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Record W3042506108 · doi:10.1680/jstbu.19.00230

Bond characteristics of glass-fibre-reinforced polymer bars in high-strength concrete

2020· article· en· W3042506108 on OpenAlexaff
Jennifer Lu, Hamdy M. Afefy, Hossein Azimi, Khaled Sennah, Mahmoud Sayed-Ahmed

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Structures and Buildings · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEmbedmentMaterials scienceBond strengthComposite materialBar (unit)Fibre-reinforced plasticConcrete coverSteel barPolymerBondBonding strengthStructural engineeringReinforced concreteAdhesiveLayer (electronics)EngineeringGeology

Abstract

fetched live from OpenAlex

Fibre-reinforced polymer bars are rapidly becoming an approved alternative to conventional reinforcing steel bars, especially for severely exposed structures such as bridges. This paper reports on an experimental study on the bond characteristics of both sand-coated and ribbed-surface glass-fibre-reinforced polymer bars embedded in high-strength concrete (HSC). A total of 145 pull-out tests were conducted to examine the effect of varying parameters on the bond characteristics, namely embedment length, bar diameter, surface treatment and concrete cover. In addition, 45 pull-out tests were conducted to investigate the effect of providing headed-end anchorage for the sand-coated bars. The experimental results showed that the sand-coated bars exhibited better bond strength than the ribbed-surface bars, which entailed lower development lengths. Thus, based on the experimental results, expressions for the development length of both sand-coated and ribbed-surface bars embedded in HSC were developed and compared against the results of formulae in available design standards. The comparison revealed that ACI 440-1R-06 produced lower conservative results than CAN/CSA S806-12.

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 score0.148
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Structures and BuildingsSame topicInnovative concrete reinforcement materialsFrench-language works237,207