Minimizing Traffic Tire Pressure as a Concept of Increasing Pavement Elastic Modulus using Transport and Road Research Laboratory Formula
Bibliographic record
Abstract
Development of vehicle industry leads to an increase in truck axle weights. Moreover, as the axle loads have increased, the use of higher tire pressures have become more popular in the truck market. Recent increase in tire loads and pressures have raised questions regarding their effects on pavement performance, service life and maintenance cost. Reducing the adverse impacts of increasing tire pressure are considered as one of the most important issues in which many researchers around the world are interested. The main objective of this research is to minimize the effect of increased tire pressure on the performance of flexible pavement. The concept of minimizing truck tire pressure will be gained through the application of the following steps: a) determining the optimum tire pressure with the optimum elastic modulus of (wearing surfaces, base course and subgrade) where fatigue and rutting age are equal, b) determining the most effective elastic modulus on the performance of the pavement which can cause a significant decrease in the optimum tire pressure. Moreover, the pavement age will be determined as a function of the tire pressure and the most effective elastic modulus. Analysis of this paper shows that, increasing traffic tire pressure leads to a significant decrease in pavement fatigue age while the effect of increased traffic tire pressure on the pavement rutting age can be neglected. Moreover, subgrade elastic modulus is the key element which leads to a marked decrease in the optimum tire pressure. Moreover, the optimum tire pressure should not exceed 0.87 N/mm 2 with 72.50 N/mm 2 optimum subgrade elastic modulus. Keywords: Tire Pressure, Pavement Elastic Modulus, Fatigue and Rutting Age Formula and Axle Weights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".