A probabilistic framework for estimating roof panel failure in low-rise buildings subjected to tornado loads
Bibliographic record
Abstract
This study develops a probabilistic risk assessment framework for evaluating the vulnerability of low-rise buildings subjected to tornado-induced wind loads. The framework integrates aerodynamic loads and a parametric tornado wind field model, and employs Monte Carlo simulations to estimate the probabilities of roof panel failures during a tornado event. Aerodynamic loads obtained from boundary layer wind tunnel testing, adjusted using the modification factors in ASCE 7–22, are incorporated into this framework. The framework captures the progressive roof panel failures by accounting for changes in internal pressures as a tornado passes over the building. The framework allows consideration of the tornado translation path (characterized by heading direction), the building’s location relative to the tornado center, and the position of openings on the walls. Through this framework, the likely location of the first roof panel failure can be identified probabilistically, and the occurrence probability of various damage states at different tornado intensities can be determined. These outcomes further enable estimation of tornado intensity based on observed damage levels. An illustrative example is included in the paper to demonstrate the application of the framework. This paper also discusses limitations in current aerodynamic and wind field modeling approaches, highlighting the need for improved understanding of vertical wind effects, turbulence characteristics, spatial correlation of wind pressures across building surfaces, and internal pressures modeling.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".