Influence of Fines Content and Particle Shape on Limiting Void Ratios of Sand Mixtures: DEM and AI Approaches

The mechanical behavior of sand–fines mixtures is governed by their limiting void ratios, which are sensitive to fines content and particle morphology. Conventional empirical correlations often fail to generalize to a wide range of soils, limiting their applicability in engineering design. This study develops an integrated approach combining laboratory calibration, discrete element method (DEM) simulations incorporating realistic particle morphologies and machine learning to predict maximum and minimum void ratios. Glass beads were first tested to validate DEM contact parameters, after which sand particles obtained through 3D scanning were employed to capture morphological effects. Correlation and partial least squares analyses confirmed fines content as the dominant factor, while particle shape also contributed to packing behavior. A fully connected neural network (FCNN) was trained to establish predictive relationships, demonstrating closer agreement with DEM simulations than traditional empirical formulations. The proposed approach provides a reliable and generalizable tool for evaluating packing characteristics and offers new insights into the role of particle morphology in the mechanical response of sand–fines mixtures.