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Record W4402651026 · doi:10.1051/e3sconf/202456928001

Reinforcement of piling platforms with geogrids over dewatered docks with very soft soils: Wood Wharf, London

2024· article· en· W4402651026 on OpenAlexaff
Patricia Guerra-Escobar, P. Bernardini, Rozhan Saeed

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsTerrafix Geosynthetics (Canada)
Fundersnot available
KeywordsWharfReinforcementSoil waterGeotechnical engineeringEngineeringGeologyMarine engineeringStructural engineeringSoil science

Abstract

fetched live from OpenAlex

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 14,000m 2 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.196
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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