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Journal Articles Structural Safety Year : 2019

Parametric models averaging for optimized non-parametric fragility curve estimation based on intensity measure data clustering

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Abstract

Seismic fragility curves give the probability of exceedance of the threshold of a damage state of a structure, or a non-structural component, conditioned on the intensity measure of the seismic motion. Typically, fragility curves are constructed parametrically assuming a lognormal shape. In some cases, which cannot be identified a priori, differences may be observed between non-parametric fragility curves, evaluated empirically based on a large number of seismic response analyses, and their estimations via the lognormal assumption. Here, we present an optimized Monte Carlo procedure for derivation of non-parametric fragility curves. This procedure uses clustering of the intensity measure data to construct the non-parametric curve and parametric models averaging for optimized assessment. In simplified case studies presented here as illustrative applications, the developed procedure leads to a fragility curve with reduced bias compared to the lognormal curve and to reduced confidence intervals compared to an un-optimized Monte Carlo-based approach. In the studied cases, this procedure proved to be efficient providing reasonable estimations even with as few as 100 seismic response analyses.
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Dates and versions

hal-03484490 , version 1 (20-12-2021)

Licence

Attribution - NonCommercial - CC BY 4.0

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Konstantinos Trevlopoulos, Cyril Feau, Irmela Zentner. Parametric models averaging for optimized non-parametric fragility curve estimation based on intensity measure data clustering. Structural Safety, 2019, 81, pp.101865. ⟨10.1016/j.strusafe.2019.05.002⟩. ⟨hal-03484490⟩
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