A series of high-pressure compression tests with various stress paths under monotonic and cyclic loadings were performed to examine the compression and particle crushing characteristics of silica sand in a dense state. Different stress paths were particularly designated to individually investigate the influences of mean and shear stress on particle crushing. Test results show that the degree of particle crushing increases with the stress level and is markedly affected by the stress paths. For each compression test following a designated stress path, an increase in the cyclic loading number also enhances the degree of particle crushing. Particle morphology is affected by the accompanying occurrence and evolution of grain damage. The relative breakage expresses a linear relationship with the maximum volumetric strain during monotonic and repeated compression loadings. A good correlation between the relative breakage and plastic work per unit volume is obtained for silica sand at various mean stresses regardless of the stress history. However, this correlation is affected by cyclic loading number because of different crushing mechanisms. During the entire cyclic compression process, the increasing rate of relative breakage is lower than the plastic work increment for dense silica sand as the cyclic loading progresses.

The calibration of discrete element method (DEM) simulations is typically accomplished in a trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal with these issues, the sequential quasi-Monte Carlo (SQMC) filter is employed as a novel approach to calibrating the DEM models of granular materials. Within the sequential Bayesian framework, the posterior probability density functions (PDFs) of micromechanical parameters, conditioned to the experimentally obtained stress–strain behavior of granular soils, are approximated by independent model trajectories. In this work, two different contact laws are employed in DEM simulations and a granular soil specimen is modeled as polydisperse packing using various numbers of spherical grains. Knowing the evolution of physical states of the material, the proposed probabilistic calibration method can recursively update the posterior PDFs in a five-dimensional parameter space based on the Bayes’ rule. Both the identified parameters and posterior PDFs are analyzed to understand the effect of grain configuration and loading conditions. Numerical predictions using parameter sets with the highest posterior probabilities agree well with the experimental results. The advantage of the SQMC filter lies in the estimation of posterior PDFs, from which the robustness of the selected contact laws, the uncertainties of the micromechanical parameters and their interactions are all analyzed. The micro–macro correlations, which are byproducts of the probabilistic calibration, are extracted to provide insights into the multiscale mechanics of dense granular materials.