# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 5 Conclusions  
As the construction object of tunneling is a non-continuous, non-uniform geotechnical body, its mechanical properties are very complex. Moreover, in the course of tunnel construction and operation, it is difficult to rely on a single monitoring method, calculation tool, and indoor test to predict structural diseases, assess risks, and select mechanical parameters. Therefore, researchers have begun to use the monitoring of the surrounding rock displacement and stress-strain trends to deduce the nature of the geotechnical body, and they tend to utilize the rapid and accurate analysis capabilities of computers to solve the above tasks. At the same time, the rapid development of computer vision and data mining technology has solved the errors caused by the lack of tunnel monitoring data, weak disease identification technology, and strong interference of objective factors; thus, the deep learning method in the intelligent solution of tunnel engineering problems has become a new development trend. Through a brief introduction to the development of deep learning, this paper focuses on the intelligent application of the deep learning algorithm in tunnel disaster prevention and prediction problems, such as those pertaining to mountain tunnels, urban subway tunnels, and undersea tunnels. The main conclusions are as follows:  
1. The development of deep learning has experienced an evolution from the conception of the network structure to the depth of network. In the progression of the big data era and the information age, the step towards the intelligent stage of the \"big data + deep network structure\" framework has come, and different deep network structures have gradually been put forward. However, due to the different theoretical bases and structures of all kinds of networks, there is no specific network model that can be considered the most suitable for solving all engineering problems. 2. With the development of computer vision, deep learning shines brilliantly in intelligent recognition and detection tasks such as image and voice detection, and the deep learning algorithm is used to quickly monitor and diagnose the various types of tunnel diseases. The prediction and judgment of the failure mode of the lining structure and the recognition and classification of a palm face and an image of rock are the main research directions at present. The CNN network structure has also become the most widely used deep network structure in tunnel engineering. 3. In the prediction of rockburst disasters in a mountain tunnel, our country already has certain intelligent analysis abilities, and it can use deeper neural networks to solve prediction problems such as the rockburst intensity grade, influencing factors, and so on. However, more in-depth research is needed on the prediction of the time of rockburst occurrence and the mechanism of rockbursts in different geologies, as well as collapse, water gushing, and other disasters. 4. Due to the dense population and concentrated distribution of buildings in the city, the difficulty of underground space construction is also increasing sharply with the rapid development of the city; so, the identification of tunnel lining cracks and water seepage and the prediction of surface settlement are particularly important in urban tunnel construction. The CNN is often used to identify lining diseases, while the RNN is often used to predict settlement values. The appropriate depth learning algorithm should be selected according to different working conditions and different construction methods. Random forest, support vector machine, and other machine learning methods are also good choices. 5. At present, the intelligent analysis of subsea tunnels is mainly considered from three perspectives, namely, surrounding rock stability, durability, and tunnel leakage, and the analysis method mainly depends on numerical simulation.