Kinetics Decoupling Method for Thermo-Photo Coupling Aging Effects of Asphalt Considering Aging Time and Depth: A Chemical Reaction Kinetics Study
Bibliographic record
Abstract
The thermo-photo coupling aging of high-viscosity modified asphalt (HVMA) is essentially a complicated chemical reaction process, and the chemical reaction kinetics theory can provide a novel chemical perspective to elucidate the aging mechanism of HVMA. The aim of this study is to achieve kinetics decoupling of the thermo-photo coupling aging process at different aging times and depths based on chemical reaction kinetics theory, with the purpose of clarifying the spatiotemporal distribution characteristics of thermal aging and photoaging. Firstly, Fourier transform infrared spectroscopy was conducted to investigate the chemical composition changes of HVMA at different aging times and depths. Then, the aging gradient distribution submodel, as well as thermal aging and photoaging kinetics submodels, were constructed to calculate the contribution rates of thermal aging and photoaging at different aging times and depths, thus achieving the kinetics decoupling of the thermo-photo coupling aging process. The results showed that the proposed aging kinetics combination model can ideally fit the thermal aging and photoaging characteristics of HVMA. No notable aging gradient phenomena were detected during thermal aging, but a significant aging gradient characteristic was observed during photoaging. At the surface, the photoaging rate constant was the highest, and it slowed down after the aging depth reached 200 μm. The photoaging exhibited a dominant effect at the surface, with a contribution rate exceeding 96%. As the aging depth increased, the contribution rate of photoaging decreased, whereas that of thermal aging increased. With extended aging time, the aging dominant depth of photoaging gradually increased. A decoupling cloud map was constructed to achieve the kinetics decoupling of the thermo-photo coupling aging process under various aging conditions, durations, and depths.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".