## 1 Introduction
Although machine learning (ML) techniques have been widely applied in various fields, there has been limited research on their use in geotechnical engineering. However, researchers have started exploring the potential of ML techniques for soil classification and estimation of soil parameters using CPT data. Some geotechnical researchers applied ML techniques to predict various geotechnical properties such as landslide [31], slope stability [32; 33; 34], soil type [12], and shear wave velocity [13] utilizing CPT data. In our study, we aim to evaluate the performance of four commonly used ML algorithms, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision tree (DT), for soil classification using CPT data. This study has the potential to address the gap in the existing literature and offer valuable insights into the efficacy of ML algorithms for soil classification through CPT data. Furthermore, the findings of this study could help improve the efficiency and accuracy of soil classification in geotechnical engineering, which could have significant implications for geotechnical engineering. The selection of the ML model for a classification task is based on several factors, including desired accuracy, dataset size, generalization ability, interpretability, and robustness. For our specific soil classification problem, we chose to evaluate the performance of the ANN, DT, SVM and RF algorithms, each with its own strength and weakness. ANNs are known for their ability to capture complex non-linear relationships in the data [35], while RF is the ML algorithm that utilizes multiple decision trees to enhance the accuracy and robustness of the model [36; 37]. SVM can handle high-dimensional data and nonlinear decision boundaries [38; 39], and DT is easy to interpret and visualize, and can handle both categorical and numerical data. By selecting these four algorithms, we aimed to strike a balance between complexity and interpretability and compare the performance of the models. Our choice of algorithms provides a diverse set of models that can handle various aspects of the classification task, including complex relationships, high-dimensional data, and interpretability. By evaluating their performances, we hope to gain insights into which algorithm is the most suitable for our specific soil classification problem. The performance of the ML models can be compromised by various factors if not properly addressed. One of the critical factors that can affect the models' performance is the selection of hyperparameters. By carefully selecting and tuning the hyperparameters, we can improve the models' robustness and ensure that they can perform optimally in real-world applications [40]. Grid search (technique in which sets of predefined hyperparameter values are defined) is one of the most commonly used methods for hyperparameter tuning [41]. Bayesian optimization is another approach that uses a probabilistic model to estimate the performance of different hyperparameter configurations [41; 42]. ML models rely on input features to make predictions, and the quality and relevance of those features can have a significant impact on the performance of the models [43]. Performing feature importance such as permutation feature importance [44] and eliminating irrelevant features from the dataset can significantly enhance the performance of ML models. Feature importance is the process of determining the most important features in a dataset for a given model. In ML, outliers are one of the factors that contribute to the poor performance of ML models. According to the literature [45; 46], outliers are data points that deviate significantly from the surrounding data points. Abnormal data readings during CPT operations can primarily occur due to human or procedural errors, such as the addition of a rod [47]. These outliers are not representative of the actual CPT measurements and should be detected and removed during the data preprocessing stage. The structure of this paper comprises six sections.