# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Mountain Tunnel
#### 3.1.1 Rockburst Disaster  
Rockburst is one of the main geological disasters in deep-buried long tunnels, often occurring in hard rock mass under high ground stress. It is characterized by apparent randomness and suddenness [45]. Due to the brittle damage of deep surrounding rock caused by excavation, unloading, or external disturbances, the elastic strain energy accumulated in the rock body is suddenly released. This results in damaging phenomena such as blasting, spalling, and ejection of the surrounding rock. Such events often cause catastrophic damage to underground engineering construction and pose a serious threat to the safety of construction personnel. Consequently, the prediction of rockburst grade and the development of intelligent early warning systems have become worldwide challenges [46]. When the DL method is used to predict, monitor, and prevent such tragedies, it is essential to choose the neural network parameters. The ability of the parameters to intuitively reflect lithology characteristics, in situ stress, initial stress field characteristics, and the development characteristics of surrounding rock joints and fissures significantly influences the network's learning process and the model's accuracy. [PERSON] et al. [47] selected compressive strength, tensile strength, elastic energy index, and tangential stress as discriminant indexes. They used these as input-layer neurons and included two binary neurons in the output layer to represent the classification of rockburst intensity, thereby establishing an artificial neural network model for rockburst prediction. Based on the original parameter selection, [PERSON] [48] summarized the research results of relevant scholars on influencing factors of rockburst prediction. He added a total of nine types of correlation indicators, including point load strength (I\({}_{\text{s}}\)), tunnel burial depth (H), and burst tendency index (W\({}_{\text{et}}\)), and combined these with the improved BP neural network algorithm to evaluate the propensity of rockburst occurrence during tunnel construction. Among them, the point load strength I\({}_{\text{s}}\) depended on the rock damage load and the distance between the loading points, while the blasting tendency index W\({}_{\text{et}}\) is the rock need to reach the peak strength of the rock samples in the elastic strain energy storage energy and plastic deformation dissipation of the ability of the ratio; the value can be calculated by the rock samples of the stress-strain curve obtained. [PERSON] et al. [49] proposed a deep neural network (DNN) rockburst prediction model (DA-DNN) based on dropout regularization and an improved Adam optimization algorithm. By analyzing rockburst examples, the maximum tangential stress of surrounding rock, uniaxial compressive strength and tensile strength, and elastic energy index of rock are also used as evaluation indexes for rockburst prediction. Throughout the analysis process, the network structure itself enables autonomous selection and screening of data, avoiding the interference caused by human participation. Therefore, the prediction model can perform a complex deep relationship learning process and handle a limited amount of data with some noise. Although the concept of these physical parameters is fuzzy, their values will be some deviation from the real parameters of rock mass. However, the significance of the parameters analysis is that they can be used to obtain computational analysis results that are consistent with the measured information. In the meantime, they provide a basis for the prediction and evaluation of projects in the later stage or under the same conditions. At the same time, the network model used in the DL process requires a large amount of data for training and verification. Generally, the greater the number of samples, the higher the potential performance ceiling of the network model becomes. However, the harsh tunnel environment poses significant challenges to data collection and acquisition.