# TBM Enclosure Rock Grade Prediction Method Based on Multi-Source Feature Fusion
## 3 Case Study
### Data Preprocessing
The category with the smallest distance is then selected as the nearest neighbour category. The noise data is then generated and added to the original data. The formula for generating noise data is as follows:  
\[y_{\text{noise}}=y_{\text{original}}+\eta\times\epsilon \tag{27}\]  
where \(y_{\text{noise}}\) is the reflected energy intensity class after adding noise, \(=y_{\text{original}}\) is the original reflected energy intensity class, \(\eta\) is the noise weight, and \(\epsilon\) is the random noise value, which usually follows a normal distribution \(\epsilon\sim N(0,\sigma^{2})\). The visual depiction of the raw data and the data following the addition of noise is presented in Figure 9a illustrates the raw reflected energy data, with the distance travelled (m) represented. Specifically, Figure 9a illustrates the raw reflected energy data, with the distance travelled (m) represented on the horizontal axis and the HSP grade on the vertical axis. The data points are distinguished by different colours to indicate different HSP classes, and each data point corresponds to a specific mileage and HSP class. Figure 9a illustrates the raw reflected energy data, with the distance travelled (m) represented on (b) illustrating the data after the introduction of noise to the original dataset. The introduction of noise to the original dataset results in noise predominantly concentrated at the beginning and end of the mileage, and a deviation from the original data distribution. A clear visualisation of the impact of noise on data distribution is afforded by a comparison of Figure 9a, illustrating the raw reflected energy data, with the distance travelled (m) represented on (a) and (b). The introduction of noise results in a more dispersed distribution of data points on the graph. However, through the judicious application of noise weighting and classification methods, the preservation of the overall trend and characteristics of the data is possible. From the 116 recorded data variables for the Chuanxi TBM left-line tunnel, this study selected five excavation parameters as the initial TBM input parameters: cutterhead rotational speed (\(RPM\)), advance rate (\(v\)), penetration rate (\(P\)), total thrust (\(F\)), and cutterhead torque (\(T\)). Utilising these parameters, rock fragmentation indices such as the fragment coefficient (\(FC\)), the total fragment coefficient (\(TFC\)), and the weight ratio (\(WR\)) were calculated using Equations (28)-(30) and incorporated as additional inputs. Furthermore, real-time coordinate data (\(X,Y,Z\)) from the shield tail guidance system were added as three-dimensional spatial data. The amalgamation of multi-source data from three distinct origins--TBM operational data, 3D spatial data, and HSP data--culminated in a total of 12 input features. \[FPI=\frac{F}{NP} \tag{28}\]\[TPI=\frac{T}{0.3 DNP} \tag{29}\]  
\[\text{WR}=2\pi\times 10^{3}\frac{T\cdot RPM}{F\cdot\
u} \tag{30}\]  
where \(N\) denotes the number of tools installed on the cutter disc, and \(D\) represents the diameter of the roadheader.