Deterioration of Concrete by the Oxidation of Sulphide Minerals in the Aggregate
Bibliographic record
Abstract
Cases of degradation of concrete associated to iron sulphides in aggregates were recently recognized in the Trois-Rivieres area, Canada. The aggregate used to produce concrete was an anorthositic gabbro containing various proportions of pyrite, pyrrhotite, chalcopyrite and pentlandite. Quantitative microanalysis on sulphide minerals show that pyrrhotite contains small amount of Ni, Co, Cu and As substituting for Fe in the mineral structure. Considering element substitution, x value in the chemical formula (Fe1-xS) was calculated to 0.099 in the pyrrhotite studied. Petrographic examination of damaged concretes showed the presence of oxidized pyrrhotite. The observation of polished samples shows, in several cases, that the pyrite is intact while the pyrrhotite presents evident signs of oxidation. In the presence of water and oxygen, pyrrhotite oxidizes to form iron oxyhydroxides and sulphuric acid. The acid then reacts with the phases of the cement paste and provokes the formation of gypsum and ettringite. These minerals were observed by SEM-EDS (scanning electron microscope/energy dispersive x-ray spectrometer) and their precipitation causes a volume increase that creates expansion and cracking of the concrete.
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 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) | 0.000 | 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".