Bibliographic record
Abstract
Abrasion resistance in concrete is related to the ability of the surface to resist being worn away by rubbing and friction. High-quality concrete is recognized as enhancing both abrasion and macrotexture durability under traffic loading. Pavement friction is the result of two primary frictional force components: adhesion and hysteresis. Adhesion is dependent on the microtexture of the surface, while hysteresis depends on its macrotexture; both microtexture and macrotexture significantly affect friction. Macrotexture is also significant in preventing hydroplaning because of its effect on the surface drainage of pavements. Improving the texture durability of concrete may provide important benefits in delivering long-term friction performance. A microtexture modification with nanotechnology improves the friction response in rigid pavements and enhances the durability of concrete materials by how it affects the deterioration mechanism at the molecular level. Nanomaterials can improve the calcium silicate hydrate component in hardened concrete; this action is crucial to enhancing the strength and durability of the cement paste. Therefore, applications using nanotechnology in concrete materials are receiving increased attention. This paper presents the results of a study in which several concrete samples were produced with different proportions of nanosilica. The results show that nanosilica enhances the compressive strength and abrasion resistance in concrete materials. Because of the relationship between abrasion response and macro texture, it can be concluded that nanomaterials increase macrotexture durability and therefore improve the safety of concrete pavements in wet conditions.
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 | 0.000 | 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) | 0.003 | 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".