Development of Micro-Indentation Tests for the Specification Grading of Asphalt Cements
Bibliographic record
Abstract
This paper documents and discusses the development of instrumented micro-indentation tests for the specification grading of asphalt cements. Seven recovered asphalts from a northern Ontario trial were tested with a flat-ended punch indenter in both static creep and dynamic oscillatory tests on thin films at low temperatures. The rheological properties determined included: creep stiffness and slope of the creep stiffness master curve (S and m-value); elastic/total work of indentation (We/Wt); elastic recovery (ER); viscous creep compliance (Jv); dynamic storage, loss and complex moduli (E’, E”, and E*); and dynamic phase angle (delta). Dynamic tests were generally found to be more reproducible than static (creep) tests, probably due to the intricacies in contact detection for the latter. Indentation creep stiffness, viscous creep compliance, and phase angle correlated well with pavement performance. Previous investigations revealed that creep properties determined on rolling thin film oven/pressure aging vessel (RTFO/PAV) residues for these sections failed to show any correlation with performance. This is likely due in part to the unrealistic film thicknesses of 1.2 and 3.2 mm and temperatures of 163 and 100°C employed in RTFO and PAV aging methods. Another look at thin film aging protocols is warranted since thinner films should allow us to age the asphalt cement more realistically at lower temperatures and pressures. More accurate asphalt aging and conditioning protocols combined with the newly developed indentation tests will allow us to reliably and precisely select asphalt cements to ensure their maximum durability.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".