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Behavior of an Atypical Embankment on Soft Soil: Field Observations and Numerical Simulation

2009· article· en· W2086904900 on OpenAlexfundno aff
Paulo J. Venda Oliveira, Luís Lemos, Paulo Coelho

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaInternational Council for Canadian Studies
KeywordsGeotechnical engineeringIsotropyLeveeMohr–Coulomb theoryPore water pressurevon Mises yield criterionGeologyOverburden pressureAnisotropyLateral earth pressureCritical state soil mechanicsYield (engineering)Yield surfaceComputer simulationMechanicsGeometryConstitutive equationFinite element methodMaterials scienceStructural engineeringEngineeringMathematicsPhysicsComposite material

Abstract

fetched live from OpenAlex

This paper compares the behavior of an embankment with nonsymmetric geometry built on soft soil with that predicted numerically using four elastoplastic soil models. Two of these models are based on isotropic conditions (Modified Cam-Clay on its own or in association with Von Mises) and two other are derived from anisotropic conditions (Melanie on its own or conjugated with Mohr Coulomb). The performance of the models, whose parameters are derived from experimental data, is checked against triaxial tests results. For the embankment, the measured and computed displacements and excess pore pressure are compared, with the isotropic models performing best. The maximum horizontal displacements versus settlements, the change in excess pore pressure versus vertical stress, the extent of the yield domain and the contours of the effective vertical and horizontal stress increments are also examined. The numerical results are explained based on the characteristics of the numerical models, namely the size and shape of the yield surface. The embankment, despite its nonsymmetric geometry, exhibits some similarities with typical behavior.

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 score0.031
Threshold uncertainty score0.660

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 head0.203
Teacher spread0.194 · 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

Citations35
Published2009
Admission routes1
Has abstractyes

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