Numerical modeling of time-dependent deformation and induced stresses in concrete pipes constructed in Queenston shale using micro-tunneling technique
Bibliographic record
Abstract
Effects of time-dependent deformation (TDD) on a tunnel constructed using the micro-tunneling technique in Queenston shale (QS) are investigated employing the finite element method. The TDD and strength parameters of the QS were measured from tests conducted on QS specimens soaked in water and lubricant fluids (LFs) used in micro-tunneling such as bentonite and polymer solutions. The numerical model was verified using the results of TDD tests performed on QS samples, field measurements of some documented projects, and the closed-form solutions to circular tunnels in swelling rock. The verified model was then employed to conduct a parametric study considering important micro-tunneling design parameters, such as depth and diameter of the tunnel, in situ stress ratio (Ko), and the time lapse prior to replacing LFs with permanent cement grout around the tunnel. It was revealed that the time lapse plays a vital role in controlling deformations and associated stresses developed in the tunnel lining. The critical case of a pipe or tunnel in which the maximum tensile stress develops at its springline occurs when it is constructed at shallow depths in the QS layer. The results of the parametric study were used to suggest recommendations for the construction of tunnels in QS employing micro-tunneling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".