## 1 Introduction
However, raw vibration data are high-dimensional and complex due to noise contamination, high-frequency content, and abrupt waveform variations [41]. These characteristics complicate feature extraction in unsupervised learning due to the lack of labeled guidance. * The discrete nature of raw vibration data or their low-level features can degrade training efficiency and fitting accuracy [42]. To address this, deep AE architectures are often employed; however, they introduce a large number of parameters requiring optimization, which increases computational cost and the risk of overfitting. * Many unsupervised learning-based methods rely on manually defined damage thresholds based on reconstruction loss. This subjectivity can lead to inconsistent damage detection outcomes. * Most studies focus on detecting damage and assessing its severity in a comparative manner but do not explicitly address spatial localization, limiting their practical application in SHM. * While many damage detection methods are validated solely through numerical examples, experimental validation on physical structures remains limited. To address these challenges, this study proposes an unsupervised damage detection framework that integrates transmissibility-based features with an AE and an OC-SVM for automated damage identification and localization. In the first step, the auto-encoder is trained on the measured local TFs from the baseline (healthy) state. The learned representation is then utilized to reconstruct the measured TFs for an unknown health state. Based on the deviations between the reconstructed output from that of the measured, specifically the reconstruction error, the OC-SVM classifier is utilized for classifying damaged states as anomalies. This detection stage is augmented by a localization approach that fuses data from all sensors in the final stage. The major contributions of this study are as follows:  
* The proposed methodology utilizes local TFs derived directly from response measurements in the frequency domain, reducing the computational burden associated with time-domain data. Additionally, it eliminates the need for preprocessing steps such as natural frequency and mode shape extraction, along with the errors introduced by frequency-domain feature selection. * The proposed framework integrates an AE with an OC-SVM, requiring only baseline-state training data for damage detection. This eliminates the need for labeled datasets and supervised learning, making the approach suitable for monitoring civil engineering structures where damage-state data are either unavailable or difficult to obtain. * A novel damage index is proposed, enabling the spatial localization of damage within the resolution of the sensor network. * The methodology is validated using experimental data from a scaled model of an arch bridge, demonstrating its applicability in real-world scenarios beyond numerical simulations. The remainder of this paper is organized as follows. First, the two-step feature extraction and damage detection localization methodology is introduced. The employed AE-based framework for feature extraction and the OC-SVM classifier to detect damage with the extracted features is discussed next. The numerical simulations demonstrating the performance of the AE by fine tuning the hyperparameters including the activation function, the number of hidden layer neurons and the number of epochs is followed by the presentation of the experimental work on a 1/4-scaled arch bridge to evaluate the proposed damage detection method. This paper concludes with a discussion of the results and the final remarks on the conclusions drawn.