Mechanical analysis and interpretation of excavation damage zone formation around deep tunnels within massive rock masses using hybrid finite–discrete element approach: case of Atomic Energy of Canada Limited (AECL) Underground Research Laboratory (URL) test tunnel
Bibliographic record
Abstract
The construction of an underground opening leads to changes in the in situ stress regime surrounding the excavation. The opening influences the rock mass owing to the redistribution of the stresses and results in the disturbance of the surrounding ground. At great depths, massive to slightly or moderately fractured rock masses are usually encountered, and under high stresses, they are more likely to behave in a brittle manner during an excavation. While constitutive models have been developed and proposed for the numerical simulation of such excavations using continuum mechanics, this brittle response cannot be simulated accurately enough, since the material behaviour is governed by fracture initiation and propagation. On the contrary, discontinuum approaches are more suitable in such cases. For the purposes of this paper, the brittle behaviour of hard, massive rock masses and the associated spalling failure mechanisms were simulated by employing a finite–discrete element method (FDEM) approach using Irazu software. The generated numerical model was utilized to replicate field conditions based on the observations at the Atomic Energy of Canada Limited (AECL) Underground Research Laboratory (URL) test tunnel located in Pinawa, Manitoba, Canada. The model results are compared with field observation data to explicitly demonstrate the suitability of the method.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".