## 1 Introduction
[PERSON] [55] employed three distinct machine learning models, namely multivariate adaptive regression splines (MARS), random forest, and gradient boosting machine models, for the purpose of predicting the California bearing ratio (CBR) of expansive soil subgrade amended with constituents such as sawdust ash, ordinary Portland cement, and quarry dust. The outcomes of the study demonstrated that the random forest model exhibited enhanced predictive efficacy as compared to the MARS and gradient boosting machine models. [PERSON] et al. [56] employed a diverse set of machine learning algorithms to assess and forecast the behavior of soils characterized by varied plastic properties when subjected to expansive behavior under inundation conditions. The results highlighted that the support vector machine (SVM) achieved higher accuracy compared to other methods such as the artificial neural network (ANN), logistic regressor (LR), and random decision forest (RDF). Geotechnical engineers have shown a growing interest in machine learning due to its remarkable ability to model nonlinear problems with multiple variables. Machine learning algorithms can establish non-linear relationships between variables and provide reasonably accurate predictions [57]. In fact, several geotechnical studies have shown that the models developed by machine learning are much more effective than those developed by multiple linear regression (MLR) [53; 57; 58]. In contrast to many previous studies and models that often overlooked the interactive effects of parameters, our research uniquely addresses the combined influence of the sand content, initial degree of saturation, and dry unit weight on soil swelling. While existing studies have made valuable contributions by focusing on individual factors such as the initial degree of saturation and dry unit weight, they have not adequately considered two critical aspects: the impact of sand content on soil swelling characteristics and the determination of optimal applied stress and dry unit weight to reduce volume changes in expansive soil. This study stands out for its significant importance in predicting the optimal applied stress and dry unit weight for mitigating soil volumetric changes, leveraging machine learning as a highly effective tool for this purpose. The study introduces a novel approach in the application of established machine learning algorithms. Although the algorithms themselves are not novel, their application to predict optimal applied stress and dry unit weight for expansive soils, while considering the combined influence of multiple parameters, represents a unique and valuable contribution to the field, marking a notable advancement in the understanding and application of these predictive models. To achieve the objectives of this study, the definitions of 'optimal unit weight' and 'optimal applied stress' are paramount, precisely delineated through thorough laboratory investigations employing loaded swelling pressure tests. The primary goal is to identify the specific dry unit weight and applied stress levels, referred to as the 'optimal unit weight' and 'optimalapplied stress,' at which swelling amplitudes in expansive soils become negligible. This is explicitly defined in the experimental work of the study, specifying that the condition for negligible swelling amplitudes is when they reach zero.