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Record W4285165378 · doi:10.11159/ijci.2022.006

Effect of Sand to Aggregate Ratio on the Properties of Self-Compacting High-Performance Concrete

2022· article· en· W4285165378 on OpenAlexvenueno aff
Abdullah Alshahrani, Tianyi Cui, Sivakumar Kulasegaram

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2022
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
Fundersnot available
KeywordsAggregate (composite)Geotechnical engineeringMaterials scienceComposite materialGeology

Abstract

fetched live from OpenAlex

Self-compacting high-performance concrete (SCHPC) combines the properties and advantages of self-compacting concrete and high-performance concrete in both fresh and hardened states. For the SCHPC mix design, sand to aggregate ratio is a crucial parameter and plays an important role in governing the properties of SCHPC mix. This paper presents the results of an experimental investigation on the flowability, passing ability and mechanical properties of SCHPC mixes for various sand to total aggregate (S/A) ratio and water to cementitious material (w/cm) ratio. Tests were conducted on specimens using four (w/cm) ratios: 0.26, 0.30, 0.35 and 0.40 and two (S/A) ratios: 48% and 53%. All the mixtures were tested using slump flow test, J-Ring test, and L-box test in the fresh state as well as compressive strength, splitting tensile strength, and unit weight in the hardened state. The test results revealed that a lower S/A ratio (0.48) enhanced the flowability whereas the higher S/A ratio (0.53) enhanced the passing ability. The lower S/A ratio (0.48), containing greater proportion of coarse aggregate, generally improved the mechanical properties of SCHPC compared to the mixes with the higher S/A ratio (0.53).

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)0.000
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 score0.257
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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