Seismic control of tall buildings using vertically distributed multiple tuned mass dampers
Bibliographic record
Abstract
Summary Tuned mass damper (TMD) is a seismic vibration control device used to reduce wind and seismic vibrations of structures. Although TMD is attractive to many researchers due to its simplicity, optimizing its parameters and positions is very challenging. The sensitivity of TMD to structure's frequency changes is among its weaknesses and if parameters of this system are not optimally tuned, the efficiency of this system decreases. To solve this problem, multiple tuned mass dampers (MTMDs) have been proposed. In this research, in order to study and compare single tuned mass damper (STMD) with MTMDs vertically distributed according to modal analysis, a 20‐story building is used. The structure is analyzed in OpenSees under seven ground motions with a peak ground acceleration (PGA) of 1.0 g. To optimize TMD parameters, particle swarm optimization (PSO) algorithm is used and the results are compared to those obtained from Den Hartog's approach. To be able to use PSO algorithm and optimize TMD design parameters, Matlab and OpenSees are linked together. In this paper, more than one vibration mode is used to tune and distribute dampers to overcome higher mode effects in high‐rise buildings. The results showed that depending on their different layouts and different optimization methods used, MTMDs reduce the average maximum responses of the structure by up to 12.1%. This is while STMD is able to reduce maximum responses of the structure by 4.3%.
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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".