Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning

Abstract

This study aimed to examine the shear strength characteristics of sand–granular rubber mixtures in direct shear tests. Two different sizes of rubber and one of sand were used in the experiment, with the sand being mixed with various percentages of rubber (0%, 10%, 20%, 30%, and 50%). The mixtures were prepared at three different densities (loose, slightly dense, and dense), and shear stress was tested at four normal stresses (30, 55, 105, and 200 kPa). The results of 80 direct shear tests were used to calculate the peak and residual internal friction angles of the mixtures, and it was found that the normal stress had a significant effect on the internal friction angle, with an increase in normal stress leading to a decrease in the internal friction angle. These results indicated that the Mohr–Coulomb theory, which applies to rigid particles only, is not applicable in sand–rubber mixtures, where stiff particles (sand) and soft particles (rubber) are mixed. The shear strength of the mixtures was also influenced by multiple factors, including particle morphology (size ratio, shape, and gradation), mixture density, and normal stress. For the first time in the literature, genetic programming, classification and regression random forests, and multiple linear regression were used to predict the peak and residual internal friction angles. The genetic programming resulted in the creation of two new equations based on mixture unit weight, normal stress, and rubber content. Both artificial intelligence models were found to be capable of accurately predicting the peak and residual internal friction angles of sand–rubber mixtures.

Determination of the Basic Geotechnical Parameters of Blast-Furnace Slag from the Kremnica Region

“…The tension intervals were set at 50-100-200-400 kPa. At these vertical stresses σ, the maximum shear stresses τ were reached, which are plotted in the figure (Figure 7) [39,40]. We can see (Figure 7) the shear box displacement/shear stress plot is more scattered in the case of sample G2/GP than that of sample G3/G-F.…”

“…The tension intervals were set at 50-100-200-400 kPa. At these vertical stresses σ, the maximum shear stresses τ were reached, which are plotted in the figure (Figure 7) [39,40].…”

Effects of Dry Density and Moisture Content on the Kaolin–Brass Interfacial Shear Adhesion

“…Firstly, this study only used kaolin clay as the testing material, which may not represent the behaviour of other types of clay or soil mixtures. Future studies could consider different types of clay mixed with sand or recycled materials [21,22] to investigate the effect of soil composition on interfacial shear adhesion. Secondly, this study only performed the test at one displacement rate of 5.0 mm/min, which may not capture the dynamic loading conditions encountered in real-world applications.…”

Advancements in Understanding Interface Friction: A Combined Experimental and Machine Learning Approach Using Multiple Linear and Random Forest Regressions

“…Despite these known factors, as yet, no detailed study that combines all of these into a single, easy-to-understand model has been conducted, mainly because these factors interact in complicated ways. In artificial intelligence, machine learning algorithms have helped solve complex problems in the geotechnical field, such as understanding soil mechanics behaviour [11][12][13] and improving the use of recycled materials in soil stabilisation [14][15][16][17][18][19].…”