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Record W4400134332 · doi:10.1061/jccof2.cceng-4564

Bundling Effect on Bond and Development Length of Sand-Coated GFRP Bars

2024· article· en· W4400134332 on OpenAlexaff
L. Kaufman, Amir Fam

Bibliographic record

VenueJournal of Composites for Construction · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsQueen's University
Fundersnot available
KeywordsFibre-reinforced plasticMaterials scienceComposite materialBondBond strengthBond lengthStructural engineeringAdhesiveLayer (electronics)CrystallographyEngineering

Abstract

fetched live from OpenAlex

One of the gaps in the new Building Code Requirements for Structural Concrete Reinforced with Glass Fiber–Reinforced Polymer (GFRP) Bars is design provisions for bundled GFRP bars. This study aims at establishing the bundling factor for the development length (Ld) of sand-coated GFRP bars embedded into normal strength concrete in bundles of two and three. A total of 12 notched beams were tested in flexure with spans ranging from 1 to 3 m, where the bars and surrounding concrete are under realistic tensile stress as opposed to pull-out tests. The embedment length (Le) of the bars varied from 17 to 87 times bar diameter (db), to establish a correlation with maximum tensile stress attained, thereby enabling calculating Ld. For each bundled arrangement, counterpart beams with spaced bars were also tested for comparison. It was found that bundling bars reduced the maximum attained longitudinal tensile stress at bond failure by 18%–30% for two bars and 20%–36% for three bars, within the studied Le range of 17–87db. At the full design tensile strength (ffu), Ld of bundles of two and three bars was 1.4 and 1.5 times larger, respectively, than spaced bars. As the tensile stress ratio (ff/ffu) reduced from 1.0 to 0.34 (as in compression-controlled failure), Ld of bundles increased up to 1.9 and 2.5 times the spaced bars, respectively. An expression for this variable bundling factor is proposed.

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 score0.252
Threshold uncertainty score0.461

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 head0.008
GPT teacher head0.228
Teacher spread0.221 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

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