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Record W2017492666 · doi:10.1115/imece2011-63411

Natural Frequency Improvement of a Suspended FGM Bridge

2011· article· en· W2017492666 on OpenAlexaff
Mousa Hadipour, Mohammad Taghi Ahmadian, S. G. Lashkari, Ahmad Barari

Bibliographic record

VenueVolume 8: Mechanics of Solids, Structures and Fluids; Vibration, Acoustics and Wave Propagation · 2011
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsVibrationNatural frequencyMaterials scienceBridge (graph theory)Galerkin methodSuspension (topology)Structural engineeringFunctionally graded materialCeramicMaterial propertiesComposite numberComposite materialAcousticsEngineeringFinite element methodPhysicsMathematics

Abstract

fetched live from OpenAlex

In this paper application of Functionally Graded Materials (FGMs) in suspension bridge structures for the purpose of vertical vibration improvement is investigated. Functionally graded materials are inhomogeneous composites, which are usually made from a mixture of metal and ceramic. Initially the classical equation of motion is modified based on the FGM model and natural frequencies of structure are extracted by Galerkin method. The material properties of structure vary continuously in the longitude direction according to power law form. In this regard, combination of materials is structured in such a way that the desired frequencies of the bridge are achieved by designing the proper power law of the FGM. Natural frequencies are evaluated to avoid any resonant due to environmental vibration. A bridge structure based on FGM made of steel and aluminum oxide is designed and improved for proper frequency. For the simple case of homogenous structures results are compared with those reported in the literature and very good agreements are obtained.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.762

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 head0.193
Teacher spread0.183 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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