Dynamic Behavior of Ribbon Floating Bridges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it