Bibliographic record
Abstract
One of the important and major defects seen in asphalt pavement is rutting or permanent deformation.Given the recent reports, millions of dollars are paid for repairing rutted pavements.The only solution for this problem is to evaluate the mixture quality in design stage.In present work, the prediction of asphalt mixtures behavior was studied using additives and such a behavior was related to one of rapid pavement test tools (wheel track).Uniaxial dynamic creep test was employed to evaluate mixtures behavior versus iterated load as well as to find creep parameters and to specify flow number in asphalt samples.In recent decades, the polymer and nanoparticles are widely used to modify the bitumen used in road pavements so that using modified bitumen in the asphalt, road construction executives have significantly increased service life of roads and thereby improved the operational life of them.The bitumen used in asphalt mixtures composes a very low weight value of this mixture (4-6%) but has a considerable effect on the asphalt efficiency.In this study, SBS polymer (as a modifier of thermoplastic elastomer with diverse weights (4-6%)) was mixed with MWNT carbon nanotubes having 1-3% weights and 60-70 bitumen of Tehran refinery and the corresponding effect was studied on diverse properties of asphalt mixtures.Conducting dynamic creep and wheel track tests, adding nanotube was found to have a significant impact on rut depth reduction and rutting strength increase.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.918 | 0.917 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".