## 1 Introduction
They attempted to eliminate the effect of morphological characteristics by selecting three sands with different particle sizes (coarse, medium, and fine) but similar particle shapes (angularity, roundness, sphericity, and roughness). The samples were prepared at a similar void ratio, and the test was carried out using direct shear. The results showed that particle size has a slight influence on the peak friction angle but not on the mechanism of shearing, with coarse sand particles taking longer to reach the peak compared to fine sand particles. In contrast, an experiment by [PERSON] et al. [24] investigated the effect of sand and gravel size on shear strength using both direct shear and triaxial tests in the laboratory. The results showed that as the mean particle diameter D\({}_{50}\) increased, the angle of shearing resistance also increased, leading to higher shear strength. Similar results were reported by [16; 25; 26], who found that peak and residual shear strength increase as particle size increases, whereas in glass beads, interparticle friction between two glass beads increases as sphere size increases [27]. Interestingly, particle size also affects the compressibility of the granular structure, with smaller particles leading to greater compression compared to larger particles [1]. Researchers in the engineering, geotechnics, as well as the medical field have become more interested in artificial intelligence (AI) techniques over the last two decades. A variety of machine learning algorithms have been utilised with significant success, including multiple linear regression (MLR) and random forest regression (RFR), which we have adopted in our research. In a study by [PERSON] et al. [28], the two models, MLR and RF, were compared for estimating soil extracellular enzyme activities in reclaimed coastal saline land. The authors report that the RF model performed better than the MLR model in predicting the activities of soil amylase and urease, which are important indicators of soil carbon and nitrogen cycling. The article also identifies the main factors affecting soil extracellular enzyme activities, such as soil water content, total nitrogen, and pH. Another study by [PERSON] et al. [29], who also used MLR and RF models, investigated the prediction of soil organic carbon (SOC) in a coastal reclamation zone of eastern China. The authors compared the effects of different factors on SOC dynamics and found that soil pH, chloride, and silt contents were the most important factors influencing SOC. Results from the study indicated that the RF model also performed better than MLR due to its superiority in handling non-linear relationships between SOC and the predictors. The RF model showed substantially reduced error indices (ME, MSE, and RMSE), as well as a higher R\({}^{2}\). Another interesting technique is the Adaptive Neuro-Fuzzy Inference System (ANFIS) introduced by [PERSON] in 1993. ANFIS integrates the elements of neural networks and fuzzy logic, demonstrating capabilities of learning and generalisation [30]. The system has found diverse applications across various domains. It has been used for predicting skin permeability in drug-delivery scenarios [31], controlling quality and predicting characteristics in food-processing technology [32], determining heavy metal concentrations in water resources [33], and even predicting the security index of ad hoc vehicular networks [34]. Moreover, it has shown efficacy in predicting the higher heating value of biomass [35] and modelling thermal error [36]. In addition, a recent article [37] presents a method to control the cooling of machine tool spindles using ANFIS. The method adjusts the coolant pump frequency based on the spindle speed and thermal state, achieving high accuracy and efficiency in reducing thermal deformation and energy consumption.