Evaluation of Coating Systems for Steel Structures
Bibliographic record
Abstract
Abstract In the last decade, the formulation of coatings used for steel structures has seen major changes. These changes mainly concern environmental aspects (green products) and a desire to enhance workers’ safety through the reduction of Volatile Organic Compounds (VOC). The Hydro Quebec network is ageing and priority must be ascertained for painting steel structures. In order to upgrade the coatings used by the company, an investigation and assessment were done of different coating systems used mainly for lattice towers and steel poles (aboveground). The evaluated coatings were chosen based on the paint companies’ recommendations and on the utility’s specific needs. Twenty-three coating systems were included in an accelerated aging study. To represent different situations encountered in the field, the selected coatings were applied on three different substrates, aged galvanized, new steel and rusted steel using the appropriate surface preparation for each one. Testing was done according to cyclic tests as per ASTM D 5894. To facilitate the evaluation, the coatings were divided into four main chemical families: epoxy, polyurethane, alkyd, and acrylic. This paper presents the approach that was used and the results obtained. As some of the coatings tested presented anti-graffiti characteristics, a laboratory experiment to test different graffiti media and different cleaners was also conducted. The results are presented in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".