Effectiveness of using tuned-mass dampers in reducing seismic risk
Bibliographic record
Abstract
Strong earthquakes cause tangible and intangible seismic losses and disrupt service and function of buildings and infrastructure. The losses could be mitigated by increasing seismic design levels or by installing additional energy dissipation devices, such as tuned-mass dampers (TMDs). Although TMDs are useful for reducing structural responses, their effectiveness in terms of the expected lifecycle cost of buildings including the cost of TMDs, is rarely discussed. A parametric study on the expected lifecycle cost of a building with TMDs is carried out using a two-degree-of-freedom system whose non-linear hysteretic behaviour of the main structure is represented by the Bouc-Wen model. Probabilistic models of seismic demand to structures with TMDs are developed by using 381 ground motion records. The models are employed to assess the expected lifecycle cost of structures with TMDs and to investigate the effectiveness of TMDs in reducing seismic risk. The results indicate that the effectiveness of TMDs decreases as the seismic excitation level increases and its use on occasion could worsen structural performance against earthquake loading. The reduction in the expected damage cost in terms of the initial construction cost by installing TMDs is limited.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".