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Record W2050715933 · doi:10.1139/l01-037

Role of synthetic fibers in delaying steel corrosion cracks and improving bond with concrete

2001· article· en· W2050715933 on OpenAlexvenueno aff
Rami H. Haddad, Ahmed Ashteyat

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceCorrosionSlippageCrackingComposite materialBond strengthCuring (chemistry)PolypropyleneSteel barFiber-reinforced concreteConcrete coverFiberAdhesive

Abstract

fetched live from OpenAlex

An experimental study was conducted to investigate the bond behavior between corroding reinforcing steel and surrounding synthetic fiber reinforced concrete. Pullout concrete and fiber reinforced concrete specimens were prepared at concrete cover to steel bar diameter ratios of 3.1 and 3.7 with three embedded lengths of 100, 200, and 300 mm. Fiber reinforced concrete was prepared using polypropylene at 0.15% and 0.30% or nylon fibers at 0.3% by mix volume. After moist curing for 90 days, pullout specimens, originally contaminated by up to 11 kg/m 3 NaCl, were subjected to a special treatment to accelerate corrosion in steel. The bond stress–slippage relationship was evaluated at different corrosion levels. The findings indicated that the use of fibers delayed initiation of cracking, due to steel corrosion, and improved the ultimate bond strength at cracking and post-cracking stages. The percentage improvement in ultimate bond strength at the latter stages reached as high as 52% and 87%, respectively. It was noticed that, prior to cracking, corrosion of steel bars resulted in an increase in bond strength, regardless of the concrete cover to bar diameter ratio used, or whether fibers were used or not. After cracking (due to steel corrosion), pullout specimens prepared with polypropylene fibers showed better resistance than that of corresponding ones with nylon fibers.Key words: fibers, reinforced concrete, corrosion, cracking, bond strength, slippage.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.988

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 head0.004
GPT teacher head0.160
Teacher spread0.156 · 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 designSimulation or modeling
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

Citations33
Published2001
Admission routes1
Has abstractyes

Explore more

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