Comparison of international wind loading codes with a proposed Computational Fluid Dynamics framework considering the slenderness of buildings
Bibliographic record
Abstract
This study compares the latest editions of five international wind loading codes -namely, the American code (ASCE 7-22), the Japanese code (AIJ-2019), the Australian/New Zealand code (AS/NZS 1170.2:2021), the European code (EN 1991-1-4:2018), and the Canadian code (NBCC 2020)- against a proposed Computational Fluid Dynamics (CFD) framework. The comparison is based on 12 isolated square buildings situated in open terrain, with height-to-plan-dimension ratios (H/B) ranging from 1 to 12. The objective is to classify each code according to the H/B ratio, identify its strengths and limitations, and highlight the scenarios where wind tunnel testing becomes essential. The influence of building natural frequency is also examined. Numerical results reveal that, for along-wind loads, ASCE 7-22 aligns well with CFD predictions for H/B ≤ 6, when the directionality factor is not considered. AIJ 2019 and NBCC 2020 show good agreement for H/B ≤ 8, AS/NZS 1170.2:2021 for H/B ≤ 5, and EN 1991-1-4:2018 for H/B ≥ 6. For across-wind base moments, AS/NZS 1170.2:2021 matches the CFD results well at H/B ratios of 3 and 4. In terms of acceleration, EN 1991-1-4:2018 provides the best match for along-wind acceleration, while NBCC 2020 performs best for cross-wind acceleration. Furthermore, the findings confirm the necessity of employing wind tunnel testing or a CFD-based approach when the building exceeds an H/B ratio of 4, as across-wind effects become dominant beyond this threshold. • Using the Computational Fluid Dynamics (CFD) method, this study conducted 96 simulations. • CFD analysis results have been compared against 5 international wind loading standards and wind tunnel data. • This study concludes by classifying each wind code based on the building height-to-plan dimension ratio (H/B). • The influence of varying building frequencies was investigated for each wind standard. • This study identifies a threshold H/B ratio beyond which a wind tunnel test is necessary.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".