# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 3 Case Study
### Data Preprocessing  
The tunnel boring machine (TBM) data for this project was collected at approximately 85,000 data points per day, sampled at 1 s intervals from the TBM time series data. During the TBM construction process, the automatic acquisition system records two types of data: those pertaining to normal working states and those pertaining to non-working states. In addition to these non-working state data, the TBM may contain abnormal values such as zero digging parameters during the digging process due to the influence of equipment failure, among other factors. This data can interfere with the subsequent model training. Consequently, the outliers are eliminated through the implementation of a binary state discriminant function [34]. \[h(x)= \begin{cases}1&(x>0)\\ 0&(x\leq 0)\end{cases}  \tag{21}\]  
\[H=h(V)h(F)h(P)h(T) \tag{22}\]  
where \(h(x)\) is the binary state discriminant function, whose value is 0 when the value of the independent variable is 0. The time series functions of the cutterhead speed (V), total thrust (F), cutterhead penetration (P), and cutterhead torque (T) are recorded over time during the tunnelling process. In the event that the dichotomous state discriminant function \(H=0\), the data record is considered an outlier and must be eliminated. However, TBM operational data is by nature a time record rather than a consistent geographical record. The data points are evenly distributed in time but not in space [35]. \begin{table}
\begin{tabular}{c c c} \hline
**Surrounding Rock Rank** & **Length/m** & **Proportion/\%** \\ \hline II & 4100 & 10.8 \\ III & 24,070 & 63.4 \\ IV & 7973 & 21.0 \\ V & 1822 & 4.8 \\ \hline \end{tabular}
\end{table} 
Table 2: Tunnel surrounding rock grades statistics. Figure 6: Distribution of major lithologies in the project’s tunnels. As a result, the original TBM time data was categorised based on the precise mileage (i.e., spatial position) at which each TBM instant occurred. The raw time data is grouped according to the actual mileage (spatial location) of the TBM at each moment in time. The time points are denoted by \(t_{i}\), and the corresponding spatial locations (mileages) are designated as \(s_{i}\). The data should then be grouped by spatial location, with a grouping interval of 0.1 m, i.e., \(\Delta s=0.1\). \[[s_{k},s_{k+1})=[k\cdot\Delta s,(k+1)\cdot\Delta s) \tag{23}\]  
where \(k\) is an integer indicating the kth mileage segment. For each mileage segment \([s_{k},s_{k+1})\), the average of all time data within the segment is calculated. Let \(N_{k}\) denote the number of data points within the segment and \(t_{k,j}\) be the jth time point within the segment, then the average value, denoted as \(\bar{t_{k}}\), is  
\[\bar{t_{k}}=\frac{1}{N_{k}}\sum_{j=1}^{N_{k}}t_{k,j} \tag{24}\]  
Lastly, TBM data with a total length of 2.291 km was obtained from the western Sichuan tunnel project dataset. This section extends from DK1223+959 on the left line to DK1226+250 on the same line [36]. In addition, Horizontal Seismic Profiling (HSP) was utilised to investigate the geological conditions ahead of the tunnel face. The process of Horizontal Seismic Profiling (HSP) entails the concurrent implementation of low-frequency seismic sources and receivers in a single horizontal plane along the tunnel walls, thereby facilitating the acquisition of seismic reflection data. Subsequent data analysis is primarily based on travel time analysis, where travel times are used directly to locate reflective interfaces, providing relatively intuitive imaging results [37]. The primary advantage of the HSP method lies in its capacity to analyse reflected wave signals using both time and frequency domain techniques, thereby enabling indirect inference of lithological variations in front of the tunnel face. The HSP system utilised in this study is the HSP217 Advanced Geological Prediction Instrument.