Impact of support stiffness on the performance of negative stiffness dampers for vibration control of stay cables
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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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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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