Wind Load Prediction on Tall Buildings in a Stochastic Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is common practice for wind engineers to develop design load predictions based on wind loads that have been determined from a deterministic set of building properties, wind tunnel results and description of wind climate. This many-to-one relationship (many inputs, one load) has thus far served the wind engineering community well as it reduces a complicated problem into a simple account of loads and effects. A stochastic approach considers uncertainty in the design inputs, such as natural frequencies and damping ratio. While this type of approach has been described previously in the literature, the current approach provides a framework in which wind loads can be predicted in practical design scenarios. The current study focuses on tall, slender buildings in an effort to better understand the impact that uncertainties in extreme wind climate, damping ratio and natural frequency have on predicted wind loads, and how these uncertainties contribute to the overall reliability of the structure. A method is described that allows for the direct calculation of probability of failure based on a stochastic relationship between load and resistance.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it