Deicing salt scaling resistance of concrete incorporating supplementary cementing materials: laboratory and field test data
Bibliographic record
Abstract
In this study, sidewalk sections were made in the field using seven concrete mixtures, applying a finishing and curing practice that is commonly used in Montréal, Canada. For each of the sidewalk sections, large slabs (1.2 m × 1.2 m) were cast from which specimens were cored and tested in the laboratory for determining their basic mechanical properties and deicing salt scaling resistance following ASTM C672 test procedures. Also, during the casting of the sidewalk, companion specimens were cast on site, using concrete from the same batch, and were subjected to the same tests as the “cored” specimens. The resistance to deicing salt scaling of these “laboratory specimens” was evaluated according to ASTM C672 and to BNQ NQ 2621–900 (2002 standard of the province of Quebec, Canada) test procedures. The results were compared with the performance of the sidewalk sections after four winters of outdoor exposure. The visual evaluation of the sidewalks after four winters has confirmed the severity of the ASTM C672 procedure and the adequateness of the BNQ procedure to better evaluate the deicing salt scaling resistance of concrete made with supplementary cementing materials (SCMs). The field evaluation should, however, continue for a longer period of time to increase the confidence in the BNQ test or to allow for changes as needed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".