Evaluation of ground loss ratio with moving trajectories induced in double-O-tube (DOT) tunnelling
Bibliographic record
Abstract
This paper investigates the influence of moving trajectories on ground loss ratio (GRL) due to the double-O-tube (DOT) tunnelling method. DOT tunnelling has three moving trajectories: pitching, yawing, and rolling, which have different behaviours during tunnel construction compared with those from single circular shield tunnelling. These moving trajectories cause overexcavation during tunnelling. The calculation method of gap area between the DOT shield machine and linings is evaluated in this research. Based on the superposition concept, the modification equation of GLR is proposed, which takes both moving trajectory and grouting volume into consideration. A field DOT tunnelling case is analysed to determine the correlation between moving trajectories and ground settlement. The influence of tail grouting is discussed by adjusting the grouting volume in different periods. The finite element method is also employed by setting the modified ground loss ratio (GLR′) as the contraction increment of linings. Results from both the measured and simulated settlements verify the reasonability of the proposed equation and the effect of moving trajectories on ground loss.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".