Microindentation Specification Grading of Thin Film Aged Asphalt Cements from a Northeastern Ontario Pavement Trial
Bibliographic record
Abstract
This paper discusses the development of thin film aging and indentation test methods for the specification grading of asphalt cements to better predict long-term pavement performance. Samples were conditioned using various thin film oven (TFO) and pressure aging vessel (PAV) protocols to realistically age them with different film thicknesses. Moisture was introduced to the PAV for some runs to see how this affects low temperature properties. Dynamic tests were performed using a 200-micrometer flat punch indenter on samples of various film thicknesses at -14°C and -20°C. The low temperature rheological properties tested included the phase angle (δ), dynamic storage (E'), loss (E''), and complex (E*) moduli, as well as stiffness of the dynamic loading curve (S). The properties that were most useful in terms of determining the long-term pavement performance were phase angle and stiffness, with higher phase angles and lower stiffness values indicating asphalts that were least likely to crack under cold conditions in service. The aging method that was best able to replicate the phase angles and stiffness results of recovered asphalts from test sections in a northeastern Ontario pavement trial employed thinner films (0.8 mm) in the presence of moisture utilizing regular PAV pressure, temperature, and time. This investigation shows that long-term pavement performance can be accurately predicted using a short-term aging method and flat punch microindentation, while requiring only minimal amounts of asphalt binder.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".