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Conditional simulation of spatially variable seismic ground motions based on evolutionary spectra

机译:基于演化谱的空间可变地震地震动条件模拟

摘要

This paper proposes a computational procedure for the conditional simulation of spatially variable seismic ground motions for long span bridges with multiple supports. The seismic ground motions, with part of their time histories measured at some supports, are regarded as zero-mean nonstationary random processes characterized by predefined evolutionary power spectral density. To conditionally simulate unknown seismic ground motion time histories at other supports, the Kriging method is first described briefly for the conditional simulation of a random vector comprised of zero-mean Gaussian variables. The multivariate oscillatory processes characterized by the evolutionary power spectral density matrix are then introduced, and the Fourier coefficients of the oscillatory processes and their covariance matrix are derived. By applying the Kriging method to the random vector of the Fourier coefficients and using the inverse Fourier transform, unknown nonstationary seismic ground motion time histories can be simulated. A numerical example is selected to demonstrate capabilities of the proposed simulation procedure, and the results show that the procedure can ensure unbiased time-varying correlation functions, especially the cross correlation between known and unknown time histories. The procedure is finally applied to the Tsing Ma suspension bridge in Hong Kong to generate ground accelerations at its multiple supports using limited seismic records.
机译:本文提出了一种多支撑大跨度桥梁空间可变地震地震动条件模拟的计算程序。地震地震动,其时间历史的一部分在某些支撑处测量,被认为是零均值非平稳随机过程,其特征在于预定义的演化功率谱密度。为了有条件地模拟其他支撑处的未知地震地面运动时间历史记录,首先简要介绍了Kriging方法,以对由零均值高斯变量组成的随机向量进行条件模拟。然后介绍了以演化功率谱密度矩阵为特征的多元振荡过程,并推导了该振荡过程的傅立叶系数及其协方差矩阵。通过将Kriging方法应用于傅立叶系数的随机向量并使用傅立叶逆变换,可以模拟未知的非平稳地震地震动的时间历史。通过数值算例验证了该仿真程序的有效性,结果表明该程序可以保证无时变相关函数,尤其是已知和未知时间历史之间的互相关。该程序最终应用于香港的青马悬索桥,利用有限的地震记录在其多个支撑处产生地面加速度。

著录项

  • 作者

    Hu L; Xu YL; Zheng Y;

  • 作者单位
  • 年度 2012
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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