Design of Tuned Mass Damper Used to Enhance the Response of Structure under Seismic Action
Bibliographic record
Abstract
Today there is a real desire to build skyscrapers in economical cities. Where they are considered the weakest if they are exposed to an earthquake due to their height, lightweight, flexibility, and low damping resistance. Therefore, many studies and techniques presented to control vibration dangers. The newer technique to enhance vibration responses of structures is the Tuned Mass Damper (TMD). In this study, a two-story building was designed and analyzed, with Tuned Mass placed on the upper floor and friction damping between movable hose clamp ball bearings and a fixed shaft. The Friction Tuned Mass Damper is formed by combining this bearing and bearing shaft system with the Tuned Mass Damper (FTMD). The coefficient of friction and equivalent viscous damping ratio of the proposed FTMD coil were experimentally obtained based on different states of the tuned mass. To explore the potential of the proposed FTMD to suppress vibrations on a two-degree-of-freedom structure exposed to the step input, numerical modeling and simulation were done using matlbe. In addition, to verify the simulation results, a parallel experimental validation of FTMD was performed. The proposed FTMD device was able to significantly enhance the damping ratio of the core structure, according to the results of both experiments and simulations. The selected steel slide shaft had a near-perfect damping coefficient, the proposed FTMD may significantly reduce the amplitude of the structural resonant peak over the studied excitation frequency domain.
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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 | 0.001 | 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".