## 7 Conclusions  
This study introduced a purely response-based methodology for structural damage detection and localization by leveraging transmissibility functions combined with unsupervised machine learning techniques. Unlike traditional model-based methods, the proposed approach requires only response measurements from the baseline (undamaged) state, elim  
Figure 13: Damage localization for the experimental arch using the flexibility-based approach. inating the need for excitation input or analytical modeling. The damage detection phase is implemented through a one-class classification model trained on baseline transmissibility amplitudes, while localization is achieved by evaluating frequency-wise sensor-pair differences and identifying the most affected pair. The methodology was validated using both numerical simulations and experimental data. Simulations on a fixed-fixed beam subjected to progressive damage demonstrated high sensitivity to local stiffness reductions, even under realistic noise levels. Experimental validation was performed on a scaled masonry arch bridge model, where structural and material complexities more closely reflect real-world applications. The proposed transmissibility-based approach successfully detected early-stage damage and provided meaningful localization results based on frequency-domain data fusion. In comparison with the modal flexibility method--which relies on mode shapes derived from system identification--the transmissibility-based framework offered several advantages. It captured dynamic changes across a broad frequency band, thereby improving sensitivity to damage near localized regions. While the flexibility-based method showed sharper localization peaks in some cases due to normalization, it required manual mode selection and extensive preprocessing. In contrast, the transmissibility approach is less sensitive to such subjective steps and better suited for automation. The main contribution of this study lies in demonstrating that transmissibility-based learning, when combined with unsupervised classification, can provide an effective and scalable framework for structural health monitoring. Its application to a complex laboratory-scale structure marks a significant step beyond previous studies, which often focus on idealized systems or rely on supervised methods. The approach also supports the use of baseline-only data, which is particularly relevant for existing structures where damage-state data are not available. Practical considerations, such as the need for impact testing across multiple locations, may pose challenges in terms of accessibility and testing duration, especially in large or operationally sensitive structures. Nevertheless, advancements in mobile sensing systems and automated data acquisition offer promising opportunities to streamline this process. Furthermore, the use of alternative excitation sources--such as ambient vibrations or operational loads--could eliminate the need for active input, thereby increasing the adaptability of the proposed approach to field conditions. Although the current methodology relies on repeated baseline measurements, future research may focus on real-time data acquisition and adaptive algorithms capable of continuously updating the baseline model, enhancing the feasibility of real-time structural health monitoring. Moreover, while the sensor configuration in this study was based on the expected damage locations and the structural characteristics of the test model, future work may focus on a comprehensive sensitivity analysis of sensor placement to derive generalized guidelines applicable to various structural systems. Conceptualization, O.G. and B.G. ; Methodology, B.G. ; Software, B.G. ; Validation, O.G. and B.G. ; Formal analysis, O.G. and B.G. ; Investigation, O.G. and B.G. ; Resources, O.G. and B.G. ; Data curation, O.G. ; Writing--original draft, O.G. and B.G. ; Writing--review & editing, O.G. and B.G. ; Visualization, B.G. ; Supervision, O.G. ; Project administration, O.G. ; Funding acquisition, O.G. and B.G.