Numerical DEM simulation of AE in plate fracture and analogy with the frequency of seismic events in SCRs

Birck, Gabriel; Riera, Jorge Daniel; Iturrioz, Ignácio

doi:10.1016/j.engfailanal.2018.06.024

Cited by 15 publications

(6 citation statements)

References 10 publications

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“…This method was used by Riera [7] to solve structural dynamics problems in an orthotropic continuum, with stiffness coefficients derived for a linearly elastic solid by Nayfeh and Hefzy [37], and by using the explicit direct numerical integration of the equations of motion. The LDEM was also employed to represent the behavior of quasi-brittle specimens in [10,38], showing a good correlation with numerical methods and experimental results, not only in terms of final configuration and global parameters, but also in the simulation of acoustic emission.…”

Section: Introductionmentioning

confidence: 86%

Analysis of Acoustic Emission Activity during Progressive Failure in Heterogeneous Materials: Experimental and Numerical Investigation

et al. 2022

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This work focuses on an experimental and numerical investigation into monitoring damage in a cube-shaped concrete specimen under compression. Experimental monitoring uses acoustic emission (AE) signals acquired by two independent measurement apparatuses, and the same damage process is numerically simulated with the lattice discrete element method (LDEM). The results from the experiment and simulation are then compared in terms of their failure load, final configurations, and the evolution of global parameters based on AE signals, such as the b-value coefficient and the natural time approach. It is concluded that the results from the AE analysis present a significant sensitivity to the characteristics of the acquisition systems. However, natural time methods are more robust for determining such differences, indicating the same general tendency for all three data sets.

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Section: Introductionmentioning

confidence: 86%

Analysis of Acoustic Emission Activity during Progressive Failure in Heterogeneous Materials: Experimental and Numerical Investigation

et al. 2022

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“…The time-varying b-value can well reflect the changing state of the crack inside the rock at different loading stages, as well as the scale extension of the microcracks inside the rock. [31][32][33] The G-R relation between magnitude and frequency of seismic activity proposed by Gutenberg and Richter as follows 13 lg N = a À bm where m is the magnitude, N is the magnitude within DM-the number of specimens, and a and b are the constants. For the estimation of b-value, the most common method is the maximum likelihood method…”

Section: B-value Calculation and Analysismentioning

confidence: 99%

Identification of crack development in granite under triaxial compression based on the acoustic emission signal

Wang

Xue

et al. 2021

International Journal of Distributed Sensor Networks

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To explore the development mechanism of cracks in the process of rock failure, triaxial compression tests with simultaneous acoustic emission monitoring were performed on granite specimens using the MTS rock mechanics test system. The frequency-domain information of the acoustic emission signal was obtained by the fast Fourier transform. The Gutenberg–Richter law was used to calculate the acoustic emission signals and obtain the b-value dynamic curve in the loading process. Combined with the stiffness curve of granite specimens and acoustic emission signal in the time domain and frequency domain, the crack development characteristics in different stages were analyzed. The results showed that the acoustic emission signals of granite samples under triaxial compression can be divided into four stages: quiet period 1, active stage 1, quiet period 2, and active stage 2. b-value attained its maximum value in the active phase 2 when it is close to the sample loss, and then drops rapidly, which means the propagation of cracks and the formation of large cracks. The acoustic emission signal’s dominant frequency was not more than 500 kHz, mostly concentrated in the medium-frequency band (100–200 kHz), which accounted for more than 80%. The proportion of signals in each frequency band can reflect the distribution of the three kinds of cracks, while the change in low-frequency signals can reflect the breakthrough of microcracks and the formation time of macrocracks in granite samples. By fully analyzing the characteristics of acoustic emission signals in the time domain and frequency domain, the time and conditions of producing large cracks can be determined accurately and efficiently.

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“…Hazzard and Young [2000] and Hazzard and Young [2002] introduced methods for modeling AE within PFC DEM software [Itasca, 2015]. Many studies extended and validated the methodology by comparing numerical and experimental Guttenberg-Richter b-values, refining energy calculations, and generating synthetic seismograms [Birck et al, 2018, Hazzard and Damjanac, 2013, Khazaei et al, 2015, Lisjak et al, 2013, Zhang et al, 2017. In all cases, compressive tests yield clouds of simulated AE that compare well to experimental AE observations.…”

Section: Acoustic Emissions In the Intrinsic Process Zonementioning

confidence: 99%

Modeling acoustic emissions in heterogeneous rocks during tensile fracture with the Discrete Element Method

Caulk¹

2020

Open Geomechanics

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A computationally efficient and open sourced methodology designed for the investigation of rock matrix heterogeneities and their effect on pre-and post-fracture Acoustic Emission (AE) distributions is presented. First, an image analysis method is proposed for building a statistical model representing rock heterogeneity. The statistical model is generalized and implemented into a discrete element contact model where it efficiently simulates the presence of defects and locally tough regions. The coupling of the heterogeneity model, discrete element model, and acoustic emission model is demonstrated using a numerical three point bending test. The shape parameter of the statistical model, which controls heterogeneity magnitude, is found to control the spatial width of the acoustic emission distribution generated during failure. The same acoustic emission distribution trend is observed in literature for rocks containing various magnitudes of heterogeneity. Further analysis of the numerical AE activity reveals that larger AE events are located directly along the fracture and they are linearly related to their number of constituent interactions. As such, an AE interaction count threshold is identified to distinguish between fracture and damage AE activity. These results demonstrate the ability of the presented methodology to investigate the location and energy release associated with large fracture events for various levels of heterogeneity.

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Numerical DEM simulation of AE in plate fracture and analogy with the frequency of seismic events in SCRs

Cited by 15 publications

References 10 publications

Analysis of Acoustic Emission Activity during Progressive Failure in Heterogeneous Materials: Experimental and Numerical Investigation

Analysis of Acoustic Emission Activity during Progressive Failure in Heterogeneous Materials: Experimental and Numerical Investigation

Identification of crack development in granite under triaxial compression based on the acoustic emission signal

Modeling acoustic emissions in heterogeneous rocks during tensile fracture with the Discrete Element Method

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