Evaluating the Potential for Hot Mix Asphalt Rutting Performance Using Laboratory and Digital Imaging Technique
Bibliographic record
Abstract
The objective of this study is to evaluate the non-structural rutting resistance of six typical Superpave™ mixes used in Ontario for surface course using conventional and advanced methods. Hamburg Wheel Rut Tester (HWRT), Dynamic modulus test, and Digital Imaging Processing (DIP) technique were used in the evaluation. These mixes include two Superpave SP12.5 and four SP12.5 FC2 mixes. Six Superpave Performance Grading (PG) binders and three traffic levels were used in the design of these mixes. The effect of aggregate type and binder type in improving the rutting resistance was investigated. Manual method was used to quantify the shear upheave for all mixes. The common devices in measuring Hot Mix Asphalt (HMA) rutting ignore the effect of shear flow and only measure the effect of densification which might affect the ranking of mixes according to rutting susceptibility. DIP was used for further analysis of aggregate effect on HMA rutting resistance. This included estimating aggregate contacts, segregation and orientation of two dimensional cross section images after loading. This method provides internal structural analysis of HMA in order to understand the failure mechanism in rutting and its relationship with each individual component characteristics. Dynamic modulus test was also conducted to investigate the correlation between the HMA stiffness and rutting. It was found that Dynamic modulus |E*| is very effective for evaluating the resistance of HMA mixtures against rutting due to the strong correlation. The results of this study also showed that DIP provides an indication of HMA rutting potential. Aggregate contacts showed a good correlation with mixture rutting resistance measured manually and by using HWRT. Overall, imaging analysis would assist in the design of long lasting pavement.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".