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Record W4412785997 · doi:10.3390/ma18143367

Bond Behavior of Glass Fiber-Reinforced Polymer (GFRP) Bars Embedded in Concrete: A Review

2025· review· en· W4412785997 on OpenAlexafffund
Saad Saad, Maria Anna Polak

Bibliographic record

VenueMaterials · 2025
Typereview
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaMinistère des Transports
KeywordsFibre-reinforced plasticEmbedmentBondBond strengthReinforcementMaterials scienceComposite materialDurabilityBar (unit)Structural engineeringCorrosionGlass fiberAdhesiveEngineering

Abstract

fetched live from OpenAlex

Glass Fiber-Reinforced Polymer (GFRP) bars are becoming increasingly common in structural engineering applications due to their superior material properties, mainly their resistance to corrosion due to their metallic nature in comparison to steel reinforcement and their improved durability in alkaline environments compared to CFRP and BFRP reinforcement. However, GFRP bars also suffer from a few limitations. One of the main issues that affects the performance of GFRP reinforcing bars is their bond with concrete, which may differ from the bond between traditional steel bars and concrete. However, despite the wide attention of researchers, there has not been a critical review of the recent research progress on bond behavior between GFRP bars and concrete. The objective of this paper is to provide an overview of the current state of research on bond in GFRP-reinforced concrete in an attempt to systematize the existing scientific knowledge. The study summarizes experimental investigations that directly measure bond strength and investigates the different factors that influence it. Additionally, an overview of the analytical and empirical models used to simulate bond behavior is then presented. The findings indicate the dependence of the bond on several factors that include bar diameter, bar surface, concrete strength, and embedment length. Additionally, it was concluded that both traditional and more recent bond models do not explicitly account for the effect of different factors, which highlights the need for improved bond models that do not require calibration with experimental tests.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.574
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.293
Teacher spread0.274 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations8
Published2025
Admission routes2
Has abstractyes

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