Bibliographic record
Abstract
When tunnelling at great depth in hard brittle rock or when mining-induced stresses cause stress-fracturing of brittle rock, the resulting rock fragments cannot fill the original space. As during a rock blast, geometric bulking occurs when brittle rock is fractured, and the volume occupied by fractured rock is much larger. Near underground excavation, this volume increase causes convergences including floor heave because the fractured rock can only move into the excavation. Unfortunately, analytical tools such as the convergence confinement method (CCM) or the ground reaction curve (GRC) do not account for this rock mass bulking action. Similarly, numerical continuum model, while accounting for material dilation, do not account for the unidirectional expansion (bulking) of the fractured rock. The purpose of this thesis is to combine semi-empirical relations of bulking, established based on field measurements and numerical discontinuum models, with the analytical GRC-method and with 2D numerical models (specifically Phase2TM) to provide a means for estimating the impact of bulking on tunnel convergence. The outcome of this thesis therefore is to provide a means of bulking enhance convergence prediction by analytical and numerical solutions. This is presented for circular tunnels, to facilitate use of analytical solutions, in different rock mass types (plastic and brittle) and for various stress states (stress ratio k = 1 and 0.5) as well as for mining conditions with associated stress changes. The examples presented in this thesis demonstrate that rock mass bulking in brittle rock often dominates tunnel convergence. It is also shown that bulking by extension failure primarily affects the shallow radial displacement profile (near the excavation wall) whereas shear-related bulking, if not suppressed by sufficient confinement, causes deeper-seated radial displacements. The practical implication of this work is that rock support experiences significantly more radial strain and deformation than predicted by conventional analytical and numerical solutions. These models therefore tend to underestimate the straining of installed rock support.
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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 | 0.001 | 0.000 |
| 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".