Intelligent Control Methodology for Smart Highway Bridge Structures Using Optimal Replicator Dynamic Controller
Why this work is in the frame
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Bibliographic record
Abstract
Control algorithms are an essential part of effective semi-active vibration control systems used for the protection of large structures under dynamic loading. Adaptive control algorithms, which are data-driven methods, have recently been developed to replace model-based control algorithms, thus improving efficiency. The dynamic parameters of semi-actively controlled infrastructures will change after significant vibration loading. As a result, these structures require real-time, effective control actions in response to changing conditions, which classical controllers are unable to provide. To improve the efficiency of the semi-active controller, the optimal control algorithm was developed in this study. The algorithm is the integration of the replicator dynamics with an improved non-dominated sorting genetic algorithm (NSGA), which is NSGA-II. The optimal parameters of replicator dynamics (total resources, growth rate, and fitness function), which represent the behavior of the actuators, were obtained through a multi-objective optimization process. The new control system was then used to reduce the vibrations of the isolated highway bridge, which is equipped with semi-active control devices known as MR dampers. Moreover, the current study improved the performance of the structural control system with minimum energy consumption by assigning a specific growth rate to each control device. In order to reduce the vibrations of the highway bridge, the results show that the performance of the optimal replicator controller is better than the performance of the classical control algorithms. Doi: 10.28991/CEJ-2023-09-01-01 Full Text: PDF
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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