The 2003 Canadian Geotechnical Colloquium: Mechanistic interpretation and practical application of damage and spalling prediction criteria for deep tunnelling
Bibliographic record
Abstract
Spalling and strain bursting has long been recognized as a mechanism of failure in deep underground mines in hard rock and in deep infrastructure tunnels. The latter is a significant growth industry, particularly in Europe where subalpine base tunnels in excess of 10 m wide and dozens of kilometres long are being driven by tunnel boring machine (TBM) through alpine terrain at depths greater than 2 km. In more massive granitoid or gneissic ground, these tunnels have experienced significant spalling damage. En route to a practical predictive technique for this condition, the author utilizes a number of analytical and micromechanical tools to validate a simple empirical predictive model for tunnel spall initiation. The true nature of damage and of yield, as the result of extensile damage accumulation, in hard rocks is examined using these tools. Based on the resultant conceptual model, the author expands on the empirical damage threshold, using a spalling limit to differentiate stress paths that lead to crack propagation and spalling from those that incur stable microdamage prior to conventional shear failure at higher relative confinements. Finally, the composite and robust in situ yield model is applied to nonlinear modelling for support design.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".