A Continuum Modelling Study of Macro-Geometry choices and Their Impact on Excavation Damage Zone Development in Brittle Rock at Depth
Bibliographic record
Abstract
Canadian and International experts agree that deep underground repositories are the single best solution regarding safe, long-term nuclear waste disposal (IAEA 2009). Numerous European and Asian countries have already begun extensive research and preliminary design analyses associated with site assessment for high-level fuel storage facilities known collectively as deep geological repositories (DGRs) (ANDRA 2005, Armand et al. 2003, NAGRA 2002, Martino and Chandler 2004). In Canada, Kincardine, ON, is currently the proposed site that will host the low- to medium-level nuclear waste generated from the Bruce, Pickering and Darlington nuclear power plants. The project conceptualization, as well as the specific sites, have undergone extensive public critique and analyses. The project is in the final licensing stage of the process. To ensure long term stability and safety of the DGRs, the prediction and understanding of the excavation damage zone (EDZ) around the associated shafts, placement room tunnels, storage voids and access tunnels is paramount. The general EDZ consists of component zones of damaged, fractured and influenced rock moving radially away from the center of the excavation. The outer “influenced” zone consists of rock that has been elastically strained, while the “damaged” zone undergoes small scale, discontinuous crack damage, subsequently increasing the permeability of the surrounding rockmass. The “fractured” zone is located nearest to the excavation wall/face, where continuous (connected) fractures dominate. Numerous details within a preliminary design of the tunnels and intersections within a DGR will influence the development and ultimate impact of the EDZ on ultra-long-term repository safety. For the various DGR tunnelling projects that include the placement room tunnels, access tunnels and vertical shafts, details of shape will dictate the ultimate support demands and safety requirements. Using case examples, as well as 2-dimensional and 3-dimensional continuum numerical modelling simulations, this thesis will illustrate key impacts of macro-geometry choices, such as shape and dimensions of placement room and access tunnels, as well as details, which include corner geometry, aspect ratio, excavation sequence and methodology, on EDZ and highly damaged zone (HDZ) development in underground infrastructure.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".