Rutting Resistance of HMA Rehabilitated with Micro-Surfacing
Bibliographic record
Abstract
The work presented here is a study on the measurement and prediction of the rutting resistance of previously rutted asphalt mixes rehabilitated with a layer of micro-surfacing manufactured with virgin and recycled aggregates at different stages of aging. The experimental procedure consisted of rutting tests on hot mix asphalt slabs already degraded and repaired with virgin and recycled micro-surfacing. Then, the evolution of the behavior of micro-surfacing cast on the hot mix asphalt slabs is observed according to loading cycles of the pavement rutting tester MLPC. Before rutting tests, slabs are subjected to 24 hours at 50°C and aged for 2 days and 5 days at 85°C in the oven. The results showed rutting percentages of 6.3% for hot mix asphalt slabs aged for 2 days and 7.2% for 5 days. These hot mix slabs repaired with virgin micro-surfacing have rutting percentage of about 9.2 % for 2 days of aging and 6.5% for 5 days of aging. While, the HMA slabs repaired with recycled micro-surfacing have rutting percentage of about 8.1% for 2 days of aging and 5.9% for 5 days of aging. These results allowed the development of a prediction model based essentially on three predictor variables including cycle number, rutting state and percentage of water in the micro-surfacing material. The developed model shows a strong correlation between the predicted rutting values and the rutting values measured with the MLPC rut tester. Thermal aging in oven has a positive impact on the resistance to permanent deformation of new asphalt mixes and those rehabilitated with micro-surfacing. The parameters of rutting state and contribution water are significant in the rutting prediction model, while the cycle number remains a non-significant parameter in the model but determinant.
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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 | 0.001 | 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 | 0.001 |
| 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".