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Record W2023221781 · doi:10.1680/eacm.2011.164.3.171

Improving damage resistance of a composite pole using a computer experiment strategy

2011· article· en· W2023221781 on OpenAlexafffund
Damoon Motamedi, Abbas S. Milani

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Engineering and Computational Mechanics · 2011
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComposite numberStructural engineeringPower transmissionMinimaxPower (physics)Computer scienceMaterials scienceComposite materialEngineeringMathematicsMathematical optimization

Abstract

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Transmission poles are widely used for transmitting electricity from power plants to industrial and commercial sites as well as residential areas. The total cost of a transmission line is partly driven by the maintenance cost of poles and wires. To reduce this cost, designers can choose more durable materials and optimal design strategies. In recent years, the use of polymer composite transmission poles has become more popular and they have been implemented in a number of power systems. In this paper, a tapered composite pole under lateral wind load is modelled and its damage resistance under a set of composite failure criteria is investigated. The composite pole is made of glass fibre-reinforced polyester and is manufactured by filament winding. By focusing on fibre orientation angles in the pole layers, a computer experiment (simulation) strategy is designed along with a maximin space-filling sampling criterion. The goal is to choose the best combination of ply angles that can minimise pole damage while maintaining minimal computational cost. A 37% improvement in the matrix cracking failure index was achieved from the initial value of 1·00 (i.e. a damaged pole) to the optimised value of 0·63 (no damage).

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 score0.637
Threshold uncertainty score0.591

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 head0.188
Teacher spread0.178 · 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

Citations4
Published2011
Admission routes2
Has abstractyes

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