Fracture Behavior of Intact Rock Using Acoustic Emission: Experimental Observation and Realistic Modeling
Bibliographic record
Abstract
Abstract It is well known that acoustic emission (AE) is a powerful nondestructive testing tool for examining the behavior of materials deforming under stress. One can use it to monitor fracture or damage in a rock mass by listening to AE events during failure under compressive loads. In this paper, an experimental study on the AE source location in square cylinder granite specimens under uniaxial compression is reported. In order to determine the three-dimensional location of AE events, eight AE sensors were mounted on the specimen. The AE source location was determined via the acquisition of eight channel AE sensors after filtering, processing, reporting, and visualizing seismic data. On the basis of the laboratory experiment results, the granite sample was numerically simulated via the Burgers model using the discrete element program PFC2D (Particle Flow Code in Two Dimensions) to further study the mechanism of fracture initiation and propagation in intact rock. In PFC2D, materials may be modeled as either bonded (cemented) or unbonded (granular) assemblies of particles. It can describe nonlinear behavior and localization with accuracy that cannot be matched by typical finite element programs. The consistency of stress-strain curves obtained with PFC2D and with the test results shows that PFC2D is a practical tool for reproducing AE events in rock, and use of the Burgers model is feasible in the field of rock failure and provides an analysis of the microcracking activity inside the rock volume to predict rock fracture patterns under uniaxial loading conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".