Prediction and analysis of surface settlement due to shield tunneling for Xi’an Metro
Bibliographic record
Abstract
This study describes a new modified prediction method of surface settlement (SS) for Xi’an Metro. The estimation method of SS and its characteristic parameters, volume loss (VL), maximal SS, and settlement trough width (STW) are reviewed and discussed in this paper. The gap parameter (GP) is applied to estimate VL; however, the calculation method of GP and its influence factors have not been clarified entirely. In this study, six influence factors are introduced into the new GP model, and the detailed solutions are presented. This estimation method is able to take into account the support pressure of the shield head at the tunnel face, the lining support pressure around the tunnel opening, the filling effect of tail grouting, yawing, and pitching of the shielding machine, and the long-term deformation of the remoulded surrounding soil. Based on Xi’an Metro line 2, the soil behaviors and measured SS characteristics are deeply investigated. The upper and lower bounds of the total GP of the 15 cases are predicted. Comparison of the predicted SS troughs with field observations can show reasonable agreement. It is suggested that the new estimation method can be used effectively in estimating the SS induced by the shield tunneling method.
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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 | 0.001 |
| 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) | 0.000 | 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".