## 1 Introduction
* Use a machine learning model to determine the optimal dry unit weight for expansive soil as a fill material, or the optimal stress to achieve the lowest volumetric change. * Design a suitable foundation that can deliver the required stress based on the building's specifications. * Isolate any external factors that could influence soil moisture content, such as nearby trees, to prevent tree roots from absorbing moisture from beneath the structure [24]. The scientific literature has documented various experimental methods for characterizing the phenomenon of soil swelling, with some of the most commonly used methods including those outlined in references [26; 27; 28; 29]. In order to gain a deeper understanding of the behavior of expansive soils, other researchers have sought to develop new models, as seen in references [30; 31; 32]. These models can be used to estimate the behavior of expansive soils based on simple and easily performed experiments, such as those that utilize Arterberg's limits and grain-size analysis. However, the development or modification of such models requires prior knowledge of the relationships between data that depend on a multitude of external factors related to soil composition, which are difficult to define using only traditional statistical methods due to their interdependence. Given the complexity of expansive soils, numerous researchers have strived to integrate machine learning (ML) with geotechnical reliability analysis, with the objective of enhancing computational accuracy and efficiency. This pursuit has yielded a variety of successful applications [33; 34; 35; 36; 37]. In the realm of geotechnical reliability analysis, the fundamental objective of machine learning (ML) is to reconstruct intricate high-dimensional implicit performance functions by leveraging insights from meticulously curated datasets. These datasets predominantly encompass diverse input stochastic variables such as liquid limit, plastic index, unit weight, and degree of saturation. Correspondingly, the datasets include relevant quantities of interest, such as swell amplitude or swell pressure. Utilizing this methodology, the application of the ML analysis model offers an efficient means of accurately predicting outcomes in the geotechnical field. This accuracy is confirmed through rigorous training and thorough validation, demonstrating the model's ability to meet intended performance benchmarks with precision and computational efficiency. Geotechnical analysis has effectively utilized a wide range of machine learning (ML) algorithms. These algorithms include artificial neural networks (ANN) [38; 39; 40], support vector machines (SVM) [41], relevant vector machines (RVM) [42], gradient boosting regression (GBR) [43], decision tree regression (DTR) [43], K nearest neighbor regression (KNR) [43], random forest regression (RFR) [43], particle swarm optimization (PSO) [44], extreme learning machines (ELM) [45], multivariate adaptive regression splines (MARS) [45; 46], and extreme gradient boosting (XGBoost) [37; 47]. Machine learning (ML) has been extensively used by researchers to model the behavior of and solve problems associated with expansive soil. For instance, [PERSON] et al. [48] and [PERSON] and [PERSON] [49] used ANNs to model swelling, and they concluded that the ANN technique was superior to multivariate regression analysis. ANNs were also employed by [PERSON] et al. [50] to predict the vertical surface movement of soil due to shrinkage and swelling. In addition, ANNs were utilized by [PERSON] et al. [51] and [PERSON] [52] to estimate the amplitude and swelling pressure of unsaturated clay. Similarly, [PERSON] et al. [53], [PERSON] and [PERSON] [54], and [PERSON] and [PERSON] [38] estimated the swelling pressure of expansive soils using ANNs. Furthermore, ANNs were used by [PERSON] et al. [39] to predict the free swell index of the expansive soil. These studies have demonstrated the effectiveness of the artificial neural network technique.