Seismic Performance Evaluation of Building-Damper System under Near-Fault Earthquake
Bibliographic record
Abstract
The building-damper system designed by a seismic code is usually considered to be able to withstand the attack of strong earthquakes. However, near-fault earthquakes, especially those with the forward-directivity effect, might cause early and unexpected failure of code-designed dampers and consequent severe structural damage. In this paper, by taking into account near-fault earthquakes, seismic performance of the building-damper system and damper failure’s influence are evaluated systematically. A 9-storey steel building is designed by the Chinese seismic code as the benchmark model, and five typical dampers, including buckling-restrained brace damper (BRB), friction damper (FD), self-centering damper (SCD), viscous damper (VD), and viscoelastic damper (VED), are adopted. It was found that the building-damper systems show a large response and possible damper failure under the near-fault earthquake excitations. Then, the influence of damper failure is investigated, which reveals that damper failure would significantly affect seismic performance of the building-damper system, especially for the building-SCD system. Subsequently, by introducing the artificial near-fault earthquake excitation, the influences of different pulse parameters, such as pulse velocity amplitude, pulse period, and the number of significant pulses, are studied. It shows that the pulse velocity amplitude and pulse period obviously affect the seismic performance, while the number of significant pulses presents little influence.
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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) | 0.000 | 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) | 0.001 | 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".