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Record W2322136730 · doi:10.1061/41077(363)37

Dynamic Wind Analyses of Transmission Line Structures

2009· article· en· W2322136730 on OpenAlexafffund
Ferawati Gani, Frédéric Légeron, Mathieu Ashby

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsHydro-QuébecUniversité de SherbrookeNatural Sciences and Engineering Research Council of Canada
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversité de Sherbrooke
KeywordsTowerTransmission lineStructural engineeringWind engineeringStatic analysisDynamic loadingWind speedComputer scienceLine (geometry)Finite element methodEngineeringMathematicsPhysicsGeometryMeteorologyTelecommunications

Abstract

fetched live from OpenAlex

Transmission line (TL) structures are generally designed with a simplified static-equivalent approach described in the industry documents (IEC 60826, CENELEC, ASCE 74). These static equivalent methods are based on extensive in-situ testing and have been shown to be appropriate for typical TL structures. However, wind loading is a dynamic loading, and for certain cases it is difficult to capture dynamic response of the structure by a static equivalent method. As well, static equivalent method has limitations and the industry documents do not give any directions to design structures out of their scope. In this article, a set of numerical tools were used in order to assess wind-structure interaction for TL structures. At the present, the dynamic/turbulent wind loading data were numerically generated. The TL structures were modelled using finite element (FE) method that takes into account the inherent nonlinearities. Two wind loading studies are presented here: (i) a guyed tower; and (ii) a river crossing. The guyed tower was studied to show that its dynamic behaviour can have significant impact on wind effect on the structure which cannot be captured by static equivalent method provided in the industry documents. The river crossing was studied to understand better the implications of applying wind loads extrapolated from the ones used for normal TL structures, as compared to dynamic wind loading. For each case, comparisons with results obtained with static equivalent method are presented and discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.610

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)0.0010.000

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 head0.273
Teacher spread0.262 · 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 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
Published2009
Admission routes2
Has abstractyes

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