# Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
###### Abstract  
Seismic response assessment requires reliable information about subsurface conditions, including soil shear wave velocity (\(V_{s}\)). To properly assess seismic response, engineers need accurate information about \(V_{s}\), an essential parameter for evaluating the propagation of seismic waves. However, measuring \(V_{s}\) is generally challenging due to the complex and time-consuming nature of field and laboratory tests. This study aims to predict \(V_{s}\) using machine learning (ML) algorithms from cone penetration test (CPT) data. The study utilized four ML algorithms, namely Random Forests (RFs), Support Vector Machine (SVM), Decision Trees (DT), and eXtreme Gradient Boosting (XGBoost), to predict \(V_{s}\). These ML models were trained on 70% of the datasets, while their efficiency and generalization ability were assessed on the remaining 30%. The hyperparameters for each ML model were fine-tuned through Bayesian optimization with k-fold cross-validation techniques. The performance of each ML model was evaluated using eight different metrics, including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (\(R^{2}\)), performance index (\(PI\)), scatter index (\(SI\)), \(A10-I\), and \(U_{85}\). The results demonstrated that the RF model consistently performed well across all metrics. It achieved high accuracy and the lowest level of errors, indicating superior accuracy and precision in predicting \(V_{s}\). The SVM and XGBoost models also exhibited strong performance, with slightly higher error metrics compared with the RF model. However, the DT model performed poorly, with higher error rates and uncertainty in predicting \(V_{s}\). Based on these results, we can conclude that the RF model is highly effective at accurately predicting \(V_{s}\) using CPT data with minimal input features. Keywords:shear wave velocity; cone penetration test; machine learning; Random Forests; support vector machine; decision trees; eXtreme gradient boosting; regression +
Footnote †: journal: Medical  
samples collected from the site and offer a controlled environment for testing, providing detailed information on soil behavior under varying stress conditions. However, retrieving high-quality intact soil samples is a challenging task that requires specialist equipment [14]. Furthermore, it is important to note that the properties of collected samples may significantly change over time due to variations in stress conditions, temperature fluctuations, and moisture content. A viable alternative is to correlate \(V_{s}\) to cone penetration test (CPT) data, a relatively easier approach. Many empirical correlations have been developed over the past couple of decades to estimate \(V_{s}\) from CPT data [15; 16; 17; 18; 19]. The CPT test involves pushing a cone-shaped instrument into the ground at a constant rate while measuring the resistance of the soil. Two measurements are typically taken during this test: cone tip resistance (\(q_{c}\)) and sleeve friction (\(f_{s}\)) [20; 21]. The CPT test provides continuous and reliable soil data, making it an efficient and cost-effective method in geotechnical engineering practice. This wealth of CPT data has attracted the attention of many geotechnical researchers to further improve the prediction accuracy of \(V_{s}\) employing machine learning (ML) algorithms [22; 23; 24; 25; 26]. ML algorithms have shown great promise in accurately predicting \(V_{s}\) from CPT data. The ML algorithms can learn complex relationships between input variables (e.g., \(q_{c}\) and \(f_{s}\) records) and output variables (e.g., soil \(V_{s}\)) from large datasets without the need for explicit mathematical models.