# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Urban Subway Tunnel
#### 3.2.1. Lining Cracks and Water Leakage  
Cracks in the tunnel lining will seriously affect the tunnel's stability and durability, reduce the tunnel's service life, and even more seriously, endanger the safety of trains and passengers. Therefore, it is crucial to inspect the tunnel structure in a timely manner to identify and detect the cracked lining structure. As far as subway tunnels are concerned, the shield method is often used for construction. In the process of shield machine propulsion, lining segments are assembled, and grouting is carried out outside the lining ring, reducing the impact on road traffic and nearby residents. It is precise because the tunnel lining is formed by segments, the splicing strength is low, and the cracking along the joint is easy to occur. With the aggravation of the cracking degree, the water seepage area will continue to expand, and the two often show a physical correlation. At present, the detection methods of tunnel lining cracks and water leakage mainly adopt manual inspection methods, which use the naked eyes of inspectors to observe the tunnel surface and realize the identification work. However, due to the dark engineering environment, artificial level, and subjective judgment factors, this method will inevitably have errors, lower accuracy, and efficiency. Therefore, the rapid acquisition of tunnel lining images and the automatic identification of cracks and seepage areas based on computer vision are the new development trends in the design of detection methods. In recent years, scholars have delved into metro tunnel lining defect detection and identification technologies based on deep learning (DL). Their research has led to the development of advanced algorithms, as illustrated in Figure 8. [PERSON] et al. [18; 65] proposed an optimized depth convolution network model V-6 based on the GoogLeNet structure. This model effectively classifies image features like water leakage, cracks, seams, and pipelines, achieving an impressive accuracy rate of 95.24%, surpassing GoogLeNet in recognition performance. To address limitations in disease location feature extraction from large-scale images, six faster R-CNN detection models were designed to detect cracks, water leakage, and other issues simultaneously. Despite some limitations due to sample size, these models show promise in improving detection outcomes. [PERSON] et al. [66] utilized fully convolutional networks (FCN) to semantically segment images of cracks and water leakage in subway shield tunnels. Their dual-flow algorithm incorporates physical correlations between the two issues, with separate channels for crack and water leakage area segmentation. This approach outperformed shallow algorithms like the region growth and adaptive threshold methods. By employing the Mask R-CNN depth convolution network structure, researchers successfully segmented water leakage images, accurately identifying leakage traces in tunnel linings and preventing the misinterpretation of concrete spots as leaks. This method is already applicable in monitoring and identifying diseases in shield tunnel linings, showcasing the significant speed and accuracy improvements facilitated by DL methods in traditional lining defect detection. In the early stage of disease identification and detection research, the analysis model is usually based on the edge detection algorithm, the mathematical morphology operation, and the traditional machine learning algorithm. Although it has achieved good results, its recognition process is still highly dependent on manual labeling features. It is easy to misjudge. With the continuous growth in the quantity of tunnel's under construction and the continuous accumulation of operational data, the deep learning algorithm has led to a great leap forward in development in the identification and detection of tunnel lining structural diseases because of its ability to deal with massive generalization.