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Record W4413184753 · doi:10.1016/j.epsr.2025.112096

Estimation of corrosion exposure of transmission lines due to de-icing salt applied on roads

2025· article· en· W4413184753 on OpenAlexaff
Shaoqi Yang, Luc Chouinard, Meysam Hassanipour, Shengmiao Li, Jean-Marc Meango, Miguel Diago

Bibliographic record

VenueElectric Power Systems Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsHydro-QuébecMcGill University
Fundersnot available
KeywordsIcingElectric power transmissionEstimationCorrosionSalt (chemistry)Environmental scienceTransmission (telecommunications)Forensic engineeringEngineeringMaterials scienceChemistryMeteorologyGeographyTelecommunicationsElectrical engineeringMetallurgy

Abstract

fetched live from OpenAlex

This paper proposes a comprehensive corrosion exposure model for overhead electric transmission conductors near roads in northern climates. The proposed model accounts for the level of traffic, the frequency of winter precipitation, de-icing salt spreading practices, wind speed, and wind direction for predicting the level of chloride contamination during winter as a function of the relative position of a transmission line to the road. The exposure to chlorides is used in combination with relative humidity, air temperature, and SO 2 levels to estimate annual rates of corrosion for the line. The exposure model is successfully validated with inspection data collected over several spans at six sites. The yearly corrosion hazard is also evaluated using the ISO 9223 annual rate of corrosion classification for conductors at 6 different locations with similar service life, which compare well with field measurements of residual zinc layers. The next step in the research is to improve estimates of yearly corrosion rates and residual life by accounting for the effective time of exposure during the service life, the indirect exposure of steel wires due to the shielding effect from of aluminium wires, and the variability of the zinc layer thickness.

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)0.002
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 score0.043
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.014
GPT teacher head0.298
Teacher spread0.285 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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