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Record W4411032979 · doi:10.1680/jstbu.24.00213

Bond strength of steel bars in steel-fibre-reinforced normal and self-compacting concretes

2025· article· en· W4411032979 on OpenAlexaff
Mohammad Kazem Sharbatdar, Ali Rostamian, Ali Kheyroddin, Khaled Sennah

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Structures and Buildings · 2025
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceComposite materialBondBond strengthStructural engineeringEngineeringAdhesiveLayer (electronics)

Abstract

fetched live from OpenAlex

Pull-out tests were conducted to assess the bond stresses between two different sizes of steel bars and normal concrete (NC) and self-compacting concrete (SCC) with and without steel fibres (SFs), considering different conditions. The results showed that the pull-out capacities of the pre-cracked specimens were reduced by 20.4% when compared with uncracked specimens. Moreover, the pre-cracked NC specimens exhibited 10% lower tensile strength than their SCC counterparts. In addition, the specimens with SF had a higher bond stress than those without SF. Additionally, an increase in bar diameter from 12 mm to 16 mm led to a 22% improvement in bond stress. In summary, the addition of SFs to concrete generally enhanced the bond stress between the rebars and the concrete, with macro SFs exhibiting a particularly notable effect, leading to an 18% increase in bond. ACI 318-19 was found to produce conservative results of the developed steel bar length with safety margins of 120% and 79% for regular and pre-cracked specimens when compared with the expressions proposed in this work. Comparing available test results with the proposed models, the test results were very close to or more than the proposed models' results, depending on the bar diameter.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.203
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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