# 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
#### 3.2.2 Prediction of Settlement Value  
Land subsidence in China mainly occurs in large- and medium-sized regions in the east and middle of China, especially in the Yangtze River Delta region and areas with immense groundwater exploitation. The construction process of the subway tunnel is carried out in the rock mass; the initial stress state of the original stratum will inevitably change significantly due to excavation disturbance. After the stratum stress is adjusted, its macroscopic performance is the movement and deformation of the rock mass position [67]. The ground settlement caused by subway tunnel construction gives rise to many factors, including objective factors such as the direction of surface runoff and the characteristics of excavated strata, and is closely related to subjective factors like tunnel section shape and construction technology level. Therefore, it is essential to control the tunneling speed, shield machine posture, and the grouting pressure of the shield tail in the construction process [68] to monitor the surface subsidence and the deformation of surrounding buildings. As to the problem of predicting surface settlement values in urban tunnel construction, traditional prediction methods include empirical methods [69], theoretical methods [70], and model test methods [71], etc. However, the above methods are based only on the simplification of the tunnel structure and the construction parameters; their applicability is limited. In addition, the excavation process of tunnel-surrounding rock is a difficult self-added value system, and the above methods also struggle to achieve the real-time prediction of surface settlement and the optimization of construction machinery parameters. Figure 8: Algorithm flow of lining disease recognition. With the increasing construction scale of urban subway tunnels in recent years, surface settlement prediction methods based on rock mechanics and numerical simulation have also been developed and great breakthroughs have been made. Tunnel deformation monitoring methods based on numerical simulation and tunnel stability analysis methods based on mechanical analysis and rock classification, as well as deep learning algorithms based on artificial intelligence theory, provide the theoretical basis for, and solution to, these kinds of random and fuzzy engineering problems. The recurrent neural network (RNN) can use its recurrent hidden layer in the DL method to realize the time serialization of monitoring data and generate input data with the \"memory\" ability. So, the ability of the network to predict the tunnel settlement is better than that of the ML algorithms such as BP, RBF, and other algorithms. [PERSON] et al. [72] predicted the development trend of land subsidence by using the non-linear autoregressive with eXogenous inputs neural network (NARXNN) time series prediction model with external input according to the analysis results of monitored land subsidence data. The network model takes the construction impact factor x(t) as an input unit. This impact factor combines the construction characteristics, changes in environmental conditions, and spatial and temporal effects of settlement at the measurement points. In addition, the use of input and output units with delay effects improves the accuracy and dynamic description of the prediction model, which means that the inversion results are closer to the actual situation. [PERSON] et al. [73] used stratum parameters, tunnel section parameters, and shield machine parameters as network input indicators. RNNs such as LSTM, GRU (gated recurrent unit), and traditional BP algorithms are chosen as prediction models to analyze the prediction of the maximum ground settlement caused by the subway tunnel construction. The results show that RNN models outperform the BP network model in various evaluation metrics such as root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R\({}^{2}\)). [PERSON] et al.