GUST RESPONSES OF BRIDGES TO SPATIALLY VARYING WIND EXCIATIONS AND CALIBRATION OF WIND LOAD FACTORS
Bibliographic record
Abstract
The study is focused on two aspects relating to the bridge design: the gust factor and wind load factors. The concept and the assessment of the gust factor developed by Alan G. Davenport are essential for bridge design under buffeting forces. However, systematic parametric investigation of the standard deviation of fluctuating wind-induced bridge responses and the gust factor, by considering the spatio-temporal varying along and cross winds and aerodynamic damping has not be given, although a simple to use approximate equation is available. Such an investigation is given in the present study by including/excluding the aeroelastic self-excited forces. The results obtained are used to assess the bias associated with this simple approximate equation, and can be adopted by bridge design codes for predicting the gust factor.\nSince the gust factor depends on the dynamic structural characteristics and the characteristics of wind speed, wind load factors are calibrated by incorporating this dependency for both the simple procedure and detailed procedure in the current Canadian Highway Bridge Design Code (CHBDC). Based on the calibration results by considering a target reliability index of 3.5 for a service period of 75 years, a wind load factor of 1.4 for simple procedure, and an equation to evaluate wind load factor for detailed procedure are recommended for the future edition of die CHBDC. Furthermore, the calibration results indicate that an increase of dead load from 1.20 to 1.25 for the ULS combination 4 given in the current CHBDC is desirable to achieve increased reliability consistency in the bridge design
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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 | 0.001 | 0.002 |
| 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".