Seismic retrofit of a reinforced concrete flat-slab structure: Part II — seismic fragility analysis

2007 ◽  
Vol 29 (6) ◽  
pp. 1178-1188 ◽  
Author(s):  
Mary Beth D. Hueste ◽  
Jong-Wha Bai
2012 ◽  
Vol 446-449 ◽  
pp. 2313-2316
Author(s):  
Li Li Yuan ◽  
Jian Min Wang ◽  
Neng Jun Wang ◽  
Wen Ting Jiang

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 238
Author(s):  
Phuong Hoa Hoang ◽  
Hoang Nam Phan ◽  
Duy Thao Nguyen ◽  
Fabrizio Paolacci

Uncertainty quantification is an important issue in the seismic fragility analysis of bridge type structures. However, the influence of different sources of uncertainty on the seismic fragility of the system is commonly overlooked due to the costly re-evaluation of numerical model simulations. This paper aims to present a framework for the seismic fragility analysis of reinforced concrete highway bridges, where a data-driven metamodel is developed to approximate the structural response to structural and ground motion uncertainties. The proposed framework to generate fragility curves shows its efficiency while using a few finite element simulations and accounting for various modeling uncertainties influencing the bridge seismic fragility. In this respect, a class of single-bent bridges available in the literature is taken as a case study, whose three-dimensional finite element model is established by the OpenSees software framework. Twenty near-source records from different sources are selected and the Latin hypercube method is applied for generating the random samples of modeling and ground motion parameters. The Kriging metamodel is then driven on the structural response obtained from nonlinear time history analyses. Component fragility curves of the reinforced concrete pier column are derived for different damage states using the Kriging metamodel whose parameters are established considering different modeling parameters generated by Monte Carlo simulations. The results demonstrate the efficiency of the proposed framework in interpolating the structural response and deriving the fragility curve of the case study with any input conditions of the random variables.


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