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Record W7130691979 · doi:10.1680/ecsmge.60678.vol2.107

The geotechnical aspects of the London Bridge Station redevelopment

2015· book-chapter· en· W7130691979 on OpenAlexaff
G.C. Bunce, A. Wiles, M. Haliburton, M. Parry, R. Melillo, M. Back

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicCivil and Structural Engineering Research
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsDemolitionRedevelopmentSettlement (finance)Bridge (graph theory)TrainWork (physics)

Abstract

fetched live from OpenAlex

ABSTRACT The London Bridge Station Redevelopment comprises a complete reconstruction of the rail station to improve the capacity and number of through trains in central London. The works are being carried out while the station remains in operation. This paper gives a summary of the ground conditions encountered and describes the design approach for new piles required for the new station. Where possible the existing foundations were re-used and this paper describes the assessment method of these foundations in order to allow safe adoption. A large number of the new piled foundations were installed from within the existing brick arches, with limited headroom and very close to some of the existing 178 year old shallow foundations. The paper describes the approach to the controls necessary for this operation to ensure continued safe use of the station. The demolition of part of the existing station and re-loading with the new foundations affected existing facilities such as water mains and the LUL Jubilee Line. A detailed assessment of the heave and settlement resulting from these operations and the resulting impact on the third party assets was carried out using a 3D finite element programme and a summary of this work is given in the paper.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0130.002

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 head0.025
GPT teacher head0.235
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2015
Admission routes1
Has abstractyes

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