Measured performance of a 26 m deep top-down excavation in downtown Shanghai
Bibliographic record
Abstract
Via a long-term comprehensive instrumentation program, the performance of a 26 m deep metro station excavation constructed by the top-down method in downtown Shanghai was extensively examined. The measured excavation responses included diaphragm wall deflections, wall settlements, ground settlements, uplifts of interior steel columns, axial forces of propping struts, groundwater table levels, and settlements of adjacent buildings and utility pipelines. Based on the analyses of field data, the following major findings were obtained: (i) the concrete struts along with the floor slabs effectively suppressed later wall movements and consequently reduced the chance that the maximum wall deflections would occur above the excavation surfaces, (ii) with the progress of excavation to a lower depth, the diaphragm walls underwent a serrated settlement pattern over time, (iii) no significant post-excavation wall deflection occurred, (iv) the relationship between the interior column uplifts and the maximum wall deflections can be described by a linear equation, and (v) most system loads due to soil removal were carried by the concrete struts along with the floor slabs. The struts sustained mainly the released earth pressures due to the exposure of the adjacent portions of diaphragm walls, and the soil removal distant from the struts imposed limited effects on the strut axial forces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".