## 1 Introduction  
In recent times, underground urban metro tunnel infrastructures have become of great importance in the public transportation system due to road traffic overcrowding. In a modern metropolis, with an increasing trend to use the metro, the subway system has become a more convenient and leading mode to alleviate the growing road traffic pressure. Tunnel structural health during operational conditions is closely linked with public safety. Due to operational incidents and the complexity of underground environment conditions, the circumstances of damage (changes in geometric and material properties) affect the service life and safety of metro tunnel structures. Considering these issues, well-timed and accurate monitoring of the life-threatening responses of the structure is essential for structural maintenance and the prevention of mishaps [1, 2, 3, 4]. Previous research and recurrent occurrences of damage consequences in metro tunnel structures have piquedresearchers' interest in paying more attention to damage identification, and the monitoring of underground tunnel structures by taking into account the requirements of a large number of existing structures before any mishaps occur. Structural health monitoring technology offers the corresponding maintenance through a process of instantaneous monitoring, data analysis, damage identification and state evaluation of structures to provide a valid guarantee for the regular operation of civil structures and the safety of people's lives and property. Among them, as an essential part of structural health monitoring, structural damage identification has attracted more and more attention [5; 6; 7; 8]. With advancements in science and technology, computational approaches based on machine learning are subversively renovating the activities of human life, such as the genetic algorithms used in [9], artificial neural networks (ANN)-based techniques proposed in [10], and swarm intelligence techniques [11]. These approaches, including SHM, have been investigated by many researchers in numerous applications. One of the most significant and promising uses of deep learning is in the convolutional neural network (CNN) used in SHM methods, which has been widely studied in real-world applications. CNN is considered one of the most innovative and effective deep learning tools, and it has been used in a variety of applications, including face recognition, natural language refining, and fault detection [12; 13]. The dissimilarity between CNN and conventional ANN is the capability of the CNN layers to consolidate neurons in three dimensions (3D): height, width, and length. As a result, many CNN applications have performed admirably in structural health monitoring areas based on accelerometer vibration signals. The fast and precise method was suggested for primary fault detection systems and motor condition monitoring, applying 1D-CNN [14]. The authors [15] have used the same method for tackling fault detection using CNN on raw vibration signals as previously used in [16]. In [17], real-time damage identification and localization methods based on raw acceleration signals through 1D-CNN were proposed. As a result, ref. [18], in order to improve diagnosing accuracy and reliability, sensor fusion was incorporated into the CNN structure. They gathered the raw training process data of CNN from multiple accumulator sensors and examined the information both temporally and spatially. Their technique, as concluded, obtained superior assessment functionality compared with other traditional methods that extract features manually. In [19], the authors presented a technique created for wireless sensor networks training a single, assigned 1-D CNN for an individual wireless sensor in a sensor network on the locally available data as a solution to overcome data communication and synchronization issues. The proposed method eliminated the need for any filtering or preprocessing since it worked directly on the raw signals acquired from the ambient vibration environment. Similarly, ref.