DOI: 10.3390/buildings16132584 ISSN: 2075-5309

Coupled Effects of Fines Content and Particle Shape on the Maximum Shear Modulus of Gap-Graded Soils: A DEM Investigation

Yuyang Tian, Xuan Wang, Xingyang Liu, Xingxing Ai

The maximum shear modulus (Gmax) of soils is a critical parameter in geotechnical design and advanced constitutive modeling. While the effects of fines content (FC) and particle shape on Gmax have been studied extensively, their collective influence on gap-graded soils remains poorly understood. This study employs the discrete element method (DEM) to systematically investigate the combined effects of fines content and particle shape on Gmax, while strictly controlling all other variables. Numerical simulations were conducted on single-sized and binary mixtures with varying fines content of 0, 10%, and 20%, using five distinct particle shapes (with regularity indices ranging from 0.65 to 1.00) under different void ratios and confining pressures. The results show that Gmax decreases with increasing FC but increases with greater particle irregularity. A coupled effect between FC and particle shape is observed, wherein the influence of particle shape becomes more pronounced at higher fines content. Micromechanical analysis indicates that the mechanical void ratio (em) effectively normalizes the effect of FC on Gmax but does not fully account for the influence of particle shape due to variations in the proportion of inactive particles. Based on the Hardin model, empirical prediction equations for Gmax are proposed by incorporating both FC and particle shape indices. The comparison between two-dimensional (2D) and three-dimensional (3D) shape descriptors demonstrates that the 3D regularity index provides a more accurate characterization of particle shape, leading to an increase in the coefficient of determination (R2) from 0.90 to 0.94 and thereby improving predictive performance. This study highlights the importance of particle characteristics and the need for 3D shape parameters for reliable stiffness predictions in gap-graded granular materials.

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