Research on rock crack classification based on acoustic emission waveform feature extraction technology
Rock fracture mode has practical significance for the prediction and prevention of engineering disasters, and the inversion of fracture mode by the waveform signal not only reduces the experimental error of the mechanical strength measurement but also simplifies the type and quantity of disaster pre...
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Published in | Lithosphere Vol. 2022; no. Special 11 |
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Abstract | Rock fracture mode has practical significance for the prediction and prevention of engineering disasters, and the inversion of fracture mode by the waveform signal not only reduces the experimental error of the mechanical strength measurement but also simplifies the type and quantity of disaster prediction source data. For the relationship between crack mode and mechanical strength, the acoustic emission (AE) waveform signal is studied. Six coarse-grained sandstone samples were tested by uniaxial compression, AE, and scanning electron microscopy. The results show that the number of microhole cracks in rock is positively correlated with tensile-shear cracks and negatively correlated with mechanical strength. The quadratic function regression curve of the proportion of shear cracks and mechanical strength is more realistic. When crack ratio is less than 0.31, the number of shear cracks is positively correlated with the mechanical strength and vice versa. The waveform mutation coefficient k is defined as the overall change description. It is found that the increase of signal mutation has a positive impact on the mechanical strength of rock. The fitting function of crack and the signal mutation near the peak of rock can be divided into six risk zones in a two-dimensional plane. In addition to these exciting results and discoveries, the determination of the number of tensile-shear cracks and its relationship with mechanical strength provide innovative methods and ideas for crack pattern discrimination and rock burst risk assessment of roadway surrounding rock. |
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AbstractList | Rock fracture mode has practical significance for the prediction and prevention of engineering disasters, and the inversion of fracture mode by the waveform signal not only reduces the experimental error of the mechanical strength measurement but also simplifies the type and quantity of disaster prediction source data. For the relationship between crack mode and mechanical strength, the acoustic emission (AE) waveform signal is studied. Six coarse-grained sandstone samples were tested by uniaxial compression, AE, and scanning electron microscopy. The results show that the number of microhole cracks in rock is positively correlated with tensile-shear cracks and negatively correlated with mechanical strength. The quadratic function regression curve of the proportion of shear cracks and mechanical strength is more realistic. When crack ratio is less than 0.31, the number of shear cracks is positively correlated with the mechanical strength and vice versa. The waveform mutation coefficient k is defined as the overall change description. It is found that the increase of signal mutation has a positive impact on the mechanical strength of rock. The fitting function of crack and the signal mutation near the peak of rock can be divided into six risk zones in a two-dimensional plane. In addition to these exciting results and discoveries, the determination of the number of tensile-shear cracks and its relationship with mechanical strength provide innovative methods and ideas for crack pattern discrimination and rock burst risk assessment of roadway surrounding rock. AbstractRock fracture mode has practical significance for the prediction and prevention of engineering disasters, and the inversion of fracture mode by the waveform signal not only reduces the experimental error of the mechanical strength measurement but also simplifies the type and quantity of disaster prediction source data. For the relationship between crack mode and mechanical strength, the acoustic emission (AE) waveform signal is studied. Six coarse-grained sandstone samples were tested by uniaxial compression, AE, and scanning electron microscopy. The results show that the number of microhole cracks in rock is positively correlated with tensile-shear cracks and negatively correlated with mechanical strength. The quadratic function regression curve of the proportion of shear cracks and mechanical strength is more realistic. When crack ratio is less than 0.31, the number of shear cracks is positively correlated with the mechanical strength and vice versa. The waveform mutation coefficient k is defined as the overall change description. It is found that the increase of signal mutation has a positive impact on the mechanical strength of rock. The fitting function of crack and the signal mutation near the peak of rock can be divided into six risk zones in a two-dimensional plane. In addition to these exciting results and discoveries, the determination of the number of tensile-shear cracks and its relationship with mechanical strength provide innovative methods and ideas for crack pattern discrimination and rock burst risk assessment of roadway surrounding rock. |
Author | Gao Chengdeng, Gao Chengdeng Ding Ziwei, Ding Ziwei Li Xiaofei, Li Xiaofei Jia Jindui, Jia Jindui Tang Qingbao, Tang Qingbao Wang Shaoyi, Wang Shaoyi |
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Snippet | Rock fracture mode has practical significance for the prediction and prevention of engineering disasters, and the inversion of fracture mode by the waveform... AbstractRock fracture mode has practical significance for the prediction and prevention of engineering disasters, and the inversion of fracture mode by the... |
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SubjectTerms | acoustical emissions Asia cement China classification clastic rocks cracks data processing Engineering geology failures Far East geologic hazards Mel cepstrum coefficients natural hazards porosity rock bursts rock mechanics sandstone sedimentary rocks Shaanxi China tensile strength waveforms Xianyang China |
Title | Research on rock crack classification based on acoustic emission waveform feature extraction technology |
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