## 4 Field Application
Its buried depth was 646 m, we set the size of the model as long \(\times\) wide = 360 m \(\times\) 150 m, the fixed end restraint was set at the bottom, the overlying pressure (\(\gamma H\) = 33.6 KN/m\({}^{3}\)\(\times\) (646 \(-\) 150) m = 16.67 MPa) was added above the model, the horizontal stress (8.3 MPa) was added on both sides of the model, and the surrounding rock masses all adopted the M-C failure criterion, as shown in Figure 17. The physical parameters of the surrounding rock mass, dominated by sericite and carbonaceous phyllite, were: average density \(\rho\) = 3.42 g/cm\({}^{3}\), average elastic modulus \(E\) = 5.52 GPa, Poisson ratio \(\
u\) = 0.38, internal cohesion \(c\) = 175 kPa, internal friction angle \(\varphi\) = 27\({}^{\circ}\). Using the BP neural network toolkit in MATLAB, the forward training of the rock mass parameters and deformation at Point 4 (Table 7) was performed, which produced the neural network model of Point 4, referred to as net4. Similarly, the training samples for the other point were generated and so were their neural network models, namely net1, net2, net3, and net5, respectively. Finally, the deformation measured at each point was input into the corresponding neural network model and the surrounding rock mass parameters were inverted. For the purpose of validation, the inverted mechanical parameters of the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multirow{2}{*}{ \begin{tabular}{c} \(E_{0}\) \\ **/GPa** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \(\sigma_{r}\) \\ **/MPa** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \(E_{1}\) \\ **/GPa** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \(\eta_{1}\) \\ **/GPa-d** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} \(\eta_{2}\) \\ **/(GPa-d)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Displacement** \\ **/(GPa-d)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Displacement** \\ **/(GPa-d)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Displacement** \\ **/(GPa-d)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Displacement** \\ **at Point 2** \\ **/(mm)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Displacement** \\ **at Point 3** \\ **/mm** \\ \end{tabular}  } \\ \hline
1 & 2.69 & 36.80 & 0.80 & 16.38 & 93.71 & 7.31 & 9.11 & 13.11 & 27.94 & 11.50 \\ \hline
2 & 2.69 & 41.80 & 1.30 & 21.38 & 98.71  
surrounding rock mass were substituted into the Midas numerical model to determine the deformation at each point of the surrounding rock mass (Table 9). The relative error between the measured displacement and the numerically simulated displacement obtained through inversion was within 15%, indicating the good consistency and high applicability of the proposed method. Therefore, one can build a monitoring system for the largely-deformed surrounding rock mass section, according to the on-site information (such as the lithology combination, underground water status, bedding thickness, rock mass texture, surrounding rock mass integrity, maximum principal stress, and on-site measured rock deformation) for the purposes of gaining real-time information of the tunnel's surrounding rock mass parameter, adjusting the support plan in a timely manner, and providing references for design and construction.