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New Robust Linearized Seismic Analysis Method For Tall Guyed Telecommunication Masts

2011· article· en· W2054839868 on OpenAlexaff
S. Ali Ghafari Oskoei, Ghyslaine McClure

Bibliographic record

VenueJournal of Structural Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsMast (botany)Structural engineeringServiceability (structure)Induced seismicityEngineeringStiffnessInertiaNonlinear systemIcingCivil engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

Design of tall telecommunication masts is usually governed by serviceability criteria under high wind conditions, typically combined with icing in cold climates. However, there is a need for seismic design checks for guyed masts constructed in zones with moderate to high seismicity, as is routinely prescribed for buildings in modern codes. There have been few efforts towards proposing a robust simplified method of general applicability for the seismic analysis of tall masts. These structures can be represented by the simple concept of a continuous beam-column (the lattice mast) on nonlinear elastic supports (the guy cable clusters at various stay levels). In this study, the guy cables are replaced by equivalent linear lumped parameters (stiffness, mass, and viscous damping) and the effects of their interaction with the mast stiffness and inertia on the structural characteristics are discussed. The approach was tested with nine case studies of real telecommunication masts subjected to five different seismic inputs and further validated with more analysis for two selected masts under the effects of 81 recorded Californian earthquakes.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.235
Teacher spread0.218 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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