## 1 Introduction
### Research Scope  
A series of tests on expansive soils, including the Atterberg limits test, unconfined compressive test, and Proctor test, were carried out in this study, resulting in a dataset of 225 records with 5 input features and 2 labels. To develop individual predictive models for the unconfined compressive parameters, eight ML approaches were considered, including three classical ones (the proposed ANN-GMDH, ANN, and SVR), four ensemble ones (DTR, RFR, GBR, and XGBoost), and symbolic modeling (SR). The proposed ANN-GMDH is intended to improve the ANN's prediction accuracy by incorporating new relationships between input features. The suggested ML models' performances were carefully tested and compared. This pioneering research marks a significant advance in the field of soil mechanics. To date, no research effort has systematically applied a comprehensive suite of advanced \begin{table}
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**Reference** & **Model** & **Description** \\ \hline [PERSON] 1996; [PERSON] & \multirow{4}{*}{ANN} & These papers focus on the development of statistical models and neural network-based models to predict swelling potential. It has been demonstrated that neural models outperform statistical models in terms of prediction accuracy. \\  & & This study employs artificial neural networks (ANNs) to forecast soil surface movements caused by changes in moisture caused by climate variations. Based on climatic changes, ANN predictions match field measurements of soil movements caused by moisture changes as well as a physics-based method based on soil water content measurements. \\ [PERSON] 2018; [PERSON] & \multirow{4}{*}{ANN} & ANNs were used to predict the swelling pressure and amplitude of unsaturated clay \\
2010; [PERSON] 2009; [PERSON] 2011 & \multirow{4}{*}{ANN} & ANNs were used to predict the free swell index (\(FSI\)) of the expansive soil. The ANN model predicted the \(FSI\) more accurately than the multiple linear regression model. \\  & & Three distinct machine learning models were employed in this study to predict the California Bearing Ratio (\(CBR\)) of an expansive soil subgrade treated with regular Portland cement, quarry dust, and sawdust ash. The findings of the study revealed that the random forest model surpassed both the gradient boosting machine and MARS models in terms of predictive accuracy. \\  & & This work used a broad range of machine learning techniques to evaluate and estimate the swelling of soils with different plastic characteristics under expanding behavior during inundation. \\ Eyo 2022 [74] & ANN, SVR, LR, RFR, and others & According to the findings, the support vector machine (SVR) outperformed other techniques, including random decision forests (RFR), logistic regressors (LRs), and artificial neural networks \\ Benbouras 2021 [75] & SVR, RFR, ANN, DNN, LASSO, ELM, and others & This study used many advanced algorithms to predict the swelling index of expansive soil. The results show that the random forest method yielded more effective and accurate results than the other methods in modeling geotechnical phenomena. \\  & & This work developed a prediction model of the uniaxial tensile strength using three ensemble machine learning approaches and three classical methods. Based on all metrics, the results indicate that the stacked generalization (SG) and XGBoost perform best, with the ANN, RF, MARS, and SVM following closely after. \\ \hline \hline \end{tabular}
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Table 2: Overview of machine learning models used in the literature to predict expansive soil behavior. machine learning models to analyze both \(q_{u}\) and \(E_{t}\) properties of expansive soil while accounting for the influence of sand. Our work fills this crucial gap by integrating advanced machine learning techniques with experimental data on expansive soil properties.