The impact of broken rock angle of repose on truck and shovel capacity and tire life
Bibliographic record
Abstract
This article outlines a closed-form solution to calculate truck body volumetric capacity. This solution also provides a means of evaluating broken rock density and swell factor, which may be further extended to determine shovel dipper and excavator bucket fill factor. The key to the analysis is an evaluation of the broken rock angle of repose. The analysis procedure defined in this article alleviates the need for using assumed values in operational planning. Further, the volumetric capacity and broken rock angle of repose and density may then be used to evaluate the load distribution within a truck body. This is critical to understanding the impact on tire loading and performance, which may be evaluated and compared to the tire manufacturer recommended tonne -kilometer-per-hour limit. The results of the analysis proposed here indicate that for the truck body designs investigated, the SAE International body design standard angle of repose assumption of 26.6° (2:1 slope) is much lower than most materials hauled (> 33°). Also, the broken rock density assumption in the same SAE International body design standard at 2.0 t/m3 is often too high, and does not permit the target load distribution of 1/3:2/3 front:rear axle to be achieved, effectively placing greater load on the front tires than the rear tires, adversely affecting front tire performance.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".