RS3 Feasibility Modelling of a Semi-Circular Notched Sample Spalling Experiment
Bibliographic record
Abstract
Spalling in rock has been an area of focus for researchers since it was first observed in tunnels and boreholes.The earliest form of spalling tests are in-situ tests, but due to high costs and the risks associated with allowing an excavation to fail, the focus of spalling research has shifted to a safe and controlled laboratory environment.Numerous tests exist today that can recreate the conditions for spalling with unique geometry and loading conditions.A review of currently established tests allows for gaps in the literature to be identified, ultimately leading to a new test.The Semi-Circular Notched Sample spalling test (SCNS test) was conceptualized as a rectangular specimen with a semi-circular notch at the mid-height.Due to the compression of a circular notched sample, a stress concentration is created, and compressive stresses at the notch are magnified.The condition for loading was analyzed in RS3, and a full-area compressive load was identified.Results from the modelling and physical testing are compared via strain results from the RS3 simulation, and the strain gauges equipped on the physical test.Based on the comparison between numerical models and experimental data, the model predictions matched successfully with the physical samples.This indicates viability in the SCNS test's ability to re-create the spalling phenomena under the simplest of testing 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".