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Record W2180240334 · doi:10.1680/jmacr.15.00276

Mechanical properties of hybrid fibre-reinforced concrete – analytical modelling and experimental behaviour

2015· article· en· W2180240334 on OpenAlexfundno aff
Aref A. Abadel, Husain Abbas, Tarek Almusallam, Yousef Al-Salloum, Nadeem A. Siddiqui

Bibliographic record

VenueMagazine of Concrete Research · 2015
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
FundersKing Abdulaziz City for Science and TechnologyUniversity of Ottawa
KeywordsMaterials scienceKevlarFiber-reinforced concreteComposite materialToughnessUltimate tensile strengthVolume fractionFlexural strengthPolypropyleneCompressive strengthStructural engineeringReinforced concreteComposite number

Abstract

fetched live from OpenAlex

Mechanical properties of hybrid fibre-reinforced concrete (HFRC) are studied and the role of constituent fibres on overall response of HFRC is investigated. HFRC was produced using different proportions of hooked-ended steel, crimped polypropylene and plain Kevlar fibres with a total fibre volume fraction of 1·2% and 1·4%. The test results indicate that the incorporation of hybrid fibres in concrete improve the compressive and tensile strengths moderately and toughness considerably. Analytical models are developed to quantify the effect of individual fibres on compressive and tensile strengths, stress–strain curves and flexural toughness ratio of HFRC in terms of a comprehensive fibre reinforcing index. The proposed models show a marked improvement over existing models. The existing models are for single (steel) fibres only, whereas the proposed models are general and applicable to HFRC containing any number of fibres.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
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)0.0010.000

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 head0.109
GPT teacher head0.322
Teacher spread0.213 · 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 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

Citations104
Published2015
Admission routes1
Has abstractyes

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