# Soil Classification by Machine Learning Using a Tunnel Boring Machine's Operating Parameters
## 5 Results  
### Classification Results by Soil Particle Size (Step 1)  
The first step of soil classification in Figure 5 is classifying soil into three types. The first classification of fine or coarse by the USCS is completed using a No. 200 sieve (particle size 0.075 mm). If the amount of material passing through this sieve (F) is at least 50%, \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Fomulas for Measuring Performance**} & \multicolumn{2}{c}{**Definition of the Terms**} \\ \hline Accuracy & \(\mathrm{TP}+\mathrm{TN}\) & Accuracy is the proportion of the total number of predictions that are correct \\ \hline Precision & \(\mathrm{TP}\) & Precision is the ratio of the total number of correctly classified positive examples \\ \(\mathrm{TP}+\mathrm{FP}\) & \(\mathrm{and the total number of predicted positive examples}\) \\ \hline Recall & \(\mathrm{TP}\) & Recall is also referred to as true positive \\ \(\mathrm{TP}+\mathrm{FN}\) & \(\mathrm{rate}\) or sensitivity \\ \hline F1-score & \(2\times\dfrac{\mathrm{Precision}\times\mathrm{Recall}}{\mathrm{Precision}+ \mathrm{Recall}}\) & F1-score is a weighted average of the recall (sensitivity) and precision \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification performance measures. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Algorithm**} & \multicolumn{2}{c}{**Hyperparameter**} \\ \hline Tree-Based Methods & Decision Tree & max depth: 5 \\ \hline \multirow{6}{*}{Ensemble Learning Method, Boosting} & AdaBoost & learning rate: 0.1, n estimators: 300 \\ \cline{2-3}  & \multirow{3}{*}{Gradient Boosting} & learning rate: 0.5, max depth: 4, max features: 3, n estimators: 500 \\ \cline{2-3}  & & \(\mathrm{xG}\) boosting & learning rate: 0.2, max depth: 4, n estimators: 400 \\ \cline{2-3}  & \multirow{3}{*}{Light GBM} & learning rate: 0.1, max depth: 5, n estimators: 300 \\ \cline{1-1} \cline{2-3}  & & \(\mathrm{xG}\) boosting & \(\mathrm{xG}\) boosting \\ \cline{1-1} \cline{2-3}  & & \(\mathrm{xG}\) boosting & \(\mathrm{xG}\) boosting \\ \cline{1-1} \cline{2-3}  & & \(\mathrm{xG}\) boosting & \(\mathrm{xG}\) boosting \\ \cline{1-1} \cline{2-3}  & & \(\mathrm{xG}\) boosting & \(\mathrm{xG}\) boosting \\ \cline{1-1} \cline{2-3}  & & \(\mathrm{xG}\) boosting & \(\mathrm{xG}\) boosting \\ \hline \hline Support Vector Machine & SVM & C: 2.0, gamma: 2.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of the hyperparameters of the algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Positive (1)** & **Negative (0)** \\ \hline Positive (1) & True Positive (TP) & False Positive (FP) \\ \hline Negative (0) & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of classification. the soil is fine and classified as class 0. Lower pass rates indicate coarse soil, which is subdivided into class 1 (12% \(<\) F \(<\) 50%) and class 2 (F \(\leq\) 12%) (Table 8). Table 9 lists the results for each learning model using the above soil classification criteria. The accuracy value indicates how similar the predicted data are to the actual data, with values closer to 1 denoting better classification performance. In case there is an imbalance in the model class, the precision, recall, and F1-score should also be considered when assessing model performance (Table 6). The decision tree and AdaBoost models have accuracy values of 0.759 and 0.781, respectively, indicating mediocre classification performance. The support vector machine and ensemble boosting models (i.e., gradient boosting, XG boosting, and Light GBM) have high accuracy values of at least 0.974; their other performance indicators (precision, recall, and F1-score) are also good. The classification performance results in the first stage of soil classification indicate the suitability of all four of these models.