# Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
## 6 Conclusions  
This study utilized various ML algorithms, including RF, SVM, DT, and XGBoost, to predict \(V_{S}\) from CPT data. To train and test these ML models, we used a previously published open-source CPT dataset. The hyperparameters of each ML model were fine-tuned through Bayesian optimization with cross-validation techniques. Eight performance metrics, namely \(RMSE\), \(MAE\), \(MAPE\), \(R^{2}\), \(A10-I\), \(SI\), \(PI\) and \(U_{95}\), provided quantitative evaluation of the models. Based on our results, the following conclusion can be drawn:  
* The RF model outperformed the other ML models, achieving the lowest error metrics on both the training and testing datasets. Specifically, it achieved an \(RMSE\) of 0.46 and 0.96, an \(MAE\) of 0.24 m/s and 0.5 m/s, and an \(MAPE\) of 0.17% and 0.36%, respectively. The model also demonstrated low scatter, with \(SI\) values of 0.003 and 0.006, and \(PI\) values of 0.002 and 0.004 on the training and testing datasets, respectively. Additionally, the RF model achieved \(R^{2}\) and \(A10-I\) values of 1 on both datasets, indicating a perfect fit. Furthermore, the RF model recorded the lowest uncertainty, with a \(U_{95}\) value of 1.24 on the training dataset.
* The SVM and XGBoost models also exhibited strong performance, with slightly higher error metrics compared with the RF model. These two models ranked second and third, respectively, following the RF model, which achieved the highest performance. However, the DT model performed poorly, with higher error rates and uncertainty in predicting \(V_{s}\).
* The RF model demonstrated its overall superior performance and high accuracy in predicting soil \(V_{s}\), even when trained with minimal input features. Hence, owing to its excellent performance across multiple metrics, the RF model can be integrated into a software package for rapid and accurate prediction of soil \(V_{s}\).
* In summary, while this study relied solely on CPT data for training ML models, it is important to recognize the limitations of the CPT, particularly its primary suitability for fine-grained soils. To further enhance the application of ML models in soil characterization, future research should consider incorporating experimental results and data for coarse-grained soil types.  
Conceptualization, A.T.C. and R.P.R.; methodology, A.T.C.; software, A.T.C.; validation, A.T.C. formal analysis, A.T.C.; writing--original draft preparation, A.T.C.; writing--review and editing, R.P.R.; visualization, A.T.C.; supervision, R.P.R. All authors have read and agreed to the published version of the manuscript.  
This publication was financially supported by Szechenyi Istvan University.  
Not applicable.  
The CPT data can be downloaded from the following link: [[https://www.tugraz.at/en/institutes/ibg/research/computational-geotechnics-group/database/](https://www.tugraz.at/en/institutes/ibg/research/computational-geotechnics-group/database/)]([https://www.tugraz.at/en/institutes/ibg/research/computational-geotechnics-group/database/](https://www.tugraz.at/en/institutes/ibg/research/computational-geotechnics-group/database/)) (accessed on 12 May 2023).  
The authors declare no conflict of interest.