Dynamic Behavior of Ribbon Floating Bridges
Bibliographic record
Abstract
Floating bridges are economical means for crossing water bodies, especially in times of emergency or war. A special type of floating bridge, a ribbon pontoon floating bridge, is designed, built, and stocked by the military and emergency management organizations to be deployed in times of need. Lightweight and quickly erected, such bridges use the buoyancy of water to support their weight and imposed traffic loads. With increasing vehicular weights and the need for fast traversing times, analytical tools capable of designing and analyzing floating bridges are necessary. This development is ideal for optimizing vehicle weights and spacing to achieve greater economic efficiency. An analytical and experimental research program designed to study the dynamic behavior of ribbon pontoon floating bridges under two-axle vehicular loading is presented. This analytical method yielded maximum bridge displacements comparable to the experimental results. In most cases, analytical results were higher than experimental results; this difference provided a level of conservatism for design. Midspan displacements were accurately predicted as the vehicle traversed the floating bridge. However, at heavier vehicle weights, the analytical model failed to predict midspan displacement accurately at axle locations beyond midspan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".