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Strengthening of Gulfport 230 kV Wooden Transmission Structures with Glass-Fiber-Reinforced Polymer Wrap

2010· article· en· W2162089596 on OpenAlexafffund
A. Shahi, Jeffrey West, M.D. Pandey

Bibliographic record

VenueJournal of Composites for Construction · 2010
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of WaterlooNatural Sciences and Engineering Research Council
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTaguchi methodsMaterials scienceFibre-reinforced plasticComposite materialGlass fiberDesign of experimentsParametric statisticsOrthogonal arrayResponse surface methodologyFiberStructural engineeringComputer scienceMathematics

Abstract

fetched live from OpenAlex

This paper presents a statistically designed experimental program to present and analyze an effective and practical strengthening system for deteriorated crossarms of the Gulfport timber structures using glass-fiber-reinforced polymer (GFRP) wrap. In this research, a total of 14 strengthened and six control specimens with a length of approximately 3.1 m were tested to failure. The experimental program was conducted in two phases: feasibility study and parameter optimization designed by using the Taguchi method and analysis of variation (ANOVA). The feasibility study concluded that the proposed strengthening system was sufficient for increasing the strength of the deteriorated crossarms of the Gulfport structures well above the critical end-of-life (EOL) design threshold value of the crossarms. The parametric optimization of the strengthening system identified the critical variables to be the crack-repair material, the width of the wrap, and sanding the surface. Using statistical advantages of the Taguchi method of experimental design, the mean strength of the strengthened specimens was estimated, and a 95% confidence interval for that mean was calculated. This estimated mean was 70% higher than the EOL threshold of the crossarms.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.888

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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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 designBench or experimental
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
Published2010
Admission routes2
Has abstractyes

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