An assessment of damper placement methods considering upfront damper cost
Bibliographic record
Abstract
Previous studies have shown that earthquake repair costs can be minimised by using large levels of supplemental damping and uniform damper placement. However, it is not always feasible to achieve this damping due to structural or financial restraints. Iterative damper placement methods may be able to achieve higher levels of damping than simple methods such as uniform damping for the same total damper cost. In this work, six damper placement methods were assessed based on structural and non-structural repair costs. The total damper cost was constrained to be the same in each case. The scope of work was limited to linear fluid viscous dampers, concentric braced frames and regular structures. The iterative methods were found to provide a greater total damping coefficient to the structures than the simple methods. This resulted in a higher supplemental damping ratio and lower repair costs. If upfront funds are limited, or if architectural constraints prevent the placement of dampers in lower storeys, then iterative methods provide the most favourable total-building seismic performance. However, the conclusions should be extended cautiously. Although iterative methods are favourable when the upfront damper investment is strictly limited, in terms of total-building seismic performance, it is advantageous to provide a large damping ratio using uniform damping.
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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 | 0.003 | 0.012 |
| 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".