## 3 Case Analysis
### Analysis of Results  
To verify the model trained by the method in engineering applications, reliability and fitness analyses in model learning (as shown in Figure 6) were undertaken. The results show that the fitness gradually decreases with the increase in the number of iterations; that is, the group tends to be optimal, and the speed is reduced most in the first 12 iterations. It is almost unchanged in the following 88 iterations. It can be seen that this method has a breakneck optimization speed and can quickly obtain the optimal global solution and analyze the test results after the model learning is completed (as shown in Figure 7), and find the classification of the method. The results are basically consistent with the results of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multirow{2}{*}{**Speed**} & \multirow{2}{*}{**Torque**} & \multirow{2}{*}{**Sped**} & \multirow{2}{*}{**Rotating**} & \multirow{2}{*}{**Sped**} & \multirow{2}{*}{**\& **R/KN**} & \multirow{2}{*}{**Strike**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Water**} & \multirow{2}{*}{**Deliivery**} & \multirow{2}{*}{**Deliage**} & \multirow{2}{*}{**Rock**} \\  & & & **/(kN-m)** & & **/(min-1)** & & **//N** & & **/L** & **Grade** \\ \hline
1 & 0 & 0.94 & 41 & 7.05 & 370 & 1611 & 93 & 90 & 3 \\
2 & 31 & 1.56 & 50 & 8.11 & 491 & 1659 & 80 & 79 & 3 \\
3 & 46 & 1.44 & 53 & 7.68 & 485 & 1666 & 93 & 91 & 3 \\
4 & 26 & 0.96 & 38 & 4.42 & 397 & 1652 & 95 & 92 & 4 \\
5 & 24 & 0.85 & 40 & 4.69 & 397 & 1652 & 93 & 90 & 4 \\
6 & 59 & 0.67 & 40 & 2.31 & 335 & 1652 & 96 & 92 & 5 \\
7 & 49 & 0.77 & 39 & 3.35 & 387 & 1652 & 89 & 85 & 5 \\
8 & 53 & 1.52 & 40 & 8.05 & 393 & 1652 & 96 & 93 & 3 \\
9 & 65 & 1.47 & 40 & 8.1 & 494 & 1679 & 83 & 80 & 3 \\
10 & 57 & 0.88 & 39 & 4.31 & 392 & 1652 & 91 & 87 & 4 \\
11 & 41 & 0.81 & 41 & 3.96 & 392 & 1650 & 93 & 90 & 4 \\
12 & 30 & 1.71 & 54 & 7.96 & 481 & 1662 & 92 & 89 & 3 \\
13 & 27 & 1.54 & 52 & 8.21 & 476 & 1664 & 81 & 85 & 3 \\
14 & 33 & 0.92 & 37 & 4.43 & 391 & 1651 & 88 & 83 & 4 \\
15 & 52 & 1.01 & 39 & 4.61 & 384 & 1650 & 91 & 89 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Samples of drilling parameters in the Qingdao Jiao Zhou Bay Subsea Tunnel. Figure 5: Matlab dynamic surrounding rock grading interface. the actual analysis (BQ classification method). It can be seen that the method is feasible in the advanced classification of surrounding-rock-mass categories, which provides a scientific basis for the construction unit to reasonably arrange the construction progress and reduce the hidden dangers of the project. The method proposed in this paper is compared with traditional methods (Q method, RMR method, and BQ method). First, in terms of the results of surrounding rock classification, the method proposed in this paper remains largely consistent with the traditional method (Table 3). Second, in terms of applicable conditions, the Q classification method is applicable in both soft and hard rock masses, and the advantages of the Q classification method are more obvious in dealing with extremely soft rock formations; the RMR method is more applicable to hard rocks affected by joints and is not suitable for use in soft rock formations; the BQ classification method is applicable to all kinds of rocks except for expansive enclosing rocks and has a wider range of use; in the above traditional methods, among the rock formations to which  
Figure 6: Curve of fitness and evolutionary algebra (parameter c1 = 1.5 c2 = 1.5 termination algebra = 100). Figure 7: Model prediction for surrounding rock grades in comparison with actual rock grades. they are applicable, the values derived from the above traditional methods have a high degree of confidence in the rock formations to which they are applicable. The proposed method of predictive classification of surrounding rocks based on drilling parameters is applicable in both homogeneous and non-homogeneous rock masses of hard rocks but is less effective in all soft rocks.