2022
DOI: 10.1007/s11771-022-4971-3
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Experimental investigation on synergetic prediction of granite rockburst using rock failure time and acoustic emission energy

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Cited by 25 publications
(7 citation statements)
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“…The bottom of an oil tank can be regarded as a two-dimensional circular plate, as shown in Figure 2 [19][20][21][22]. When the stress on the bottom of the oil tank changes, the measured defect site will generate continuous or discontinuous AE signals.…”
Section: Mathematical Modeling Of Acoustic Emission Detectionmentioning
confidence: 99%
“…The bottom of an oil tank can be regarded as a two-dimensional circular plate, as shown in Figure 2 [19][20][21][22]. When the stress on the bottom of the oil tank changes, the measured defect site will generate continuous or discontinuous AE signals.…”
Section: Mathematical Modeling Of Acoustic Emission Detectionmentioning
confidence: 99%
“…When the stress on the bottom of the oil tank changes, the measured defect site will generate continuous or discontinuous AE signals. Information about the location of AE signals can be acquired by obtaining the distance and time taken for the signals to reach three or more AE sensors [18][19][20][21][22]. Assuming the signal transmitted from the defect position reaches sensor i after t i , and the transmission speed of sound in the tank bottom plate is v, the following formula can be obtained:…”
Section: Mathematical Modeling Of Ae Detectionmentioning
confidence: 99%
“…Since the rock particles and cementation components are random, the strength of microunits is also random, and assuming that the microunit strength obeys the Weibull statistical distribution [16].…”
Section: Analysis Of the Damage Evolution Law Characterized By Acoust...mentioning
confidence: 99%
“…A large number of scholars have carried out researches on the application of acoustic emission in rock fracture monitoring and achieved fruitful results. Yang [16] studied the microfracture behavior and acoustic emission characteristics of rock under hydraulic coupling through machine learning. Wang et al [17] used acoustic emission technology to explain the law of granite crack growth by carrying out true triaxial tests on granite and building a cooperative prediction model for rock failure time.…”
Section: Introductionmentioning
confidence: 99%