Bibliographic record
Abstract
The observed behaviour of shales and shaly rocks in southern Ontario and elsewhere has demonstrated an interesting combination of high in situ horizontal stresses and potential for substantial time dependent deformations. Tunnelling in such challenging ground conditions can be problematic. Studies using visco-elastic closed form solutions have established that lining stiffness and the timing of its installation are important factors in the development of ground-induced stresses in the lining. Traditional tunnel sequencing has often resulted in a time lag between excavation and final support installation that allowed dissipation of a significant portion of the time dependent deformations that would otherwise have resulted in additional lining stresses. However, modern tunnelling uses tunnel boring machines and single-pass pre-cast concrete segmental linings that install a relatively stiff lining system in close proximity to the recently excavated tunnel face, and this exacerbates development of time dependent loads. This paper describes the development and implementation of a two-dimensional, plane strain finite element model to calculate both the stress and time dependent ground and lining interaction using a numerical stepwise integration of the tunnel construction process and its effects. The work extends previously developed closed-form visco-elastic solutions that were limited to the installation of a cast-in-place lining (Lo and Yuen, 1981 and
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".