Real-Time Flow Behavior of Hot Mix Asphalt (HMA) Compaction Based on Rheological Constitutive Theory
Bibliographic record
Abstract
Compaction is the most critical stage during pavement construction, but the real-time rheological behavior in the compaction process of hot mix asphalt has not received enough attention. Rheological properties directly reflect the of mixture performance, the intrinsic directly reflects the influencing factors of compaction, and the pavement compactness and service life. Therefore, it is important to interpret the rheological properties of the asphalt mixture during the compaction process. In this paper, the improved Nishihara model was used to study the viscoelastic-plastic properties of the hot mix asphalt in the compaction process. Firstly, the improved Nishihara model was briefly introduced. Subsequently, the stress and strain correlation curves are obtained by the MTS (Material Testing System) compaction test, and the strain-time curve is fitted to determine the model parameter values. Finally, the parameters are substituted into the constitutive equation to obtain the strain-time curve and compared it with the test curve. The results show that the improved Nishihara model effectively depicts the real time behavior of the asphalt mixture in the compaction progress. The viscos and plastic parameters present certain differences, which reflects that the gradation and temperature have certain influence on the compaction characteristics of the mixture.
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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) | 0.000 | 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) | 0.001 | 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".