Reinforcement of piling platforms with geogrids over dewatered docks with very soft soils: Wood Wharf, London
Bibliographic record
Abstract
Wood Wharf is a major redevelopment of 23 acres designed to provide residential and office buildings as well as public spaces for shops, restaurants, parks and community uses. The development is located on the Isle of Dogs, Canary Wharf, London. This paper describes the design and results of the piling mats required to construct the buildings in the area of the South Dock, where 14,000m2 of land had to be reclaimed. Cofferdams were installed to create a watertight retaining structure to facilitate dry construction methods in the reclaimed area behind the new quay wall. To construct the different buildings, CFA and LDA piles between 8m to 34m deep were installed for the foundations. The soil profile primarily comprised Made Ground, Dock Sediments and River Terrace Deposits with very low undrained shear strengths between 2.5 to 42 kPa. The rigs to be used on site were 225 tons with applied pressures in the range of 300 to 470kPa and loadings of more than 1500kN. A series of reinforced piling mats were required to support the heavy rigs and reduce the applied pressures over the foundation soil. The design of the working platforms reinforced with geogrids was conducted in two stages, first to improve the modulus of the platform using recycled granular material, and second to provide safe platforms to support the heavy rigs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".