Sound Absorption and Friction Responses of Nanoconcrete for Rigid Pavements
Bibliographic record
Abstract
This paper reports the influence of nanosilica on friction and sound absorption responses of concrete for application in rigid pavements. The paper also discusses the feasibility of applying nano-lotus leaf as a coating for concrete material. Previous research on friction and sound absorption characteristics of concrete pavement primarily emphasized creating different surface textures through macrotexture modifications. The microstructure can also have a significant effect on surface texture, friction, and sound absorption characteristics of concrete. This research studied the friction and sound absorption properties of concrete on the basis of microtexture modification that used nanomaterials such as nano-silica and nano-lotus leaf. Several laboratory concretes were produced by using different proportions of nanosilica, which partially replaced cement by weight. The fresh concretes were tested for workability, wet density, and air content. The hardened concretes were tested for compressive strength, friction, and sound absorption. The British pendulum test was used to determine the friction number. An impedance tube was used to determine the sound absorption coefficient. Preliminary results indicate that nanosilica can increase friction and sound absorption of concrete pavements. In addition, the results show that nano-lotus leaf can be applied as a coating on concrete material for rigid pavements to improve retention of their friction properties during the wet season.
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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".