Scaling wind loads for incremental dynamic analysis applications
Bibliographic record
Abstract
Incremental Dynamic Analysis is a powerful tool for the performance assessment of structures where a full range of responses can be mapped. Currently, an open discussion among researchers is the scaling of wind loads at increasing hazard intensities. In common practice, local aerodynamic pressure data from wind tunnel testing are normalized with respect to the mean wind velocity. Then, the value is linearly scaled up to provide wind loads at considered limit states. The main issue in the linear scaling of winds is the non-consideration of cross-correlation between different time histories and the mean wind velocity. To address this issue, the Wieringa gust model is applied to account for the dependency of gustiness on mean wind speed, thereby updating the scaling coefficients for both mean and turbulent wind components. This methodology is demonstrated through the application of wind IDA on a high-rise steel hospital in Montreal, Canada. The building is designed to meet the code requirements for wind and earthquake loads. Finite element models that incorporate geometrical and material nonlinearities of building's lateral force-resisting systems are developed in OpenSees. These nonlinear models are used to analyze the impact of linear gust scaling on the building's performance under varying wind intensities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".