Direct adaptive neural controller for the active control of earthquake-excited nonlinear base-isolated buildings
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Bibliographic record
Abstract
This paper presents a nonlinearly parameterized controller for the adaptive control of base-isolated buildings subjected to a set of near-fault earthquakes. The control scheme is based on discrete direct adaptive control, wherein the system response is minimized under parameter uncertainties. Stable tuning laws for the controller parameters are derived using the Lyapunov approach. The controller utilizes a linear combination of nonlinear basis functions, and estimates the desired control force online. The measurements that are necessary to generate the control force to reduce the system responses under earthquake excitations are developed based on the adaptive systems theory. The main novelty in this paper is to approximate the nonlinear control law using a nonlinearly parameterized neural network, without an explicit training phase. A perturbed model is used to initialize the controller parameters in order to simulate the uncertainty in the mathematical modeling that typically exists in representing civil structures. Performance of the proposed control scheme is evaluated on a full-scale nonlinear three-dimensional (3-D) base-isolated benchmark structure. The lateral-torsion superstructure behavior and the bi-axial interaction of the nonlinear bearings are incorporated. The results show that the proposed controller scheme can achieve good response reductions for a wide range of near-fault earthquakes, without a corresponding increase in the superstructure response. Copyright © 2011 John Wiley & Sons, Ltd.
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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) | 0.001 | 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)
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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