Comparing fluid viscous damper placement methods considering total‐building seismic performance
Bibliographic record
Abstract
Summary Nonstructural damage has been found to critically influence economic losses and building downtime following earthquakes. Attaining a target level of seismic performance mandates the harmonization of structural and nonstructural performance. Retrofitting buildings with fluid viscous dampers (FVDs) can improve interstorey drifts and floor accelerations, 2 structural parameters that characterize seismic demand on structural and nonstructural systems. The distribution of dampers within a building is a critical decision; however, no conclusive optimal placement method has been identified due to performance variations between storeys and between structural parameters. This paper compares 6 frequently used damper placement methods considering structural and nonstructural repair costs calculated using FEMA P‐58. The scope is limited to linear FVDs, concentric braced frames, and regular structures. Comparisons of the placement methods are based on constraining the added viscous damping coefficient to be the same in each case; this may give different results to methods based on damper cost. No placement method produced optimal results for both interstorey drifts and accelerations. Iterative methods that seek to optimize seismic performance did not achieve that objective. The iterative methods optimize local parameters assuming the performance of each is independent, and optimizing for a single parameter may worsen another parameter that impacts earthquake damage. The storey shear strain energy method and uniform damping generally produced repair costs that are more favorable than, or equal to, the other placement methods. It appears unlikely that large repair cost improvements can be achieved using an “optimal” damper placement method for low‐rise and mid‐rise structures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".