Comparative Dynamic Load Effects of Tracked and Wheeled Military Vehicles on Bridges
Bibliographic record
Abstract
The perceived and observed differences in the dynamic behavior between wheeled and tracked military vehicles should be considered when applying appropriate dynamic load effect values for bridge design and assessment. Based on available test data, tracked military vehicles appear to impose less severe dynamic load effects when compared with wheeled vehicles under similar crossing conditions. In exploring a range of crossing conditions, a review of test data was used to make a general comparison of the dynamic loading amplification between tracked and wheeled military vehicles. To expand the range of crossing conditions tested for an instrumented bridge, additional data were collected for an artificially induced roughness of the bridge surface. By combining the test results from previous studies with the results from this testing program, the relative dynamic loading amplification between tracked and wheeled military vehicles can be quantified for situations with similar crossing conditions. Given this comparison, it may be appropriate to use a dynamic load allowance (DLA) of as low as 70% of the code-specified DLA for wheeled vehicles when evaluating the capacity of bridges subjected to military tracked traffic. This is especially relevant when considering maneuver options for main battle tanks during military combat operations.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".