Determining Effect of Environmental Corrosivity on Atmospheric Corrosion of Weathering Steel
Bibliographic record
Abstract
Weathering steel (WS) has been used in bridges since the 1960s as a substitute for carbon steel. WS contains small amounts of alloying elements, such as Cu, Cr, and Ni that promotes the formation of a stable oxide layer called a “patina”. The patina acts as a corrosion barrier, removing the need for coatings and reducing maintenance costs. However, in Canada WS bridges often require painting due to corrosive field conditions. While nitrates and sulfates are common air pollutants, the use of de-icing salt in Canadian climates is the main source of chloridein urban and rural environments that contributes to corrosion. Therefore, the effect of atmospheric corrosion environments on WS needs to be further investigated to better inform bridge owners on factors that could affect the integrity of their structures. This study uses electrochemical methods combined with surface analysis methods such as X-ray diffraction (XRD) and scanning electron microscopy (SEM) to study the effects of the anions Cl-, NO3 -, and SO4 2- on the patina formation of 350AT WS. In this work, the lower threshold limits for chloride and sulfate-initiated corrosion were determined by wet electrochemistry. The results indicate that a concentration of 100 ppm is the lower limit for chloride and a concentration of 10 ppm is the limit for the more aggressive sulfate. Subsequently, WS was exposed to various anion combinations and concentrations in wet-dry cycles and the formed patina was examined for corrosion protection. The XRD and SEM results show that the patina’s composition depends on the exposure medium. In the presence of 10 ppm of SO4 2-, the formation of a dense oxide layer occurs consisting of goethite (α-FeOOH), while in the presence of Cl-, the formation of less protective oxides are observed such as lepidocrocite (γ-FeOOH) and akaganeite (β-FeOOH). Figure 1
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".