Title

Non-Parametric Fragility Curves For Bridges Using Recorded Ground Motions

Keywords

Concrete bridge columns; Kernel density estimation; Lognormal assumption; Non-parametric fragility curves

Abstract

Seismic fragility curves are commonly used to assess the vulnerability of structures to earthquakes by representing the probability of structural failure as a function of an earthquake intensity measure. The classical approach for computing fragility curves assumes the curves to have a lognormal shape. This approach is therefore parametric. Since fragility curves play an important role in the framework of performance-based earthquake engineering, it is of utmost importance either to validate the accuracy of the lognormal assumption or to propose an assumption-free approach to compute these curves. In a recent work, the authors validated the lognormal assumption for a linear steel frame. However, in a more realistic case study of this steel frame with non-linear material behavior, the lognormal assumption showed insufficient accuracy. In this paper, we consider a different type of structure, i.e. a typical reinforced-concrete bridge column subject to recorded ground motions. We compute fragility curves for the column, first with the classical parametric approach and subsequently with a non-parametric method based on kernel density estimation. Two different intensity measures are considered, namely the peak ground acceleration and the pseudo-spectral acceleration. The results show the limitations of the classical lognormal approach for this type of structure and prove kernel density estimation to be a promising tool for establishing seismic fragility curves.

Publication Date

1-1-2014

Publication Title

Proceedings of the International Conference on Structural Dynamic , EURODYN

Volume

2014-January

Number of Pages

2831-2838

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84963619024 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84963619024

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