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Bond of FRP bars in air-entrained concrete: Experimental and statistical study

2021· article· en· W3191170009 on OpenAlexfundno aff
Sándor Sólyom, Matteo Di Benedetti, György L. Balázs

Bibliographic record

VenueConstruction and Building Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
FundersNational Research, Development and Innovation OfficeNemzeti Kutatási, Fejlesztési és Innovaciós AlapEuropean CommissionPultrall
KeywordsFibre-reinforced plasticMaterials scienceBond strengthBondReinforcementComposite materialStructural engineeringBond lengthAdhesiveEngineering

Abstract

fetched live from OpenAlex

The combined use of Fibre Reinforced Polymer (FRP) and Air-Entrained Concrete (AEC) can be an alternative to traditional steel-reinforced concrete as this system is less affected by the corrosion of the reinforcement and by the freeze-thaw cycles induced concrete degradation. However, the viability of this system hinges on the bond performance of the reinforcing bars. A total of 236 pull-out specimens were prepared and tested to study the effect of air-entraining admixtures (AEA) on the bond behaviour of FRP bars to concrete with varying compressive strengths. Failure modes and bond stress-slip curves were reported and discussed. The bond energy, calculated as the area under the bond stress-slip diagram, was also analyzed. The experimental peak bond stresses (bond strength) were compared to the theoretical ones characterized by the formula proposed for steel bars by the Model Code 2010. In addition, the statistical significance of the effect of AEA on bond characteristics was determined, yielding a reduction factor to account for the effect of AEA on bond strength. The test results show that the bond strength of FRP bars in AEC was statistically significantly lower than in normal concrete. However, the decrease is sufficiently small that could be accounted for, during the design stage, by merely increasing the reinforcement development length.

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 score0.012
Threshold uncertainty score0.504

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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

Citations36
Published2021
Admission routes1
Has abstractyes

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