桥(图论)
调谐质量阻尼器
阻尼器
计算机科学
振动
控制理论(社会学)
振动控制
PID控制器
结构工程
工程类
算法
控制(管理)
控制工程
人工智能
物理
声学
医学
内科学
温度控制
作者
Kumar Rajnish,Anoop Kodakkal,Daniel H. Zelleke,Rishith E. Meethal,Vasant A. Matsagar,Kai-Uwe Bletzinger,Roland Wüchner
出处
期刊:Proceedings of the Institution of Civil Engineers
[Thomas Telford Ltd.]
日期:2023-01-17
卷期号:: 1-23
标识
DOI:10.1680/jbren.21.00090
摘要
The implementation of machine learning for the real-time prediction of the suitable value of the damping ratio of a semi-active tuned mass damper (SA-TMD) is investigated to ensure enhanced vibration control in vehicle–bridge interaction (VBI) problems. The response assessment of the uncontrolled, tuned mass damper (TMD)-controlled, and SA-TMD-controlled bridge models is performed under the Japanese Shinkansen (SKS) train model. The energy-based predictive (EBP) control algorithm is implemented for the bridge fitted with the SA-TMD. The EBP algorithm-controlled SA-TMD results in more effective suppression of the bridge vibration as compared to the passive TMD. However, the effectiveness of the EBP algorithm reduces for more complex VBI systems because of the increased computational time delay. To circumvent the effect of the delay, a control strategy is proposed based on the weighted random forest (WRF) algorithm. The WRF algorithm is trained based on the data obtained from the EBP algorithm-controlled bridge and implemented to suppress the vehicle-induced vibration of the bridge using SA-TMD. The results demonstrate that the implementation of the newly proposed WRF algorithm-based control strategy nullifies the effects of the computational time delay. Furthermore, it is established that the WRF algorithm suppresses the bridge vibration more effectively than the EBP algorithm.
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