Evaluation of Mechanical Performance of Asphalt Emulsion Stabilized Base Course Composed of Reclaimed Asphalt Pavement and Asphaltenes
Bibliographic record
Abstract
Abstract Reclaimed asphalt pavement (RAP) is a material that is produced by milling old asphalt pavement that can be mixed with virgin asphalt binder and aggregates to fabricate a recycled asphalt mixture. Using RAP in pavement structure can promote sustainability and reduce construction costs. In this study, asphaltenes, which is a waste material derived from oil sands deasphalting operation, was added as a modifier to recycled mixtures composed of different contents of RAP. A proctor test was conducted to determine the optimum fluid contents. Mix designs were performed for mixtures with 50 %, 75 %, and 100 % RAP with asphalt emulsion and different asphaltenes contents. Mechanical properties of the modified mixtures were evaluated by conducting the indirect tensile strength (ITS) test, creep compliance and strength test, indirect tensile asphalt cracking test (IDEAL-CT), and Hamburg wheel tracking test. The optimum emulsion content was determined to be 1.5 % based on sample performance using the ITS test. The results indicate that asphaltenes improves the strength up to the design-specified limits. The asphaltenes-modified samples show lower creep compliance and fracture energy (FE) values than the unmodified sample, indicating these samples are potentially more prone to cracking. Further, statistical analysis shows the difference in FE is significant at the lowest testing temperatures (−20°C). Asphaltenes-modified mixtures have greater cracking resistance at the intermediate temperature (25°C) and rutting performance at high temperature (40°C) than the unmodified mixture. However, based on FE and rutting resistance index, the improvements are not significant. Overall, the 100 % RAP mix with 1 % asphaltenes had the best tensile strength, cracking resistance, rutting resistance, and low temperature properties compared to other modified mixes.
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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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".