Performance of plant-produced asphalt mixtures for balanced mix design implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several transportation agencies in Canada are currently relying on volumetric properties of asphalt mixtures to accept or reject the final mix design. The existence of various pavement defects on Canadian roads indicates that volumetric mix design procedure alone does not guarantee adequate long-term pavement performance. Therefore, transportation agencies are finding ways to increase durability of their asphalt mixtures to accomplish a road network that is more sustainable, safer and more economical. The balanced mix design (BMD) approach integrates two or more performance test criteria into mix design and acceptance to produce asphalt mixtures that are resistant to cracking and permanent deformation. The main objective of this study is to assess cracking and rutting performance of plant-produced asphalt mixtures to validate current volumetric mix design methods and investigate ways to optimise mix performance for moving towards an efficient BMD framework. Six plant-produced mixtures were collected from different pavement construction projects to prepare specimens for cracking and rutting evaluation. Cracking performance was determined using the Illinois flexibility index test and rutting performance was determined using the Hamburg wheel-tracking test. Results showed that polymer-modified binders, recycled materials and reduction of nominal maximum aggregate size contributed to better rutting performance. Nevertheless, limestone aggregates, recycled asphalt shingles and low asphalt content reduced cracking resistance and did not lead to an efficient BMD framework.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it