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Record W4288057888 · doi:10.1680/jmacr.21.00067

Evaluation of self-consolidating concrete shear strength by means of push-off test

2022· article· en· W4288057888 on OpenAlexaboutno aff
Brunela Francine da Cunha, Roberto Caldas de Andrade Pinto, Gustavo Savaris

Bibliographic record

VenueMagazine of Concrete Research · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsReinforcementEurocodeShear (geology)Aggregate (composite)Materials scienceSelf-consolidating concreteStructural engineeringComposite materialShear strength (soil)Geotechnical engineeringCompressive strengthEngineeringGeology

Abstract

fetched live from OpenAlex

Self-consolidating concrete (SCC) can be characterised by its flowability, stability and passing ability. Although SCC tends to have a more uniform microstructure in the concrete–reinforcement interfacial transition zone, the reduction of the nominal size and coarse aggregate content in the concrete mix may adversely affect its shear strength. Therefore, the aim of this research was to evaluate the shear strength of SCC by means of push-off tests of 45 specimens, produced with one conventional concrete and two SCC mixtures, with two different coarse aggregate contents (reference and reduced by 30%). The transverse reinforcement ratio ranged from 0.46% to 2.28%, using closed stirrups crossing the shear plane. The increase of reinforcement ratio resulted in improvements in shear strength for specimens with one, two and three stirrups, while specimens with four and five stirrups had a premature failure owing to concrete crushing near the notch. No significant difference in shear strength was identified owing to concrete type or coarse aggregate. The experimental results were also compared with codes estimates, and Eurocode 2 presented the best fit to experimental results when compared with the American code ACI 318 and the Canadian standard CSA A23.3.

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)0.010
metaresearch head score (Gemma)0.003
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 score0.614
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.323
Teacher spread0.277 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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