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作者:

Tan, Ping (Tan, Ping.) | Liu, Liang-Kun (Liu, Liang-Kun.) | Li, Xiang-Xiu (Li, Xiang-Xiu.) | Zhang, Ying (Zhang, Ying.) | Zhou, Fu-Lin (Zhou, Fu-Lin.)

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摘要:

In this paper, Gauss-legendre integral formulation and Gauss-legendre integral points estimation method are proposed to solve the reliability of stochastic structure under seismic excitation, based on the first-passage method modified by Vanmarcke. Numerical simulation shows that Gauss-legendre integral method is only suitable for system whose dimension is less than 4, offering great precision and higher computational efficiency. By contrast, Gauss-legendre integral points estimate method possesses excellent precision and much higher computational efficiency. Both the mean and variation coefficient of structural reliability solved by those two methods are converged with increase in variability coefficient of structure parameters. ©, 2015, Nanjing University of Aeronautics an Astronautics. All right reserved.

关键词:

Computational efficiency Efficiency Gaussian distribution Numerical methods Reliability Seismology Stochastic systems

作者机构:

  • [ 1 ] [Tan, Ping]Earthquake Engineering Research & Test Center, Guangzhou University, Guangzhou; 510405, China
  • [ 2 ] [Liu, Liang-Kun]School of Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Li, Xiang-Xiu]School of Civil Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Zhang, Ying]Earthquake Engineering Research & Test Center, Guangzhou University, Guangzhou; 510405, China
  • [ 5 ] [Zhou, Fu-Lin]Earthquake Engineering Research & Test Center, Guangzhou University, Guangzhou; 510405, China
  • [ 6 ] [Zhou, Fu-Lin]School of Civil Engineering, Beijing University of Technology, Beijing; 100124, China

通讯作者信息:

  • [liu, liang-kun]school of civil engineering, beijing university of technology, beijing; 100124, china

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来源 :

Journal of Vibration Engineering

ISSN: 1004-4523

年份: 2015

期: 2

卷: 28

页码: 211-216

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 1

ESI高被引论文在榜: 0 展开所有

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