INFLUENCE OF DILATION ON ROCK MASS DISPLACEMENT AROUND UNDERGROUND EXCAVATIONS—A CASE STUDY OF DONKIN-MORIEN TUNNEL IN CANADA
Bibliographic record
Abstract
Based on the established rock dilation angle model considering both confining pressure and plastic shear strain from previous study by authors,the relationship between peak internal friction angle(?) and dilation angle(ψ) for different rocks is established.Based on the conclusion of ψ ? and ψ peak≈ ? peak at the zero confining pressure from theoretical analysis and experimental observations,an important assumption is made,i.e.the dilation behavior of a rock mass resembles that of the intact rock.The rock dilation angle model is generalized for rock mass using Hoek-Brown criterion and GSI system;and subsequently the proposed rock mass dilation angle model is implemented in FLAC3D using VC++ language.Utilizing extensometer data from Donkin-Morien tunnel project in Canada,the dilation angle model is verified by investigating the ground deformation near the excavation boundary.It is demonstrated that constant dilation angle values can not capture the displacement distributions near the excavation boundary satisfactorily.However,when the confining pressure and plastic shear strain dependent rock mass dilation are considered,the predicted rock mass displacements induced by gradual excavation are in good agreement with the field measurement results.This model provides a reasonable means to consider dilation during rock mass failure near the excavation boundary.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".