# Soil Classification by Machine Learning Using a Tunnel Boring Machine's Operating Parameters
## 3 Dataset
### Data Preparation  
A TBM is large-scale equipment for mechanized construction. Various sensors monitor hundreds to thousands of items such as excavation time, load, pressure, speed, temperature, position, and direction in order to check the operation of the various mechanical devices inside the machine and control the position and attitude of the equipment. Data on \(\sim\)880 items were collected at the test site. In this study, a dataset for machine learning was constructed by matching the TBM operating data generated during excavation with the ground data at the excavation locations. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Cohesive Soils** & \multicolumn{2}{c}{**Non-Cohesive Soils**} \\ \hline N & Consistency & N & Density \\ \hline \(<\)2 & Very soft & & \\ \hline
2-4 & Soft & 0–4 & Very loose \\ \hline
4-8 & Medium & 4–10 & Loose \\ \hline
8–15 & Stiff & & \\ \hline
16–32 & Very stiff & & \\ \hline \(>\)32 & Hard & 30–50 & Dense \\ \cline{3-4} \cline  
During TBM construction, it is difficult to secure additional ground data beyond those obtained from prior geotechnical investigation. Therefore, the operation and ground data were matched based on the locations of 23 boreholes used for geotechnical investigation, and ~19,000 data were used for analysis. When the geotechnical data obtained from dozens of boreholes and continuous excavation data were matched, the target which was of operation data could be subordinated to the geotechnical data due to the number of data and deviation. Therefore, this study followed [PERSON] and [PERSON] [9] in assuming that geotechnical parameters were implicitly reflected in TBM excavation data. The only geotechnical data used to make class labels for classification prediction were the N-value and the pass rate of the No. 200 sieve in the USCS. Among the 880 features of operating data generated during TBM excavation, features that do not affect classification prediction of this study (e.g., voltage, temperature, location, and attitude information) were excluded from the dataset. Correlations between 20 features (e.g., force, torque, rotational speed, speed, stroke, and pressure) measured at the cutterhead and TBM body were checked, and six representative features were then obtained considering all EPB-type TBM excavation sites. In machine learning, since one feature is expressed as one axis during analysis, if there are many features for learning, the interpretation power of the machine learning model decreases. Therefore, in the data preparation stage for machine learning, it is necessary to reduce features, that is, to make the model intuitively easy to understand through dimension reduction. This study selected the features with good statistical distribution to minimize the feature among the 20 features in Figure 2. Features with unbiased data were preferentially selected, and features with multicollinearity problems as shown in (8) of Figure 2 were excluded. From features (7)-(20) of Figure 2, the statistical distribution was not good (variation poor). Therefore, input variables with good statistical distribution were first selected for data-based learning, and six features were finally selected through correlation verification. The selected features were penetration rate, thrust force, cutterhead torque, screw torque, screw revolution speed, and earth pressure. The correlations of the selected features are shown in Figure 3 and a statistical analysis is summarized in Table 4.  
[PERSON] and [PERSON] [9] described that the machine data includes ground characteristics; Figure 4 shows that each feature has a different distribution depending on the ground characteristics. When the fine-soil component (particle diameter 0.075 mm) was small, (1) thrust speed average was a left-skewed distribution, whereas (2) thrust force was a right-skewed distribution. The peak of the distribution for (3) screw revolution shifted approximately from 5 to 11 rev/min. As the fine-soil component decreased, the distribution