Evaluation of Selected Performance Properties of Nanoclay-Modified Asphalt Binders
Bibliographic record
Abstract
Asphalt binders are often modified with additives such as acid, polymer, or a combination of multiple additives to achieve improved performance to sustain heavy loads and adverse weather conditions. According to some previous researches, nanoclay can be a good alternative of currently practiced Styrene-Butadiene-Styrene (SBS) modification, and the former is expected to reduce the overall cost of the asphalt binder. Three types of nanoclay (Cloisite 10A, 11B, and 15A) were blended with asphalt binders prepared from two different sources (Arabian Crude and Canadian Crude). A blending protocol has been developed to blend nanoclay with the base binders. Mechanical properties including viscosity, rutting parameter have undergone significant changes after the nanoclay modification. It was also observed that nanoclay modified binders offer different moisture susceptibility while bonding with different aggregates; the nanoclay modified asphalt binder exhibits better bonding with gravel than sandstone. Mechanistic properties such as viscosity and rutting parameter are found to be highly correlated with the chemical compositions. Binders from the Canadian crude showed more colloidal stability than binders from the Arabian crude after nanoclay modification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".