Investigation of acoustic events during shear loading of layered rock bridge; Particle flow code approach
Bibliographic record
Abstract
Abstract This research investigates acoustic emission (AE) phenomena during shear failure of layered rock bridges under different normal stresses using experimental punch tests and numerical simulation. Firstly, particle flow code (PFC) was calibrated by UCS and Brazilian experimental test results and then the shear behavior of the numerical model was verified by experimental punch test outputs. Finally, acoustic phenomena during shear failure of layered rock bridges were discussed. Rectangular specimens were utilized, incorporating a combination of different layers. These layers included a pairing of soft and hard materials, as well as variations such as a two-layered model comprising hard gypsum and soft gypsum. Furthermore, three-layered models were examined, featuring a soft interlayer in one case and a hard interlayer in another. Additionally, a four-layered model was employed for the investigation. In each model, two vertical edge fissures were introduced, with fissure lengths set at 20, 40, and 60 mm. The angle between bedding layers and shear loading direction was 90°. The results indicate that cracks initiate at the notch tip and propagate vertically until they meet the upper boundary. The frequency of significant Acoustic hits is tied to factors like crack initiation and material properties. Increasing the number of layers in the specimen leads to more Acoustic hits. The sequence of Acoustic hits between major hits is influenced by parameters such as bedding number and material properties. This correlation is attributed to different gypsum types on the shear surface. The findings from numerical bedding models mirror those from physical samples.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".