Impact of Damper Stiffness and Damper Support Stiffness on the Performance of a Negative Stiffness Damper in Mitigating Cable Vibrations
Bibliographic record
Abstract
Due to the superior performance of negative stiffness damper (NSD), its application to the vibration control of bridge stay cables attracts much research attention in recent years. In the current study, an experimental study on the dynamic response of a cable-NSD system is conducted to investigate the effect of negative damper stiffness and damper support stiffness on the efficiency of NSD. In particular, the impact of the latter, which was only reported in a recent analytical study, will be verified in the lab. A numerical simulation is performed to not only validate the experimental results, but also provide a comprehensive evaluation on the influence of various system parameters on NSD performance. An NSD design tool is developed to predict optimum damper size and the corresponding maximum achievable modal damping ratio of a cable-NSD system. Results show that when the stability criterion is satisfied, choosing stronger negative damper stiffness would enhance NSD efficiency. The impact of support stiffness on NSD performance depends on the magnitude of damper stiffness. Attach an NSD to a cable having larger sag and/or higher bending stiffness would yield a lower maximum achievable system modal damping ratio.
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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) | 0.000 | 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 | 0.000 |
| 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".