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To investigate the fracture characterizations of rocks under high strain rate tensile failure, a series of dynamic Brazilian tests was conducted using Split Hopkinson pressure bar (SHPB), and a high-speed digital camera at a frame rate of 50,000 frames per second (FPS) with a resolution of 272 × 512 pixels was adopted to capture the real-time images and visualize the failure processes. Using the extracted cracks and image processing technique, the relationship between loading condition (impact velocity), crack propagation process (crack velocity, crack fractal characteristic, and crack morphological features), and dynamic mechanical properties (absorbed energy and strain-stress parameters) was explored and analyzed. The experimental results indicate that (1) impact velocity plays a critical role in both crack propagation process and dynamic mechanical properties, (2) the crack fractal dimension is positively correlated with crack propagation velocity and has a linear relationship with the proposed morphological feature of crack, (3) mean strain rate and max strain of rocks under SHPB loading both decrease with the increase of crack propagation velocity, and (4) the energy absorbed by the rocks increases with increasing impact velocity and has a strong negative correlation with a proposed novel crack descriptor. Experimental studies pertaining to the measurement of crack propagation path and velocity, in particular, some crack feature extraction approaches, present a promising way to reveal the fracture process and failure mechanisms of rock-like materials.

In order to accurately identify and quantitatively calculate the surface cracks of rock mass under SHPB impact loading, an automatic crack detection algorithm was proposed and evaluated by the experiment. In SHPB experiment, cracks on the rock surface can effectively reflect its current state and better analyze the damage process. Firstly, the SHPB system was used to impact 12 groups of rock specimens under different impact velocities. A high-frame camera with 50,000 FPS was used to capture the damage process of the rock mass; using the manual annotation method, we got a dataset of SHPB damage images including a total of 310 original images and 310 corresponding cracked annotations. Secondly, a deep convolution network model named CrackSHPB was designed based on a deep learning algorithm. e algorithm can automatically identify the crack on the rock surface during impact damage process and further provide a quantitative result of cracks, crack area. Finally, after the crack on the rock surface in each frame image was identified automatically through the model, cracks were quantitatively analyzed by the proposed algorithm, the growth rate of cracks was calculated, and their evolution law was concluded. e crack identification algorithm proposed in this paper can provide a more accurate quantitative method for rock damage by cracks on the rock surface, and evolution law can further explain the failure process of rock at high strain rate.