基于混合模态分解的斜拉桥挠度影响线识别方法

IDENTIFICATION OF DEFLECTION INFLUENCE LINES OF CABLE-STAYED BRIDGES UPON MIXED MODAL DECOMPOSITION

  • 摘要: 为有效剔除车致桥梁响应中的动力成分,获得更加精确的准静态加载时程曲线,提出基于经验变分混合模态分解的影响线识别方法,对比经验模态分解,变分模态分解和排列熵优化变分模态分解三种方法应用效果并进行了斜拉桥挠度影响线识别试验验证。将车辆转化为单位集中荷载,构建车辆信息矩阵;结合吉洪诺夫正则化方法剥离车轴效应,识别出斜拉桥挠度影响线的稳定解;通过数据仿真及实际工程案例验证所提方法。研究发现,相较于其他三种单一处理方法,经验变分混合模态分解对于动力效应的剔除效果较好,与仿真模拟的相对误差仅为2.77%,在车速增加至120 km/h时,其识别误差不超过4%。

     

    Abstract: In order to effectively remove the dynamic components in the vehicle-induced bridge response and to obtain more accurate quasi-static loading time course curves, an influence line identification method based on empirical variational hybrid modal decomposition is proposed, and the three methods of Empirical Mode Decomposition,Variational Mode Decomposition and Permutation Entropy-Variational Mode Decomposition are compared and verified by the deflection influence line identification test of cable-stayed bridge. The vehicle is transformed into a unit concentrated load, the vehicle information matrix is constructed, The axle effect is peeled off by the Tikhonov regularization method to identify the stable solutions of the deflection influence lines of cable-stayed bridges, The method proposed is verified by the data simulation and by the actual engineering cases. It is found that: the empirical variational hybrid modal decomposition is the more effective in removing the dynamic effects compared with the other three single processing methods, with a relative error of only 2.77% with respect to the simulation, and its identification error is no more than 4% when the vehicle speed is increased to 120 km/h.

     

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