An Experimental Study of Semiactive Modal Neuro-control Scheme Using MR Damper for Building Structure
Bibliographic record
Abstract
In this study, a semiactive modal neuro-control scheme which combines the modal neuro-control algorithm with a semiactive MR damper is proposed, and its effectiveness is experimentally verified through a series of shaking table tests. A modal neuro-control scheme uses modal coordinates as inputs of neuro-controller. Hence, it is more convenient to design the controller compared with conventional neuro-control schemes. A Kalman filter is introduced to estimate modal states from measurements. Moreover, the clipped algorithm is adopted to provide an appropriate command voltage to an MR damper. For shaking table tests, a scaled three-story shear building model is considered. Two types of semiactive modal neuro-controllers are trained with a reproduced El Centro earthquake for their own purposes. The performance of the proposed semiactive modal neuro-control scheme is compared with that of the passive-optimal case. In the experiments, the proposed semiactive modal neuro-controllers show better performance than the passive-optimal case, especially in adaptability over various excitations and reducing inter-story drifts as well as accelerations. Moreover, the proposed scheme can be designed for specific purpose which fulfills the designer's requirement (e.g., focusing on reducing inter-story drifts). Therefore, the proposed semiactive modal neuro-controller can be effectively used in reducing seismic responses of large engineering structures.
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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 | 0.001 | 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".