Mobilised strength components in brittle failure of rock
Bibliographic record
Abstract
In deep underground excavations in hard rocks where stresses easily exceed the micro-crack initiation stress level inside the rock mass, proper consideration of the behaviour of rockmass during the brittle fracturing process in constitutive modelling is of paramount importance. Current empirical and conventional experimental methods for obtaining the deformational behaviours of hard rocks under loading do not lead to results that can be matched with in situ failure observations. This paper demonstrates that this problem is not necessarily a matter of the general notion of size effect but rather can be related to the different circumstances under which the cohesive and frictional strength components are mobilized in laboratory compression tests and around underground openings. It is also demonstrated that the propagation of the failed or breakout zone (depth and extent) is a function of the strain-dependent brittleness index IBε introduced in this paper, which explicitly considers the relative delay in friction mobilization relative to the rate of cohesion loss as functions of plastic strain. This new brittleness index characterizes the entire stress–strain curve (pre- to post-peak stages) and represents the involved micro-mechanisms during the brittle failure process: that is, initiation, propagation, and coalescence of cracks. This study shows that brittleness of rock is the most dominant factor, in controlling breakout shape, which explains the failure of stress-based criteria adopted by many researchers in predicting the stress-induced breakout depth around openings.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".