## 3 Datasets  
For our study, we used publicly available CPT datasets contributed by [47], which were accessible in the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE) database. The CPTs were collected from an area measuring 50 by 50 m. Each CPT was performed to a depth of 5 m below the ground surface, and the measurement spacing of qc and fs was 5 mm. Further information about the specifics of the CPTs can be found in [47, 49, 50]. We preprocessed the datasets using MS Excel (see Figure 3) to categorize the soil behavior types based on [PERSON]'s classification [48]. In order to reduce bias and ensure that the training and testing datasets are representative of the overall dataset, we shuffled the dataset and divided it into training and testing datasets using (80, 20) ratio. The steps followed to preprocess data are as follows:  
(1) Combine the individual CPT soundings into the appropriate columns (e.g. depth, qc, and fs) using Power Query in MS Excel and remove the missing values. (2) Calculate the inter quartile range (IQR) values for the qc and fs columns using Excel's built-in functions such as QUARTILE. (3) Determine the upper threshold values for outlier detection by multiplying the IQR by three and adding the third quartile. (4) Identify the outlier values in the qc and fs columns using conditional formatting. (5) Remove the outlier from the dataset and replace the values with the threshold value. (6) Estimate the \(R_{f}\), total vertical stress \((\sigma_{v})\), effective vertical stresses \((\sigma_{v}^{\prime})\), and Ic. Table 2 presents a statistical summary of the datasets organized into 222,100 rows and 7 columns. The frequency distribution of each soil type in the dataset is shown in Figure 4. The distribution analysis demonstrated that soil type 5 has the highest frequency and represents over 50% of the total dataset. Soil type 4 has the second highest frequency and represents over 30% of the dataset. Soil types 2, 3, 6, and 7 (minority class) have much lower frequencies and represent less than 20% of the dataset combined, indicating an imbalanced dataset. Balancing this highly imbalanced dataset using oversampling or under sampling techniques may be possible, but it can also affect the natural variability of the soil, potentially leading to biased predictions and incorrect soil classification. To avoid this, we opted to train the ML models on the imbalanced datasets and evaluate their performances using appropriate evaluation metrics such as sensitivity, precision, and F1_score, instead of artificially generating or discarding soil samples that could impact the true variability of the soil. \begin{table}
\begin{tabular}{c c c} \hline \hline
**SBT Classification** & **Ic Boundaries** & **Soil Type ID/ML Labels** \\ \hline Organic soils: peats & Ic \textgreater{} 3.6 & 2 \\ Clays: clay to silty clay & 2.95 \textless{} Ic \textgreater{} 3.6 & 3 \\ Silt mixtures: clayy silt and silty clay & 2.6 \textless{} Ic \textgreater{} 2.95 & 4 \\ Sand mixtures: silty sand to sandy silt & 2.05 \textless{} Ic \textgreater{} 2.6 & 5 \\ Sands: clean sand to silty sand & 1.31 \textless{} Ic \textgreater{} 2.05 & 6 \\ Gravelly sand to dense sand & Ic \textless{} 1.31 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Soil behavior type classification based on Ic boundaries (adapted from [8,48]).