## 2 Materials and Methods
### TBM Tunneling Big Data Preprocessing  
#### 2.2.1 Characterization and Extraction of TBM Rock-Breaking Data  
Figure 3 shows that TBM tunneling can be divided into several excavation cycles during operation. However, there is a large amount of invalid data between the excavation cycles, i.e., the shutdown phase. Therefore, it is necessary to eliminate invalid data with the following rejection principle:  
\[f(x)=T\;\times\;n\;\times\;v\;\times\;F \tag{1}\]  
\[\begin{cases}f(x)=0,&\text{Shatdown section}\\ f(x)\
eq 0,&\text{Tunneling section}\end{cases} \tag{2}\]  
where \(T\) is the cutterhead torque, \(n\) is the cutterhead rotational speed, \(v\) is the advance rate, and \(F\) is the total thrust.  
Despite the shutdown phase, a complete TBM excavation cycle can be divided into four phases, startup, ascending, stable, and end, with the first three phases presented in Figure 4 [8]. Since all parameters in the end phase dropped sharply to 0, they were not included. At the moment of interactions between the TBM cutterhead and the surrounding rock, the advance rate significantly decreased to develop a concave down point due to vibration responses. The cutterhead torque and thrust increased significantly, indicating the beginning of the ascending phase after the startup phase. When the data in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Project Parameters** & **KS Tunnel in Xinjiang, China** & \multicolumn{2}{c}{**Water Conveyance Tunnel Project in**} \\  & & **Lanzhou, China** \\ \hline Tunnel length/(km) & 283.41 & 31.57 \\ Mean buried depth/(m) & 428 & 500 \\  & Tuffaceous sandstone, granite, quartz & Quartz diorite, quartz schist, granite, metamorphic and sandstone \\  & & \\ TBM types & Open full-face TBM & Double-shield TBM \\ Number of rolling cutters & 48 & 37 \\ Cutter diameter/(m) & 7 & 5.48 \\ Rated torque/(kN-m) & 4410 & 3458 \\ Maximum thrust/(kN) & 19,076 & 22,160 \\ Cutterhead rotating speed/(r/min) & 0–10.9 & 0–10.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geological characteristics and TBM parameters of tunnel projects.  
Figure 1: Geological profiles of the study area.  
ascending phase stabilized, the stable phase occurred. It should be noted that the total thrust increased with time in the startup phase because the TBM cutterhead has not yet touched the surrounding rock. The cutterhead needed to be pushed forward to the surrounding rock, and the total thrust started to increase. When the advance rate in the startup phase gradually increased, the total thrust also increased with time. In this study, the data in the stable phase were used to analyze the relationship between surrounding rock information and excavatability evaluation indexes during TBM tunneling. Therefore, a scientific and feasible division principle is needed to identify the stable phase in a complete excavation cycle.  
If a complete excavation cycle except the stable phase is considered as a whole, the overall fluctuation interval can be determined, i.e., between 0 and the value at the stable phase. Therefore, the data of a complete excavation cycle can be divided into two categories: stable and other phases. Based on this feature, the K-means clustering algorithm was introduced to identify the stable phase in the excavation cycle. K-means clustering is an unsupervised learning clustering algorithm that is widely used in data mining, and the simple implementation process is shown in Figure 5. The basic principle is to iterate to find the minimum distance between each data point and its cluster centroid and then to assign the data points to the nearest clusters [30]. Figure 6 shows the clustering results of  
Figure 2: Processes of acquiring parameters related to the uniaxial compressive strength of rock: (**a**) field sampling; (**b**) rock core samples; (**c**) laboratory tests.