Evaluation of the effect of rolling correction of double-o-tunnel shields via one-side loading
Bibliographic record
Abstract
During construction of a double-o-tunnel (DOT), rolling will inevitably occur due to the following: (i) nonuniformity of subsoil condition, (ii) manufacturing errors in the DOT shield machine, (iii) different pulling forces at two sides of the DOT, (iv) effect of assembled segments, (v) loss of grout, and (vi) inappropriate operation. In engineering practice, rolling correction using a one-side load at the elevated side is a cost-effective method. The weight of the one-side load is determined by the experience of the engineers and (or) through observation of the returned rolling angle. However, these methods cannot predict the value of the one-side load before applying it. This paper presents a series of finite element analyses that was performed to investigate the relationship among the one-side load, rolling angle, and subsoil deformation. The analytical results show that the proposed approach can predict field-observed data well. It is concluded that analytical results can be used as guidance for DOT construction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".