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Record W4395668283 · doi:10.18280/mmep.110418

Increasing the Punching Shear Capacity of Flat Plate Reinforced Concrete Utilizing CFRP Warp and Bar

2024· article· en· W4395668283 on OpenAlexvenueno aff
Bilal Raheem Abed, Salmia Beddu, Zarina Itam, Muslim Abdul-Ameer Khudhair

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsnot available
Fundersnot available
KeywordsPunchingBar (unit)Structural engineeringShear (geology)Materials scienceReinforced concreteComposite materialGeologyEngineering

Abstract

fetched live from OpenAlex

This research investigates the use of carbon fiber reinforced polymer (CFRP) sheet and bar in concrete to increase punching shear capacity.The study focuses on the use of 1000×1000×100 mm 3 reinforced flat plates and their flexure in both directions.The flat plates are subjected to a single-point load and a column stub.CFRP has a high modulus of elasticity and low coefficient of thermal expansion.Fifteen reinforced concrete flat plates were investigated, with three without FRP as control flat plates and twelve divided into two groups.The results showed that using CFRP composites to reinforce slabs at columns postponed the first slab flexural fractures, resulting in an increase in slab 2's ultimate punching shear capacity of 127.4 kN to 141.2 kN and a rise in sustained deflection from 33 mm to 46 mm.Strengthening slabs with CFRP delays the onset of concrete cracking and increases the initial slab stiffness by about 20% compared to an non-reinforced slab.Ultimate punching shear capacity improved using CFRP, making it on par with the slab without apertures.This research highlights the importance of using CFRP in concrete manufacturing systems to prevent failures and ensure long-term stability.

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 score0.248
Threshold uncertainty score0.933

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 head0.025
GPT teacher head0.209
Teacher spread0.184 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMathematical Modelling and Engineering ProblemsSame topicStructural Behavior of Reinforced ConcreteFrench-language works237,207