Investigating Effect of Friction-yielding Top Plate of Rigid Connections on Seismic Response of Special Moment Frames
Bibliographic record
Abstract
Acceptable seismic performance, ease and low cost in design and implementation are advantages of passive dampers, but fixed performance parameters corresponding to the type and amount of input energy reduce their efficiency. In this research, a new two-level passive damper in rigid connections with variable stiffness, strength, and energy absorption capacity is introduced and its seismic performance in 5, 10, and 15-story steel frames is evaluated with nonlinear dynamic analysis using SAP2000 software. The results show that, despite the different dynamic parameters in the selected seismic records, such as the frequency content and duration of ground motions, the performance of the structures under all earthquakes has improved significantly, which confirms the effectiveness of the proposed damper in rigid connections on improving the seismic performance structures. Besides, results prove the proposed damper effectiveness on decreasing the structural response such as maximum displacement and base shear. The average displacements reduced by 61%, 51% and 16% compared to those of BSEEP-4ES connections for the 5, 10 and 15-story frames, respectively. Besides, maximum base shear forces reduced by average of 29% and 15% compared to those of BSEEP-4ES connections for the 5 and 10-story frames, respectively.
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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 | 0.000 | 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".