Tuned Sloshing Dampers in Tall Buildings: A Practical Performance-Based Design Approach
Bibliographic record
Abstract
It is becoming increasingly common to employ tuned sloshing damper (TSD) systems to reduce the wind-induced motion of tall buildings due to their affordability and apparent simplicity. However, TSD systems are relatively new to the high-rise construction industry, and, due to their unfamiliarity, design and construction teams may perceive these devices as having considerable risk. Since the implementation of these systems requires a performance-based design approach (rather than a prescriptive approach) to ensure serviceability performance objectives are achieved, knowledge of their function and operation is paramount to their efficient design and installation. The goal of this study is to reduce the perceived risks by describing the function of TSDs, as well as the practical aspects of the design and installation process. The process is described in four phases: concept design, detailed design, construction, and tuning and commissioning. The key tasks associated with each phase are defined and common challenges identified. This paper does not present new theory to further advance the TSD research; instead, it summarizes current theory and presents practical guidance accumulated from years of experience with the design, installation, and as-built performance verification of many TSD systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".