Crack Pinning in Asphalt Mastic and Concrete: Regular Fatigue Studies
Bibliographic record
Abstract
The effects of finely dispersed fillers on the fatigue performance of asphalt binders and asphalt concrete mixes at relatively low temperatures are examined. A series of model binder systems containing glass spheres with narrow particle size distributions were used to study the effect of filler particle size on the fatigue performance of the asphalt mastic. Two mastic systems containing ground limestone fillers, which possessed significantly different gradations, also were tested. Fatigue performance was evaluated by applying a constant torsional strain to each specimen in a dynamic rheometer at 10°C and 40 Hz. Testing at various strain levels allowed the relationship between fatigue life and strain to be determined for the different systems. The results indicate that as the particle size of the filler decreases, the fatigue life of the asphalt mastic increases. This observation is a direct result of the mode of fatigue failure in the asphalt mastics and is in agreement with Evans’s theory on crack pinning for failure in filled brittle solids. Constant stress asphalt concrete fatigue tests on both dense- and gap-graded systems prepared with the two different ground limestone fillers show that the particle size does not significantly affect the fatigue life of the mixes. These results also confirm that crack pinning is the major mechanism responsible for improved fatigue performance.
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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) | 0.000 | 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 | 0.000 |
| 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".