An experimental system to evaluate impact shear failure of rock discontinuities
Bibliographic record
Abstract
Conventionally, the evaluation of shear failure of discontinuities in rocks and other geomaterials has been conducted under static shear loading. In such methods, the shear failure behaviors of rock discontinuities are significantly influenced by loading velocities. To evaluate the shear failure process under dynamic loading, in this paper, we propose a new experimental methodology by taking advantages of recently available high-speed optical and mechanical measurement techniques. The methodology utilizes the Hopkinson bar to apply impact loading, and the diagnostics include a dynamic stress wave acquisition system, a digital image correlation (DIC) system, and an acoustic emission (AE) monitoring system. To improve the accuracy of the DIC analysis, an advanced digital speckle pattern and an updated water transfer printing are used to obtain the optimized and consistent speckle pattern. A flexible piezoelectric film sensor is first introduced to acquire AE signals in order to locate AE events accurately. A dynamic impact shear experiment indicates that the normal stress has a significant effect on the peak shear stress of rock discontinuities and the peak shear stress itself is rate dependent. The displacement field along shear directions is quantified using the DIC method, and the initial AE source locations during the impact shear process are determined using the AE monitoring system. We thus conclude that the dynamic impact shear system can systematically characterize the dynamic impact shear process with quantitative details and can further be implemented to study other dynamic impact failure behaviors of rock discontinuities under in situ stresses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".