Unified Fatigue Tests for Asphalt Materials Using Incremental Repeated-Load Permanent Deformation (iRLPD) Methodology
Bibliographic record
Abstract
With the increased use of recycled materials in asphalt mixtures, a practical fatigue test is needed for determining effect of additives on durability of asphalt mixtures. The current fatigue tests face many challenges in identifying fatigue performance of asphalt mixtures. This is partly because the test variables are different from the conditions in the field. Pavement Systems has developed iRLPD fatigue test for asphalt mixture, where all test variables and their levels are set to simulate field conditions. There are also companion iRLPD fatigue test for asphalt binder and mastic, which follow the same incremental methodology and measures the same fatigue index as those for the mixtures. Therefore, the fatigue resistance of the binder, mastic, and mixture are well correlated. This paper will discuss the iRLPD test results of the FHWA’s accelerated loading facility (ALF) mixtures for a fatigue study. The iRLPD fatigue tests were able to show the difference in fatigue property of extracted binder, recovered mastic, and mixtures of ALF due to use of RAP and RAS. The fatigue indices are also shown to be highly correlated with the number of heavy load repetitions to the first observed cracks in the ALF lanes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".