Effect of Surface Preparation on the Deicing Salt-Scaling Resistance of Concrete With and Without SCM: Laboratory and Field Performance in the Presence of Sodium Chloride Deicing Salt
Bibliographic record
Abstract
Abstract Deicing salt scaling is a form of surface deterioration of concrete, which describes the progressive raveling of mortar, sometimes even coarse aggregate, from the surface. This paper presents findings from both laboratory and field studies on the deicing salt-scaling resistance of concrete incorporating various proportions of fly ash (25 % and 50 %) and slag (30 % and 60 %). In the laboratory study, concrete slabs finished with different methods as well as concrete with non-finished surfaces were tested for deicing salt-scaling resistance. For the field study, larger concrete slabs were cast and cured in the laboratory and then placed outdoors. Deicing salt solution was applied on the slabs following each snowfall during the winter seasons to maintain an ice-free condition on the surface of the slabs. The results from the laboratory study indicate that the reduced salt-scaling resistance of concrete incorporating supplementary cementing materials is not solely depended on the mechanical strength of the concrete itself. It is also shown that concrete containing high levels of fly ash and slag maybe more sensitive to finishing techniques.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".