Differential settlement remediation for new shield metro tunnel in soft soils using corrective grouting method: case study
Bibliographic record
Abstract
In the Yangtze River Delta of China, a large number of metro tunnels have been constructed in soft soils. The excessive and differential tunnel settlement may impair the serviceability of the metro system. The treatment of such excessive and differential settlement in rheologic and sensitive soft soils is a challenge because the tunnel may incur a larger settlement due to construction disturbances. In this paper, a case study of the differential settlement treatment of the new shield tunnel of Ningbo Metro line 2 is presented. A maximum tunnel settlement of 214 mm was observed several months after construction of the tunnel was completed. To uplift the deviated tunnel axis, a grouting and lifting measure named “bottom grouting, inner support, real-time monitoring and immediate adjusting” is proposed. The settlement treatment section is successfully uplifted with an average value of 30 mm, and the maximum final uplift amount of the tunnel is 41 mm, which reached the target value of uplift. The maximum convergence deformation caused by the grouting is 10 mm, which is less than the maximum acceptable deviation, i.e., 15.5 mm. The corrective grouting method and the valuable monitoring data presented in this study can provide a reference for projects with similar problems in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".