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Record W4404502828 · doi:10.3390/buildings14113680

Experimental and Numerical Investigation of Shear Strengthening of Simply Supported Deep Beams Incorporating Stainless Steel Plates

2024· article· en· W4404502828 on OpenAlexaff
Ahmed Hamoda, Saad A. Yehia, Mizan Ahmed, Khaled Sennah, Aref A. Abadel, Ramy I. Shahin

Bibliographic record

VenueBuildings · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsToronto Metropolitan University
FundersKing Saud UniversityKafrelsheikh University
KeywordsMaterials scienceStructural engineeringShear (geology)Numerical modelsComputer simulationComposite materialEngineering

Abstract

fetched live from OpenAlex

In this study, the effectiveness was investigated of shear strengthening techniques in reinforced concrete (RC) deep beams incorporating stainless steel plates (SSPs). Four RC deep beams were tested under incremental static loading until failure to examine the proposed strengthening techniques. The key parameters considered in this study included the arrangement of the externally bonded SSPs. The experimental findings demonstrated that strengthening using SSPs led to substantial improvements in their performance compared to the unstrengthened control beam. The use of SSPs increased the ultimate shear capacity by 129 to 175% over the control specimen. Finite element models (FEMs) were developed to simulate the responses of the tested beams strengthened using SSPs. Parametric studies were then conducted using the validated FEM to investigate to identify the effects of the area of SSPs on the shear capacity of the beams. The parametric studies concluded that increasing the plate thickness resulted in the enhanced shear capacity of the deep beam specimens up to a critical point upon which the increases in the thickness have insignificant effects on the shear strength. The accuracy of the design equations given by European and American codes in predicting the shear strength of the deep beams is examined.

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 score0.105
Threshold uncertainty score0.597

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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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