Analysis of Corrosion Products Formed on Some of Ontario’s Weathering Steel Bridges
Bibliographic record
Abstract
Abstract Ministry of Transportation Ontario has been using weathering steel for bridge girders since1968. The main advantage of using weathering steel is that, under normal weathering cycles, it will form a tough outer oxide layer referred to as ‘patina’ that will protect the steel from unabated corrosion. However, recently the ministry has observed that the patina formed on girders over the driving lanes is debonding from the parent steel material and often has to be physically removed before it becomes a safety hazard. In contrast, other locations of the girders (between the ends of girders and the driving lanes) exhibited a more stable patina. Hence, in order to determine the root cause for de-bonding of the patina, steel core samples and corrosion products (scraped oxide layer) were collected from several locations of interest. These samples were examined using scanning electron microscopy, energy dispersive X-ray analysis, inductively coupled plasma/mass spectrometry, laser Raman spectroscopy and Mossbauer spectroscopy. The results from these investigations indicate that the patina at debonded regions exhibited greater amounts of chloride species and lower levels of sulphur species compared to more stable patina areas. Moreover, the de-bonded patina regions exhibited greater amounts of akaganeite (β-FeOOH-Cl), lepidocrocite (γ-FeOOH) and hydrated iron (IN) oxyhydroxide, while greater amounts of goethite (α-FeOOH) were observed in the other regions. The corrosion product analysis data are presented in this paper and their significance towards the root cause failure analysis is discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".