Seismic fragility curves for structures using non-parametric representations

  • Chu Mai
  • Katerina Konakli
  • Bruno SudretEmail author
Research Article


Fragility curves are commonly used in civil engineering to assess the vulnerability of structures to earthquakes. The probability of failure associated with a prescribed criterion (e.g., the maximal inter-storey drift of a building exceeding a certain threshold) is represented as a function of the intensity of the earthquake ground motion (e.g., peak ground acceleration or spectral acceleration). The classical approach relies on assuming a lognormal shape of the fragility curves; it is thus parametric. In this paper, we introduce two non-parametric approaches to establish the fragility curves without employing the above assumption, namely binned Monte Carlo simulation and kernel density estimation. As an illustration, we compute the fragility curves for a three-storey steel frame using a large number of synthetic ground motions. The curves obtained with the non-parametric approaches are compared with respective curves based on the lognormal assumption. A similar comparison is presented for a case when a limited number of recorded ground motions is available. It is found that the accuracy of the lognormal curves depends on the ground motion intensity measure, the failure criterion and most importantly, on the employed method for estimating the parameters of the lognormal shape.


earthquake engineering fragility curves lognormal assumption non-parametric approach kernel density estimation epistemic uncertainty 


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The authors are thankful to the anonymous reviewers for various valuable comments that helped improve the quality of the manuscript. Discussions with Dr. Sanaz Rezaeian, who provided clarifications on the stochastic ground motion model used in this study, are also acknowledged.


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© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Risk, Safety & Uncertainty Quantification, Institute of Structural EngineeringETH ZürichZürichSwitzerland

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