# Soil Classification by Machine Learning Using a Tunnel Boring Machine's Operating Parameters
## 5 Results
### Classification Results by N-Value (Step 2)  
The second step of the classification model in Figure 5 is to subdivide the previously classified coarse and fine soil. This step uses the N-value from SPT to further classify coarse soil as dense or loose and fine soil as soft or stiff. The relevant ranges of the considered criterion, the N-value, differ depending on whether the soil is coarse or fine: non-cohesive (coarse) soil is classified using Table 2 as loose or dense based on a threshold N-value of 10, and cohesive (fine) soil is classified as stiff or soft relative to a threshold N-value of 5. Table 10 shows the classification classes in the second step.  
Table 11 summarizes the performance of the models during step 2 in classifying soils previously categorized as cohesive or non-cohesive in step 1. The decision tree and AdaBoost show accuracy values of 0.835 and 0.879, respectively, for cohesive soils and 0.794 and 0.855, respectively, for non-cohesive soils. Similar to the performance in step 1 (Section 4.1), the single algorithm support vector machine and the ensemble boosting models gradient boosting, XG boosting, and Light GBM show higher accuracy values (here, at least 0.943), and their other performance indicators are also high.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class Range**} & \multicolumn{3}{c}{**Description**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Cohesive Soils**} & \multicolumn{2}{c}{**Non-Cohesive Soils**} \\ \hline
0 & N \(\geq\) 5 & stiff & N \(>\) 10 & dense \\ \hline
1 & N \(<\) 5 & soft & N \(\leq\) 10 & loose \\ \hline \hline \end{tabular}
\end{table}
Table 10: Criteria for classification in step 2.  
Figure 8: Summary of accuracy and fit times for cross-validation.  
The four models with accuracy values of at least 0.943 have similar performance, so they can then be distinguished by their speed of operation on the same PC (as in Section 4.1). Light GBM is considered optimal, as it offers excellent classification performance and fast learning. Figure 9 shows the results of classifying and predicting the density and consistency of the entire learning dataset using Light GBM.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multicolumn{8}{c}{**Test Dataset**} \\ \cline{2-10}  & \multicolumn{4}{c}{**Cohesive Soils**} & \multicolumn{4}{c}{**Non-Cohesive Soils**} \\ \cline{3-10}  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{Decision Tree} & class 0 & \multirow{3}{*}{0.835} & 0.84 & 0.94 & 0.89 & \multirow{3}{*}{0.794} & 0.80 & 0.43 & 0.56 \\ \cline{2-10}  & class 1 & & 0.82 & 0.61 & 0.70 & & 0.80 & 0.96 & 0.87 \\ \hline \multirow{3}{*}{AdaBoost} & class 0 & \multirow{3}{*}{0.879} & 0.87 & 0.97 & 0.92 & \multirow{3}{*}{0.855} & 0.86 & 0.65 & 0.74 \\ \cline{2-10}  & class 1 & & 0.91 & 0.69 & 0.78 & & 0.87 & 0.95 & 0.91 \\ \hline \multirow{3}{*}{Gradient Boosting} & class 0 & \multirow{3}{*}{0.955} & 0.96 & 0.97 & 0.97 & \multirow{3}{*}{0.963} & 0.94 & 0.94 & 0.94 \\ \cline{2-10}  & class 1 & & 0.93 & 0.92 & 0.93 & & 0.97 & 0.97 & 0.97 \\ \hline \multirow{3}{*}{XG Boosting} & class 0 & \multirow{3}{*}{0.956} & 0.96 & 0.97 & 0.97 & \multirow{3}{*}{0.964} & 0.94 & 0.94 & 0.94 \\ \cline{2-10}  & class 1 & & 0.94 & 0.92 & 0.93 & & 0.98 & 0.98 & 0.97 \\ \hline \multirow{3}{*}{Light GBM} & class 0 & \multirow{3}{*}{0.956} & 0.97 & 0.97 & 0.97 & \multirow{3}{*}{0.962} & 0.94 & 0.94 & 0.94 \\ \cline{2-10}  & class 1 & & 0.93 & 0.93 & 0.93 & & 0.97 & 0.97 & 0.97 \\ \hline \multirow{3}{*}{Support Vector Machine} & class 0 & \multirow{3}{*}{0.943} & 0.95 & 0.93 & \multirow{3}{*}{0.93} & 0.94 & 0.94 & 0.93 & 0.94 \\ \cline{2-10}  & class 1 & & 0.92 & 0.90 & 0.91 & & 0.97 & 0.98 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Evaluation of classification performance (step 2) for EPB TBM datasets.  
Figure 9: Prediction results by classification models (step 2: N-value): (a) cohesive soils; (b) non-cohesive soils.