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author:

Gu, Yin (Gu, Yin.) [1] (Scholars:谷音) | Zheng, Wen-Ting (Zheng, Wen-Ting.) [2] | Zhuo, Wei-Dong (Zhuo, Wei-Dong.) [3] (Scholars:卓卫东)

Indexed by:

EI Scopus PKU CSCD

Abstract:

A method is proposed to calculate seismic probabilistic risk considering the randomness of the material and the seismic wave, and the seismic hazard. It avoids the cumbersome integration simultaneously. Based on the probability distribution of the structural material parameters, a method of Latin Hypercube Sampling (LHS) is adopted to consider the randomness of the material parameters of the structural members. A sample set of ground motion - bridge combination is formed according to the selected seismic waves. A nonlinear finite element fiber model for the typical short-tower cable-stayed structural system is established. The damage index of the key components is identified. The seismic fragility analysis of lower-tower cable-stayed bridge is carried out with incremental dynamic analysis (IDA), and the PGA-structural damage probability function is fitted. Monte Carlo sampling methods is used for discretion of seismic hazard probability functions, which avoids the cumbersome integration. The seismic probabilistic risk assessment is made for a typical lower-tower cable-stayed bridge.

Keyword:

Cables Cable stayed bridges Damage detection Electron transitions Hazards Monte Carlo methods Probability distributions Random processes Risk assessment Seismic response Seismic waves Structural analysis Towers

Community:

  • [ 1 ] [Gu, Yin]College of Civil Engineering, Fuzhou University, Fuzhou 350108, China
  • [ 2 ] [Zheng, Wen-Ting]College of Civil Engineering, Fuzhou University, Fuzhou 350108, China
  • [ 3 ] [Zhuo, Wei-Dong]College of Civil Engineering, Fuzhou University, Fuzhou 350108, China

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Source :

Engineering Mechanics

ISSN: 1000-4750

CN: 11-2595/O3

Year: 2013

Issue: 8

Volume: 30

Page: 96-102,110

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 13

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 8

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