The prediction of rock fragmentation is critical to improve the efficiency and economy of blasting excavation. In this study, an attempt is made to predict the entire fragmentation size distribution using a least square support vector machine (LSSVM) model. In addition, three optimization algorithms—the bacterial foraging algorithm (BFO), artificial fish swarm algorithm (AFSA), and adaptive particle swarm optimization (APSO)–were used to determine the appropriate parameters of the LSSVM model. In the constructed LSSVM-BFO, LSSVM-AFSA and LSSVM-APSO models, the hole spacing, row spacing, change per delay and stemming were used as the input parameters, while the statistical rock fragmentation size was assigned as the output. The LSSVM model was also employed as a control group for comparing with the optimized models. The above-mentioned models were trained and tested based on a database comprising of 10 datasets collected from in-site testing of Altashi Water Control Project in China. The performance of the proposed models was compared by several statistical criteria. The viability and efficiency of the LSSVM-BFO model were confirmed with an R² of 0.9960 and an RMSE of 1.8044, which were better than those of the LSSVM-AFSA, LSSVM-APSO and LSSVM. Last but not least, sensitivity analysis was also executed. The result of sensitivity analysis demonstrated when the size of rock fragmentation of prediction is less 80mm, the most effective parameter will be stemming; otherwise the most effective parameter will be hole spacing.

Granular materials are widely prevalent in nature and industry, and the discrete element method (DEM) is the most effective method for the mechanical analysis of granular materials. Because of the difficulty inherent in friction coefficient determination, the friction behavior of granular geomaterials has always been a difficult problem in DEM. In order to study the variation rules of the sliding friction coefficients in DEM, an experiment designed to determine the sliding friction coefficient between particles with different shapes and different contact forms and an experiment designed to determine the sliding friction coefficient between particles and different boundaries were proposed, and the experimental results were fitted. In order to verify the reliability of the experiments, the fitting results were embedded into DEM software, and several packing experiments and simulation experiments were carried out and compared. The simulated results are detailed and discussed with reference to the experimental results as part of this paper. The results indicate that the simulation results are highly consistent with the experimental results, and the sliding friction coefficient obtained by the proposed experiment is in accordance with that determined theoretically. The sliding friction coefficient determined by fitting according to the experimental results can determine the sliding friction coefficient in DEM simulation and improve the accuracy of simulation results.

The normal contact force determines the behavior of a particle system. To investigate the normal contact force in a one‐dimensional sphere chain subjected to impact load, by comparing the simulation results of the existing typical normal contact force models embedded in the discrete element program, an improved normal contact force model was proposed in this paper. The improved model consists of two parts: the Cundall model for loading and the Daniel model for unloading. Moreover, a systematic test was designed to verify the accuracy and applicability of the improved model. The results showed that the calculated contact force curves agree well with the experimental results. Furthermore, the improved model is implemented in the solution algorithm without need for complex numerical methods and parameters fitting, leading to more efficient simulations.

Accurate characterization of soil dynamic response is paramount for geotechnical and protective engineering. However, the impact properties of unsaturated cohesive soil have not been well characterized due to lack of sufficient research. For this purpose, impact tests using the Split Hopkinson Pressure Bar (SHPB) were elaborately designed to investigate the dynamic stress–strain response of unsaturated clay with strain rates of 204~590 s⁻¹. As the strain rate increased up to 500 s⁻¹, a lock-up behavior was observed in the plastic flow stage, which can be explained as the breakage and rearrangement of soil gains under a high level of stress. Further, the strain rate dependency of the dynamic strength was quantitatively characterized by the Cowper Symonds (CS) model and the CS coefficients were determined to be the intercept of 55 and slope of 0.8 in the double logarithmic scale of Dynamic Increase Factor (DIF) and strain rate space. Furthermore, the SHPB test was reproduced using a modified Material Particle Method (MPM), which involves an improved dynamic constitutive model for unsaturated soil considering the strain rate effect. The simulated stress–strain curves basically agree with the experimental results, indicating the feasibility of MPM for investigating the dynamic properties of unsaturated soil under SHPB impact loading.