Determining the Best Insertion Site of Fluid Viscous Dampers to Optimize and Reduce Incurredcosts in Adjacent Buildings
Bibliographic record
Abstract
In the past decade, researchers developed the idea of connecting buildings with intelligent activated, semi-active and inactivated damper systemsreduce adjacent buildings response to wind and earthquake. One of the most important damper devices in non-active control is fluid viscous damper.Fluid dampers due to viscous fluidsshow high resistance. High resistance of viscous fluidsagainst the flow is the basicfunctionof fluid viscous dampers. Deformation speed a fluid viscous damper is proportional to the acted forces. Therefore the aim of this paper is to determine the insertion site of fluid viscous dampersto optimize and reduce the consuming costs in adjacent buildings. For this purpose, four different models of connected adjacent buildings with common and different shear stiffness in the software SAP 2000 has been modeled. This study shows that it is not necessarytwo adjacent buildings connected by a damper on all floors, but the less damper in appropriate selected locations can help reduce the earthquake response. And by placing the fluid viscous dampers in selected certainfloors provides more useful structural system for reducing the effects of earthquakes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".