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Record W7056398332

Field study of pile – prefabricated vertical drain (PVD) interaction in soft clay

2020· article· en· W7056398332 on OpenAlexaboutno aff

Bibliographic record

VenueUTS ePRESS (University of Technology Sydney) · 2020
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPileCompressibilitySettlement (finance)LeveePore water pressureParasitic dragDrainageSoil structure interaction
DOInot available

Abstract

fetched live from OpenAlex

© 2020, Canadian Science Publishing. All rights reserved. Piles and prefabricated vertical drains (PVDs) are two well-established inclusions used by geotechnical practitioners when dealing with soft compressible foundations. Induced movements in highly compressible soil can adversely influence the pile response by inducing additional movements and stresses in the piles. Especially, undesirable soil–pile interaction often leads to the development of excess pore-water pressure during pile installation and negative skin friction caused by the settlement of compressible soil surrounding the piles. Additional drainage by PVDs prior to the installation of a pile could reduce excess pore-water pressure, lateral soil movement, and negative skin friction on the pile. In this paper, full-scale field testing on two trial embankments built on soft soil is reported and the relative behaviour of these two embankments is compared and discussed. Soft soil underneath both embankments was consolidated before one pile was installed at the centre of each embankment. The pore-water pressure, lateral soil movement, surface settlement, and associated strain at the pile shaft were recorded. The pile capacity was tested immediately and 3 h after pile installation. The monitoring and testing results indicated that preconsolidation with PVDs before piling can effectively reduce the excess pore-water pressure, lateral soil movement, and downdrag on the pile.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.423

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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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
Published2020
Admission routes1
Has abstractyes

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