Impact of Damper Stiffness and Damper Support Stiffness on the Efficiency of a Linear Viscous Damper in Controlling Stay Cable Vibrations
Bibliographic record
Abstract
Accurate prediction of optimum damper size and its corresponding maximum attainable modal damping ratio is essential for the design of a linear viscous damper to control cable vibrations on cable-stayed bridges. The stiffness within the damper and the damper support would affect both the required damper size and the resulting equivalent modal damping ratio of the damped cable and thus influence the damper efficiency. An experimental study on a cable-damper system is conducted to investigate the individual and the combined effects of damper stiffness and damper support stiffness on the performance of a linear viscous damper. A finite-element model of the corresponding cable-damper system is developed to verify the experimental results and further study these two parameters within the typical ranges of cable and damper properties used on real bridges. Results show that higher damper stiffness and/or lower damper support stiffness would have an adverse impact on damper performance. Increasing the stiffness of a damper and/or its support would result in a larger optimum damper size. However, the maximum attainable damping ratio would decrease with larger damper stiffness but increase if the support is more rigid. To facilitate practical design, a set of asymptotic relationships has been proposed, of which the optimum damper size and the maximum achievable damping ratio are expressed concisely as functions of nondimensional damper properties in terms of its location, stiffness, and support stiffness. Design examples are given to illustrate the various applications of the proposed refined damper design tool.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".