Performance Properties and Carbon Emissions of Cold Mix Cold Laid Asphalt Rubber Mixture
Bibliographic record
Abstract
Based on the indoor experiments and a test section constructed in the G5 national expressway project, a comprehensive study was implemented regarding the performance properties and environmental characteristics of cold mix cold laid asphalt rubber mixture. Results showed that the Marshall stability and dynamic stability of the mixture were greater than those of the hot mix dense graded asphalt crushed stone. However, its residual stability was slightly lower than the latter. Both the high-temperature stability and water stability could meet technical requirements for pavement middle-layer and lower-layer asphalt mixtures. Additionally, the total in-construction carbon emissions of cold mix asphalt rubber mixture were 2,970.77 kg per 1,000 m2 surface layer, mainly caused during the mixture transportation, paving, and rolling stages. This part accounted for 47.1% and 51.9% of the total emissions. The in-construction emissions of hot mix dense graded asphalt-treated permeable base was 6,151.80 kg per 1,000 m2 surface layer, which mainly resulted from the heating of aggregates and asphalt as well as the fuel consumption of transportation, paving, and rolling machinery. The carbon emissions of hot mix asphalt mixture were about twice that of cold mix asphalt rubber mixture. It suggests cold mix asphalt rubber mixture greatly reduced carbon dioxide and harmful gas emissions and lowered personnel labor intensity, showing outstanding environmental advantages.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".