Jiangbo Wei

The mechanical characteristics of crystalline rocks are affected by the heterogeneity of the spatial distribution of minerals. In this paper, a novel three-dimensional (3D) grain-based model (GBM) based on particle flow code (PFC), i.e. PFC3D-GBM, is proposed. This model can accomplish the grouping of mineral grains at the 3D scale and then filling them. Then, the effect of the position distribution, geometric size, and volume composite of mineral grains on the cracking behaviour and macroscopic properties of granite are examined by conducting Brazilian splitting tests. The numerical results show that when an external load is applied to a sample, force chains will form around each contact, and the orientation distribution of the force chains is uniform, which is independent of the external load level. Furthermore, the number of high-strength force chains is proportional to the external load level, and the main orientation distribution is consistent with the external loading direction. The main orientation of the cracks is vertical to that of the high-strength force chains. The geometric size of the mineral grains controls the mechanical behaviours. As the average grain size increases, the number of transgranular contacts with higher bonding strength in the region connecting both loading points increases. The number of high-strength force chains increases, leading to an increase in the stress concentration value required for the macroscopic failure of the sample. Due to the highest bonding strength, the generation of transgranular cracks in quartz requires a higher concentrated stress value. With increasing volume composition of quartz, the number of transgranular cracks in quartz distributed in the region connecting both loading points increases, which requires many high-strength force chains. The load level rises, leading to an increase in the tensile strength of the numerical sample.

Abstract Prediction of the height of a water-flowing fracture zone (WFFZ) is the foundation for evaluating water bursting conditions on roof coal. By taking the Binchang mining area as the study area and conducting an in-depth study of the influence of coal seam thickness, burial depth, working face length, and roof category on the height of a WFFZ, we proposed that the proportion of hard rock in different roof ranges should be used to characterise the influence of roof category on WFFZ height. Based on data of WFFZ height and its influence index obtained from field observations, a prediction model is established for WFFZ height using a combination of a genetic algorithm and a support-vector machine. The reliability and superiority of the prediction model were verified by a comparative study and an engineering application. The results show that the main factors affecting WFFZ height in the study area are coal seam thickness, burial depth, working face length, and roof category. Compared with multiple-linear-regression and back-propagation neural-network approaches, the height-prediction model of the WFFZ based on a genetic-algorithm support-vector-machine method has higher training and prediction accuracy and is more suitable for WFFZ prediction in the mining area.