径向基神经网络用于钢-混Π型梁原始断面涡振性能的预测
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U448.27;TU311.3

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国家自然科学基金资助项目(51978077);长安大学中央高校基本科研业务费专项资金资助项目(300102210212,300102210208)


Radial basis function networks used in prediction of vortex⁃induced vibration of Π⁃shape bridge⁃decks
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    摘要:

    钢?混Π型梁主梁断面在大跨斜拉桥的主梁设计中被广泛采用,此类断面易出现涡激振动现象,引起桥梁结构安全问题且降低行车舒适性。首先利用既有风洞试验结果校核涡激振动响应的CFD计算模型,并利用校核后的CFD方法得到学习样本数据库。利用学习样本对径向基(RBF)神经网络进行训练,并优化神经网络的设置参数,以此建立钢?混Π型裸梁的开口率和宽高比2个形状参数与涡激振动响应的关系,探索Π型桥梁断面的涡振规律。研究表明,Π型梁涡振响应与2个形状参数均呈非线性关系;2个形状参数对涡振响应的的影响可进一步指导气动措施的选择及优化。

    Abstract:

    Steel-concrete Π -shaped cross-section is prevalent in the design of cable-stayed bridges. Nevertheless,the vortex-induced vibration(VIV)is prone to occur for this section type,which can cause structural safety problems and reduce the driving comfort. In this paper,the results of the wind tunnel tests of Π-shape prototype deck are used to certify the script in numerical simulation of vertical VIV. Then,the data set could consist of the results from wind tunnel tests and computational fluid dynamics(CFD),which is used to describe the relationship between the two shape parameters―aspect ratio and aperture ratio,and responses of vibration. The radial basis function artificial neural network is trained by the learning sample,and the setting parameters of artificial neural network should be optimized to improve the precision to study the mechanism of the VIV in the Π -shape prototype deck. The results indicate that the responses of VIV have non-linear relationship with two shape parameters. And the relationship can be used to guide the selection and optimization of the aerodynamics control measures.

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李加武,党嘉敏,吴拓,高广中.径向基神经网络用于钢-混Π型梁原始断面涡振性能的预测[J].振动工程学报,2021,34(1):1~8.[LI Jia-wu, DANG Jia-min, WU Tuo, GAO Guang-zhong. Radial basis function networks used in prediction of vortex⁃induced vibration of Π⁃shape bridge⁃decks[J]. Journal of Vibration Engineering,2021,34(1):1~8.]

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  • 在线发布日期: 2022-05-12
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