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Record W2099715299 · doi:10.1139/l07-061

A simplified method for seismic analysis of rooftop telecommunication towers

2007· article· en· W2099715299 on OpenAlexfundvenueno aff
Rola Assi, Ghyslaine McClure

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Cheng Kung UniversityNational Science Council
KeywordsStructural engineeringTowerAccelerationSeismic analysisSuperposition principleModal analysisIncremental Dynamic AnalysisModalPeak ground accelerationEarthquake simulationEngineeringComputer scienceGround motionFinite element methodMathematicsMathematical analysisPhysics

Abstract

fetched live from OpenAlex

A simplified method is presented in this paper for the estimation of forces at the base of telecommunication towers mounted on building rooftops due to seismic excitation. Although some codes and standards propose simplified methods for the evaluation of base shear forces for towers founded on ground, no method yet exists for the evaluation of overturning moments. The proposed simplified method is based on numerical simulations using truncated modal superposition, which is widely used for seismic analysis of linear structures. The method requires the prediction of input seismic acceleration at the building–tower interface, the definition of an acceleration profile along the building-mounted tower, and the determination or evaluation of the mass distribution of the tower along its height. The method was developed on the basis of detailed dynamic analyses of three existing towers assumed to be mounted separately on three buildings. It was found that the method yields conservative results, especially for the overturning moments.Key words: self-supporting towers, earthquake, horizontal excitation, dynamic analysis, acceleration, modal superposition.

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)0.001
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 score0.905
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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

Citations16
Published2007
Admission routes2
Has abstractyes

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