Aging characteristics evaluation of polyphosphoric acid modified asphalt based on thermal analysis kinetics and artificial neural network methods
Bibliographic record
Abstract
Polyphosphoric acid (PPA) modified asphalt is cost-effective and efficient, but its aging stability requires more precise quantitative analysis. This study investigate the aging behavior of PPA-modified asphalt considering the effects of PPA dosage, aging temperature and time. Softening points were measured after the rolling thin film oven tests (RTFOT) and pressure aging vessel (PAV) tests. Analysis of variance was used to evaluate the factors influencing the softening point. First-order reaction for the aging of PPA-modified asphalt was validated. Aging kinetics and back-propagation neural network model were developed to predict softening points under various aging conditions. Fourier transform infrared (FTIR) was used to explored the mechanism under PAV-aging. The results show that higher PPA dosages negatively affect asphalt’s aging resistance. PPA dosage having the greatest influence on softening points. The aging behavior of matrix asphalt, 0.5%PPA, and 1.0%PPA follows first-order reaction kinetics, allowing the aging kinetics model to accurately predict their softening points. Delayed RTFOT effectively simulates the softening point variation of PPA-modified asphalt during long-term aging. The back-propagation neural network model accurately predicts changes in the softening point of PPA-modified asphalt. The differences in softening points of PPA-modified asphalt after PAV-aging are primarily related to S=O, P–O, and P=O groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".