Influence of Hydrogreen Bioasphalt on Viscoelastic Properties of Reclaimed Asphalt Mixtures
Bibliographic record
Abstract
The incorporation of reclaimed asphalt pavement (RAP) into asphalt mixtures exposes some challenges from the design perspective because of the aged asphalt binder in RAP. Steps are being taken to offset the addition of stiff materials, often with the use of rejuvenating additives. This paper summarizes the laboratory evaluation of one of the available bio-rejuvenating agents called BituTech RAP. High RAP content mixtures used in Manitoba, Canada, were evaluated to study the impact of BituTech RAP on the viscoelastic properties of asphalt mixtures to overcome any possible moisture damage or thermal cracking problems that might arise in such a wet–freeze environment. The laboratory experiment consisted of the production and test of mixtures that contained 15% and 50% RAP, with and without BituTech RAP. The 2S2P1D analogical model was used to generate the complex modulus (E*) of the various evaluated mixtures and to assess the influence of BituTech RAP on the storage and loss moduli. The addition of BituTech RAP improved the moisture resistance of the mixtures that contained RAP, as observed after three freeze–thaw cycles. The addition of BituTech RAP restored the thermal cracking properties of the mixtures revealed by the thermal stress restrained specimen test. The use of BituTech RAP could result in cost savings without the need to use a softer binder, as long as the high-temperature properties of the mixtures were not jeopardized.
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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 | 0.000 | 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) | 0.001 | 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 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".