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Record W2073732949 · doi:10.1139/t01-085

Effect of ageing on stiffness of very loose sand

2002· article· en· W2073732949 on OpenAlexfundvenueno aff
John A. Howie, T Shozen, Y. P. Vaid

Bibliographic record

VenueCanadian Geotechnical Journal · 2002
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAgeingShearing (physics)StiffnessMaterials scienceGeotechnical engineeringModulusCreepComposite materialCompression (physics)Stress (linguistics)IsotropyGeology

Abstract

fetched live from OpenAlex

The paper presents the results of laboratory triaxial compression tests to study the stiffness of very loose Fraser River sands. The stiffness has been shown to be very dependent on the time of confinement prior to shearing and the stress ratio at which the sample is aged. Higher stress ratios resulted in very low initial moduli with no ageing, but the moduli increased by several hundred percent during the first 1000 min of ageing. For ageing at a stress ratio of 1.0 (i.e., isotropic ageing), the initial moduli were higher than those for ageing at high stress ratios, but the stiffness increased by only about 60% during the first 1000 min of ageing. The rate of stiffness increase was approximately linear with the logarithm of time up to ageing times of 10 000 min (>1 week). Ageing at any stress ratio resulted in reduced contractive volumetric strain during subsequent shearing, reflecting a change in soil structure during ageing. The dε v /dε a ratio under triaxial compression loading decreased as the ageing stress ratio increased. The results suggest that close attention must be paid to the age of laboratory samples prepared to study the stress–strain response of sands at strains up to about 0.1%, particularly in studies on loose sand.Key words: sands, ageing, creep, modulus.

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 score0.176
Threshold uncertainty score0.690

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 head0.184
Teacher spread0.178 · 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
Published2002
Admission routes2
Has abstractyes

Explore more

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