Case study of tunnel ground reaction modeling in horizontally bedded rock using continuum and fracture network models
Bibliographic record
Abstract
Anisotropic, jointed rock masses can result in relatively complex conditions that are not easily approximated with numerical models using general rock mass strength parameters. In this case study, numerical models using either rock mass continua or fracture networks are examined in the context of tunnels within horizontally bedded shales with high horizontal stress. This case study demonstrates the scale and orientation effects of rock mass discontinuities on the understanding of rock mass reaction and potential failure mechanisms. A case history from the Hanlan tunnel project in Mississauga, Canada is reviewed. Continuum models for the tunnels were created using rock mass strength parameters from the Generalized Hoek-Brown Failure Envelope using laboratory testing data and Geological Strength Indices. Due to high horizontal stress and the relatively low Geological Strength Indices in the horizontally bedded rock mass, continuum models of the tunnels exhibit extensive conjugate shear failure planes through the tunnel haunches, resulting in very large zones of plasticity that are not typically observed based on local tunneling experience. Adjusting the rock mass strength parameters for a pseudo-intact rock condition and explicitly modeling fracture networks changes the model ground reaction and more accurately reflects observed behaviour.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".