Evaluation of stiffness to predict rutting resistance of hot-mix asphalt: a Canadian case study
Bibliographic record
Abstract
This paper investigates the relationship between the stiffness and rutting resistance of hot-mix asphalt. Ten different types of hot-mix asphalt were examined. The Superpave mix design method was utilized to produce nine mixes; the remaining mix was designed using the Marshall method. The asphalt mixes were tested for stiffness and rutting resistance under the Centre for Pavement and Transportation Technology research program at the University of Waterloo. The stiffness was determined by the laboratory resilient and dynamic moduli tests. The dynamic modulus test was conducted at six different loading frequencies and five different temperatures. The rutting test was executed by the Hamburg Wheel Rut Tester and the French Laboratory Rutting Tester to obtain rutting depth. The regression analysis was performed to examine the relationships of resilient and dynamic moduli with rutting depth. The results of the regression analysis revealed that resilient modulus did not correlate well with rutting depth. In contrast, dynamic modulus showed strong correlation with rutting depth for a number of loading frequencies and temperatures. The strong relationship was observed at the higher temperatures of +46.1 oC and +54.4 oC. Moreover, the relationship between dynamic modulus and rutting depth was better for lower loading cycles/wheel passes applied in the rutting test. It was also noticed that dynamic modulus exhibited a better relationship with rutting depth obtained from the French Laboratory Rutting Tester. The overall findings indicate that the dynamic moduli obtained at 0.1–1.0 Hz and +46.1–(+54.4) oC are useful to predict the rutting resistance of hot-mix asphalt.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 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) | 0.000 | 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 teacher head, 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".