An Experimental Study of Semiactive Modal Neuro-control Scheme Using MR Damper for Building Structure
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it