## 5 Conclusions  
This study investigates the effect of sand in expansive soil on unconfined test parameters and presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils. Focusing on the impact of sand content, saturation level, and dry density, the following conclusions were made:  
* The modulus of elasticity (\(E_{t}\)) and unconfined compressive strength (\(q_{u}\)) are positively correlated with the soil's dry unit weight. * Increasing sand content, while keeping dry unit weight constant, reduces modulus of elasticity and unconfined compressive strength due to expanded voids decreasing suction forces. * A rise in the degree of saturation results in a notable but nonlinear decrease in both modulus of elasticity and unconfined compressive strength, becoming less pronounced at higher saturation levels. * For samples at maximum dry unit weight, adding sand initially increases strength and elasticity up to a threshold of 30% sand content, beyond which the effect reverses. * Optimal sand content for maximum strength and elasticity at full saturation is 30%. Further into the study, equations derived from symbolic regression are introduced to predict the modulus of elasticity and unconfined compressive strength. These equations consider a range of factors including void ratio, degree of saturation, liquid limit, and sand percentage, thereby providing a robust predictive framework for soil properties. Feature importance analysis reveals the following:  
* Degree of saturation is the most influential variable, showing a negative correlation with \(q_{u}\) and \(E_{t}\). * Void ratio shows a significant negative correlation. * Dry unit weight demonstrates a positive correlation. * Sand content exhibits a negative correlation. * Liquid limit has the least impact, with a positive correlation. The ANN-GMDH model, integrating GMDH features, outperforms other models in predicting soil behavior. Both ANN and SVR models are effective, with ANNs excelling in capturing complex patterns. This study's limitations arise from depending on a specific dataset tailored to a particular range for each parameter, as indicated in Table 4. Additionally, Afterberg limits should be positioned slightly above and near line A on the Casagrande chart. This commitment to achieving accurate predictions may, however, pose constraints on the generalizability of the findings. Although the evaluated models produced reasonable results, there is still room for improvement. Future work could explore new methods such as deep learning or reinforcement learning. Additionally, enhancing the models' prediction capabilities might require incorporating other characteristics, such as the plasticity index, clay percentage, maximum dry density, and optimum saturation degree. Expanding the dataset and refining risk mitigation strategies related to expansive soils should be a focus of future research, contributing to the continuous advancement of geotechnical engineering practices. These enhancements aim to further solidify the applicability and effectiveness of the proposedapproach in real-world scenarios. In conclusion, while this study provides valuable insights, addressing these limitations via broader datasets and refined strategies will be crucial for improving the reliability and robustness of predictive models in geotechnical engineering. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14114819/s1](https://www.mdpi.com/article/10.3390/app14114819/s1)]([https://www.mdpi.com/article/10.3390/app14114819/s1](https://www.mdpi.com/article/10.3390/app14114819/s1)). Conceptualization, A.A., M.O.A. and R.P.R. ; investigation, A.A. and M.O.A. ; experimental work, A.A.; writing--original draft preparation, A.A., H.H.H. and C.L. ; modeling, A.A., R.P.R., H.H.H. and C.L. ; writing--review and editing, R.P.R., M.O.A. and A.A.; supervision, R.P.R. and M.O.A. All authors have read and agreed to the published version of the manuscript.