Performances of a large-scale deep excavation with multi-support types and zoned excavation technique in Shanghai soft soil
Bibliographic record
Abstract
This paper presents a comprehensive field investigation on a large-scale deep basement excavation in Shanghai soft soil propped by a multi-support system. Because of its large size, irregular shape, and different excavation depths, the excavation site was divided into Zone A and Zone B to optimize the construction process and improve the construction efficiency. The excavation was constructed using the “bottom-up” method following the principles of stratification and zone excavation. A notable innovation in this project is the implementation of three different support subsystems as a multi-support system to accommodate different deformation requirements in different areas. The excavation was densely instrumented to monitor the behaviors of retaining walls, columns, axial forces of struts, and surrounding ground throughout the whole construction process. The wall deformation and ground surface settlement of the three support subsystems are comprehensively compared to investigate the performances of the three support subsystems. The comparison of the corner-effect envelope summarized from nine reported cases indicates that the multi-support system can effectively alleviate the spatial corner effects of the excavation. The zoned construction technique in conjunction with the multi-support system presented in this case study provides an efficient and economic approach for large-scale deep excavation in soft soils.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".