MétaCan
Menu
Back to cohort
Record W2142063874 · doi:10.1139/l05-118

Shear strength of reinforced concrete beams with a fiber concrete matrix

2006· article· en· W2142063874 on OpenAlexfundvenueno aff
Fariborz Majdzadeh, Sayed Mohamad Soleimani, Nemkumar Banthia

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceVolume fractionFiber-reinforced concreteShear (geology)Composite materialStructural engineeringToughnessFiberShear strength (soil)EngineeringGeology

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the influence of fiber reinforcement on the shear capacity of reinforced concrete (RC) beams. Both steel and synthetic fibers at variable volume fractions were investigated. Two series of tests were performed: structural tests, where RC beams were tested to failure under an applied four-point load; and materials tests, where companion fiber-reinforced concrete (FRC) prisms were tested under direct shear to obtain material properties such as shear strength and shear toughness. FRC test results indicated an almost linear increase in the shear strength of concrete with an increase in the fiber volume fraction. Fiber reinforcement enhanced the shear load capacity and shear deformation capacity of RC beams, but 1% fiber volume fraction was seen as optimal; no benefits were noted when the fiber volume fraction was increased beyond 1%. Finally, an equation is proposed to predict the shear capacity of RC beams.Key words: shear strength, fiber-reinforced concrete, RC beam, stirrups, energy absorption capacity, steel fiber, synthetic fiber.

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)0.000
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
Metaresearch0.0000.001
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)0.0010.000

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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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

Citations88
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicInnovative concrete reinforcement materialsFrench-language works237,207