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Record W2424474415 · doi:10.1002/pc.24102

Modeling and mechanical characterization of braidtruded fiber reinforced polymer rebar

2016· article· en· W2424474415 on OpenAlexafffund
M. Ivey, Cagri Ayranci, Jason P. Carey

Bibliographic record

VenuePolymer Composites · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsRebarAramidMaterials scienceComposite materialFibre-reinforced plasticUltimate tensile strengthCorrosionComposite numberFiberReinforcementFailure mode and effects analysisStructural engineering

Abstract

fetched live from OpenAlex

Traditional steel rebar used for concrete reinforcement can be susceptible to corrosion. One solution to this problem is to substitute fiber reinforced polymer (FRP) rebar in the place of steel. However, conventional unidirectional FRP rebars are limited by a linear elastic failure mode at relatively low ultimate strains. In this study, FRP rebar was developed to behave in a pseudoductile manner, where a progressive failure was achieved based on the composite architecture. Hybrid FRP rebar consisting of a unidirectional carbon fiber core encased in a braided aramid fiber overwrap was manufactured, and a model was developed to reflect the tensile response of the rebar based on constituent material properties and geometric parameters. Experiments were conducted in order to validate the model, which was successful in reflecting the pseudoductile tensile behavior exhibited by the rebar. POLYM. COMPOS., 39:1582–1593, 2018. © 2016 Society of Plastics Engineers

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 score0.066
Threshold uncertainty score0.605

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 head0.204
Teacher spread0.195 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

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