Seismic retrofit of MRF buildings using decentralized semi‐active control for multi‐target performances
Bibliographic record
Abstract
Summary Numerous structures have been designed and built without taking earthquake ground motions or outdated seismic design codes into account. In order to improve the seismic performance of existing structures, many retrofit approaches based on performance‐based design have been developed. However, some of these approaches are inapplicable due to structural limitations or because they were developed with the assumption of single‐degree‐of‐freedom, which does not take higher modes into account. To overcome the limitations of these traditional methods, a multi‐performance‐based control design (MPBCD) methodology has been proposed by integrating a decentralized semi‐active control algorithm, magnetorheological dampers, and an advanced multi‐objective optimization method to provide various sets of retrofit control designs to satisfy multiple target performances under multiple seismic intensities without changing structural cross‐section sizes or material properties. This MPBCD method provides engineers with numerous sets of control designs (i.e., control device layouts with control design parameters) to help them select proper control designs to retrofit existing building structures and improve seismic performance. 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 | 0.000 |
| 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) | 0.001 | 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".