<p>Correctly identifying of fracture characteristics is essential for understanding the failure mechanism of rockburst. This study investigates the influence of initial burial depths on impact rockburst behavior in sandstone through a series of laboratory experiments. The results show that rockburst intensity increases with initial burial depths, as evidenced by the enlarged fragment ejection range, increased depth and extent of the V-shaped notch, and a rising trend in fragment mass. Additionally, the mesoscopic fracture evolution processes of rockburst are systematically analyzed. Specifically. Based on the combined the framework of Artificial Neural Network (ANN) -based classification and crack scale, four failure stages of rockburst are revealed, including: (i) micro-tensile crack dominated stage, (ii) mixed micro-tensile and shear crack stage, (iii) large-scale tensile and micro-shear crack stage, and (iv) shear crack dominated stage. Finally, the influence mechanism of the initial burial depths is interpreted from the perspectives of energy accumulation and crack evolution. With increasing burial depth, the energy storage capacity of sandstone is enhanced, leading to higher energy release during failure and thus more intense rockburst. In addition, deeper burial conditions promote more pronounced slab-like fracturing, which facilitates violent fragment ejection.</p>

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Experimental study on the fracture process and failure mechanism of impact rockburst

  • Dongqiao Liu,
  • Kai Gu,
  • Yiguo Peng,
  • Mengyao Cui,
  • Zheng Zhou,
  • Kai Ling

摘要

Correctly identifying of fracture characteristics is essential for understanding the failure mechanism of rockburst. This study investigates the influence of initial burial depths on impact rockburst behavior in sandstone through a series of laboratory experiments. The results show that rockburst intensity increases with initial burial depths, as evidenced by the enlarged fragment ejection range, increased depth and extent of the V-shaped notch, and a rising trend in fragment mass. Additionally, the mesoscopic fracture evolution processes of rockburst are systematically analyzed. Specifically. Based on the combined the framework of Artificial Neural Network (ANN) -based classification and crack scale, four failure stages of rockburst are revealed, including: (i) micro-tensile crack dominated stage, (ii) mixed micro-tensile and shear crack stage, (iii) large-scale tensile and micro-shear crack stage, and (iv) shear crack dominated stage. Finally, the influence mechanism of the initial burial depths is interpreted from the perspectives of energy accumulation and crack evolution. With increasing burial depth, the energy storage capacity of sandstone is enhanced, leading to higher energy release during failure and thus more intense rockburst. In addition, deeper burial conditions promote more pronounced slab-like fracturing, which facilitates violent fragment ejection.