Transportation Optimization of Ribbon Floating Bridges: Analytical and Experimental Investigation
Bibliographic record
Abstract
Floating bridges are an economical and practical alternative for crossing water obstacles, especially in times of emergencies and conflicts. Ribbon pontoon floating bridges are a special type of floating bridge designed, built, stockpiled and deployed by the military and emergency management organizations in times of need. They are light-weight, fast to erect, and use the buoyancy of water to aid in supporting their self-weight and traffic loads imposed on the bridge. With increasing vehicular weights and fast bridge traversing time requirements, it has become necessary to develop reliable analytical tools capable of designing and analyzing floating bridges. It is critical to ensure that ribbon pontoon floating bridges can accommodate heavier vehicles, and at the same time reduce the spacing between successive vehicles to achieve greater transportation and economic efficiency. This paper presents the outline and results of an analytical and experimental research program designed to study the dynamic behavior of ribbon pontoon floating bridges under two-axle vehicular loading. An innovative experimental model was designed, constructed, and used in the experimental study. The developed analytical model predicted, with reasonable accuracy, maximum bridge displacements at different vehicle speeds and weights when compared with the experimental results.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".