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Record W4411377288 · doi:10.3390/cmd6020025

Microstructural and Mechanical Characterization of Corroded Aluminum Wires from ACSR Strand

2025· article· en· W4411377288 on OpenAlexafffund
Laurent Gaillet, Alan Rondineau, Sébastien Langlois, Marc Demers, Lamine Dieng

Bibliographic record

VenueCorrosion and Materials Degradation · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Gustave EiffelHydro-QuébecUniversité de Sherbrooke
KeywordsAluminiumCharacterization (materials science)Materials scienceMetallurgyNanotechnology

Abstract

fetched live from OpenAlex

Aluminum Conductors Steel-Reinforced (ACSR) conductors are typically used in overhead transmission lines. Corrosion is an important degradation mechanisms that might affect the lifetime of this essential electricity network component. Considering the complexity of conductors, it is difficult to predict the damage of these conductors in corrosive environments. The objective of this paper is to evaluate the effect of grease and conductor geometry on the mechanical properties of aluminum strand composing the envelope of ASCR conductors. Thus, ACSR wires and strands have been evaluated in corrosion by the mean of accelerated corrosion tests. Tensile, fatigue and torsion test results are presented to examine the effect of corrosion on aluminum strands. The influence of corrosion on mechanical characteristics is established by a decrease in ductility, maximum elongation and tensile strength for the longest exposition (336 days). This significant reduction in the internal layer of ungreased wires confirms the importance of the galvanic corrosion mechanism of aluminum wires. This evolution concerns only aluminum wires of non-greased conductors, confirming the crucial role of grease as protection against corrosion.

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 score0.016
Threshold uncertainty score0.620

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)0.0010.000

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 head0.011
GPT teacher head0.248
Teacher spread0.236 · 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
Published2025
Admission routes2
Has abstractyes

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Same venueCorrosion and Materials DegradationSame topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207