Impact of support stiffness on the performance of negative stiffness dampers for vibration control of stay cables
Bibliographic record
Abstract
Bridge stay cables are susceptible to dynamic excitations due to their low intrinsic damping and lateral stiffness. Installation of transverse passive dampers near the cable-deck anchorage on a rigid/flexible support is one of the practical measures to mitigate cable vibrations. The limited performance of conventional positive stiffness dampers (PSDs) has led to the emergence of negative stiffness dampers (NSDs). Recent research has found that unlike PSD, NSD would perform more effectively in the presence of a flexible support. In this study, the impact of damper support stiffness on the NSD control performance is investigated. Based on an existing analytical design formula for achieving a target damping ratio, the design of NSD for a given support condition, the design of damper support for a given NSD, and the design of the entire NSD-support system are addressed. An optimization algorithm is proposed to identify the optimum combination of NSD parameters and damper support stiffness. The NSD design is refined through numerical iterations to minimize the impact of assumptions made in developing the analytical formulation. A numerical example is presented for a 325 m long stay cable equipped with an optimized NSD and subjected to harmonic excitation. The optimized NSD performance is compared with an optimal active linear-quadratic regulator (LQR) controller. Results show that the presence of flexible support leads to a cost-efficient NSD with smaller size and lower level of negative stiffness. Moreover, the optimized NSD is shown to be as effective as LQR to suppress cable vibrations.
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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 | 0.000 |
| 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".