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Record W2759481879 · doi:10.5539/mas.v11n10p110

Shear and Flexural Behavior of Reinforced Concrete Deep Beams Strengthened with CFRP Composites

2017· article· en· W2759481879 on OpenAlexvenueno aff
Hana Al-Ghanem, Aya Al-Asi, Mu’tasim Abdel-Jaber, Maha Alqam

Bibliographic record

VenueModern Applied Science · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
FundersUniversity of Jordan
KeywordsMaterials scienceFlexural strengthComposite materialShear (geology)StiffnessFailure mode and effects analysisCarbon fiber reinforced polymerReinforced concreteStructural engineering

Abstract

fetched live from OpenAlex

The current research studies the shear and flexural behavior of reinforced concrete (RC) deep beams strengthened with externally bonded carbon fiber-reinforced polymers (CFRP). Using two types of CFRP composites including sheets and laminates, different configurations for shear and flexural strengthening of deep beams were experimentally investigated. In total, twenty specimens of deep beams with cross-sectional dimensions of 190 mm width, 400 mm depth and an overall length of 1 900 mm were casted and tested to failure. Concerning the cracks’ formation, failure’s modes, ultimate strength and overall stiffness, the performance of the strengthened beams compared to the control beams were evaluated. From the test results, the effectiveness of CFRP technique on enhancing both the shear and flexural capacity of deep beams is verified; however, the efficiency differs variedly depending on the material and the strengthening scheme. Regarding the shear strengthening, using the continuous wrap of two sheets records the highest increase in the ultimate strength with a value exceeds 86% compared only to 36% with the inclined laminates. On the other hand, an enhancement of about 51% is achieved through the flexural strengthening with two layers of sheets and 26% when the laminates are used; both are accompanied by a divergent in the failure mode from flexure to shear.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.800

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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 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

Citations17
Published2017
Admission routes1
Has abstractyes

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