Contact Pressures and Energies Beneath Soft Tires: Modeling Effects of Central Tire Inflation–Equipped Heavy-Truck Traffic on Road Surfaces
Bibliographic record
Abstract
Much has been made of the use of tires with low-inflation-pressure systems called central tire inflation (CTI) systems. Great benefits have been noted in numerous trials, including the reduced requirement for truck maintenance, improved gradeability, longer tire life, improved ride for drivers, reduced road rutting, and reduced road maintenance. Reports of successful field trials in the literature are confirmed by other supporting theoretical studies. However, the studies reported in the literature tend to relate to unsealed, unbound roads. Given that in some quarters there is now a desire to extend the benefits of the use of CTI to sealed roads, new questions arise. A full-scale, laboratory study was carried out at the University of Ulster, Northern Ireland. Both the normal and shear contact stresses were measured with a high-speed data logger connected to electronic sensors in the apparatus’s bed plate, as a tire ran over them. Tire pressures and loads were varied in this factorial study. The contact stresses were measured, and conclusions based on their distribution across the tire “contact patch” were presented. In addition, contact energies were inferred, and the consequences for sealed pavements were suggested.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".