###### Abstract
While these features are conceptually sound, each presents limitations or challenges requiring careful consideration. Modal parameters, for example, are global properties of the system; however, structural damage does not always impact global dynamic characteristics, especially in lower modes of vibration, potentially making modal properties insensitive to certain types of damage. Furthermore, environmental factors such as temperature, moisture, and measurement noise may alter modal characteristics to a degree comparable to damage effects, thereby masking damage indicators. While modal frequencies lack the capability to localize damage, mode shapes or mode shape curvatures are introduced to fulfill this role, as they can provide spatial information about damage locations. However, these features require measurements from a large number of sensor arrays to ensure completeness and highly accurate estimations for reliable results. In fact, all the aforementioned limitations of modal parameters as damage features require, first and foremost, the extraction of these parameters from sensor data using modal identification algorithms, increasing the computational cost. Similarly, features derived from statistical time series models, such as those AR models, are sensitive to noise, which can compromise detection accuracy in low-damage scenarios. Selecting the correct model and model order introduces additional computational complexity, which can hinder the use of these features in practical applications. In contrast, transmissibility functions (TFs) paired with anomaly detection algorithms effectively address many of these limitations. Fundamentally, a transmissibility function represents the frequency-dependent ratio of the output response (e.g., acceleration) at one point on a structure to the output response at another point, when the structure is subjected to excitation. Unlike frequency response functions (FRFs), which require knowledge of the input force, TFs are derived solely from response measurements. This inherent characteristic, combined with their ability to capture damage-sensitive information across a broad frequency range, enhances robustness and detection reliability by reducing reliance on global system properties. The potential of TFs for damage detection has been explored in several foundational studies. Early analytical and foundational studies laid the groundwork for transmissibility function (TF) concepts. [PERSON] and [PERSON] [25] provided the mathematical basis of TFs and analyzed their relationship with mechanical properties of mass-spring systems. [PERSON] et al. [26] introduced transmissibility coherence and proposed a damage index based on this feature to detect and estimate damage severity. [PERSON] et al. [27] extended the concept by proposing power spectral density transmissibility within a model-update framework. [PERSON] et al. [28] investigated damage sensitivities of TFs on lumped mass models and offered a procedure suitable for large-scale structures with limited sensors. [PERSON] et al. [29; 30] contributed with clustering and dimensionality reduction techniques combined with TFs to reduce computational costs, demonstrating growing recognition of TFs as valuable damage-sensitive features. In parallel, data-driven and machine learning-based approaches utilizing TFs have emerged. [PERSON] et al. [31] reviewed TF fundamentals within system identification, focusing on operational modal analysis and model updating. [PERSON] et al. [32] applied TFs as inputs to artificial neural networks for damage detection in simulated bridge data. [PERSON] et al. [33] proposed integrating TF-based datasets with deep learning models using one-dimensional convolutional neural networks. [PERSON] and [PERSON] [34] provided analytical insights into TF mechanisms for damage localization, emphasizing their flexibility. [PERSON] et al. [35] developed damage detection methods based on TFs employing different distance metrics.