Impact of Damper Stiffness and Damper Support Stiffness on the Performance of a Negative Stiffness Damper in Mitigating Cable Vibrations
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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