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A general framework for the estimation of analytical fragility functions based on multivariate probability distributions
HighlightsGeneral analytical fragility model.Two marginal distribution and dependence described by copula model.Applicable to continuous damage measures and discrete damage states.Parametric models more robust than non parametric statistics.Use of classical information criteria (Bayesian information criterion) for model selection.
AbstractA fragility curve is a function that expresses the probability of failure of a structure or component as a function of the intensity of external aggression. This paper proposes a general framework for the development of analytical fragility functions from data based on the copula approach. Such a model allows for any kinds of marginal distributions and dependence structures so that it can be applied to various types of fragility data, analytical or empirical. The fragility function is then derived from the joint distribution of intensity and damage measures. The Bayesian information criterion is used to select the most plausible model among the candidate joint distributions, given the data. The practical implementation of the methodology is illustrated by an analytical test case and by the evaluation of seismic fragility curves for a reinforced concrete building. Several candidate marginal distributions, in agreement with the nature and the physical properties of the variables (e.g. common intensity and damage measures take only positive values) are evaluated. In particular, seismic intensity measures are lognormal random variables according to seismological models. This paper is focused on bivariate distributions but the case of vector valued intensity measures can be treated accordingly.
A general framework for the estimation of analytical fragility functions based on multivariate probability distributions
HighlightsGeneral analytical fragility model.Two marginal distribution and dependence described by copula model.Applicable to continuous damage measures and discrete damage states.Parametric models more robust than non parametric statistics.Use of classical information criteria (Bayesian information criterion) for model selection.
AbstractA fragility curve is a function that expresses the probability of failure of a structure or component as a function of the intensity of external aggression. This paper proposes a general framework for the development of analytical fragility functions from data based on the copula approach. Such a model allows for any kinds of marginal distributions and dependence structures so that it can be applied to various types of fragility data, analytical or empirical. The fragility function is then derived from the joint distribution of intensity and damage measures. The Bayesian information criterion is used to select the most plausible model among the candidate joint distributions, given the data. The practical implementation of the methodology is illustrated by an analytical test case and by the evaluation of seismic fragility curves for a reinforced concrete building. Several candidate marginal distributions, in agreement with the nature and the physical properties of the variables (e.g. common intensity and damage measures take only positive values) are evaluated. In particular, seismic intensity measures are lognormal random variables according to seismological models. This paper is focused on bivariate distributions but the case of vector valued intensity measures can be treated accordingly.
A general framework for the estimation of analytical fragility functions based on multivariate probability distributions
Zentner, Irmela (author)
Structural Safety ; 64 ; 54-61
2016-09-22
8 pages
Article (Journal)
Electronic Resource
English
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