Development of high performance asphalt mastic using fine taconite filler
Bibliographic record
Abstract
Low temperature cracking is a very serious distress for asphalt pavements built in Northern U.S. and Canada. As temperature rapidly decreases, thermal stresses develop in the restrained asphalt surface layer and, when the temperature reaches a critical value, cracking occurs. A “simple” solution to overcome this problem is to use a very soft asphalt binder with high relaxation properties, which limits the accumulation of high stress and the formation of cracks. However, these types of binder may lead to significant permanent deformation at high temperature (e.g., rutting) and, therefore, cannot be used for real pavement constructions. In this research, the possibility of obtaining stiffer asphalt binders by adding fine taconite filler available in Minnesota was investigated. Two different types of asphalt binders were selected and from each binder, three different types of asphalt mastics were prepared based on the amount (i.e., level) of taconite particles used as filler: 5%, 10% and 25%. Bending Beam Rheometer (BBR) and Dynamic Shear Rheometer (DSR) tests were performed to evaluate the low and high temperature properties of asphalt binder and corresponding mastics. From these experimental works, creep stiffness, m -value, thermal stress and shear complex modulus were calculated and then graphically and statistically compared. It was observed that asphalt mastic containing 5% taconite filler presents similar properties at low temperature and better performances at high temperature compared to the corresponding asphalt binder. On the other hand, asphalt mastics containing higher amount of taconite fines (10% and 25%) are much more brittle compared to the original binder at low temperature, even though higher rutting resistance was observed at high temperature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".