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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. The 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. The 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.

Crack Detection and Evolution Law for Rock Mass under SHPB Impact Tests