## 5 Results and Discussion
### Predictability of Models  
In this section, the results of typical ML models were presented. All three models showed good prediction when the linear fit almost overlapped with the best fit on both the training set and testing set (Figure 11). Out of the three models, ANN showed the best performance when all prediction points on the training and testing set were almost closest to the perfect fit. Based on the analysis results, it can be confirmed that the ML models were successful in predicting UCS of the cement-stabilized soil and optimized ANN was the most suitable model for this data set.  
Table 7 presented some previous research results on ML applications in determining USC of some soil type. The results of the present study and previous studies show the expected effect of the ML technique in determining the UCS of soils with most of the R reaching between 0.8 and 0.95 on the testing data set. However, due to the use of different data sets, the comparison among these results make no sense. A project that combines datasets from different studies is needed to create a large database for building generalized models in the UCS prediction of soil reinforcement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Average** & **Min** & **Max** & **SD (a)** \\ \hline \multirow{2}{*}{GB} & Training & 203.49 & 161.85 & 229.3 & 11.92 \\  & Testing & 319.23 & 211.68 & 436.57 & 34.91 \\ \hline \multirow{2}{*}{ANN} & Training & 115.29 & 86.01 & 155.6 & 12.71 \\  & Testing & 292.2 & 171.95 & 413.51 & 41.57 \\ \hline \multirow{2}{*}{SVM} & Training & 159.5 & 125.69 & 193.39 & 11.87 \\  & Testing & 309.76 & 215.35 & 434.12 & 37.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the 300 samplings using MAE criteria.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Average** & **Min** & **Max** & **SD (a)** \\ \hline \multirow{2}{*}{GB} & Training & 0.968 & 0.968 & 0.982 & 0.005 \\  & Testing & 0.912 & 0.798 & 0.973 & 0.029 \\ \hline \multirow{2}{*}{ANN} & Training & 0.980 & 0.968 & 0.991 & 0.004 \\  & Testing & 0.925 & 0.850 & 0.981 & 0.031 \\ \hline \multirow{2}{*}{SVM} & Training & 0.965 & 0.950 & 0.979 & 0.006 \\  & Testing & 0.903 & 0.759 & 0.957 & 0.031 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the 300 samplings using R criteria.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Average** & **Min** & **Max** & **SD (a)** \\ \hline \multirow{2}{*}{GB} & Training & 284.41 & 215.58 & 327.34 & 20.23 \\  & Testing & 446.79 & 298.94 & 629.76 & 58.71 \\ \hline \multirow{2}{*}{ANN} & Training & 231.2 & 163.03 & 292.94 & 23.16 \\  & Testing & 419.82 & 221.04 & 599.35 & 65.91 \\ \hline \multirow{2}{*}{SVM} & Training & 292.7 & 229.11 & 341.08 & 19.97 \\  & Testing & 446.67 & 299.93 & 641.16 & 58.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the 300 samplings using RMSE criteria.  
Figure 11: Measure and predicted values of unconfined compressive strength (UCS) of cement-stabilized soil using the training set: (**a**) gradient boosting (GB); (**c**) ANN and (**e**) support vector machine (SVM) and testing set: (**b**) GB; (**d**) ANN and (**f**) SVM.