Real-world installations of tuned liquid column dampers for wind-induced vibration control of tall buildings
Bibliographic record
Abstract
The tuned liquid column damper (TLCD) is regarded as one of the most cost-effective, reliable, and low-maintenance passive structural vibration control devices, which offers a quantifiable amount of supplemental damping. Being an inherently long-period system, it is suitable for the wind-induced vibration mitigation of tall and slender structures. The limitation of unidirectional vibrational control has been overcome by several configurational variations that have also led to the performance enhancement of the original damper. The current work traces the development of the TLCD and presents case studies on its real-world installation in six landmark buildings worldwide, namely, the Hotel Cosima, Tokyo, Japan; the One Wall Center, Vancouver, Canada; the Random House Tower, New York City, USA; the Comcast Center, Philadelphia, USA; the First World Towers, Incheon, South Korea; and the 461 Dean Street, New York, USA. Each case study has been systematically categorized into description of the structure, design of the TLCD system, and the functioning of the installed damper system. The overall observations from the case studies indicate the successful performance of the TLCD in improving the serviceability of flexible buildings, while also identifying important damper design features as well as future directions of improvement and application.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".