Failure of double friction pendulum bearings under pulse‐type motions
Bibliographic record
Abstract
Summary Although the behavior of friction sliding bearings is well understood, the failure behavior has not been thoroughly investigated. However, predicting and understanding the failure of bearings is an important key in designing isolated structures to minimize their collapse in extreme events, and thus, this study is critical. Because of its relative simplicity and particular availability in certain markets, the failure of the double friction pendulum (DFP) bearing at its physical displacement limit is investigated. The bearing is modeled with a rigid body model including inertia for each of the bearing components. A nonlinear viscoelastic impact model is included to simulate the impact between bearing components. As isolation systems are particularly vulnerable to long‐period excitations, analytical pulses are used as input excitations to investigate the influences of pulse parameters on the failure of DFP. The influences of DFP design parameters are investigated as well. To confirm that the response to the analytical pulses correctly represents the behavior under long‐period ground motions, wavelet analysis to is performed on 14 pairs of pulse‐type ground motion records to extract their pulses, and the failure prediction made from the extracted analytical pulse is compared with the failure from the real ground motions. It is found that using the extracted pulses provides a good estimation for the failure prediction of the ground motions. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 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".