## 5 Training Model and Prediction for Subsidence
### Training Phase One: Feature Extraction from Machine Data  
As discussed in the previous section, we used engineering judgment to select some of the items. Sensitivity analyses of the other items are incomplete and could be served as asubject for future research. Eleven items were chosen in the training phase: penetration, chainage, thrust cylinder (Gr. A), torque cutting wheel, current pressure real lance one, pressure excavation chamber one, rotation speed one screw conveyor drive, calculated excavated material depending on advance one, the actual quantity of excavated material belt scale one, and stroke thrust cylinder. Data were recorded while the TBM was in operation on each of the approximately 160 working days over the course of a year. Machine data recorded when the TBM was idling were excluded. Because the machine data were recorded about every 10 s, there was a maximum of 8460 records in a day, but the actual number of daily records was fewer due to ring building, cutter-head intervention, equipment breakdown, or maintenance. The machine data were turned into a sequencing batch comprising 1000 records taken every 10 s, producing a sequence 2.8 h long. A maximum of 500 iterations of training was carried out for each batch. The window size for training a batch was 10, the input size of the model was 1100, which was 11 by 10 flattened, and the output size was one, which represented penetration. Figure 9 depicts the complicated dynamics taken from the various TBM sensors in response to the driving of the machine. We expected that the machine data from training could be organized into meaningful features, such as the amount of excavation work completed, control of chamber pressure, maintenance of muck balance, and speed of the TBM drive. In order to analyze the sensitivity of chosen parameters with respect to penetration, the Pearson correlation coefficient (PCC) was used as shown in the Equation (7),  
\[\mathrm{r}_{\mathrm{x,y}}(\mathrm{x}_{\mathrm{i}})=\frac{\mathrm{n}\sum_{ \mathrm{i=1}}^{\mathrm{n}}\mathrm{x}_{\mathrm{i}}\mathrm{y}_{\mathrm{i}}- \sum_{\mathrm{i=1}}^{\mathrm{n}}\mathrm{x}_{\mathrm{i}}\sum_{\mathrm{i=1}}^{ \mathrm{n}}\mathrm{y}_{\mathrm{i}}}{\sqrt{\mathrm{n}\sum_{\mathrm{i=1}}^{ \mathrm{n}}\mathrm{x}_{\mathrm{i}}^{2}-(\sum_{\mathrm{i=1}}^{\mathrm{n}} \mathrm{x}_{\mathrm{i}})^{2}}\sqrt{\mathrm{n}\sum_{\mathrm{i=1}}^{\mathrm{n}} \mathrm{y}_{\mathrm{i}}^{2}-(\sum_{\mathrm{i=1}}^{\mathrm{n}}\mathrm{y}_{ \mathrm{i}})^{2}}} \tag{7}\]  
where \(\mathrm{r}_{\mathrm{x,y}}(\mathrm{x}_{\mathrm{i}})\) is the PCC of the effect of i-th influential factor on penetration (\(\mathrm{y}_{\mathrm{i}}\)), \(\mathrm{x}_{\mathrm{i}}\) is the value of influential factors, and n is the total number of samples. Figure 10 shows the time series excavation records in the first box and thr PCCs of the chosen parameters against the penetration of TBM driving in the following boxes on days 1, 178 and 358, which were the first, midst, and last stages of the TBM excavation, respectively. From a few cases of PCC analyses, chainage show low Pearson correlation, which are rather dependent than the influential parameters on penetration. Torque, thrust, pressure A (current pressure at real lance one) and screw RPM show mild positive correlation, whereas pressure B (ex excavation chamber one pressure), the calculated and actual muck volume, and  
Figure 9: Sequence of chosen items of TBM data for machine learning. network. thrust cylinder stroke show a negative correlation. This is in line with the common intuition that driving parameters, such as thrust and torque, are positively influential to penetration, and that align chamber pressure for the support of excavation face is negatively influential, i.e., it maintains a low penetration. Evolution of the Pearson correlation coefficients with respect to penetration for the entire duration of TBM excavation are shown in Figure 11. Figure 11a-d show negative, neutral, and positive correlation parameters and the combination of all, respectively.