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Numerical Modelling of Shear Crack Angles in Frp Shear-Strengthened Reinforced Concrete Beams

2010· article· en· W2220047517 on OpenAlexafffund
Ahmed Godat, Pierre Labossière, K. W. Neale

Bibliographic record

VenueAustralian Journal of Structural Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFibre-reinforced plasticShear (geology)Structural engineeringMaterials scienceReinforced concreteBeam (structure)Slip (aerodynamics)Finite element methodDeflection (physics)Composite materialEngineeringPhysics

Abstract

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SummaryThe shear crack angle is a key parameter in the calculation of the fibre-reinforced polymer (FRP) contribution to the shear capacity of a shear-strengthened reinforced concrete beam. In this study, a new approach is developed to estimate the shear crack angles for such a beam. The approach is based on the FRP/concrete interface response. A non-linear finite element model was developed to simulate the behaviour of six beams grouped in three sets according to their dimensions. One unstrengthened beam of each set was used as a benchmark and its behaviour was compared to that of a beam strengthened with a U-wrap scheme. It was found that the numerical model is able to successfully simulate the behaviour of the shear-strengthened beams. The numerical predictions compare very well with previously published experimental data in terms of load-deflection relationships and carbon FRP (CFRP) axial strain profiles along the sheet length. The analysis of the slip profiles along the CFRP strip is helpful to understand the bond behaviour between the concrete and CFRP strips. The interfacial slip profiles are used to predict the shear crack angle along the shear span, and these predictions agree very well with the experimental measurements. The numerical results give failure modes that are identical to those obtained experimentally.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.226
Teacher spread0.210 · 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.

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

Citations2
Published2010
Admission routes2
Has abstractyes

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Same venueAustralian Journal of Structural EngineeringSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207