Optimizing Seismic Performance of Tuned Mass Dampers at Various Levels in Reinforced Concrete Buildings
Bibliographic record
Abstract
This study aimed to rigorously evaluate the impact of tuned mass dampers (TMDs) on structural response under seismic excitation. By strategically placing TMDs at various levels within the structures, the research sought to determine their effectiveness in mitigating structural movement. A single-degree-of-freedom (SDOF) system incorporating TMDs was utilized to model structures of 10, 13, and 16 stories, each configured with TMDs at different heights. The structures were subjected to near-fault earthquakes to assess the efficacy of TMDs in reducing structural response. The findings revealed significant enhancements in structural performance when TMDs were optimally positioned. Specifically, a 50% reduction in both acceleration and displacement, alongside a 65% decrease in maximum drift, underscored the effectiveness of TMD deployment. Furthermore, the study demonstrated that distributing multiple TMDs along the height of the structure provided superior drift control. Notably, positioning TMDs within the upper one-third of the structure yielded the most pronounced improvements in acceleration, displacement, and maximum drift. Finally, the research indicates that the strategic incorporation of TMDs can significantly enhance the seismic resilience of structures. The results highlight the substantial benefits of TMDs in optimizing acceleration, displacement, and drift, thereby affirming their critical role in seismic design and retrofitting strategies.
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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) | 0.000 | 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 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".