Innovative use of nanomaterials for improving performance of asphalt binder and asphaltic concrete: a state-of-the-art review
Bibliographic record
Abstract
Rising costs of constructing and maintaining asphaltic concrete pavements present a challenge requiring an alternative solution. Nanomaterials can be cost-effectively integrated with asphalt binder to provide beneficial effects for asphaltic concrete mixture. This paper investigated seven different types of nanomaterials, namely nanoclay, carbon nanotube, nanosilica, nano-titanium dioxide, nano-zinc oxide, graphene oxide, and carbon nanofiber by examining their production methods, benefits, applications, and limitations based on the data available in published literature. Challenges and limitations discussed include economic, production, and blending problems, some of which are due to the lack of research on the topic. This study provides a framework from which the pavement engineering community can conduct experimental research on nanomaterials for applications in asphaltic concrete pavements. The review of previous studies reveals that new asphalt binders and asphaltic concrete mixtures incorporating nanomaterials can be developed for improved performance of flexible pavements. It is expected that further research can be devoted to overcoming the current challenges faced by aging transportation infrastructure through use of nanomaterials in asphalt binder and asphaltic concrete. Above all, research gaps in the present state of knowledge have been identified and certain recommendations are given for future investigations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".