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Record W2549298884 · doi:10.1139/cgj-2016-0232

Analytical model for vacuum consolidation incorporating soil disturbance caused by mandrel-driven drains

2016· article· en· W2549298884 on OpenAlexaffvenue
Darshana Perera, Buddhima Indraratna, Serge Leroueil, Cholachat Rujikiatkamjorn, Richard Kelly

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsConsolidation (business)Geotechnical engineeringCompressibilityPore water pressureLeveeDissipationPermeability (electromagnetism)GeologyMechanics

Abstract

fetched live from OpenAlex

When vacuum preloading is applied with vertical drains, the rate of consolidation can be increased, and the stability of an embankment is enhanced due to the inward lateral movement. The aim of this study is to develop an analytical solution for vacuum preloading that accurately captures the more realistic variations in compressibility and permeability in actual ground conditions as a result of drain installation. The soil samples were obtained from various locations after drain installation to determine the characteristics of soil surrounding the vertical drain in terms of compressibility and permeability. The main differences between the proposed and conventional models are described by considering the stress history and preloading pressure. The effect of pre-consolidation pressure and the magnitude of applied preloading are examined through the dissipation of average excess pore pressure and associated settlement. The analysis of a selected case history employing the writers’ solution indicates improved accuracy of the predictions in comparison to the field measurements.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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 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

Citations36
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil StabilizationFrench-language works237,207