## 5 Conclusions  
Due to the concealment of tunnel engineering, it is difficult to obtain the parameters of the surrounding rock mass after excavation. In this study, we combined the traditional Nashihara model with the Hoek-Brown failure criterion and developed the creep constitutive model for the jointed rock mass. Then, the creep parameters of the surrounding rock mass of the Ganbao tunnel were inverted and validated by integrating the on-site monitoring and the BP neural network. The following conclusions were drawn:  
1. By reviewing the typical cases of large deformation in soft rock tunnels, the main influential factors can be summarized as the lithology combination, weathering effect, and underground water status. With the classical rock mass failure criterion, it is hard to thoroughly incorporate the geological characteristics of the actual rock mass and therefore the semi-empirical semi-theoretical Hoek-Brown approach is more fit-for-purpose. 2. The geological characteristics of the engineering rock mass were quantitatively characterized using two indexes, namely, the rock mass block index (_RBI_) and the absolute weathering index (AWI). Following the Hoek-Brown criterion, the long-term strength \(\sigma_{r}\) and elastic modulus \(E_{0}\) of the rock were obtained and then substituted into the rock mass creep constitutive model based on the Nashihara model and the Hoek-Brown failure criterion. By doing so, the original five creep parameters that needed to be determined in the creep equation were reduced to three, which simplifies the calculation of the constitutive model, while well reflecting the engineering, geological, and creep characteristics of the rock mass on site. 3. Considering the fact that the actual engineering geology varies at different positions of the tunnel's surrounding rock mass, a specific BP neural network model was built for each monitoring point. Then, the rock mass parameters at each point were inverted from the on-site measured deformation. At last, the inverted parameters were input into the numerical model to calculate the deformation at each point, which was compared with the corresponding measured deformation. The resultant errors \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Monitoring Point**} & \multirow{3}{*}{**Measured Deformation**} & \multicolumn{6}{c}{**Output: Inverted Mechanical Parameters of**} & \multirow{3}{*}{**Simulated Deformation**} \\  & & & & & & & **Deformation /**mm** & /\% \\ \cline{3-8}  & & & & & & & & \\ \hline
1 & 23.73 & 3.44 & 35.35 & 0.92 & 16.85 & 104.49 & 26.67 & 12.37 \\ \hline
2 & 28.05 & 2.91 & 31.89 & 0.81 & 12.39 & 97.20 & 30.69 & 9.41 \\ \hline
3 & 40.76 & 2.12 & 21.67 & 0.59 & 10.01 & 9.13 & 45.57 & 11.81 \\ \hline
4 & 51.50 & 1.71 & 16.91 & 0.48 & 9.19 & 8.11 & 58.54 & 13.67 \\ \hline
5 & 34.56 & 2.39 & 22.63 & 0.71 & 11.15 & 88.00 & 37.51 & 8.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameter inversion of surrounding rock mass and error analysis. were all within 15%, satisfying the engineering requirements and demonstrating the reliability of the proposed method. 4. The values of the rock mass \(GSI\) (Figure 2) were all determined according to the engineering geology handbook, relevant standards, and existing literature. Therefore, these values can be adjusted as per the field condition. The inversion of the surrounding rock mass parameters is highly affected by the basic parameters of the engineering geological characteristics and the in-situ stress field of the tunnel's surrounding rock mass. Hence, during applications of the proposed method, the basic parameters need to be accurately measured to improve the accuracy of the inversion. 5. This method is suitable for tunneling in unsupported rock mass or plainly supported tunnels after excavation. Writing--original draft preparation, C.C. ; writing--review and editing, T.L. ; methodology, C.M. ; investigation, H.Z. ; software, J.T. ; data curation, Y.Z. ; funding acquisition, T.L.