MétaCan
Menu
Back to cohort
Record W4285167414 · doi:10.11159/ijci.2022.011

Assessing the Effect of Coarse Aggregate Size on Self-Compacting Fibre Reinforced Concrete Mix

2022· article· en· W4285167414 on OpenAlexvenueno aff
Abdulkarim Mimoun, Sivakumar Kulasegaram

Bibliographic record

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

Abstract

fetched live from OpenAlex

Steel fibres increase inhomogeneity and alter rheological and hardened characteristics of self-compacting concrete. To investigate the rheological behaviour and hardened characteristics of self-compacting concrete (SCC) and selfcompacting steel fibre reinforced concrete (SCSFRC), a wide range of normal strength self-compacting concrete mixes containing steel fibres and coarse aggregates (of size 10 mm, 20 mm) with target cube compressive strengths between 30 to 70 MPa were prepared in the laboratory. The plastic viscosity of these concrete mixes were estimated to be in the range between 20-50 Pa s , and the slump flow time t500 of each mix was recorded to ensure that the flow and passing ability for workability of the mixes satisfy the recommended British and European standards. This work mainly focuses on the properties of fibre reinforced self-compacting concrete containing 0.5% and 1% (by volume fraction) steel fibres and the effect of coarse aggregates on their rheological behaviour and flow characteristics. The effect of steel fibre content on the strength of hardened concrete is also investigated. In addition, comparison of flow behaviour between SCC and SCSFRC is also presented.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.259
Teacher spread0.253 · 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.

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

Explore more

Same venueInternational Journal of Civil InfrastructureSame topicInnovative concrete reinforcement materialsFrench-language works237,207