Viscoelastic Dampers for Vibration Control of Building Structures: A State-of-Art Review
Bibliographic record
Abstract
Due to its high effectiveness and low cost, viscoelastic damper (VED) is a commonly used type of passive energy dissipation device to reduce structural vibrations and responses against earthquakes and strong winds. Over the past decades, scholars have developed new types of VEDs to be installed at different structural locations. These VEDs offer better post-disaster recoverability and smarter behaviors for structures. Nonetheless, existing efforts of various VEDs and the technologies supporting VEDs were seldomly summarized. This article presents a critical state-of-art review of the existing research on VEDs, hybrid VED devices, and the design methods for structures installed with VEDs. First, the VEDs are classified based on the design locations in building structures, including VEDs used as coupling beams and damping walls, installed in braces and beam-column joints, and used to connect parallel structures. In addition to these classic VEDs, the study presents the high-performance VEDs and the corresponding techniques, such as the combined usage with other materials and/or devices. Furthermore, as an important contribution to the presented work, various design methods for structures enhanced by VEDs were systematically summarized. These methods considered different evaluation parameters aiming at different design targets. Finally, this article identifies and highlights research challenges in the existing studies. Possible improvements that could be made in the future were also provided.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".