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Record W2159088281 · doi:10.3970/cmes.2007.021.177

Geometrically Nonlinear Analysis of Reissner-Mindlin Plate by Meshless Computation

2007· article· en· W2159088281 on OpenAlexaff
P.H. Wen, Y.C. Hon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCollocation (remote sensing)Regularized meshless methodRadial basis functionInterpolation (computer graphics)Singular boundary methodNonlinear systemMeshfree methodsCollocation methodBoundary element methodBoundary (topology)Plate theoryBending of platesMathematicsBoundary value problemDomain (mathematical analysis)Mathematical analysisBasis functionApplied mathematicsBendingFinite element methodComputer scienceStructural engineeringEngineeringDifferential equationPhysicsImage (mathematics)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we perform a geo- metrically nonlinearanalysis of Reissner-Mindlin plate by using a meshless collocation method. The use of the smooth radial basis functions (RBFs) gives an advantage to evaluate higher order derivatives of the solution at no cost on extra-interpolation. Thecomputationalcost islow and requires neither the connectivity of mesh in the domain/boundary nor integrations of funda- mental/particular solutions. The coupled nonlin- ear terms in the equilibrium equations for both the plane stress and plate bending problems are treated as body forces. Two load increment schemes are developed to solve the nonlinear dif- ferential equations. Numerical verifications are given to demonstrate the efficiency and accuracy of the proposed method in comparing with exact solutionsand results from using thefinite element software (ABAQUS). Keyword: large deformation, Reissner-Mindlin plate theory, meshless collocation, radial basis functions.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.280
Teacher spread0.269 · 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

Citations43
Published2007
Admission routes1
Has abstractyes

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