Laboratory characterization of recycled crumb-rubber-modified asphalt mixture after extended aging
Bibliographic record
Abstract
This paper presents a limited study that characterizes the recycling of artificially aged crumb-rubber modified (CRM) mixtures depending on their recycling percentage and aggregate type. Eight mixtures [six recycled mixtures containing rubberized reclaimed asphalt pavement (RAP) and two control virgin mixtures] were designed and tested. Two types of aggregates were used in this research project. The rubberized RAP used in the study was artificially aged in the laboratory using an accelerated aging processes. The percentages of the RAP, by total weight of the mix, incorporated into the recycled mixtures were 15%, 25%, and 35%. Evaluation of all mixtures included the following testing procedures: tensile strength ratio (TSR), asphalt pavement analyzer (APA), resilient modulus, and indirect tensile strength (ITS) after long-term oven aging. The results from this study showed that (i) the recycled aged CRM mixtures (with 15%, 25%, and 35% rubberized RAP) can satisfy the current Superpave mixture requirements, including moisture susceptibility and rutting resistance, and (ii) in general, there was no significant difference between the control and the recycled CRM mixtures for the properties evaluated in this study.
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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".