Tuned Mass Dampers in Tall Buildings: A Practical Performance-Based Design Approach
Bibliographic record
Abstract
Tuned mass dampers (TMDs) are increasingly being used to reduce the motion of tall buildings during common wind events. Despite TMDs receiving extensive theoretical research for many decades, dissemination of the practical aspects of designing and installing these devices is severely lacking. Since they are relatively new to the high-rise construction industry, TMD installations may be viewed by design and construction teams as having considerable risk. This paper describes the process of implementing a TMD in a tall building to demystify the devices for practicing structural engineers, architects, general contractors, and owners. It is hoped that this demystification will help these parties understand and control the real and perceived risks associated with TMD implementations. Since prescriptive, code-based procedures are unsuitable for TMD design, a performance-based design approach must be used to ensure the TMD attains specified performance objectives. The TMD implementation process is described in four phases: concept design, detailed design, fabrication and installation, and tuning and commissioning. This paper does not present new theoretical or experimental research but instead provides a broad, practical overview of real-world TMD installations for practitioners. The content of this paper has been obtained from the design and installation of dozens of TMDs in tall buildings around the world.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".