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Record W2417214991 · doi:10.1520/gtj20150199

Observations of Multi-Scale Granular Kinematics Around Driven Piles in Plane Strain Condition

2016· article· en· W2417214991 on OpenAlexaboutno aff
Z. Chen, Mehdi Omidvar, Magued Iskander

Bibliographic record

VenueGeotechnical Testing Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringPileKinematicsGeologyDigital image correlationGranular materialDilatantCentrifugeShear (geology)Magnetosphere particle motionMaterials sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract The behavior of granular media around driven piles is not well understood. Current design guidelines still remain empirical and highly approximate. Plane-strain calibration chamber tests have been conducted to provide visual observations of the penetration mechanisms occurring during driving. Tests were performed to investigate the effects of confinement, relative density, and soil type. Digital image correlation (DIC) combined with other advanced image analysis tools were used to obtain meso-scale displacement, finite strain maps, as well as micro scale particle kinematics during pile installation. The observed shear strain map for the confined dense Ottawa sand test shows a wedge-type soil failure plane with a rigid cone of sand beneath the pile tip. The obtained volumetric strain maps show an intense compression zone directly below the pile tip, followed by a dilation zone to accommodate shear. High degree of sand compaction at the pile tip during driving also created a thin dilation strip along the pile shaft. High hoop stresses could then be sustained in the surrounding denser sand by arching. Large rotation and chaotic particle motion were also observed in the areas where there were large shear and volumetric strains. The random motion of particles near pile boundaries was further confirmed through affine/non-affine analysis of grain kinematics.

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)0.001
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 score0.355
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.043
GPT teacher head0.242
Teacher spread0.199 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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