## 1 Introduction
[20], the authors utilized an improved CNN-based method that needs only two measurement arrangements, irrespective of structure size, to overcome the CNN training limitation, which primarily requires a large number of sample data, especially for large-scale applications. It should be noted that the method investigated in the benchmark study with nine damage assumptions was capable of successfully quantifying the actual damage quantity. The authors observed that a more accurate method was needed to identify multiple damages because there are subtle differences between the measures of the various damages [21]. The major achievement of deep learning methods is that they can learn the useful features from the raw data automatically [22; 23]. Similarly, the need for signal processing methods and handcrafted features is omitted using deep learning methods. The last three years have witnessed several researchers' work on deep learning due to its superiority in feature learning capability [15; 24; 25]. Convolutional neural networks (CNN) and deep auto-encoder (DAE) are two widely used deep learning models [26]. DAE is an unsupervised learning model; this differs from CNN, which is a more efficient and simple training method. In many structural health monitoring applications, popular deep learning models such as CNN and deep auto-encoder are successfully used to find damage based on raw vibration signals that have not been changed in any way. Auto-encoders are simple learning models that transform inputs into outputs with the least possible amount of distortion [27]. [PERSON] et al. [28] proposed a sparse auto-encoder-driven method for indoor air quality monitoring in the Metro subway. [PERSON] et al. [29] investigated an auto-encoder-based damage identification technique in multi-story buildings. Pattern recognition problems can be best solved by taking into account two major features: reducing the number of dimensions and learning the relationship between vibration characteristics and structural damage. A similar auto-encoder-based framework was proposed for multi-scale data [30]. Using data-based deep learning approaches, DAE and sparse-DAE were proposed to identify structural damage and monitor structural health [31; 32]. Conclusively, the SHM state of the art approved that there is no information for the application of vibration-based NDT techniques in underground structures where DAE is used for damage identification and structural monitoring, marking the uniqueness of the recommended approach. The literature demonstrates clearly that the deep auto encoder has not been used for damage identification in underground tunnel structures. Therefore, this study proposed a deep auto encoder-based technique for damage identification in an underground metro tunnel structure. The proposed approach incorporated a deep auto encoder to abstract the hidden features of the input vector (raw vibration signals) acquired from an underground tunnel structure without any preprocessing. The autoencoder (DAE) and root mean square (RMS) was used to identify and locate to the damage of the underground metro tunnel structure. In this study, a precise finite element model of a metro tunnel structure was built in the ABAQUS software, and numerical simulation was conducted under a moving load with different severity levels of damage. This numerical investigation was based on experimental studies on the damage identification of underground tunnel structures using wavelet-based residual force vector [33] and damage detection of the underground tunnel through a frequency response function [34]. Numerous damaged and undamaged structural conditions were assessed using dynamic tunnel structure analysis to assess the efficiency and robustness of the anticipated damage identification approach based on input signal data. The results of the damage detection were evaluated under different measuring locations, and white noise was added to simulate the polluted measurements in real-world scenarios.