###### Abstract  
Recent advances in sensor technology, data acquisition, and signal processing have enabled the development of data-driven structural health monitoring (SHM) strategies, offering a powerful alternative or complement to traditional model-based approaches. These approaches rely on damage-sensitive features (DSFs) extracted from vibration measurements. This study introduces an innovative, unsupervised learning framework leveraging transmissibility functions (TFs) as DSFs due to their local sensitivity to changes in dynamic behavior and their ability to operate without requiring input excitation measurements--an advantage in civil engineering applications where such data are often difficult to obtain. The novelty lies in the use of sequential sensor pairings based on structural connectivity to construct TFs that maximize damage sensitivity, combined with one-class classification algorithms for automatic damage detection and a damage index for spatial localization within sensor resolution. The method is evaluated through numerical simulations with noise-contaminated data and experimental tests on a masonry arch bridge model subjected to progressive damage. The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. Experimental findings further confirm the proposed approach's localization capability, especially as damage severity increases, aligning well with observed damage progression. These results demonstrate the method's practical potential for real-world SHM applications. t  
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dynamic response. Meanwhile, alternative data modalities such as vision-based methods have also gained attention, especially with the rise of deep learning [3; 4; 5], although these lie outside the scope of the present study. Model-based approaches, on the other hand, rely on the assumption that structural responses can be accurately simulated using numerical models, such as finite element models. However, developing these accurate numerical models, especially for large-scale structures, is a resource-intensive process that demands significant computational power and human expertise. The complexity and extensive data requirements further complicate this task. Even with all the investment in developing and validating a numerical model, the assumptions and simplifications inherent in modeling can introduce inaccuracies, potentially undermining the damage detection process. To address these limitations, non-model-based approaches utilize damage indicators directly derived from measured data, avoiding the need for detailed physical models. These methods fit data analysis models to the measured input and/or output response data and then extract features that are sensitive to variations caused by damage. They are scalable, adaptable to changing conditions, and efficiently process large datasets. Within these non-model-based approaches, input-output methods offer greater accuracy but face challenges in measuring excitation forces, and the requirement for controlled input imposes significant limitations in real-world applications. In contrast, output-only methods, which rely solely on response data, bypass these constraints, making them more suitable for real-world monitoring of civil engineering systems. Approaches such as operational modal analysis, principal component analysis, independent component analysis, correlation analysis, time series analysis, and wavelet analysis have been proposed within this output-only category. In output-only techniques for assessing structural integrity, commonly selected features include modal parameters [6; 7; 8; 9; 10; 11; 12; 13; 14] and parameters derived from statistical time series models, such as autoregressive (AR) model coefficients or residual errors when comparing monitored data with model predictions [15; 16; 17; 18; 19; 20; 21; 22; 23; 24].