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Numerical Modeling of Shear Strengthened Reinforced Concrete Beams Using Different Systems

2013· article· en· W1986781716 on OpenAlexfundno aff
Usama Ebead, Huda Saeed

Bibliographic record

VenueJournal of Composites for Construction · 2013
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
FundersUnited Arab Emirates UniversityUniversité de Sherbrooke
KeywordsFibre-reinforced plasticMaterials scienceStructural engineeringFinite element methodShear (geology)Deflection (physics)Slip (aerodynamics)Composite materialReinforced concreteEngineering

Abstract

fetched live from OpenAlex

This research aims at creating finite-element models for fiber-reinforced polymer (FRP) shear strengthened concrete beams. It is inspired by the fact that the determination of the structural behavior of shear strengthened beams requires advanced numerical methods of which results are substantiated by credible experimental findings. The models are developed here to assess the shear and interfacial types of behavior of beams strengthened using one of three different schemes, namely, externally bonded (EB), mechanically fastened (MF), and hybrid EB/MF FRP schemes. The interfacial behavior between the EB, MF, and hybrid EB/MF FRP and the concrete is accounted for using interface elements for both vertical and inclined FRP strips. A user-defined subroutine for the microplane constitutive law for the concrete material is incorporated in the model. Results are presented in terms of the ultimate load-carrying capacities, load-deflection relationships, and interfacial stress/slip distributions. Numerical results are validated against available experimental data and show reasonable agreement. Models for hypothetical cases of MF FRP strengthened beams are created to enrich the discussion on the interfacial bearing stress distributions.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.766

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 communication0.0000.000
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 head0.014
GPT teacher head0.225
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.

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

Citations9
Published2013
Admission routes1
Has abstractyes

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Same venueJournal of Composites for ConstructionSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207