Investigation of the long-term tunnel settlement mechanisms of the first metro line in Shanghai
Bibliographic record
Abstract
The long-term performance of a metro tunnel is obviously a major concern for the authority and designers responsible for its construction, as excessive tunnel settlement would affect the serviceability and safety of the entire metro system. In this study, measured long-term settlement of the 16.4 km long tunnel of Shanghai Metro Line 1 from 1994 to 2007 (a period of 12.5 years) is reported and discussed. The tunnel settlement was continuous with time, and reached a maximum of 288 mm. To assist in the investigation of the mechanisms of long-term tunnel settlement, records of groundwater pumping and subsurface soil compression between 1985 and 2007 are analyzed. Four possible causes — namely, effects of tunnel construction, cyclic loading due to running trains, secondary compression of soft clay, and groundwater pumping in sandy aquifers — are investigated. From the measurements of subsurface soil compression, it is revealed that the observed large tunnel settlement was mainly caused by the compression of sandy Aquifer IV due to groundwater pumping. The measured compression of sandy Aquifer IV accounted for about 65% of the maximum tunnel settlement of Line 1. Soil yielding due to an increase in effective stress was induced by a significant decline in groundwater level. The observed continuing settlement of the tunnel over the 12.5 years was the result of secondary compression (creep), which is a common feature of Aquifer IV at various locations in Shanghai.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".