Semi-active control of seismic response of a building using MR fluid-based tuned mass damper
Bibliographic record
Abstract
While tuned mass dampers are found to be effective in suppressing vibration in a tall building, integrating it with a semi-active control system enables it to perform more efficiently. In this paper a forty-story tall steel-frame building designed according to the Canadian standard, has been studied with and without semi-active and passive tuned mass dampers. The building is assumed to be located in the Vancouver, Canada. A magneto-rheological fluid based semi-active tuned mass damper has been optimally designed to suppress the vibration of the structure against seismic excitation, and an appropriate control procedure has been implemented to optimize the building's semi-active tuned mass system to reduce the seismic response. Furthermore, the control system parameters have been adjusted to yield the maximum reduction in the structural displacements at different floor levels. The response of the structure has been studied with a variety of ground motions with low, medium and high frequency contents to investigate the performance of the semi-active tuned mass damper in comparison to that of a passive tuned mass damper. It has been shown that the semi-active control system modifies structural response more effectively than the classic passive tuned mass damper in both mitigation of maximum displacement and reduction of the settling time of the building.
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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".