Shield kinematics and its influence on ground settlement in ultra-soft soil: a case study in Suzhou
Bibliographic record
Abstract
Construction of tunnels using a shield in very soft soil may result in serious shield alignment problems and ground settlement. This paper presents a detailed field instrumentation program that was conducted for the construction of the Suzhou Metro Tunnel-Line S1 in China. The characteristics of the shield kinematics in ultra-soft soil and ground responses were examined, and the mechanisms of ground movements and pore water pressure corresponding to shield motions and construction parameters were interpreted and discussed. In addition, a calculation method for ground loss corrected by shield posture was established, based on which a corresponding volume of the grout required was recommended. The results indicated that the shield typically tunnels in very soft soil with increasing inclination mode characterized by a rising tail owing to the floating segment and with a changing inclination mode characterized by a sudden downward movement and subsequent steering of the shield upon encountering hard soil. Consequently, with the uplifting of the shield tail, it was found that the ground heaves and settles with a change in the shield posture. Moreover, a large longitudinal uneven deformation of the lining was found to occur in most of the above situations, while the pore water pressure was minimally affected by the shield posture.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".