Experimental and numerical studies on dynamic crack growth in layered slate rock under wedge impact loads: part II – non‐plane strain problem
Bibliographic record
Abstract
ABSTRACT Dynamic crack propagation in non‐plane strain (or 3D) slate blocks under wedge impact loads was investigated numerically in this part of the paper. A parabolic‐shaped crack trajectory was taken into consideration to model the crack propagation in slate blocks for analyzing the impact splitting of layered slate rock. Major and minor axes of the parabola were determined from the condition of equal mode I stress intensity factors (SIFs) along the crack front. Mode I SIFs were determined for experimental breaking loads for each increment of crack growth in a manner similar to that mentioned in part I of this paper. These values were compared with the plane strain material fracture toughness value obtained from experimental studies and very good agreement was obtained between them, for the case of actual load applied on the specimen. Numerical analysis of a field problem, i.e., separation of a large‐sized slate slab from the rock strata in a slate quarry using wedge impacting, was also carried out in this paper. It can be observed that a large magnitude of load is required to break large‐sized slate blocks; but this load is applied through a number of smaller load‐capacity actuators‐in‐parallel, requiring large power capacity for the hydraulic pumps. However, this required power could be reduced considerably if the load applied on the line of hydraulic actuators is cascaded across the (line of) actuators (starting from centrally placed actuators) with a small time delay (equal to the initial crushing time in slate rock).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".