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Record W2948024183 · doi:10.15377/2409-5710.2018.05.3

Influence of Particle Geometry Assessed via Image Processing on Broken Sandstone Strength

2018· article· en· W2948024183 on OpenAlexaff
Saleh Balideh, T. G. Joseph, M. Curley

Bibliographic record

VenueGlobal Journal of Earth Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeometryVoid ratioVoid (composites)Geotechnical engineeringGeologyPorositySieve analysisParticle (ecology)Particle sizeMaterials scienceMineralogyComposite materialMathematics

Abstract

fetched live from OpenAlex

Particle geometry has an impact on the behavior and strength of broken rock, where particle shape characteristics affect the ability of particles to rotate or slide relative to each other. Form, angularity and texture are three independent parameters that describe the geometry of such a particle. In this paper the geometry of crushed Berea sandstone was determined through image processing, where the results showed that form and angularity indices describe the geometry characteristics of broken Berea sandstone better than the other geometry indices. A correction coefficient that has previously been introduced to predict a sieve size distribution from image processing was shown to be a function of form index for the sandstone fragments. Triaxial compression tests were performed on the broken sandstone, showing that confining pressure and void ratio have an impact on broken rock strength. Increasing confining pressure was shown to enhance the strength of broken rock, while void ratio was shown to be inversely related to broken rock strength. Void ratio can also be affected by particle geometry. Increasing particles form index increases the void ratio where particles angularity index and void ratio are inversely related.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.221
Teacher spread0.217 · 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 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
Published2018
Admission routes1
Has abstractyes

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