Cracking and Aging of Asphalt Mixtures Using the Illinois Flexibility Index Test
Bibliographic record
Abstract
Cracking is a serious asphalt pavement distress that can affect ride quality and impact road safety. The increased use of reclaimed asphalt pavement (RAP), recycled asphalt shingles (RAS), and modified asphalt binders has led to the production of asphalt mixtures that are prone to cracking issues. In addition, asphalt aging, which occurs during mix production and construction as well as during pavement service life, is a significant factor that can exacerbate issues related to all modes of cracking. Consequently, the volumetric mix design method alone can no longer secure acceptable long-term pavement performance, and several transportation agencies in Canada currently are integrating performance tests into mix design procedures to increase longevity and durability of asphalt mixtures. This study investigated the influence of common mix design properties on cracking and aging resistance and developed preliminary cracking performance–based specifications for balanced mix design implementation. Six loose plant-produced mixtures consisting of a range of mix design properties were compacted in the laboratory to produce short-term- and long-term-aged specimens. The Illinois flexibility index test (I-FIT) was used to evaluate cracking performance of both short-term- and long-term-aged specimens. The results showed that mixtures with higher nominal maximum aggregate size (NMAS), limestone aggregates, and RAS reduced cracking performance. Conversely, incorporating RAP and RAS and using stiffer binders improved aging resistance, whereas use of limestone aggregates contributed to lower aging resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".