# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Model Performance Comparison
#### 3.2.1 Initial Model Screening Results  
The initial screening phase evaluated 14 regression algorithms using a conventional train-test split. The dataset was divided into 80% training data and 20% testing data, allowing for a straightforward evaluation of model accuracy before implementing more advanced techniques such as cross-validation and hyperparameter tuning. Table 3 presents the performance metrics of each model in terms of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R-squared (R\({}^{2}\)) score.  
The results revealed distinct patterns in model performance. Linear models (Linear Regression, Ridge, Lasso, and Elastic Net) achieved moderate R\({}^{2}\) values ranging from 0.76 to 0.84, with RMSE values between 479 and 588 kPa. The relatively limited performance of these models indicates that UCS development in cement-stabilized soils follows non-linear patterns that cannot be adequately captured by linear relationships.  
In contrast, tree-based ensemble methods demonstrated superior predictive capability. Gradient Boosting achieved the highest accuracy with an R\({}^{2}\) of 0.96 and RMSE of 253 kPa, followed closely by Random Forest (R\({}^{2}\) = 0.95; RMSE = 265 kPa) and Light-GBM (R\({}^{2}\) = 0.95; RMSE = 270 kPa). These algorithms effectively captured the complex, non-linear interactions between cement content, curing period, and UCS. Support Vector Regression (SVR) with an RBF kernel performed notably poorly (R\({}^{2}\) = 0.02), suggesting that the chosen kernel configuration was unsuitable for this particular prediction task. Other algorithms, including Decision Tree, XGBoost, CatBoost, KNN, Gaussian Process, and  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline _Linear Methods_ & & & & \\ Linear Regression & 229,558.94 & 479.12 & 405.85 & 0.8390 \\ Ridge & 229,731.25 & 479.3 & 406.75 & 0.8388 \\ Lasso & 229,581.38 & 479.15 & 406.16 & 0.8389 \\ ElasticNet & 345,532.78 & 587.82 & 516.76 & 0.7576 \\ \hline _Tree-Based Methods_ & & & & \\ Decision Tree & 83,397.33 & 288.79 & 201.92 & 0.9415 \\ Random Forest & 70,209.46 & 264.97 & 178.98 & 0.9507 \\ Gradient Boosting & 64,004.83 & 252.99 & 171.5 & 0.9551 \\ XGBoost & 83,396.61 & 288.78 & 201.92 & 0.9415 \\ LightGBM & 72,716.76 & 269.66 & 181.41 & 0.9490 \\ CatBoost & 81,478.5 & 285.44 & 200.55 & 0.9428 \\ Ada Boost & 84,803.66 & 291.21 & 193.61 & 0.9405 \\ \hline _Other Non-Linear Methods_ & & & & \\ SVR & 1,403,743.36 & 1184.8 & 1018.6 & 0.0152 \\ KNN & 83,397.33 & 288.79 & 201.92 & 0.9415 \\ Gaussian Process & 83,397.3 & 288.79 & 201.92 & 0.9415 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance grouped by algorithm type.  
AdaBoost, demonstrated good performance with R\({}^{2}\) values around 0.94, but did not match the accuracy of the top-performing ensemble methods.  
The substantial performance gap between linear and tree-based models (\(\Delta R^{2}\approx\) 0.1) supports the hypothesis that UCS development in cement-treated soils is governed by complex, non-linear processes that cannot be adequately captured by simple parametric relationships. Similar non-linear dependencies have been documented in previous research on various geotechnical properties [99; 100; 101; 102; 103].