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Record W3037245879 · doi:10.1680/jgeot.18.p.300

Evolution of particle morphology and mode of fracture during the oedometric compression of sand

2020· article· en· W3037245879 on OpenAlexaboutno aff
Dawa Seo, Changbum Sohn, Mehmet B. Cil, Giuseppe Buscarnera

Bibliographic record

VenueGéotechnique · 2020
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsnot available
Fundersnot available
KeywordsComminutionBreakageMaterials scienceCompression (physics)Fracture (geology)CushioningParticle (ecology)Grain sizeComposite materialMicroscale chemistryCrusherShearing (physics)Particle sizeGeotechnical engineeringGeologyMetallurgyMathematics

Abstract

fetched live from OpenAlex

This work uses synchrotron X-ray tomography to quantify the role of the particle shape on the interaction between collective comminution and individual grain fracture. Two sands (subrounded Ottawa and subangular Q-ROK#2) were compressed beyond their comminution pressure and imaged at the microscale. An algorithm was developed to track the sequence of breakage events stemming from compression, as well as the evolving morphology of the resulting fragments. The results show that the degree of grain shape evolution depends on the initial morphology of the particles, with subrounded particles exhibiting the most severe shape alterations. However, it was found that at high compressive stress all morphological indices approach similar values. Such a tendency is achieved when crushing involves primarily the finer grain fractions – that is, when cushioning hinders the rupture of larger particles. A quantitative assessment of the mode of particle failure also revealed that in both sands comminution (i.e. the pervasive fragmentation of individual particles) was not achieved abruptly, but was rather attained by way of successive stages of splitting and chipping. These findings suggest that in crushable granular solids the initial grain shape plays a key role during the first stages of compression leading to yielding, but its influence tends to vanish at higher pressures, when cushioning mitigates the impact of initial morphological differences and hinders further major breakage.

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 score0.010
Threshold uncertainty score0.020

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)0.0010.000

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations59
Published2020
Admission routes1
Has abstractyes

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