WATER VAPOR TRANSMISSION AND WATERPROOFING PERFORMANCE OF CONCRETE SEALER AND COATING SYSTEMS
Bibliographic record
Abstract
This paper reports the water vapor transmission and waterproofing performance of a silane-based penetrating sealer (PS), two cementitious coatings (CC1 and CC2), and a polymeric coating (PC). The sealer and coating products were applied on 100-mm concrete cubes. In total, fifteen concrete cubes including the control specimens were prepared. The treated concrete cubes were tested to determine the ability of the selected products to improve waterproofing and to transmit water vapor. The correlation between water vapor transmission and waterproofing performance was also examined. Experimental results revealed that the cementitious coating CC2 had the lowest degree of water vapor transmission but the highest degree of waterproofing performance. In contrast, the highest degree of water vapor transmission but the lowest degree of waterproofing performance were observed for the cementitious coating CC1. The penetrating sealer PS provided greater water vapor transmission but lower waterproofing ability than the polymeric coating PC and the cementitious coating CC2. The best-performing coating with regard to water vapor transmission and waterproofing was the polymeric coating PC. Furthermore, the water vapor transmission and waterproofing performance of the sealer and coating systems were strongly correlated; this suggests that one of these two properties can be predicted by testing the other.
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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) | 0.000 | 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".