Abstract
The recognition of tensile and shear cracks during hard rock cracking is critical for early warning of rockbursts in deep rock engineering. However, direct observation of cracking inside hard rocks by imaging equipment is difficult. A sound-based machine learning method for crack-type recognition in hard rock is therefore proposed. First, the sound signals of tensile and shear cracks in granite are obtained by Brazilian tension and shear tests, respectively. Then, the spectrogram conversion of the two kinds of signals is conducted to build a dataset. Next, a deep learning network EfficientNet is used to automatically extract the features of the spectrograms. Last, these deep learning–based features are used to construct a classification model of the crack types by a shallow machine learning method CatBoost. The experiments showed that the combination of two learning methods achieves high accuracy. We further validated the performance of the proposed method in laboratory cases involving biaxial and triaxial compression, as well as in real-world cases. The results indicate that the proposed method can accurately analyze the failure process of rocks by recognizing crack types. The proposed method is straightforward to implement and can provide a sound basis for making informed decisions on early warning of rockbursts.
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The code used in the study is provided as supplementary material, while the supporting data is temporarily not publicly available due to the nature of the research.
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Acknowledgements
The authors greatly acknowledge the financial support from the National Natural Science Foundation of China (Grant Nos. 52169021 and 51869003), the Innovative Team and Outstanding Talent Program of Colleges and Universities in Guangxi (Grant No. 202006) and the Interdisciplinary Scientific Research Foundation of Guangxi University (Grant No. 2022JCA004).
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Su, G., Qin, Y., Xu, H. et al. A sound-based machine learning method for crack-type recognition in hard rock. Bull Eng Geol Environ 82, 252 (2023). https://doi.org/10.1007/s10064-023-03291-4
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DOI: https://doi.org/10.1007/s10064-023-03291-4