Buffeting response analysis – the stack state-space approach
Bibliographic record
Abstract
Wind stability and design loads of long-span bridges are assessed applying experimental and theoretical methods. The commonly used approach entails the extraction of fundamental aerodynamic data of key structural elements such as the deck, towers, and cables, either experimentally or numerically, and the application of theoretical models for evaluation of structural responses to turbulent winds. This phenomenon called buffeting is extremely complex and, to date, there is no closed-form theoretical model to reproduce how the wind converts to structural responses and loads which the bridge must resist. The objective of this paper is to explore the base of the problem, namely the transformation of wind gusts to actual loads, and the response estimations. The time domain response approach has been adopted for solution of the generalized equations of motion allowing the exploration of details in the performance of various theoretical interpretations. Starting from the classic quasi-static linear model, theoretical simplifications are removed toward a more complete model of buffeting loads. Non-linear and aerodynamic coupling effects on response predictions are examined specifically aiming at improved buffeting load representations within the framework of the currently available experimental data. A new concept called stack state-space analysis has been introduced for the response solution to wind buffeting. Aerodynamic and structural data of Pierre-Laporte Bridge in Québec City, and the IABSE Working Group 10, long-span bridge validation example, are utilized as representative cases in this study. Avenues for further experimental and numerical validations of the presented new solution approach are suggested toward more accurate predictions of wind response and design loads of long-span bridges.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".