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Record W2791858315 · doi:10.5539/jmsr.v7n2p1

Shear Resisting Mechanism and Shear Strength Equation for Sandwich Beams

2018· article· en· W2791858315 on OpenAlexvenueno aff
Ataur Rahman, Tamon Ueda

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceShear (geology)Finite element methodStructural engineeringNonlinear systemBeam (structure)Shear strength (soil)MechanicsComposite materialPhysicsEngineeringGeology

Abstract

fetched live from OpenAlex

The aim of this study is to eliminate the limitation of existing shear design equations and to establish a complete set of rational as well as computationally efficient shear design equations. On this goal, a macro physical model, based on 2D nonlinear FEM analysis, for both open and full sandwich beams are developed which can clearly demostrate the contributions of different parts of the sandwich beam in resisting shear force. The proposed model also shows a satisfactory correlation between the experimental and the analytical results. A series of analytical specimens in connection with experimental one are analyzed by engaging a 2D-FEM program and a complete set of shear strength equations are derived with the help of that proposed shear resisting model. The equations for full sandwich beam show a good agreement with experimental and analytical results, whereas equations for open sandwich beam require further investigation to increase their level of accuracy and are not presented here.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.068
GPT teacher head0.352
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2018
Admission routes1
Has abstractyes

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