Estimating peak pressure coefficients for high-rise buildings: LES-based evaluation of Gumbel and XIMIS methods
Bibliographic record
Abstract
Accurately estimating peak wind pressures is essential for the safe and cost-effective design of high-rise buildings. This study evaluates LES-based peak pressure coefficient predictions for high-rise buildings, using 1-h equivalent full-scale wind tunnel data from Tokyo Polytechnic University as a reference. The research examines the effects of segment durations, number of segments, total EFS durations, and wall-specific error analysis and prediction uncertainties in LES. The Cook-Mayne conversion standardized shorter segments to a 60-min EFS duration but introduced prediction discrepancies, particularly for negative peak pressures. Findings indicate that longer total durations with moderate segment lengths yield reliable maximum pressure predictions, while shorter segment durations are more effective for minimum pressures. Wall-specific analysis reveals greater uncertainties near the ground on side and leeward walls due to recirculation and separation, and at higher elevations on the windward wall from stagnation effects. The XIMIS method yields peak estimates comparable to the Gumbel method, effectively handles limited data. While LES shows strong potential for capturing peak pressures, its accuracy in Gumbel based analysis is sensitive to segment and total simulation durations. In contrast, XIMIS offers consistent results without need for segmentation, making it particularly valuable when data availability is limited.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".