Advancing Performance Based Design through Full-Scale Simulation of Wind, Water, and Structural Interaction
Bibliographic record
Abstract
This paper provides an overview of the research programs within the Department of Civil and Coastal Engineering at the University of Florida (UF) that focus on characterizing and mitigating the effects of hurricanes on the built infrastructure. The UF wind hazard research group specializes in "full-scale" hurricane research, i.e conducting experiments at landfall to quantify loads and damage mechanisms as well as recreating the damaging nature of hurricane winds and wind-driven rain at sufficient scale to test actual building systems. These programs include the Florida Coastal Monitoring Program, which collects weather data during hurricane landfall to guide post-damage assessments. A critical component of this program includes the collection of wind pressures on residential housing. Researchers are also conducting destructive experiments on formerly occupied homes provided by the State of Florida to learn how actual buildings respond to hurricane wind loads. Computer-based numerical simulation complements this research. UF is developing numerical techniques to simulate fluctuating loads on low-rise structures. Finally, researchers have undertaken several projects to evaluate the structural and water penetration resistance of building components using full-scale testing apparatuses built at UF. The centerpieces of this research are a portable 2800 hp Hurricane Simulator, which can recreate Category 3 wind loads on residential construction, and several dynamic pressure loading actuation systems based on the Three Little Pigs project at the University of Western Ontario (UWO).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".