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Study on the Mathematical Model and Propagation Characteristics of AE Waveform Signals during Rock Fracture
Author(s) -
Xun You,
Bin Gong,
Xin Lv,
Longfei Hu
Publication year - 2021
Publication title -
advances in civil engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.379
H-Index - 25
eISSN - 1687-8094
pISSN - 1687-8086
DOI - 10.1155/2021/6685357
Subject(s) - waveform , anisotropy , acoustic emission , signal (programming language) , fracture (geology) , rock mass classification , geology , infrasound , acoustics , low frequency , fracture mechanics , viscosity , mineralogy , materials science , seismology , geotechnical engineering , physics , composite material , telecommunications , engineering , computer science , optics , radar , programming language
Rock deformation or fracture is accompanied by the phenomenon of acoustic emission (AE). Due to the heterogeneity and anisotropy of rock materials as well as the complexity of their fracture, AE signals recorded by sensors at different positions have different characteristics. To explore factors influencing these differences, this study examines the effects of the physical properties of rocks, such as heterogeneity, anisotropy, and viscosity, on AE waveform signals from the perspective of the rock material and its fracture characteristics as well as the characteristics of the propagation of different AE waveform signals. The results show that the frequency (f) of the AE signals generated by rock fracture is inversely proportional to crack length (c) and directly proportional to the rate of crack growth ( v ). During signal propagation, the comprehensive effects of such factors as the heterogeneity, anisotropy, and viscosity of rocks as well as environmental noise weaken the energy of the signals and enhance the distribution of signal frequency. Each factor differently influences the time frequency of AE. A model for the propagation of AE signals was built and verified. Finally, as for on-site rock mass engineering, the low-frequency signals should be analysed prior to analysis in rock mass disaster monitoring.

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