Abstract

This study uses a ring shear apparatus to measure the interface shear stress between five types of sand and three surfaces: steel, PVC, and stone. Experiments were conducted under normal stresses of 25, 50, and 100 kPa at a constant shear rate of 0.5 mm/min. The research examines the impact of various sand properties, including particle size distribution, median particle size, particle shape, and initial density, as well as the surface roughness and hardness of continuum materials. The results show that interface shear strength is significantly influenced by the mechanical interlock between sand particles and surface asperities, which is affected by the normalized roughness and hardness of the materials. Machine learning models, including Multiple Linear Regression and Random Forest Regression, were used to predict peak and residual shear strengths, demonstrating high accuracy. Additionally, an empirical equation was generated using eight input parameters, considering the peak and residual interface shear strength as outputs.

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