# Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
## 2 Datasets Preprocessing and Visualization  
The dataset used in this study was obtained from a previously published dataset [52]. This study utilized 61 CPT soundings, each containing over 1000 \(q_{c}\) and \(f_{s}\) recordings. These data sets were collected from various regions of Austria, including the Vienna Basin, Gastein Valley, and Zell Basin. The data is publicly accessible and 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 CPT datasets were preprocessed before applying ML training and testing techniques. The preprocessing step involved removing outliers from the data. Specifically, outliers were identified and removed from both the \(q_{c}\) and \(f_{s}\) values in the raw CPT data. Any data point that exceeded twice the interquartile range (\(IQR\)), where \(IQR\) is the difference between the third quartile (Q3) and the first quartile (Q1), was considered an outlier. Next, the target variable, which in this case was the shear wave velocity, \(V_{s}\), was estimated using Equation (1) [16]. Subsequently, the datasets were divided into a training set and a testing set, with a ratio of 0.7:0.3 for training and testing purposes. \[V_{S}=\sqrt{\left(\frac{q_{c}-\sigma_{v0}}{p_{a}}\times 10^{0.55I_{c}+1.68} \right)} \tag{1}\]  
where \(q_{c}\) represents cone tip resistance, \(\sigma_{v0}\) represents total overburden pressure, \(p_{a}\) represents atmospheric pressure, and \(I_{c}\) represents soil behavioral type index estimated as follows:  
\[I_{c}=\left(3.47-log((q_{c}-\sigma_{v0})/\sigma_{v0})^{2}+(logF_{r}+1.22)^{2} \right)^{0.5} \tag{2}\]  
\[F_{r}=f_{s}/(q_{c}-\sigma_{v0})\times 100 \tag{3}\]  
where \(f_{s}\) represents sleeve friction, \(\sigma\)\(\sigma\)\(v_{s0}\) is the effective overburden stress, and \(F_{r}\) represents normalized friction ratio. The statistical summaries of both the training and testing datasets considered in this study are presented in Table 1. To gain further insights into the relationship between the input features and the target variable (\(V_{s}\)), scatter plots are presented in Figure 1. Each scatter plot indicates the correlation between an individual input feature and the target variable. In addition, Figure 2 shows the frequency distribution of the input features and target variable, providing a visual representation of their distribution patterns. Furthermore, box plots of both the input features and the target variable are presented in Figure 3, offering an overview of their distribution. The interdependencies among input features in ML models can lead to overfitting and decreased efficiency. To assess the correlation between each input feature, a [PERSON]'s correlation analysis was conducted. Figure 4 displays the correlation coefficients among the input features in the dataset. The correlation coefficients range from \(-0.08\) to \(0.51\), indicating a combination of weak to moderate correlations among the features. The absence of highly correlated features in the correlation analysis suggests a lower risk of overfitting, as no redundant features were observed.