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Parametric models averaging for optimized non-parametric fragility curve estimation based on intensity measure data clustering
https://orcid.org/0000-0003-4358-8185
Highlights Clustering of the intensity measure data based on an enriched ground motion database. Fragility curves as threshold exceedance probabilities at cluster centroids. Optimization based on averaging of parametric models. Optimized fragility curves with smaller confidence intervals that the un-optimized.
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.
Parametric models averaging for optimized non-parametric fragility curve estimation based on intensity measure data clustering
https://orcid.org/0000-0003-4358-8185
Highlights Clustering of the intensity measure data based on an enriched ground motion database. Fragility curves as threshold exceedance probabilities at cluster centroids. Optimization based on averaging of parametric models. Optimized fragility curves with smaller confidence intervals that the un-optimized.
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.
Parametric models averaging for optimized non-parametric fragility curve estimation based on intensity measure data clustering
https://orcid.org/0000-0003-4358-8185
Highlights Clustering of the intensity measure data based on an enriched ground motion database. Fragility curves as threshold exceedance probabilities at cluster centroids. Optimization based on averaging of parametric models. Optimized fragility curves with smaller confidence intervals that the un-optimized.
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.
Parametric models averaging for optimized non-parametric fragility curve estimation based on intensity measure data clustering
Trevlopoulos, Konstantinos (author) / Feau, Cyril (author) / Zentner, Irmela (author)
Structural Safety ; 81
2019-05-13
Article (Journal)
Electronic Resource
English
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