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Record W2110670517 · doi:10.5539/esr.v1n2p303

Research on the Constitutive Model for Granular Material

2012· article· en· W2110670517 on OpenAlexvenueno aff
Wang Meng-shu, Dingli Zhang

Bibliographic record

VenueEarth Science Research · 2012
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsnot available
Fundersnot available
KeywordsGranular materialConstitutive equationAggregate (composite)Homogenization (climate)Materials scienceMicromechanicsMechanicsStructural engineeringComposite materialPhysicsEngineeringFinite element methodComposite number

Abstract

fetched live from OpenAlex

The constitutive relation of granular aggregate is one important aspect in solid mechanics research, since many problems involved are nothing like the ordinary mechanical problems. Using the energy method, this paper sets up the constitutive model for the granular material through the homogenization of granular material with structural characteristics. Several important factors that are influential to the characteristics of such material are fully considered by this model: performance of granular connector, its geometric position, directional distribution, morphology and size of statistical domain and the density of the connectors. The analysis of the model reveals that the mechanical properties of the granular material are not only related to the properties of the granular material but also to the arrangement of the granular aggregate, i.e. its fabric characteristics. In addition, the morphology of granules determines how the granules will be connected, which in turn modifies the mechanical performance of connection among granules and hence the performance of the granular aggregate. Besides, the couple stress is an important stress component, which makes classical theory no longer applicable to the granular material. Therefore, the constitutive model for the granular material is a generic model reflecting the granular material performance.

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)0.006
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.138
GPT teacher head0.397
Teacher spread0.259 · 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 designBench or experimental
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

Citations1
Published2012
Admission routes1
Has abstractyes

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