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Record W4206058647 · doi:10.18280/rcma.310605

A Comparison Between a Solid Block Made of Concrete and Others Made of Different Composite Materials

2021· article· en· W4206058647 on OpenAlexvenueno aff
Emad Toma Karash, Tymor Abed Alsttar Sediqer, Mohammad Takey Elias Kassim

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsDeformation (meteorology)Materials scienceComposite numberComposite materialStress (linguistics)Structural engineeringvon Mises yield criterionFiberEngineeringFinite element method

Abstract

fetched live from OpenAlex

In this research, three mathematical models were designed, the first consisting of concrete, the second from carbon fiber, and the third from s-glass fiber, in order to compare the strength of composite materials to different stresses and deformations, because composite materials are better than concrete in terms of weight and shape and do not need to be applied to painting operations in addition to the fact that their thermal insulation is higher than Concrete in high proportions. From the results of the comparison, it was found that the second model was the best model in terms of bearing deformations, as the deformation percentage in it did not exceed the deformation of concrete a lot, reaching (17%), which is a very small percentage, and the stresses towards pregnancy for the second and third models were much better than the bearing of the first model, but the results indicate that the Von Mises Stress in the second model is higher than the first model by a percentage (57%), while the comparison of the third model with the first was the rate of increase percentage (47%).

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 categoriesMeta-epidemiology (narrow)
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 score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.050
GPT teacher head0.311
Teacher spread0.260 · 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.

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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

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