## 1 Introduction  
Structural health monitoring (SHM) aims to evaluate large civil and mechanical infrastructures' structural integrity, reliability, and resilience [1; 2]. The proper implementation of SHM ensures the functioning of these engineering structures by finding structural defects or irregularities before they cause catastrophic structural breakdown [3]. It involves measuring and evaluating the state properties of the underlying structure and relating these to define the performance parameter. Structural Health Monitoring (SHM) is an emerging field with immense industrial significance, incorporating innovative technology and evolving into a multidisciplinary area of research and development. Despite its growing importance, a recent study has yet to prove enough in many aspects. One primary concern is the reliability quantification of SHM systems, as current assessments primarily rely on visual inspections, which can be subjective and infrequent. On the other hand, existing data processing methods faced challenges from ambient noise, the volume of measurement data, the computation complexity, etc. [4]. These limitations hinder the widespread application of SHM across various industries [5]. The last decade has seen significant advances in machine learning, particularly in deep learning. Deep learning approaches are very effective in various tasks, yielding cutting-edge outcomes across multiple engineering disciplines [6]. Recent advancement in artificial intelligence has also provided an opportunity to address these SHM challenges and elevate the capability and applicability of the SHM sector [7; 8]. Recently, [PERSON] and [PERSON] [9] proposed a 1D convolutional neural network (CNN) to detect damage in aluminium metallic structures. The proposed method can process raw signals and identify the different types of structural damage. Similarly, [PERSON] et al. [10] presented a structural damage identification method based on integrating Hilbert-Huang transformation and CNN. Similarly, the author used a DNN with an autoencoder to compress the input data and a second neural network architecture to translate the compressed input into the damage characteristic. For guided wave (GW)-based fatigue crack diagnosis (FCD) on aluminium-alloy attachment lugs, [PERSON] et al. [11] developed a CNN model. The trained multiclass CNN model predicted the crack length with an accuracy of 86.84%. Similarly, [PERSON] et al. [12] employed a deep CNN network on a bridge. The author determined the deflection signal from the fibre-optic gyroscope sensor. The author exploited a data augmentation scheme to enhance the size of the datasets. The trained model generates an efficiency of 97% and outperforms many traditional machine learning algorithms. Many authors integrate multiple deep-learning networks. For example, ref. [13] combined 1D CNN and LSTM for acceleration data obtained from a three-story frame. The combined architecture decreases the computational cost, but the network is noise-sensitive. Most of the damage detection strategies based on deep learning methods are mainly supervised and require data preprocessing and filtering methods to extract their key features. Moreover, most of the traditional deep learning methods require large amounts of training datasets, which is an expensive and time-consuming effort, requires expert knowledge, and hence generates a bottleneck in the automation system. Moreover, in SHM, there are other challenges associated with data collection and analysis such as high-dimensionality, data insufficiency, and class-imbalance. In a traditional ML approach, gathering a substantial and diverse dataset is crucial for accurately estimating the parameters of statistical models that describe all classes involved in efficient damage inspection [14]. However, in real situations, the collection of training data for all cases is often difficult, expensive, time-consuming, and resource intensive.