Vehicle roll-over stability in strong winds on long-span bridges
Bibliographic record
Abstract
High-sided, lightly loaded vehicles are known to be prone to accidents in strong, gusty winds. While wind related accidents may occur anywhere on roads, vehicles are more sensitive to wind while passing over bridges given the higher road elevations, exposure and possible speed-up effects at various bridge locations compared to ground level roads. The paper presents an extended roll-over model for estimation of vehicle stability in strong, gusty winds. The study example is of a Double-Deck Suspension Bridge where four typical vehicles were investigated, including: a tractor-trailer truck; an intercity bus; a courier van; and a full-sized SUV. Using a sectional model of the bridge deck and vehicle models built in scale, six component force and moment coefficients were measured at various lanes for the full azimuth of wind directions. The effects of the road level were also investigated. Based on expected wind turbulence properties at the bridge site and measurements of wind flow modifications at various deck locations, vehicle stability against roll-over has been predicted for various wind and vehicle speeds. For calibration purposes comparative tests and analyses on the same vehicles were carried out for the Confederation Bridge, PEI, Canada, and the results compared with the adopted policy for traffic control in strong winds on that bridge. Recommendations for traffic management in strong winds on tested bridges were drawn.
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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 | 0.000 | 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 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".