Evaluation of Surface-Related Pavement Damage due to Tire Braking
Bibliographic record
Abstract
The response of flexible pavement at near-surface is significantly affected by interfacial tire-pavement contact stresses. In addition to highly non-uniform vertical stresses and surface tangential shear stresses at tire-pavement interface, tire braking at an intersection causes additional significant longitudinal contact stresses on the pavement surface. In this paper, the flexible pavement responses to three-dimensional (3-D) tire-pavement contact stresses at various tire rolling conditions were determined using a developed 3-D finite element model. The hot-mix asphalt (HMA) layer was characterized as a viscoelastic material, and the transient dynamic tire loading was simulated using a continuous moving load and implicit dynamic analysis. The analysis matrix includes two typical flexible pavement structures (76 mm and 152 mm HMA thicknesses) and three tire rolling conditions (free rolling at high speed, free rolling at low speed, and braking). The study concluded that the low-speed vehicle loading and tire braking aggravates the pavement deterioration at an intersection in terms of rutting or shoving in the HMA and surface cracking at the pavement surface. During tire braking, the damage ratios for pavement surface cracking may be as high as 8 to 32 depending on HMA thickness, compared to the normal traffic loading conditions. The tire braking increases the HMA rutting or shoving potential by 2.0 to 2.6 times due to the increased shear strains in two directions. Hence, pavements for intersections should be specified, designed, and constructed differently than regular asphalt pavements to withstand the more severe loading conditions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".