## 3 Case Analysis
### Dynamic Surrounding-Rock-Mass Classification  
For the interest rate, the surrounding-rock-mass classification of the tunnel is generalized. Since the 1950s, the surrounding-rock-mass classification of the tunnel has been based on geological drilling, and the rock strength and integrity have been evaluated to judge the grade of the surrounding rock mass. This method is time-consuming and inaccurate. The grade of the surrounding rock mass of the tunnel is estimated, so some essential tunnels need to be detected using geological radar and seismic wave geology (TSP); then, it is necessary to generate a qualitative description of the surrounding rock mass of the tunnel. This necessitates the use of many time-consuming and labor-intensiveprocesses, which influences the progress of the project. To promote the safe development of underground engineering, guarantee an average construction period, and reduce economic losses and environmental damage, a real-time dynamic evaluation of an artificial intelligence surrounding-rock-mass classification system is undertaken, based on cutting-edge construction methods and standardized construction management regulations. Starting from the tunnel construction process, we offer practical guidance.  
At present, computer technology is the basis for the continuous development of theories and methods in the field of engineering. At the same time, it has also promoted the combination of various techniques and technologies and has produced more practical new theories. Computers have powerful data processing capabilities and fast logic operation capabilities. The empirical method in engineering often brings with it ambiguity and subjectivity. Errors often occur in construction, which increases construction volumes and economic losses. The machine learning method that uses a particle swarm optimization  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Drilling Parameters** & **Unit** & **Range** & **Meaning** \\ \hline Torque & kN-m & 0–8 & Torque of the drill bit during rock breaking \\ Rotational speed & r-min\({}^{-1}\) & 0–72 & Number of revolutions per minute of the drill bit \\ Propulsion & kN & 0–60 & Reaction force on the drill pipe \\ Striking energy & J & 0–750 & Energy of the percussion drill striking the rock \\ Striking times & n-min\({}^{-1}\) & 0–2200 & Number of times per minute that the rock is struck \\ Water delivery flow & L & 0–240 & Water flow delivered to the drill bit \\ Water delivery pressure & MPa & 0–10 & Pressure of water flow delivered to the drill bit \\ Discharge capacity & L & 0–240 & Flow rate of water returning from the drill hole \\ Drainage pressure & MPa & 0–10 & Flow pressure of water returning from the drill hole \\ \hline \hline \end{tabular}
\end{table}
Table 1: Drilling parameters.  
Figure 4: Digital drilling rig parameter acquisition interface.  
least squares support vector machine, and the digital drilling rig method, are applied to the classification of tunnel-surrounding rock mass. Designed as a dynamic evaluation software for the classification of tunnel-surrounding rock mass, it is a powerful method for undertaking the rapid real-time classification of surrounding rock mass in tunnels. This section uses a comprehensive method of artificial intelligence and digital drilling rigs to design a dynamic surrounding-rock-mass grade-evaluation system, based on particle swarm least squares support vector and digital rig technology, to be applied in the real-time construction of underground engineering circles. Forecasting the surrounding-rock-mass grade of the tunnel has absolute reference value. The dynamic grading interface for the surrounding rock mass is shown in Figure 5.