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Record W2052330350 · doi:10.1520/cca11916

Relationship Between Particle Shape and Void Content of Fine Aggregate

2004· article· en· W2052330350 on OpenAlexaff
Ufuk Dilek, ML Leming

Bibliographic record

VenueCement Concrete and Aggregates · 2004
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsAggregate (composite)Void (composites)Void ratioParticle (ecology)Geotechnical engineeringMaterials scienceGeologyComposite material

Abstract

fetched live from OpenAlex

Abstract Fine aggregate characteristics have an important influence on water demand and related properties of concrete. Several test methods for measurement of fine aggregate angularity are reported in the literature. These tests typically provide a single number that represents the bulk, or average angularity of the sand. An understanding of the relationship between bulk measures of angularity, individual particle geometry and shape characteristics, and concrete properties is important to aggregate producers, concrete suppliers, consulting engineers, other design professionals, particularly as existing deposits of sand are consumed and alternate sources must be developed. As part of a comprehensive research program on manufactured sand properties and their effects on fresh and hardened concrete properties, an image analysis technique was developed to determine the shape characteristics by photographing and analyzing sets of individual grains of sand. The outlines of the grains were analyzed using a variety of geometrically derived characteristics. The relationship between particle shape characteristics and a common measure of bulk angularity, void content (ASTM C 1252), was then examined. Results of the study indicated that void content was significantly influenced by the presence of deep indentations in the surface of the sand particle and deviations from a cubical particle shape.

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 score0.242
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.237
Teacher spread0.190 · 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

Citations11
Published2004
Admission routes1
Has abstractyes

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