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Record W4285007183 · doi:10.1680/jgele.21.00069

Visualization of pile penetration using mechanoluminescent-coated particles

2022· article· en· W4285007183 on OpenAlexaff
Ayumu Kondo, Daiki Takano, Eiji KOHAMA, Richard J. Bathurst

Bibliographic record

VenueGéotechnique Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsPileMaterials sciencePenetration (warfare)Wedge (geometry)Contact forceDigital image correlationGranular materialDisplacement (psychology)Structural engineeringOpticsGeotechnical engineeringComposite materialEngineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Pile model penetration tests were conducted in a transparent plane strain container filled with granular mechanoluminescent-coated particles. The pile models were constructed with flat- and cone-shaped tips. Load transmission through the analogue granular soil was captured by taking images of the light emissions from one side of the container that were triggered by inter-particle force-induced mechanoluminescence during pile penetration. Time-coincident images were taken from the other side of the container using a second camera and the corresponding displacement fields computed using the digital image correlation method. Particle contact force chains at peak loads are shown to be in reasonable agreement with particle displacement fields. Differences in both displacement fields and patterns of particle contact force chains were detectable between pile models with flat- and cone-shaped pile tips. The orientations of the particle contact force chains generated at the base of the flat-tip pile model at peak pile load were in reasonable agreement with the geometry of the wedge of soil assumed from classical bearing capacity theory.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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