MétaCan
Menu
Back to cohort
Record W1778878679 · doi:10.1139/cgj-2012-0467

Creep behavior in Virginia Beach sand

2013· article· en· W1778878679 on OpenAlexaffvenue
Hamid Karimpour, Poul V. Lade

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsCreepGeotechnical engineeringOverburden pressureBreakageMaterials scienceGrain sizeSieve analysisStress (linguistics)GeologyComminutionComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Triaxial compression tests were performed on dense specimens of Virginia Beach sand at low and high confining pressures to study time effects that relate to grain crushing due to static fatigue or delayed fracture. Experiments to study effects of loading strain rate on subsequent creep showed negligible time effects and no grain crushing at low confining pressures, while tests at high confining pressures indicated increasing amounts of creep with increasing initial loading strain rates and with increasing deviator stress at creep. Investigation of effects of grain-size distribution indicated stiffer initial response and smaller amounts of creep for more uniformly graded soils at high confining pressures. The experimental results showed that structuration effects were not present in the dense Virginia Beach sand. A long-term creep test at high confining pressure indicated continuous creep with no indication of its termination. Sieve analyses following each triaxial test showed that grain crushing, as quantified by Hardin’s relative breakage factor, was proportional to energy input and amount of creep observed for each soil specimen. The creep is due to the time-dependent static fatigue by which the grains crush and cause rearrangement of the grain structure, and this is the reason behind the time effects in granular materials.

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 score0.171
Threshold uncertainty score0.899

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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

Citations89
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Soil MechanicsFrench-language works237,207