A novel method for geometric quality assurance of rock joint replicas in direct shear testing – Part 2: Validation and mechanical replicability
Bibliographic record
Abstract
Each rock joint is unique by nature which means that utilization of replicas in direct shear tests is required in experimental parameter studies. However, a method to acquire knowledge about the ability of the replicas to imitate the shear mechanical behavior of the rock joint and their dispersion in direct shear testing is lacking. In this study, a novel method is presented for geometric quality assurance of replicas. The aim is to facilitate generation of high-quality direct shear testing data as a prerequisite for reliable subsequent analyses of the results. In Part 1 of this study, two quality assurance parameters, σmf and VHp100, are derived and their usefulness for evaluation of geometric deviations, i.e. geometric reproducibility, is shown. In Part 2, the parameters are validated by showing a correlation between the parameters and the shear mechanical behavior, which qualifies the parameters for usage in the quality assurance method. Unique results from direct shear tests presenting comparisons between replicas and the rock joint show that replicas fulfilling proposed threshold values of σmf < 0.06 mm and |VHp100| < 0.2 mm have a narrow dispersion and imitate the shear mechanical behavior of the rock joint in all aspects apart from having a slightly lower peak shear strength. The wear in these replicas, which have similar morphology as the rock joint, is in the same areas as in the rock joint. The wear is slightly larger in the rock joint and therefore the discrepancy in peak shear strength derives from differences in material properties, possibly from differences in toughness. It is shown by application of the suggested method that the quality assured replicas manufactured following the process employed in this study phenomenologically capture the shear strength characteristics, which makes them useful in parameter studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".