Analysis of the tire-pavement contact characteristics in static and dynamic conditions based on Abaqus
Bibliographic record
Abstract
Tire-pavement interaction is a contact problem that involves both static load and dynamic rolling, with complex mechanical variations influencing vehicle maneuverability. This paper develops and validates a tire-pavement finite element contact model to analyse the effects of tire and pavement factors—such as load, pressure, speed, friction coefficient, and pavement stiffness—on contact characteristics (i.e. contact area, contact pressure, and stress distribution) under both static and dynamic conditions using Abaqus. The results showed that the load is the most significant factor affecting the tire-pavement contact area under static load. The contact area decreases by approximately 8%–15%, the peak contact pressure increases by about 2.9%–13.4%, and the tire transitions from static to dynamic. In free rolling, increasing speed significantly decreases the tire-pavement contact strength. The factors influencing contact pressure in descending order were tire pressure (56.2%), rolling speed (28.4%), tire load (22.9%), friction coefficient (21.4%), and pavement stiffness (3%). The findings of this study provide insights into the tire-pavement friction behaviour, which in turn offers a foundation for tire design optimization and increased vehicle safety.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".