Considerations of rock dilation on modeling failure and deformation of hard rocks—a case study of the mine-by test tunnel in Canada
Bibliographic record
Abstract
For the compressive stress-induced failure of tunnels at depth, rock fracturing process is often closely associated with the generation of surface parallel fractures in the initial stage, and shear failure is likely to occur in the final process during the formation of shear bands, breakouts or V-shaped notches close to the excavation boundaries. However, the perfectly elastoplastic, strain-softening and elasto-brittle-plastic models cannot reasonably describe the brittle failure of hard rock tunnels under high in-situ stress conditions. These approaches often underestimate the depth of failure and overestimate the lateral extent of failure near the excavation. Based on a practical case of the mine-by test tunnel at an underground research laboratory (URL) in Canada, the influence of rock mass dilation on the depth and extent of failure and deformation is investigated using a calibrated cohesion weakening and frictional strengthening (CWFS) model. It can be found that, when modeling brittle failure of rock masses, the calibrated CWFS model with a constant dilation angle can capture the depth and extent of stress-induced brittle failure in hard rocks at a low confinement if the stress path is correctly represented, as demonstrated by the failure shape observed in the tunnel. However, using a constant dilation angle cannot simulate the nonlinear deformation behavior near the excavation boundary accurately because the dependence of rock mass dilation on confinement and plastic shear strain is not considered. It is illustrated from the numerical simulations that the proposed plastic shear strain and confinement-dependent dilation angle model in combination with the calibrated CWFS model implemented in FLAC can reasonably reveal both rock mass failure and displacement distribution in vicinity of the excavation simultaneously. The simulation results are in good agreement with the field observations and displacement measurement data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".