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Record W2101607758 · doi:10.1002/nag.921

Elastoplastic modelling of diffuse instability response of geomaterials

2010· article· en· W2101607758 on OpenAlexafffund
Richard Wan, Maurício Canêdo Pinheiro, Peijun Guo

Bibliographic record

VenueInternational Journal for Numerical and Analytical Methods in Geomechanics · 2010
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsMcMaster UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInstabilityVoid (composites)MechanicsConstant (computer programming)Constitutive equationTriaxial shear testMaterials sciencePorosityBoundary value problemBifurcationGeotechnical engineeringStructural engineeringMathematicsFinite element methodGeologyMathematical analysisComposite materialPhysicsEngineeringShear (geology)Computer scienceNonlinear system

Abstract

fetched live from OpenAlex

Abstract In this paper, we study material instabilities in geomaterials through an elastoplastic constitutive model endowed with appropriate attributes, such as stress, density and fabric dependencies. The analyses reveal the possibility of having diffuse instability, bifurcation and loss of uniqueness within the plastic limit surface. The resulting domain of bifurcation encompasses the intrinsic effects of stress–strain history, direction of loading, type of loading and fabric. The computations start at a material point level and are later on extended to an initial boundary value setting where diffuse failure of a three‐dimensional sand specimen with a random distribution of void ratio is examined. We restrict our simulations to the study of a q ‐constant laboratory experimental test under different sets of control parameters. Diffuse failure is also revealed in a slope analysis under water infiltration following similar loading paths as in the q ‐constant test. The analysis shows common material instability features observed in the above test. Copyright © 2010 John Wiley & Sons, Ltd.

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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.459
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.029
GPT teacher head0.331
Teacher spread0.302 · 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

Citations22
Published2010
Admission routes2
Has abstractyes

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Same venueInternational Journal for Numerical and Analytical Methods in GeomechanicsSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207