Effectiveness Assessment of TMDs in Bridges under Strong Winds Incorporating Machine-Learning Techniques
Publication: Journal of Performance of Constructed Facilities
Volume 36, Issue 5
Abstract
Tuned mass dampers (TMDs) are widely used to control excessive wind-induced vibration in the box girders of long-span bridges. Although the optimal design of TMDs has been investigated abundantly in the last few years, the effectiveness of TMDs in use has not been thoroughly studied. In this paper, a method combining a machine learning (ML)–based approach is developed to evaluate the TMD effectiveness. The theoretical formulation and the flowchart of the method are firstly presented, which utilizes characteristics of TMD vibration amplitude, phase shift between TMDs and the bridge, and the mode resonant frequency component. Seven commonly used ML techniques, i.e., artificial neural network (ANN), decision tree (DT), -nearest neighbors (KNN), random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGB), were adopted to generate the predictive models, and the structural health monitoring (SHM) data of the bridge were used as the training data. The wind properties and temperature were set as the input, and the TMD accelerations are set as the output. Meanwhile, the Shapley Additive Explanations (SHAP) was adopted to identify the influences of the input variables on the TMD’s performance. The result indicated that the proposed method is reliable to evaluate the effectiveness of the TMDs, and it was shown that wind velocity is the most important parameter. BecauseTMDs are often widely used to control vibration in bridges, the proposed ML-based method can be used as an effective tool to assess and/or cross-check the effectiveness of TMDs.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors greatly appreciate the financial support from the Natural Science Foundation of Jiangsu Province (Grant No. BK20211564), the National Natural Science Foundation of China (Grant No. 52078119), and FCT Project SAFESUSPENSE (Reference POCI-01-0145-FEDER-031054). The operator of Chongqi Bridge is also acknowledged for providing the chance of investigation. The conclusions and opinions in this paper are those of the authors and do not necessarily reflect those of the bridge operator.
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Journal of Performance of Constructed Facilities
Volume 36 • Issue 5 • October 2022
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© 2022 American Society of Civil Engineers.
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Received: May 28, 2021
Accepted: Apr 4, 2022
Published online: Jun 25, 2022
Published in print: Oct 1, 2022
Discussion open until: Nov 25, 2022
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