The Effects of Incremental Corner Modifications on a 200m Tall Building
Bibliographic record
Abstract
The influence of corner modifications on the wind loading and responses of tall buildings has been investigated on many occasions. Different studies have observed how the overall building response varies with the inclusion of corner cuts, chamfers, venting, and fins. However, in general only two basic cases have been considered, namely with and without every corner modified. Many structures can benefit from certain modifications, but the relationship between the location of the modification and its resulting effect relative to the predominant wind direction has not been developed. The current work explores the influence of incremental corner chamfers to the mean and dynamic response of a nominally square building. Wind tunnel tests were carried out for suburban exposure and corner chamfers with dimensions of 1/10 and 1/6.7 of the building face. Decreases in mean loading were apparent through the comparison of base bending and torsion moment coefficients. Examination of the along-wind and across-wind spectra show that the impact of corner chamfers can shift or reduce the spectral peak for particular angles, which can result in reduced dynamic response depending on the structural properties of the building. Acceleration levels for an idealized building are compared for each corner configuration. The influence of wind directionality on the acceleration levels of the idealized building are also discussed using two different wind climates.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".