Application of nanotechnology in pavement engineering: a review
Bibliographic record
Abstract
Nanotechnology utilizes the research and technology established at the nuclear and molecular or macromolecular levels to comprehend the nanoscale phenomena. In road construction, the bituminous materials are used in enormous amounts. The macroscopic mechanical conduct of these materials still largely relies on microstructure and physical characteristics at a micro and nano scale level. For many years, scientists and engineers have been exploring nanotechnology in civil engineering, but nanotechnology use in this sector has been limited. Nanotechnology has caught the interest of experts and has progressively penetrated into the field of pavement engineering. The article reviews the published works carried out to study the use of nanomaterials in asphalt pavements and illustrates the technique used to enhance asphalt features by the use of these nanomaterials. In this review, multiple nanomaterials are discussed followed by characterization of these nanomaterials, preparation techniques of modified asphalt binder, and finally the effect of nanomaterials on the efficiency of base asphalt binder as well as the asphalt mixture. The primary focus continues on the use of nanotechnology in pavement engineering to achieve a better atmosphere for society based on recognized pavement engineering requirements and difficulties.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.005 | 0.002 |
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".