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Record W3042156781 · doi:10.1139/cgj-2019-0841

Use of a bounding surface model in predicting element tests and capacity in boundary value problems

2020· article· en· W3042156781 on OpenAlexvenueno aff
Anamitra Roy, Shiao Huey Chow, Conleth O’Loughlin, Mark Randolph, Susan Reynolds Whyte

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersLloyd's Register
KeywordsCentrifugeDilatantPlasticityBoundary value problemBounding overwatchGeotechnical engineeringModulusMonotonic functionStructural engineeringScalingFinite element methodSurface (topology)Boundary (topology)MechanicsEngineeringMathematicsComputer scienceMaterials scienceMathematical analysisGeometryPhysics

Abstract

fetched live from OpenAlex

The paper examines the merit of a bounding surface plasticity model at both element and system level. The governing equations are based essentially on the parent bounding surface plasticity model reported by Dafalias and Manzari in 2004 with some simple yet practical changes to enable realistic predictions for monotonic loading along different load paths. This is achieved by scaling the influence of the state parameter based on a normalised measure of anisotropy, thus leading to suitable change in dilatancy and plastic modulus for different loading directions. The paper presents a simple optimisation technique for calibrating the model parameters, providing an objective approach to reduce the uncertainties in parameter determination that leads to good agreement with responses measured in drained and undrained triaxial tests. The model has also been implemented for the boundary value problem of a buried circular plate anchor and a surface circular footing. Comparisons of the simulated responses with those measured in centrifuge tests demonstrate the potential of the model, whilst also pointing to the challenges in capturing the global response at all strain levels, even for rather simple boundary value problems.

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 score0.017
Threshold uncertainty score0.912

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 integrity0.0000.001
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 head0.032
GPT teacher head0.203
Teacher spread0.171 · 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

Citations33
Published2020
Admission routes1
Has abstractyes

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