one-class classification (OCC) algorithms, which are well-suited for identifying deviations from normal conditions based on these key features, making them essential for the early stages of SHM automation. The OCC problem is a special type of classification issue where only data from one-class, the target class, is available. In the context of SHM, \"damage detection\" aligns with OCC, as baseline (positive) data from the undamaged structural state is readily available. However, potential damage scenarios, for which no training data exists, are numerous. With limited or no data from the damaged (negative) state, the classification boundary can only be defined using available positive data. The goal in OCC is to establish a boundary around the positive class that maximizes the inclusion of positive data while minimizing the acceptance of outliers or \"negative\" data [47]. Based on the model of the classifier, [PERSON] and [PERSON] [48] categorize OCC techniques into three types: density-based, boundary-based, and reconstruction-based. Density-based methods, such as the Gaussian, mixture of Gaussians, local outlier factor (LOF), and Parzen density methods, estimate the probability density function of the underlying distribution of the training target data points and determine whether the test data comes from the same distribution. The challenges associated with this approach are the selection of appropriate models and the well-sampled high number of training data requirements. In boundary-based methods, such as the one-class support vector machine model (OCSVM), a closed border or smooth boundary around the target data is built by introducing kernel tricks, with the task of optimizing the enclosed volume. Any test data outside the border of the boundary is considered an outlier. Boundary-based methods require fewer data samples compared to density-based methods for similar performance; however, in these models, optimizing the boundary poses a modeling challenge. In reconstruction-based methods, a model like an autoencoder is trained using the target class data, and then the test data showing a sufficiently large reconstruction error or not complying with the training data are pointed out as outliers. The k-means clustering-based classifier and auto-encoder or multi-layer perceptron (MLP) methods are the most widely used reconstruction-based models.