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Nonlinear structural model updating based on instantaneous frequencies and amplitudes of the decomposed dynamic responses

机译:基于分解后的动态响应的瞬时频率和振幅的非线性结构模型更新

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This paper proposes a new nonlinear structural model updating method based on the instantaneous frequencies and amplitudes of the decomposed dynamic responses under forced vibration. The instantaneous frequencies and amplitudes of the decomposed mono-component are first extracted by analytical mode decomposition (AMD) and Hilbert transform. Then, an objective function based on the residuals of instantaneous frequencies and amplitudes between experimental structure and nonlinear model is created for calibration of the nonlinear model. In this paper, the structural nonlinear properties are simulated by using hysteresis material parameters of Bouc-Wen model, and the optimal values of the hysteresis parameters are obtained by minimizing the objective function using the simulated annealing global optimization method. To validate the effectiveness of the proposed method, a three-story nonlinear shear type structure under earthquake and harmonic excitations is simulated as a numerical example. Then, the proposed method is verified by the shake table test of a real high voltage switch structure under forced vibration. The updated nonlinear structural model is further evaluated by the shake table test of the switch structure subjected to a new severe excitation. Both numerical and experimental results have shown that the proposed method can effectively update the nonlinear model and the updated model can be further used to predict the nonlinear responses due to new severe excitations. (C) 2015 Elsevier Ltd. All rights reserved.
机译:提出了一种基于强迫振动条件下分解动力响应的瞬时频率和振幅的非线性结构模型更新方法。首先通过分析模式分解(AMD)和希尔伯特变换提取分解后的单组分的瞬时频率和幅度。然后,基于实验结构和非线性模型之间的瞬时频率和振幅的残差创建目标函数,以对非线性模型进行校准。本文利用Bouc-Wen模型的磁滞材料参数对结构非线性特性进行了仿真,并使用模拟退火全局优化方法通过最小化目标函数来获得磁滞参数的最佳值。为了验证所提方法的有效性,以地震和谐波激励下的三层非线性剪力型结构为数值实例进行了仿真。然后,通过对真实高压开关结构在强迫振动下的振动台试验,验证了该方法的有效性。通过经受新的剧烈激励的开关结构的振动台试验,可以进一步评估更新后的非线性结构模型。数值和实验结果均表明,该方法可以有效地更新非线性模型,并且可以将更新后的模型进一步用于预测由于新的强烈激励而引起的非线性响应。 (C)2015 Elsevier Ltd.保留所有权利。

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