Multimode vibration control of stay cables using optimized negative stiffness damper
Bibliographic record
Abstract
Due to their low inherent damping and high lateral flexibility, stay cables are prone to large amplitude vibrations governed by either a single or multiple cable modes. Among the practical measures, the installation of transverse passive dampers near the cable-deck anchorage is a popular choice. Compared to conventional positive stiffness and zero stiffness dampers, negative stiffness damper (NSD) manifests superior performance in mitigating cable vibrations, especially in the case of long cables. In this study, a novel design approach is proposed to optimize NSD for multimode cable vibration control. Two design scenarios are considered. In the former, the damper size is optimized for a predetermined negative damper stiffness; whereas in the latter, the size and negative stiffness of the NSD are both optimized to achieve a required damping ratio for the dominant modes. The applicability of the proposed NSD optimum design approach is validated using 15 sample real stay cables. A numerical example is presented, of which a NSD is designed based on the proposed approach to optimize wind-induced multimode vibration control of a 460-m stay cable, and the performance is compared with that of a linear-quadratic regulator (LQR) control. Results show that the selected NSD can effectively suppress the dominant modes and has a controlling effect comparable with an active control using LQR. In addition, it is found that when there exist more than two dominant modes in vibration, designing NSD for the lowest and the highest dominant modes would also adequately control the mid-range modes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".