Geomechanics and Engineering

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Cluster and information entropy analysis of acoustic emission during rock failure process

  • Zhang, Zhenghu (State Key Laboratory of the Coastal and Offshore Engineering, School of Civil Engineering, Dalian University of Technology) ;
  • Hu, Lihua (State Key Laboratory of the Coastal and Offshore Engineering, School of Civil Engineering, Dalian University of Technology) ;
  • Liu, Tiexin (Department of Civil Engineering, Dalian Maritime University) ;
  • Zheng, Hongchun (China Three Gorges Corporation) ;
  • Tang, Chun'an (State Key Laboratory of the Coastal and Offshore Engineering, School of Civil Engineering, Dalian University of Technology)
  • Received : 2020.08.04
  • Accepted : 2021.04.02
  • Published : 2021.04.25
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Abstract

This study provided a new research perspective for processing and analyzing AE data to evaluate rock failure. Cluster method and information entropy theory were introduced to investigate temporal and spatial correlation of acoustic emission (AE) events during the rock failure process. Laboratory experiments of granite subjected to compression were carried out, accompanied by real-time acoustic emission monitoring. The cumulative length and dip angle curves of single links were fitted by different distribution models and distribution functions of link length and directionality were determined. Spatial scale and directionality of AE event distribution, which are characterized by two parameters, i.e., spatial correlation length and spatial correlation directionality, were studied with the normalized applied stress. The entropies of link length and link directionality were also discussed. The results show that the distribution of accumulative link length and directionality obeys Weibull distribution. Spatial correlation length shows an upward trend preceding rock failure, while there are no remarkable upward or downward trends in spatial correlation directionality. There are obvious downward trends in entropies of link length and directionality. This research could enrich mathematical methods for processing AE data and facilitate the early-warning of rock failure-related geological disasters.

Keywords

References

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