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Record W2973614105 · doi:10.22146/jcef.47532

Bond Strength of Fusion Bonded Epoxy-Coated Reinforcement in Concrete

2019· article· en· W2973614105 on OpenAlexaff
G. M. Sadiqul Islam, Ehsan Ahmed, Md. Sabbir Hossain, Sarwar Uddin

Bibliographic record

VenueJournal of the Civil Engineering Forum · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsThompson Rivers University
FundersUniversity of Engineering and Technology, Lahore
KeywordsEpoxyRebarBond strengthMaterials scienceComposite materialBar (unit)BondCoatingUltimate tensile strengthCompressive strengthConcrete coverStructural engineeringReinforcementAdhesiveLayer (electronics)Engineering

Abstract

fetched live from OpenAlex

Fusion-bonded epoxy-coated steel is expected to extend the service life of the reinforced concrete structure in chloride-laden environments. However, the effect of coating on the bond-strength between rebar and concrete is not well understood yet. This research, therefore, studied the effect of epoxy-coating on the bond characteristics of reinforcing bars in concrete. The bond characteristics were assessed through pullout test considering variables viz. concrete strength, embedded length and bar diameter. The load was applied to reinforcing bars embedded in concrete until bond strength between the bar and concrete exceeded. Bond strength of epoxy-coated bars was compared with that of the uncoated bars. It was found that epoxy-coating reduced the bond strength approximately 25% for Ø20mm bar and 12% for Ø16mm and Ø12mm bar. As with uncoated bar, bond strength of coated bars were also increased with concrete strength. However, the bond strength ratio between coated and uncoated bars was found almost independent of concrete strength. Based on the test results, a development length modification factor of 1.33 is proposed for Ø20mm bar and 1.15 for Ø12mm and Ø16mm bar to compensate the bond strength reduction due to the epoxy coating.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.690

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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

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