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Record W2017043951 · doi:10.1139/cgj-2014-0234

Specimen size effects on behavior of loose sand in triaxial compression tests

2014· article· en· W2017043951 on OpenAlexaffvenue
Tarek Omar, Abouzar Sadrekarimi

Bibliographic record

VenueCanadian Geotechnical Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsWestern University
Fundersnot available
KeywordsGeotechnical engineeringShearing (physics)CompressibilityTriaxial shear testShear (geology)Materials scienceDirect shear testLiquefactionIsotropyGeologySoil testSoil waterComposite materialMechanics

Abstract

fetched live from OpenAlex

Triaxial tests are often used to determine the behavior and strength characteristics of soils without due attention to the differences in specimen size. Several drained and undrained monotonic triaxial compression shear tests are performed in this study on three different specimen sizes of the same sand to investigate the influence of specimen size and scale effect on sand compression and shear behavior. The behavior of a sand specimen is strongly influenced by shear banding and specimen boundary conditions, which are manifested as specimen size effects in the test results of this study. The measured sand compressibility and shear strength parameters are employed to describe scale effects, and investigate specimen size effects in liquefaction triggering analysis. The results show that while larger specimens exhibit a less compressible behavior during isotropic compression, larger shear strengths and effective friction angles are mobilized in the smaller specimens during shearing. A number of geotechnical analyses can be significantly affected by variations in strength parameters of the same soil determined from different specimen sizes. While using small size specimens for determining shear strength parameters might result in an unconservative design, a large specimen size provides a more accurate representation of different soil strength conditions and field deformations.

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)0.001
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 score0.256
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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

Citations60
Published2014
Admission routes2
Has abstractyes

Explore more

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