Bibliographic record
Abstract
This paper addresses a fundamental question in rock mechanics: Are there Class II rocks? The historical development of servo-controlled rock testing machines is reviewed, followed by a brief review of some stiff testing machines. The pioneering work of some researchers is reviewed, and the misconception of classifying rocks into Class I and Class II is discussed. The mechanism of post-peak Class II behavior is discussed based on some recent test results. When a brittle hard rock is tested using a soft testing machine under axial-strain-controlled loading, violent failure can occur when the peak strength is reached, and the post-peak stress–strain curve cannot be obtained. However, a Class II post-peak stress–strain curve can be obtained when the rock is tested under lateral-strain-controlled loading. If a stiff testing machine is used, Class I and Class II post-peak stress–strain curves will be obtained under axial- and lateral-strain-controlled loadings, respectively. It is therefore not appropriate to classify rocks into Class I or Class II rocks. The influences of other conditions, such as rock type, confinement, and specimen height-to-diameter ratio, on the type (Class I or Class II) of post-peak stress–strain curves are also discussed. Finally, some misconceptions in the rock mechanics community, stemming from the concept of “Class II rock”, are discussed. By clarifying these concepts related to Class I and Class II behaviors, this paper seeks to clarify misunderstandings and misapplications related to post-peak strength and deformation properties in the field. Highlights • Class II post-peak behavior of brittle hard rocks is caused by lateral-strain-controlled loading. • Large post-peak rock dilation is the root cause of Class II behavior. • It is not appropriate to classify rocks into Class I and Class II rocks. • Class II curves should not be used to assess rock brittleness and ejection velocity.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".