## 3 Case Study
### Data Processing  
During the advancement of the shield machine, a point of data will be generated every minute, and the datum is recorded in the form of numerical values, but the cutter head and oil cylinder of the shield machine are not always running. The operating procedures of the shield machine can be divided into the propulsion system and the segment assembly system. During the segment assembly process, the values of the advance rate, cutterhead torque, thrust, and other parameters are all zero because they are not working. In order to analyze and organize the data better, the cutterhead torque, thrust, and other data of the shield machine will be averaged according to the propulsion state during the excavation of each ring, and then those data will be utilized for the subsequent experimental analysis. In total, 138 kinds of data were collected from the sensors on the shield machine. In this regard, this paper screened most of the parameters through the basic principal component analysis method, and referred to the previous literature ([PERSON] et al. [17] and [PERSON] et al. [31] selected T and CS as shield machine operating parameters input to the model; [PERSON] and [PERSON] [32] selected CS, T, CT, and SS as an input and considering the soil pressure (SP) as a core parameter in the excavation process of the EPB shield tunneling, which is taken as a major input parameter). Based on these articles, Table 3 presents the data characteristics of the model input parameters in the excavation process in this paper. There were 1188 rings in this shield excavation. From among the data, 80% was randomly selected as the training set, while 20% of the data was used for testing. By comparing the data characteristics of each tunneling parameter in the training set and the test set, it showed that the test set was relatively uniform and reasonable, and therefore it can be used for subsequent testing. However, the standard deviation and mean value of each parameter were quite different, which may cause the model to easily fall into the problem of gradient disappearance or gradient explosion. Therefore, in this paper, all of the input parameters will be standardized. The specific data processing method can be expressed by Equation (14):  
Figure 6: Geological profile of the tunnel between Qilin Town Station and Dongjiao Town Station. \[x^{\prime}(i)=\frac{x(i)-\overline{x}}{\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{(x(i)- \overline{x})}^{2}}} \tag{14}\]  
where \(\overline{x}\) is the average value of the input parameter, and \(n\) is the number of datasets. In terms of geological parameters, the physical and mechanical parameters of each soil and rock in the geological exploration data were collected, as listed in Table 2. Due to the lack of some geological survey report data, not all geological parameters could be used. A correlation analysis on the complete data was carried out as shown in Figure 7. It can be seen that there is an extremely strong autocorrelation between \(\gamma\) and the \(K_{\rm h}\), similar conclusions can also be found between \(K_{0}\) and \(\
u\). In order to ensure the accuracy of the subsequent model and avoid the problem of multicollinearity, three parameters with weak autocorrelation were finally selected to represent the geological parameters in this excavation process: volume weight (\(\gamma\)), coefficient of lateral earth pressure (\(K_{0}\)), saturated uniaxial compressive strength (\(\delta\)). These geological parameters were also standardized.