Optimizing parameters of tuned mass damper subjected to critical earthquake
Bibliographic record
Abstract
Summary Tuned mass damper (TMD) has been proposed as one of the vibration control methods for rehabilitation of buildings. Because the parameters of TMD can significantly affect the seismic performance of structures, many researches focused on finding the optimum parameters. Because earthquakes are random phenomena and future earthquakes in comparison with past earthquakes may be more destructive, the optimum design of TMD subjected to selected earthquakes can be nonconservative. Hence, the main contribution of this paper is to present the optimal design of TMD for the seismic vibration control of a structure subjected to a critical earthquake that produces the most severe response of a structure. In order to achieve this purpose, the parameters of TMD are optimized through minimizing the maximum displacement of the roof. First, three optimization methods are used to obtain the optimal parameters of TMD for a 10‐story shear building subjected to the critical earthquakes. Finally, the responses of the controlled and uncontrolled buildings such as the roof displacement, strokes, transfer function, and different forms of energy are compared. Results show that the optimum designs of TMD not only effectively reduce the roof displacement but also improve the seismic performance 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".