›› 2017, Vol. 38 ›› Issue (S2): 89-98.doi: 10.16285/j.rsm.2017.S2.012

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

Experimental study of rockburst early warning method based on acoustic emission cluster analysis and neural network identification

ZHANG Yan-bo1,2, YANG Zhen1,2, YAO Xu-long1,2, LIANG Peng1,2, TIAN Bao-zhu1,2, SUN Lin1,2   

  1. 1. College of Mining Engineering, North China University of Science and Technology, Tangshan, Hebei 063009, China; 2. Mining Development and Technology Safety Key Laboratory of Hebei Province, North China University of Science and Technology, Tangshan, Hebei 063009, China
  • Received:2017-06-26 Online:2017-11-23 Published:2018-06-05
  • Supported by:

    This work was supported by the National Natural Science Foundation of China (51374088, 51574102), Science and Technology Research Projects of University in Hebei(QN2016124, QN2016125), and Supported by the Graduate Student Innovation Fund of North China University of Science and Technology (2017S07).

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Abstract: Three types of AE signals are obtained by carrying out the tunnel rock burst simulation experiment and clustering rock explosion signal. The acoustic emission signals which have the characteristics of the precursory anomaly, large energy and small quantity, are preferred as precursor characteristic signals of rockburst. Before the rockburst, the precursor characteristic signal of rockburst appeared densely; it is shown obvious precursory anomaly. The window functionis used to carry out the quantitative analysis of temporal density of precursor signal and put forward the quantitative index——time intensity which can measure timing evolution characteristics of signal. Before the rockburst, the precursory signal appeared densely, is greater than or equal to 3. This situation firstly appears can be as the precursor information of rockburst; therefore the early warning threshold a can be set to 3. Based on optimization of rockburst precursor signal and early warning threshold extraction, with the help of BP neural network, intelligently identify rockburst precursor signal. The time window function is used to calculate value of precursor signal. When value of precursor signal start to achieve rockburst warning threshold ( ≥a), it starts to warn rockburst. Therefore, a real-time warning method of rockburst based on AE data of tunnel rockburst simulation experiment is established so as to provides a new idea for the construction of early warning method of rockburst.

Key words: acoustic emission(AE), cluster analysis, neural network, rockburst warning

CLC Number: 

  • TU 452

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