花岗岩破裂的声发射阶段特征及裂纹不稳定扩展状态识别

doi:10.13722/j.cnki.jrme.2021.0637

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Abstract The crucial information acquisition and identification during the accumulation and evolution process of microfractures within rock can help to analyze the rock failure status，which is of great significance to the early warning and prevention of geotechnical disasters. In this paper，the granite failure experiments based on acoustic emission(AE) monitoring were conducted to analyze the stage characteristics of the energy frequency distribution and waveform spectrum variation during the failure processes，and to provide some suggestions with multiple AE parameters for evaluating the rock failure status，as well as to establish a discriminating method for crack propagation state in the plastic stage using integrated machine learning model. The research results show that the AE parameters including b value，S value，RA value，AF value，and average frequency of gravity(AFG) show obvious difference of change trend and temporal correlation in the four failure stages of compaction，elastic，steady propagation and unsteady propagation. The parameters characterize internal fracture evolution behavior from the viewpoints of damage degree，crack scale and source type，respectively. The continuous low-level decrease of b value，steady high-level fluctuation of S value，high-level decrease of AF value，low-level increase of RA value，and sudden decrease of AFG value correspond to the unstable cracking stage. The multiple AE precursor system and risk pre-alarm and evaluation criteria，which integrate the energy frequency distribution and waveform spectrum variation，overcome the defect of single index evaluation. Based on the analysis of above parameters，A(b) and main frequency bandwidth(W) are introduced to expand the datasets. Two types of crack identification models constructed by AdaBoost and Random Forest are used to identify the stable and unstable growth stages of rock cracks，with a classification accuracy of 94.0% and 95.1% respectively. The analysis method not only realizes the effective identification of rock failure stages at the laboratory scale，but also provides references for the early warning，prevention and control of disasters in practical engineering.

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rock mechanics acoustic emission stage characteristics machine learning

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	DONG Longjun
	ZHANG Yihan
	SUN Daoyuan
	CHEN Yongchao
	TANG Zheng

Cite this article:

DONG Longjun,ZHANG Yihan,SUN Daoyuan, et al. Stage characteristics of acoustic emission and identification of unstable crack state for granite fractures[J]. , 2022, 41(1): 120-131.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2021.0637 OR https://rockmech.whrsm.ac.cn/EN/Y2022/V41/I1/120

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