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Record W27013961 · doi:10.5006/c2008-08199

Analysis of Corrosion Products Formed on Some of Ontario’s Weathering Steel Bridges

2008· article· en· W27013961 on OpenAlexaffabout
A. Coomarasamy, David Lai, F. Pianca, Tibor Turi, S. Ramamurthy, Brad Kobe, Mary Jane Walzak, J. Sawicki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsWestern UniversityEssar Steel Algoma (Canada)Ministry of Transportation of Ontario
Fundersnot available
KeywordsWeathering steelCorrosionWeatheringMetallurgyMaterials scienceForensic engineeringGeologyEngineeringGeochemistry

Abstract

fetched live from OpenAlex

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.105
Threshold uncertainty score1.000

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 head0.035
GPT teacher head0.251
Teacher spread0.216 · 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.

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

Citations6
Published2008
Admission routes2
Has abstractyes

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