An experimental method to quantify the impact fatigue behavior of rocks
Bibliographic record
Abstract
Fatigue failure is an important failure mode of engineering materials. The fatigue behavior of both ductile and brittle materials has been under investigation for many years. While the fatigue failure of ductile materials is well established, only a few studies have been carried out on brittle materials. In addition, most fatigue studies on rocks are conducted under quasi-static loading conditions. To address engineering applications involving repeated blasting, this paper proposes a method to quantify the impact fatigue properties of rocks. In this method, a split Hopkinson pressure bar system is adopted to exert impact load on the sample, which is placed in a specially designed steel sleeve to limit the displacement of the sample and thus to enable the recovery of the rock after each impact. The method is then applied to Laurentian granite, which is fine-grained and isotropic material. The results demonstrate that this is a practicable means to conduct impact fatigue tests on rocks and other brittle solids.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".