# A Review of Deep Learning Applications in Tunneling and Underground Engineering in China
## 3 Intelligent Application of Deep Learning in Tunnel Engineering
### Subsea Tunnel
#### 3.3.1 Structural Stability  
In 2010, Xiamen Xiang'an Subsea Tunnel was completed and opened to traffic. As the first subsea tunnel in China, many scholars took this tunnel as an example by which to study the structural stability of the tunnel by inverting the construction parameters. [PERSON] et al. [6] used the deformation values of the tunnel monitored on site to optimize the inversion of the equivalent modulus of elasticity of the surrounding rock and the lateral pressure coefficient. Subsequently, the numerical analysis method was used to carry out the forward calculation of the above two categories, and according to the results of the forward calculation, to feed back, check, and judge whether the initial support parameters need to be further adjusted and modified in the construction process. Finally, a scientific and reasonable evaluation of the stability of the tunnel surrounding the rock was realized. [PERSON] et al. [79] carried out a triaxial fluid-solid coupling experiment and a triaxial rheological coupling experiment on weathered rock samples in the field and simulated the rheological finite element model of the surrounding rock. The model takes the difference between the test values and the numerical calculation results as the objective function, and the Nelder-Mead optimization method was used to invert the rheological model parameters. According to the field displacement monitoring data, the inversion analysis of weathering through the surrounding rock via parameters such as mechanical parameters, plastic parameters, and damage parameters is carried out. Based on the fluid-solid coupling theory under rheological conditions, subroutines are compiled and embedded in ABAQUS finite element analysis software for numerical simulation calculation, which realizes the reliability prediction of the long-term stability of the tunnel considering rock rheology. [PERSON] et al. [80] developed an intelligent back analysis program based on a differential evolution algorithm and an elastic-plastic finite element solution program; compared the accuracy of parameters such as elastic modulus E, Poisson's ratio, internal friction angle, and cohesion C under different strategies; and analyzed the evolution trend of parameters of a certain section of the Xiang'an tunnel along with evolution algebra. [PERSON] et al. [81], who made a detailed introduction to the geological exploration, alignment selection, excavation, and support of 24 Norwegian highway undersea tunnels, such as Vardo, Nappstraumen, Oslofjord, Bemlafjord, and Eiksund, studied their risk assessment methods in depth, which served as a great help in the construction of solid undersea tunnel structures in China. [PERSON] et al. [82] proposed a risk assessment analysis and identification method for undersea tunnels. The method uses six stages of tunnel planning, feasibility study, investigation and design, bidding, construction, and operation as entry points for the analysis. It was successfully applied to the structural stability, the durability of lining structure, drainage prevention system, and risk-control measures of the fault fracture zone in the Qingdao Jiaozhou Bay subsea tunnel. Professor [PERSON] from Wuhan Institute of Geotechnics, Chinese Academy of Sciences, took the construction process of Wuhan Yangtze River Tunnel as the engineering background; used the tunnel tube sheet strain value, tunnel temperature, and Yangtze River water level as the input parameters; learnt the spatial correlation and temporal dependence of the structural development trend by using the autoencoder algorithm and RNN, respectively; and put forward a deep-learning model that could predict multiple coupling factors (autoencoder fused temporal-spatial-load network, ATSLN), which achieves a coupled analysis of the structural external load data and spatial-temporal correlation information and can accurately predict the trend of future structural mechanical behavior [83].