采用机器学习方法评估流线型箱梁颤振临界风速

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Abstract To quickly evaluate the flutter performance of a streamlined box girder in the preliminary stage of bridge design, critical flutter wind speeds of 15 different sectional models at 5 different angles of attack were tested by free vibration tests in wind tunnel and utilized to build flutter prediction models by inputting dimensional information and dynamic parameters based on four different machine learning algorithms, including support vector regression, neural network, random forest regression and Gaussian process regression.The results show that the support vector regression model has the highest prediction accuracy and get the best effect performance in the prediction of some practical bridges while the neural network model is poor, but its relative error is still far lower than that of the calculation results of three different simplified critical flutter wind speed formulas provided in the present JTG/T 3360-01—2018 Wind-resistant Design Specification for Highway Bridges.The results of the paper meet the expected requirements and the dataset can be further expanded in the future so as to provide a powerful tool for wind designers to evaluate the flutter performance of streamlined box girders with high-precision.

Key words： streamlined box girder machine learning flutter

Received: 20 November 2019 Published: 28 July 2021

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	Articles by authors
	MEI Hanyu
	WANG Qi
	LIAO Haili
	LIU Minwei

Cite this article:

MEI Hanyu,WANG Qi,LIAO Haili, et al. Predicting critical flutter wind speed of a streamlined box girder by using machine learning[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(14): 195-202.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I14/195

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