## 1 Introduction
### Sand as an Additive Material and Its Impact on Soil Parameters  
Sand is a granular material of natural origin with high strength and is often utilized as a filler material in cohesive soil mixtures to alter their plasticity, compaction, and strength properties [9; 13; 16; 17; 18]. The sand fraction significantly influences the cracking behavior of expansive soil. As the sand content increases, it can moderate the volume fluctuations caused by soil swelling and shrinking, minimizing the amount of cracking. However, an excessive sand component may modify the soil's mechanical properties, thereby influencing its overall behavior. Balancing the sand fraction is critical in engineering applications because it improves soil performance and reduces the negative impacts of cracking in expansive soil [19]. The addition of sand to expansive soils has been extensively investigated by many researchers to determine its effect on the soil parameters [10; 20; 21; 22; 23; 24; 25; 26]. Previous studies have confirmed that increasing the percentage of sand increases the unconfined compressive strength (UCS) of expansive soils. [PERSON] et al. [27] performed unconfined compressive experiments on expansive soils. They found that the compressive strength increases with increasing dry density. Increased water content, on the other hand, shows the opposite trend. It is worth noting, however, that their study only looked at two levels of added sand (20% and 40%). [PERSON] [10] performed unconfined compression tests to investigate the impact of sand on the unconfined compressive strength (UCS) of expansive soil, using a static compactor to prepare samples at moisture levels determined by the proctor test only. Their findings showed that, up to a certain point, the unconfined compression strength increased with the percentage of sand. But as the sand percentage increased further, the strength decreased. [PERSON] and [PERSON] [28] also investigated the effect of sand on the unconfined compressive strength (UCS) of expansive soil. Nonetheless, their research was limited to samples prepared with the maximum dry density and optimum moisture content with only three percentages of added sand (25%, 40%, and 70%). Sometimes, disparities in results emerged across studies, as can be seen in [PERSON] [10] versus [PERSON] and [PERSON] [28]. The primary cause of this variation was significant differences in the percentages of added sand used in each study. [PERSON] [10], for example, used percentages ranging from 10% to 80%, whereas [PERSON] and [PERSON] [28] used 25%, 40%, and 70%. Furthermore, the existing  
The symbolic regression (SR) algorithm is used effectively in many areas of civil engineering. It adeptly models the Colebrook equation governing hydraulic flow friction in hydraulics, significantly improving accuracy in turbulent scenarios [58]. Symbolic regression is used in structural engineering to estimate seismic peak drift ratio, penetration depth into concrete blocks, shear capacity of concrete beams reinforced with steel fibers, fire response of concrete structures, seismic fragility analysis, remaining fatigue life, and shear resistance in bolted connections [59; 60; 61; 62; 63; 64]. Notably, in some cases [59; 63; 64], symbolic regression equations outperformed traditional formulas. Geotechnical engineers are becoming interested in ANNs because of their exceptional capacity to represent nonlinear problems with many different parameters. ANNs can create non-linear relationships between variables and make relatively accurate predictions [65]. Several geotechnical investigations have demonstrated that ANN models outperform multiple linear regression (MLR) models [65; 66; 67; 51]. Table 2 summarizes several studies that used machine learning to predict the behavior of expansive soils, including some of the most used regression algorithms such as GBR, SVR, and DT.