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Record W3040250652 · doi:10.18280/mmep.070212

Forced Vibration Analysis of Functionally Graded Beams Carrying Moving Harmonic Loads under Random Boundary Conditions

2020· article· en· W3040250652 on OpenAlexvenueno aff
Abbès Elmeiche, Mohamed Bouamama, Abdelhak Elhannani

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2020
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsVibrationHarmonicRandom vibrationStructural engineeringBoundary (topology)Boundary value problemPhysicsAcousticsMechanicsMathematicsEngineeringMathematical analysis

Abstract

fetched live from OpenAlex

This paper introduces forced vibration analysis of functionally graded materials (FGMs) beams subjected to moving harmonic loads in different physical and geometric states, under random boundary conditions. A mathematical model was developed based on a new refined logarithmic shear deformation theory (LSBT), used the Hamilton principle combined with the introduction of weak forms into the dynamic analysis, while including rotational inertia. The raster force is designed by the Dirac-delta function expressing moving harmonic loads. The Rayleigh-Ritz solution is used to separate system variables from equations with general boundary conditions. The fundamental frequencies of free vibration analysis are determined by solving the system of equations governing the eigenvalue problems and the modal responses of forced vibration behavior are also solved numerically using Newmark's temporal integration method. The numerical results presented make it possible to clearly appreciate the contribution of this theoretical development by examining in detail the influence of several parameters on the Dynamic Amplification Factor (DAF) of the FGMs beams.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.205
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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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