Multi-objective control optimization of isolated bridge using replicator controller and NSGA-II
Bibliographic record
Abstract
Earthquakes can cause significant damage to constructed structures, leading engineers to design systems that effectively reduce damage and improve real-time vibration control. While base isolation is a commonly used passive method for seismic protection in highway structures, it has limitations such as a lack of immediate adaptation, constrained power dissipation capacity, and poor performance during earthquakes. To address the limitations of passive base isolation bearings, a hybrid control system that includes semi-active MR dampers is being introduced into isolated highway bridge structures. The aim is to enhance vibration reduction and improve overall performance. One of the major challenges in implementing this technology is developing appropriate control algorithms to handle the nonlinear behavior of semi-active devices. This paper proposes an adaptive data-driven control algorithm, informed by evolutionary game theory and a multi-objective optimization process, to optimize the distribution of voltage to semi-active MR dampers based on measurements of the damper's response to input signals. The algorithm is designed to provide optimal seismic protection. The performance of the replicator dynamics in the control system depends on three critical parameters: total population, which represents the total available resources or the sum of actuator forces; growth rate, which is the rate at which resources are distributed among control devices; and the fictitious fitness function, which regulates power consumption. Previous studies used sensitivity analysis to ascertain the best values for population size and growth rate, a time-consuming and unreliable process. This study aims to improve the performance of the system by solving a multi-objective problem. The proposed approach integrates a control algorithm with a multi-objective optimization algorithm, namely NSGA-II, to find Pareto optimal values for all parameters of the replicator dynamics. These parameters include total population, growth rate, and the fictitious function, with the aim of ensuring sustainability. By considering multiple objectives simultaneously, the proposed approach can provide a more comprehensive and effective solution for the bridge control problem. The effectiveness of this proposed approach is demonstrated through sample results Utilizing a case study centered around the Southern California Interstate 91/5 Overcrossing Highway Bridge, which is exposed to seismic activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".