DOI QR코드

DOI QR Code

Important measure analysis of uncertainty parameters in bridge probabilistic seismic demands

  • Song, Shuai (School of Civil Engineering, Qingdao University of Technology) ;
  • Wu, Yuan H. (School of Civil Engineering, Taiyuan University of Technology) ;
  • Wang, Shuai (School of Civil Engineering, Taiyuan University of Technology) ;
  • Lei, Hong G. (School of Civil Engineering, Taiyuan University of Technology)
  • 투고 : 2020.05.13
  • 심사 : 2022.01.06
  • 발행 : 2022.02.25

초록

A moment-independent importance measure analysis approach was introduced to quantify the effects of structural uncertainty parameters on probabilistic seismic demands of simply supported girder bridges. Based on the probability distributions of main uncertainty parameters in bridges, conditional and unconditional bridge samples were constructed with Monte-Carlo sampling and analyzed in the OpenSees platform with a series of real seismic ground motion records. Conditional and unconditional probability density functions were developed using kernel density estimation with the results of nonlinear time history analysis of the bridge samples. Moment-independent importance measures of these uncertainty parameters were derived by numerical integrations with the conditional and unconditional probability density functions, and the uncertainty parameters were ranked in descending order of their importance. Different from Tornado diagram approach, the impacts of uncertainty parameters on the whole probability distributions of bridge seismic demands and the interactions of uncertainty parameters were considered simultaneously in the importance measure analysis approach. Results show that the interaction of uncertainty parameters had significant impacts on the seismic demand of components, and in some cases, it changed the most significant parameters for piers, bearings and abutments.

키워드

과제정보

The research described in this paper was financially supported by National Natural Science Foundation of China (Grand no. 51808376) and China Postdoctoral Science Foundation (Grand no. 2019M651076). The authors also acknowledge Dr. Jing Liu for polishing the language.

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