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Record W4402651375 · doi:10.1051/e3sconf/202456912004

Flexural behaviour of concrete beams reinforced with fiberglass geogrid

2024· article· en· W4402651375 on OpenAlexafffundabout
Mohamed Shokr, Mohamed A. Meguid, Sam Bhat, Deniele Malomo

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsTerrafix Geosynthetics (Canada)McGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsGeogridFlexural strengthMaterials scienceComposite materialGeotechnical engineeringStructural engineeringReinforcementGeologyEngineering

Abstract

fetched live from OpenAlex

In cold regions, especially within Canada, the degradation of non-structural concrete components in challenging environmental conditions has become a pressing issue. Traditional steel reinforcements are known to be susceptible to corrosion. With anticipated climate shifts causing variations in temperature, precipitation, and freeze-thaw cycles, there's an increasing need for more resilient reinforcement materials to deter premature cracking in non-structural concrete components. This study delves into the advantages of using low-ductility fibreglass geogrids as reinforcement layers to curb crack development and augment the flexural performance of plain concrete beams. Tests were carried out on nine concrete beams, each measuring 550×150×150 mm, with diverse reinforcement setups. The emphasis was on evaluating load-deflection characteristics, energy absorption capabilities, and modes of failure. Results suggest that lowductility fibreglass geogrid reinforcement markedly enhances the flexural strength of plain concrete, outperforming control beams. Additionally, fibreglass reinforcement showcases enhanced crack resistance and postcracking behaviour than plain beams. A Finite Element Analysis (FEA) was also executed using the Abaqus software, and its accuracy was confirmed through experimental data comparisons, yielding numerical figures for midspan deflection and peak load. This research furnishes pivotal insights into the prospective use of progressive reinforcement materials to combat environmental challenges in colder climates.

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 score0.033
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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