Search

Numerical modeling of unconsolidated granular materials is comprised of multiple nonlinear phenomena. Accurately capturing these phenomena, including intergranular forces and grain deformation, depends on resolving contact regions several orders of magnitude smaller than the grain size. Here, we investigate a method for capturing the morphology of the individual particles using computed X-ray tomography, which allows for accurate characterization of the interaction between grains. Additionally, the ability of these numerical approaches to determine stress concentrations at grain contacts is important in order to capture catastrophic splitting of individual grains, which has been shown to play a key role in the plastic behavior of the granular material on the continuum level. Samples of Ottawa sand are numerically modeled under one-dimensional compression loadings in order to determine the effect of discretization approaches, such as mesh refinement, on the resulting stress concentrations at contact points between grains. The effects of grain coordination number and finite element type selection on these stress concentrations are also investigated.

Triaxial compression experiments on dry Ottawa sand are numerically simulated using Finite Discrete Element Method (FDEM) in conjunction with X-ray CT imaging. A novel framework is introduced and validated where particle shape is captured in FEM simulations using shell elements for simulating triaxial boundary value problem. The proposed approach is well suited for load cases with low/intermediate confining pressures where particle breakage is negligible. The numerical model allows for grain deformation as well as robust particle contact fabric evolution for the realistic grain morphology. For this research, the confining latex membrane is modelled as hyper-elastic material to provide flexible boundary condition on the lateral surface of the cylindrical sand specimen. Volume change measurements are obtained directly based on the convex hull of an assembly of sand grains, with no contributions or associated errors from latex membrane effects. In this paper, we attempt to explore the FDEM modeling aspect from different perspectives considering macroscale and microscale response. The influence of the frictional and frictionless boundary on the strength deformation response and localization in displacement field is discussed. Furthermore, sensitivity of mesh refinement, interparticle friction coefficient and critical shear stress on strength and volume change response is quantified.

Fracture of individual grains within a granular assembly affect the strength and deformation behavior of granular materials under large stress states. Interparticle forces, leading to contact stresses and ultimately fracture initiation, are influenced by particle morphology. A numerical method that incorporates both particle fracture and morphology can provide a more accurate model of a granular material’s macro-scale response. This research investigates an approach to modeling fracture initiation and propagation within individual grains, utilizing high resolution X-ray computed tomography (CT) imaging to consider grain morphology and an explicit finite element code optimized for high performance computing which incorporates damage mechanics. Using precisely measured force-displacement curves from single particle crushing tests of manufactured quartz spheres, the method is validated, and the effects of fracture properties on grain fragmentation are determined. This approach is then used to simulate single particle crushing of Ottawa sand, with grain morphology incorporated through discretized CT images. The effect of grain orientation on the force and applied displacement at catastrophic splitting is also explored. The proposed discrete particle based finite element framework can be applied to modeling granular assemblies, considering the effect of individual particle shape and fracture on the assembly’s deformation response through well calibrated numerical simulations.

The stress–strain and volume change behavior of sand and gravel under drained triaxial compression test conditions was modeled using feed-back artificial neural networks. A large experimental database obtained from published literature was used in training, testing, and prediction phases of three neural network based soil models. Issues related to the number of hidden units, magnitude of strain increment during feed-back, and over-training error are discussed. These models can accurately represent the effects of mineralogy, grain shape and size distribution, void ratio, and confining pressure. The observed behavior in terms of a non-linear stress–strain relation, compressive volume change at low stress levels, and volume expansion at high stress levels are captured well by these models.