Improving total-building seismic performance using linear fluid viscous dampers
Bibliographic record
Abstract
Previous research has revealed that Eurocode-compliant structures can experience structural and nonstructural damage during earthquakes. Retrofitting buildings with fluid viscous dampers (FVDs) can improve interstorey drifts and floor accelerations, two structural parameters that characterize seismic demand. Previous research focusing on FVD applications for improving seismic performance has focused on structural performance. Structural parameters such as interstorey drifts and floor accelerations are often evaluated. Complexities arise as these parameters are often competing objectives. Other studies use damage indices that are influenced by several assumptions to represent performance. The use of repair costs is a more appropriate measure of total-building seismic performance, and avoids these limitations. This study investigates the application of linear FVDs to improve total-building seismic performance considering repair costs. The energy-based method commonly used to calculate damper coefficients is modified to improve its accuracy. The optimal amount of damping with respect to repair costs (estimated using the FEMA P-58 procedure) is identified as 25–45%. This contrasts with a previously suggested optimal damping of 20–25%, based on structural parameters, that is frequently targeted. This study on the damping-repair cost relationship provides insight when selecting levels of damping for structural designs and retrofits. It also highlights that retrofit methods may be enhanced by using repair costs, rather than structural parameters. The FVD buildings significantly reduce both drift-sensitive and acceleration-sensitive damage. Structural damage is also negligible in the FVD buildings: a major step towards achieving building serviceability following an ultimate limit state level earthquake.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".