Recommendations for Quality Control of Nanomaterials and Field Construction of PCC Pavements when Nanomaterials are Incorporated
Bibliographic record
Abstract
Concrete is the most widely used material on the planet, after water. According to the Cement Association of Canada, nine billion cubic meters of concrete were used in the world in 2007. The importance of concrete in the construction industry is demonstrated by the continual focus to improve concrete performance and durability. Nanotechnology in cement based materials is an emerging field and presents significant potential. However, practical applications have been limited mostly due to the fact that nanoconcrete is still a new technology and thus lacks field experience, and is not taken account for in specific standards, technical specifications, and constructions procedures. In order to control the quality of nanomaterials and improve the construction quality of Portland Cement Concrete (PCC) pavements that contain them, this paper presents several recommendations for practitioners that are involved in design and field construction. The recommendations have been extrapolated from extensive lab research (developed in Canada, Chile and the United States), literature research, and also from the authors’ field experiences.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.017 |
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".