Probabilistic Gross Vehicle Weights and Associated Axle Loads for Military Vehicles in Bridge Evaluation and Code Calibration
Bibliographic record
Abstract
Military vehicles frequently use civilian bridges. The loading effects of military vehicles, both wheeled and tracked, are specific and different than those of civilian vehicles in normal traffic. Calibration to determine appropriate load factors for military loading of civilian bridges has not been fully performed and the corresponding levels of safety have not been quantified. This is partially due to the lack of probabilistic information of the gross vehicle weights and corresponding axle loads of military vehicles while operating in real-world conditions. This paper quantifies probabilistically the gross vehicle weight and axle loads for three military vehicles in use by NATO, each of which is representative of: military transport vehicles; armoured personnel carriers; and main battle tanks. A general means are proposed to quantify the probabilistic gross vehicle weight of military vehicles on the basis of maximum nominal payload as a proportion of the total nominal vehicle weight. Based on observed probabilistic gross vehicle weight of military vehicles, it is recommended to differentiate between military transport and military fighting vehicles as different categories of vehicles in bridge evaluation.
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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 | 0.000 | 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 | 0.001 |
| 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".