Optimal and Suboptimal Vibration Control of Structures
Bibliographic record
Abstract
Structures subjected to extreme dynamic loads such as earthquake, impulse load always creates undesirable vibration. And the unexpected extreme deformation may lead to structural partial damage or fully collapse situation. Hence it is essential to understand the dynamical behaviour of structures subjected to those type of extreme loads. Typically, structures are combined with optimal control algorithms for vibration mitigation. And the desired control force is estimated based on the measured information e.g. displacement, velocity. To do this end, a 6-storied frame is coupled with an optimal control law known as the viscous damping with negative stiffness (VDNS). Additionally, a modified passive control technique is adopted namely the equivalent viscous damping (EVD) and the results are compared with the VDNS control scheme. The performance of the early mentioned strategies are evaluated numerically by employing different type of dynamic loads such as a real earthquake data (e.g. Loma Prieta 1989), a sinusoidal load and an irregular impulse load. A significant reduction of vibration is observed by optimally tuned control law as well as the sub-optimal performance of equivalent passive systems is presented.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".