# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Mountain Tunnel
#### 3.1.2 Prediction of Collapse and Outburst and Water Inflow  
Collapse is one of the primary risk sources in tunnel engineering, often accompanied by water and mud inrush. Figure 7a shows that the upper rock mass of the tunnel collapsed during construction, with the fallen rock mass visible at the bottom of the image. Figure 7b reveals a large amount of groundwater gushing out in the upper right corner of the excavation face. In mountain tunnels, these phenomena primarily occur in areas with unstable surrounding rock, including weathering zones, fault fracture zones, and geological structural zones. Many factors contribute to tunnel collapse, including natural factors (such as precipitation and earthquakes), geological factors (such as the grade of surrounding rock, buried depth, and groundwater content), survey and design factors (such as inaccurate geological surveys and deviations in design schemes), and construction factors (such as limited choices of excavation methods and flawed construction practices). Moreover, the relationship between the influencing factors of each disaster is complicated and ambiguous, due to the significant spatial and temporal variability of atmospheric precipitation and surface runoff. Furthermore, the complexity of the disaster mechanisms and the lack of basic information make it challenging to quantitatively assess the risks of tunnel collapse and water inrush, preventing the formation of a unified risk assessment and prediction model. Currently, the prediction methods employed for this type of disaster in China fall into three categories: (1) direct forecasting and analysis using field observation and monitoring data; (2) application of non-linear theory (including grey models, fuzzy risk assessment, and neural network models) to develop a model for predicting surrounding rock deformation; (3) prediction analysis utilizing numerical analysis methods, such as finite element, discrete element, boundary element techniques, and their combinations. Since tunnel collapse and water surge represent complex processes, often characterized by discontinuity and uneven rock deformation, a highly non-linear problem, addressing this issue poses significant challenges. Numerical analysis methods, while precise, demand stringent testing conditions and incur substantial computational costs. To mitigate these challenges, non-linear theory is integrated with existing theoretical equations. This integration serves dual purposes: it offers a theoretical foundation that enhances the accuracy and feasibility of data analysis, and it provides a methodology for rapidly assessing parameter sensitivity and for cross-verifying model results. This dual approach not only leverages the strengths of both methods but also fulfills the requirements for prediction precision and accuracy. On the problems of the evaluation and diagnosis of collapse disaster, [PERSON] et al. [56] summarized the factors affecting tunnel collapse according to the case data of more than 100 tunnels and established the membership function of loss and the fuzzy analytic hierarchy evaluation model of collapse risk. A comprehensive assignment method is adopted for the weight value of factors, which can satisfy both historical and practical data and reduce the possibility of interference by human factors. [PERSON] [57] combined grey theory with cooperative non-linear theory to establish a grey-collaborative non-linear theoretical model for landslide time prediction. [PERSON] et al. [58] took the Yinsong Water Diversion Project as an example based on the monitoring data of 18 collapse accidents during tunnel construction. The parameters such as penetration rate, rotational speed, thrust, and torque of the TBM cutter are combined into the drilling efficiency index (TPI), which was used as the input variable and output targets of DBN. This approach improves the accuracy of the algorithm while reducing the amount of data.