Numerical Analysis Method for Evaluating the Response of Steel Structures Equipped with Different Friction Dampers Configuration: A Case Study
Bibliographic record
Abstract
Earthquakes can be catastrophic phenomena that cause casualties, injuries, and significant damage to buildings.The damage magnitude to individuals and assets caused by earthquakes is mostly determined by the performances of buildings to withstand seismic forces.The current study investigates the performance of friction damper under different earthquake loads considering different configuration using three-dimensional finite element software ETABS.The friction damper was represented by Bouc-Wen model and location optimization was studied.The investigation was carried out to explore the impact of damper optimum placement and configuration.The result showed that using the friction damper in the ten-story building reduced both maximum displacement and the maximum acceleration occurs during the earthquakes events.Also, the result showed the behavior of the diagonal, chevron and the upper toggle friction damper as the location of the damper change across the stories.Finally, the results demonstrate that upper toggle friction dampers significantly reduce seismicinduced displacements and accelerations include up to a 36% reduction in overall displacements and a 35% reduction in peak accelerations compared to undamped structures.Additionally, using a diagonal friction damper (DFD) resulted in a maximum displacement reduction (MTDR) of 30% and a maximum acceleration of 18%.Furthermore, the implementation of a chevron friction damper led to a 29% decrease in maximum displacement and an 23% increase in maximum acceleration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".