Prediction of fracture and dilatancy in granite using acoustic emission signal cloud

The invisibility of fracture network evolution in the rock under triaxial compression seriously restricts the correlation modeling between dilatancy behavior and fracture interconnectivity. The key to solving such a challenge is strongly dependent on the accurate modeling of the spatial correlation in fracture network, which could be indirectly re-constructed by the acoustic emission (AE) signal cloud. Considering the interaction of local fractures, a cube cluster approach is established to describe the spatial correlation. The evolutional cube clusters effectively present the geometric characteristics induced by the increasing dilatancy of fracture. Two descriptors (i.e. three-axis length sum and pore fraction) are introduced to correlate cluster model with dilatancy behavior. Most fitting results support the linear correlation between two descriptors and volumetric strain, which verifies the sensitiveness of the cube cluster model to dilatancy. More importantly, by the statistical analysis of cluster structure, the cluster model shows the potential of calculating fracture angle. Moreover, a comparison between dilatancy-based damage and porosity-based damage is made not to prove the best but provide an AE-based prediction of local damage evolution. Finally, four classical models for calculating fracture angle are compared. The deviations prove the huge difficulty of describing the development of the fracture network uniquely dependent on a fracture angle. The proximity of measured angle and cluster-based angle supports the effectiveness of predication by the cube cluster approach