Tuned Sloshing Dampers in Tall Buildings: A Practical Performance-Based Design Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it