Tire Burst Phenomenon and Rupture of a Typical Truck Tire Bead Design
Bibliographic record
Abstract
Abstract Even though relatively rare, the tire failures are very dangerous. An example of tire failure is over-pressurization that usually occurs during inflation of the tire, when the latter is inflated well beyond the pressure recommended by the tire manufacturer. When inflating tires, personnel assigned to vehicle repair and maintenance are likely to suffer severe injuries if several safety rules are ignored. Experimental data on tire burst is somewhat rare in the open literature. In order to determine the strength limits of a typical truck tire and describe the mechanism of the tire burst phenomenon, a hydrostatic burst test was first conducted on an 11R22.5 tire. From this test, tire burst pressure was determined. Over pressurizing the tire results in a high tension in the steel wire beads. As the total strain this kind of steel can withstand is rather low, their fracture will be source of the general failure. Then, an x-ray inspection and microscopic analysis were performed on the tire beads in order to characterize their behavior and failure. Furthermore, a finite element analysis was also conducted using material properties from the available literature to determine the inflation pressure resulting in failure of a new tire. The model was able to well predict the tire burst pressure by identifying the pressure at which the maximal plastic strain of steel bead wires is reached. Finally, the various tests and finite element analysis allowed to understand why, where, when, and how a truck tire fails when over pressurized.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".