## 2 Materials and Methods
### Laboratory Tests  
The materials utilized in this study included expansive clayey soil sourced from Demsarkhu, Lattakia, Syria and fine sea sand. Figure 2 displays the case study location. For further details on the characteristics of these materials, refer to [PERSON] and [PERSON] [13]. The sand and clay were mixed in varying proportions, ranging from 10% to 50% relative to the dry weight. All the laboratory tests were conducted according to ASTM. Granular gradient curves were plotted and presented in Figure 3. To ensure that the materials were free of impurities, the clay and sand samples were cleaned using sieve N200. The chemical composition of the expansive clay was determined and is presented in Table 3.  
Figure 1: Machine learning model implementation workflow. Figure 2: Case study location: Coordinates: 35\({}^{\circ}\)33\({}^{\prime}\)12.2\({}^{\prime\prime}\) N, 35\({}^{\circ}\)46\({}^{\prime}\)35.9\({}^{\prime\prime}\) E.  
According to the Unified Soil Classification System [77] and the AASHTO classification system [78], the expansive clay employed in this investigation was classified as CH and A-7-5, respectively. Prior research [79; 80] established a relationship between the Atterberg limits plot position on a plasticity chart and the soil's clay mineralogy. Plotting the liquid limit (\(LL\)) and plastic index (\(PI\)) for each percentage of sand (\(Fs\)) resulted in a plasticity chart (Figure 4) with ranges for kaolinite, montmorillonite, illite, and chlorite. The results indicated that the samples in this study with varying amounts of added sand are composed of illite. Figure 4: Effect of sand content on the Atterberg limits. Figure 3: Variation in granular gradients across tested mixtures. The standard proctor test was used to determine the optimal moisture content and maximum dry density of the sand-expansive clay mixtures (Figure 5). Refer to [PERSON] and [PERSON] [13] for detailed information on Proctor, Arterberg, and consolidation tests. In the current study, unconfined compression tests (UCTs) were performed on cylindrical samples that were twice their diameter in length. The tests were conducted at various densities and saturations for each sand percentage added, with a deformation rate of 2 mm/min. Figure 6 depicts the step-by-step procedure used in the modified sample preparation at the laboratory. Initially, sand and clay soils were thoroughly mixed to create a homogeneous mixture (A). Following that, the blend was sealed in a plastic bag and left isolated for 24 h to ensure consistent moisture distribution (B). After that, the mixture was put into an unconfined compression test mold. The specimen was compressed to the required unit weight using a hydraulic jack and static pressure (C). After being prepared, the specimen was placed in the unconfined compression apparatus (D). The apparatus applied a vertical load until the specimen failed (E). Figure 7 shows the relationship between strain and vertical stress to clarify how \(E_{t}\) and \(q_{u}\) are defined. A dataset comprising 225 unconfined compression tests was compiled from experimental outcomes for utilization in machine learning models. The input data consist of specific gravity (\(G\)), plastic index (\(PI\)), liquid limit (\(LL\)), dry unit weight (\(\gamma_{d}\)), sand content (\(Fs\)), degree of saturation (\(SR\)), optimum degree of saturation (\(SR_{opt}\)), maximum dry unit weight (\(\gamma_{dmax}\)), and void ratio (\(e_{0}\)). The dataset is used to predict the elasticity modulus (\(E_{t}\)) and unconfined compressive strength (\(q_{u}\)). Density plots and distribution histograms of the dataset are presented in Figure 8.  
Figure 5: The maximum dry density and optimal moisture values as a function of the sand content; (**A**) the change in \(\gamma_{dmax}\) as a function of \(Fs\), (**B**) the change in \(w_{opt}\) as a function of \(Fs\). Figure 6: \(Cont\).