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Record W4389924904 · doi:10.3390/civileng4040069

Developing a Sprayed-Glass Fiber-Reinforced Polymer Retrofitting System for Decommissioned Wooden Utility Poles

2023· article· en· W4389924904 on OpenAlexafffund
Amr E. Abdallah, Shukai Chen, Ehab El-Salakawy

Bibliographic record

VenueCivilEng · 2023
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaManitoba Hydro
KeywordsRetrofittingFibre-reinforced plasticMaterials scienceComposite numberUltimate tensile strengthComposite materialGlass fiberStructural engineeringStructural materialCorrosionEngineering

Abstract

fetched live from OpenAlex

Wooden utility poles are vulnerable to degradation and decay, which requires maintenance or replacement. The strengthening and retrofitting techniques for wooden poles are either prone to corrosion or encountering installation difficulties. However, the use of sprayed fiber-reinforced polymer (FRP) composites seems to be a viable solution as it has proven its efficiency and applicability for reinforced concrete members and connections. This study includes a comprehensive experimental program where the mechanical properties of the sprayed-glass FRP (GFRP) composite was evaluated in terms of tensile, compressive and shear strength, in addition to its bond strength to wood and confinement efficiency. Afterwards, the results of the material testing phase were implemented on full-scale old utility poles to evaluate their structural performance with varying composite thicknesses and sprayed zone lengths. The behavior of the retrofitted poles reflected remarkable effectiveness for the sprayed-GFRP composite and highlighted the need for a design model for the optimum length for the sprayed zone. Two simplified analytical models were introduced which predicted the failure loads and locations for the tested poles and estimated the required length for the retrofitted zone, which all agreed well with the experimental results of the tested poles.

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 categoriesMeta-epidemiology (narrow)
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 score0.365
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.262
Teacher spread0.235 · 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.

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

Citations2
Published2023
Admission routes2
Has abstractyes

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