Improving the Dynamic Behavior of Adjacent Buildings with Fluid Viscous Dampers
Bibliographic record
Abstract
Explained ways to strengthen structures against lateral dynamic loads can be divided into two broad categories. The first part is the structural systems for controlling seismic displacement and second part is the use of applying systems of control forces. Response mechanism of structures using control systems are improved and greatly reduce the risks of damage caused by earthquakes.Today the use of these control systems in buildings have been increased and it’s important to reduce vibration of structures is felt more than ever. As well as to improve the dynamic behavior of nearby buildings, control systems can be installed between adjacent buildings as activated, semi-active and inactivated systems. The main purpose of this study is the use of control systems in two similar adjacent buildings to reduce the entire system response which will be the analytical study of the impact of viscous dampers to control system performance.In order to analysis of modeling to improve the dynamic behavior of different adjacent buildings connected with dampers, two models of the original sample will be examined in this article. All examples are different from each other and to elicit response analysis and time history software SAP 2000was used. According to the results the effect of fluid viscous dampers for tall buildings compared shorter building, is less. Also, these dampers for adjacent buildings with different heights than buildings with same height are more effective.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".