Bibliographic record
Abstract
This paper suggests that more efficient and high performance tall buildings can be designed if engineers consider the dynamic performance of a building as a separate and unrelated issue to the strength needs of a tower. When considering the dynamic performance of a tower, it is often more effective to add damping to a building to improve the vibration performance rather than to add stiffness, mass or strength. Although engineers have been adding Tuned Mass Dampers (TMDs) to tall buildings for years, the typical approach has been to add material and or damping to a building after the initial wind tunnel test rather than to optimize the structure to meet the strength requirements and then resolve the acceleration issues. The authors suggest that in the future, viscous dampers in tall buildings will be much more common as they allow additional damping to be provided without increasing the weight of a building, and allow the structure to be optimized for strength and for accelerations separately. An example of this approach is shown for a 40-story tower under construction in New York City. This all steel 860,000 sq. ft. tower has a steelwork weight of 22psf and incorporates seven viscous dampers to meet the acceleration requirements. A conventional solution would have involved another approximately 1,000 tons of steel, or required the addition of a damper plus additional steelwork.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".