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Record W4313168517 · doi:10.2749/nanjing.2022.0173

Nonlinear Coupling in Cable-Supported Bridges for Non-Analogous Modes

2022· article· en· W4313168517 on OpenAlexaff
Sébastien Maheux, Jenny King, Ashraf El Damatty, Fabio Brancaleoni

Bibliographic record

VenueReport · 2022
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsNonlinear systemCoupling (piping)Structural engineeringModalVibrationStiffnessSuspension (topology)Modal analysisDisplacement (psychology)PhysicsEngineeringAcousticsMathematicsMaterials scienceMechanical engineering

Abstract

fetched live from OpenAlex

It has been shown that the nonlinear differential equations representing the structural system of a suspension bridge exhibit nonlinear modal coupling that can lead to large torsional vibrations of the bridge deck. Such nonlinear coupling could play a role in the stability of cable-supported bridges under wind effects. Therefore, this paper presents an investigation of nonlinear modal coupling in cable-supported bridges with an emphasis on coupling between pairs of non- analogous modes, i.e., modes having a weak correlation along the bridge deck between the verti- cal displacement and torsional rotation. A procedure for assessing nonlinear coupling that relies on nonlinear generalized stiffness parameters is utilized for this purpose. Results of nonlinear gen- eralized stiffness analysis for suspension bridges indicate that non-analogous modes have a weak- er nonlinear coupling compared to analogous modal pairs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.247
Teacher spread0.234 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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