Modelling short-term aging of asphalt binders using the rolling thin film oven test
Bibliographic record
Abstract
Simulation of short-term aging of asphalt binders is a widely used procedure in asphalt binder characterization for predicting the binder response to plant mixing and paving under controlled laboratory conditions. There are two laboratory test methods for evaluating the short-term aging of asphalt binders: (i) a method using rotating pans filled with a thin asphalt film termed thin film oven test (TFOT) and (ii) a method using rolling cylindrical asphalt containers termed rolling thin film oven test (RTFOT). In this paper, an attempt is made to develop generalized models for short-term aging effects using the RTFOT aging time as a benchmark. Six binder types representing two PG grades and three source suppliers are conditioned to varying levels of RTFOT aging and tested using the dynamic shear rheometer (DSR). Aging effects are modelled using independent temperature shift models for the shear modulus and phase angle. The paper discusses the sources of errors in producing generalized models and some potential applications of aging models. The research revealed that it is possible to develop and implement such models for unmodified binders.Key words: asphalt, aging, RTFOT, DSR, binder rheology, shear modulus, phase angle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".