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Record W2123740616 · doi:10.1093/gji/ggt221

Numerical simulation of acoustic emission in brittle rocks by two-dimensional finite-discrete element analysis

2013· article· en· W2123740616 on OpenAlexaff
A. Lisjak, Q. Liu, Qi Zhao, O. K. Mahabadi, Giovanni Grasselli

Bibliographic record

VenueGeophysical Journal International · 2013
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAcoustic emissionDissipationFinite element methodBrittlenessGeologySolverFracture mechanicsMechanicsElastic energyDiscrete element methodDeformation (meteorology)Structural engineeringPhysicsAcousticsComputer scienceEngineering

Abstract

fetched live from OpenAlex

Stress waves, known as acoustic emissions (AEs), are released by localized inelastic deformation events during the progressive failure of brittle rocks. Although several numerical models have been developed to simulate the deformation and damage processes of rocks, such as non-linear stress–strain behaviour and localization of failure, only a limited number have been capable of providing quantitative information regarding the associated seismicity. Moreover, the majority of these studies have adopted a pseudo-static approach based on elastic strain energy dissipation that completely disregards elastodynamic effects. This paper describes a new AE modelling technique based on the combined finite-discrete element method (FEM/DEM), a numerical tool that simulates material failure by explicitly considering fracture nucleation and propagation in the modelling domain. Given the explicit time integration scheme of the solver, stress wave propagation and the effect of radiated seismic energy can be directly captured. Quasi-dynamic seismic information is extracted from a FEM/DEM model with a newly developed algorithm based on the monitoring of internal variables (e.g. relative displacements and kinetic energy) in proximity to propagating cracks. The AE of a wing crack propagation model based on this algorithm are cross-analysed by traveltime inversion and energy estimation from seismic recordings. Results indicate a good correlation of AE initiation times and locations, and scaling of energies, independently calculated with the two methods. Finally, the modelling technique is validated by simulating a laboratory compression test on a granite sample. The micromechanical parameters of the heterogeneous model are first calibrated to reproduce the macroscopic stress–strain response measured during standard laboratory tests. Subsequently, AE frequency–magnitude statistics, spatial clustering of source locations and the evolution of AE rate are investigated. The distribution of event magnitude tends to decay as power law while the spatial distribution of sources exhibits a fractal character, in agreement with experimental observations. Moreover, the model can capture the decrease of seismic b value associated with the macrorupture of the rock sample and the transition of AE spatial distribution from diffuse, in the pre-peak stage, to strongly localized at the peak and post-peak stages, as reported in a number of published laboratory studies. In future studies, the validated FEM/DEM-AE modelling technique will be used to obtain further insights into the micromechanics of rock failure with potential applications ranging from laboratory-scale microcracking to engineering-scale processes (e.g. excavations within mines, tunnels and caverns, petroleum and geothermal reservoirs) to tectonic earthquakes triggering.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.250
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations219
Published2013
Admission routes1
Has abstractyes

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