Vehicle-wind-long span bridges interaction and its effect on speed limit and vehicle stability
Bibliographic record
Abstract
In the absent of intensive tests in the wind tunnel, this study provided an effective and accurate approach to estimate the operational driving speed limit on bridges subjected to different road conditions and wind intensities, through a convenient continuous simulation technique (CSP). A fast and rigorous simulation tool, VPSIM is developed to effectively model the vibration of vehicles travelling on bridges by considering the interactions between wind, vehicles and the bridge. The CSP, on the other hand, dramatically reduces the data generation time and makes the stability analysis of vehicles possible. The application of the proposed method on the Confederation Bridge in Canada is presented as a numerical example. Furthermore, a parallel iteration technique (PIT) is proposed to address the non-linear effects on long-span bridges, such as the cable sags and the nonlinearity due to large displacements. The efficient PIT extended the application of the proposed simulation technique to the cable-stayed bridges and the suspension bridges. To more realistically simulate the wind induced effect on these very flexible bridges, the fluttering effect is incorporated into the proposed model. A numerical example is presented in this thesis to roundup the all the techniques proposed. The simulation result override the public impression that only high-sided vehicle is sensitive to wind attacks, this research demonstrated that a light-weight vehicles are likely to suffer from instability problems on bridges under relatively low wind velocity. Besides, different types of vehicle can undergo different instability mechanism under the same wind condition and the instability mechanism of a vehicle varies with wind speed. The numerical examples also demonstrated that the allowable vehicle speed limit on cable-stayed bridges is significantly lower than that on roads.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".