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Record W1994393192 · doi:10.1139/t08-129

Finite element analysis of an embankment on a soft estuarine deposit using an elastic–viscoplastic soil model

2009· article· en· W1994393192 on OpenAlexaffvenue
Curtis Kelln, Jitendra Sharma, David Hughes, James Graham

Bibliographic record

VenueCanadian Geotechnical Journal · 2009
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsUniversity of ManitobaUniversity of Saskatchewan
FundersMinistère de l'Enseignement supérieur, de la Recherche et de l'Innovation
KeywordsLeveeGeotechnical engineeringPore water pressureViscoplasticitySettlement (finance)GeologyFinite element methodDeformation (meteorology)Foundation (evidence)Soil waterConstitutive equationEngineeringStructural engineeringSoil science

Abstract

fetched live from OpenAlex

A new elastic–viscoplastic (EVP) soil model has been used to simulate the measured deformation response of a soft estuarine soil loaded by a stage-constructed embankment. The simulation incorporates prefabricated vertical drains installed in the foundation soils and reinforcement installed at the base of the embankment. The numerical simulations closely matched the temporal changes in surface settlement beneath the centerline and shoulder of the embankment. More importantly, the elastic–viscoplastic model simulated the pattern and magnitudes of the lateral deformations beneath the toe of the embankment — a notoriously difficult aspect of modelling the deformation response of soft soils. Simulation of the excess pore-water pressure proved more difficult because of the heterogeneous nature of the estuarine deposit. Excess pore-water pressures were, however, mapped reasonably well at three of the six monitoring locations. The simulations were achieved using a small set of material constants that can easily be obtained from standard laboratory tests. This study validates the use of the EVP model for problems involving soft soil deposits beneath loading from a geotechnical structure.

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 categoriesMeta-epidemiology (narrow)
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 score0.623
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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.

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

Citations32
Published2009
Admission routes2
Has abstractyes

Explore more

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