Performance of a 56 m deep circular excavation supported by diaphragm and cut-off double-wall system in Shanghai soft ground
Bibliographic record
Abstract
The performance of a 56 m deep circular excavation supported by a double-wall system, consisting of an inner circular diaphragm wall and an outer rectangular cut-off wall, in Shanghai soft ground is studied in this paper. The surveyed data (e.g., lateral wall deflections ( δ h), horizontal displacement of soil ( δ s), ground surface settlement ( δ v), soil rebound ( δ v+), earth pressure ( P), pore water pressure ( Pw), and soil stress path) are systematically investigated. The results show that the maximum lateral wall deflections are 0.002% ∼ 0.03% of the excavation depth ( H e). The maximum ground surface settlement ( δ v m) is generally larger than δ h m, which is located at 0.4 ∼ 0.6 H e. Moreover, an innovative formula is proposed to estimate the ground settlement. It is able to distinguish various characteristics of surface settlement in different zones of the settlement profile. The ground surface settlements are mainly induced by surcharge loading and continuous excavation, and they are related to the change in confined water level. The lateral earth and pore water pressures in the active zone are relatively insensitive to the excavation, while the correlation becomes obvious in the passive zone. The findings from this study can be helpful to the design of other similar deep excavations in soft clay.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".