## 1 Introduction  
Structural degradation in civil infrastructure, driven by factors such as excessive loading, environmental stressors, and material fatigue, highlights the need for continuous monitoring to maintain safety and functionality. Limitations in traditional visual inspections, such as shortage of qualified labor and inaccessibility to critical structural components, have led to the development of vibration-based SHM techniques for assessing structural conditions. In recent years, the research in this field has increasingly focused on intelligent applications utilizing data-driven methods that do not require physical models [1]. This approach is often regarded as a statistical pattern recognition paradigm, involving the extraction of damage sensitive features from the acquired data and classification of the extracted features utilizing statistical modeling. In this framework, a significant challenge is that for most structural systems, only data from the baseline or current state are available. Considering the countless potential damage scenarios that can arise, 'unsupervised' approaches--which learn solely from the data representing the healthy condition while accounting for operational and environmental variability--are essential for classification [2; 3; 4; 5]. Given the challenges associated with measuring input excitation during data acquisition, output-only approaches relying only on vibration response measurements, emerge as more suitable for civil engineering applications [6; 7; 8]. In this regard, a number of machine learning and statistical inference techniques have been implemented to health monitoring applications. Artificial neural networks (ANNs) based on machine learning provide such a computational approach that can be applied to systems where large volumes of data are available and complex patterns need to be identified for analysis and prediction of these data. Among these, autoencoders (AEs) are a specialized class of ANNs, mainly designed for unsupervised learning, with the purpose of dimensionality reduction and feature learning [9]. An AE consists of two main components: an encoder and a decoder. The encoder maps high-dimensional input data into a lower-dimensional latent space, capturing essential features while discarding redundant information. The decoder then reconstructs the original input from this compressed representation, aiming to minimize the reconstruction error. This process enables AEs to learn intrinsic structures within the data, making them particularly valuable for anomaly detection, denoising, and representation learning. In anomaly detection tasks, AEs are trained to minimize reconstruction error only on normal data instances, thus involving high reconstruction error on anomalous data. Then, the reconstruction error is exploited to classify the input data. AEs demonstrate remarkable flexibility, enabling customization for various data types and tasks through adjustments to their architecture or objective functions. Various AE architectures have been proposed to address specific challenges and enhance performance across different domains, categorized by network structure as follows: standard, regularization-based, generative, convolutional, recurrent, semi-supervised, graph-based, robust, and masked AEs [10]. For example, convolutional AEs are commonly used in image processing tasks, while recurrent AEs excel in handling sequential data. Generative AEs focus on learning the underlying probability distribution of data rather than solely performing dimensionality reduction. Variational AEs, a subtype of generative AEs, are designed to generate new data samples and improve model generalization. With each architecture having its own advantages and limitations, specific requirements of the application domain have to be considered in selecting an appropriate architecture. The capacity of the AEs to model complex patterns and detect anomalies that are not easily identifiable makes them well-suited for vibration-based SHM applications.