Performance of nano-modified silica, alumina, and titania concrete coatings under combined cyclic environments, carbonation, and calcium chloride
Bibliographic record
Abstract
In cold regions, transportation infrastructure (e.g., concrete pavements and bridges) undergo complex degradation due to the combined effects of freeze-thaw cycles (FT), wet-dry cycles (WD), and carbonation with chloride-based de-icers; however, the effectiveness of plain and nano-modified coatings as a mitigation or rehabilitation strategy under such combined conditions remains largely unexplored. This study examines methyl methacrylate (MMA) and silane coatings, modified with 0–5 % nano-alumina or nano-titania, and evaluates colloidal silica as an alternative coating for a rehabilitation strategy. Coatings were applied to concrete with 0.4 and 0.6 w/b ratios and exposed for 12 months to combined FT, WD, carbonation (10 %), and calcium chloride (13.6 % and 21.9 %) conditions to stimulate chemical and physical damage. In addition to initial fluid transport, performance was assessed through mass change, expansion, relative dynamic modulus ( REd ), mineralogy, microstructure, and corrosion tests. Uncoated, colloidal silica, and MMA-coated specimens failed within 4–5 months, while silane and its nanocomposites showed minimal expansion (0.01–0.03 %) and superior durability. MMA nanocomposites offered moderate improvement through barrier, pozzolanic, and nucleation effects but were limited by low chloride-binding capacity; silane nanocomposites outperformed them due to synergistic hydrophobic, pore-lining, and microstructural densification effects.
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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 | 0.000 | 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) | 0.001 | 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".