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Assessing the impact of ground-motion variability and uncertainty on empirical fragility curves
https://orcid.org/0000-0003-3822-0060
Abstract Empirical fragility curves, constructed from databases of thousands of building-damage observations, are commonly used for earthquake risk assessments, particularly in Europe and Japan, where building stocks are often difficult to model analytically (e.g. old masonry structures or timber dwellings). Curves from different studies, however, display considerable differences, which lead to high uncertainty in the assessed seismic risk. One potential reason for this dispersion is the almost universal neglect of the spatial variability in ground motions and the epistemic uncertainty in ground-motion prediction. In this paper, databases of building damage are simulated using ground-motion fields that take account of spatial variability and a known fragility curve. These databases are then inverted, applying a standard approach for the derivation of empirical fragility curves, and the difference with the known curve is studied. A parametric analysis is conducted to investigate the impact of various assumptions on the results. By this approach, it is concluded that ground-motion variability leads to flatter fragility curves and that the epistemic uncertainty in the ground-motion prediction equation used can have a dramatic impact on the derived curves. Without dense ground-motion recording networks in the epicentral area empirical curves will remain highly uncertain. Moreover, the use of aggregated damage observations appears to substantially increase uncertainty in the empirical fragility assessment. In contrast, the use of limited randomly-chosen un-aggregated samples in the affected area can result in good predictions of fragility.
Highlights We examine the impact of ground-motion variability on empirical fragility curves. The absence of accelerograms leads to flatter curves with wide confidence intervals. Only a very dense network of stations can reduce the uncertainty in these curves. Relatively few random samples of buildings can lead to accurate empirical curves. Aggregated data leads to curves with large uncertainties.
Assessing the impact of ground-motion variability and uncertainty on empirical fragility curves
https://orcid.org/0000-0003-3822-0060
Abstract Empirical fragility curves, constructed from databases of thousands of building-damage observations, are commonly used for earthquake risk assessments, particularly in Europe and Japan, where building stocks are often difficult to model analytically (e.g. old masonry structures or timber dwellings). Curves from different studies, however, display considerable differences, which lead to high uncertainty in the assessed seismic risk. One potential reason for this dispersion is the almost universal neglect of the spatial variability in ground motions and the epistemic uncertainty in ground-motion prediction. In this paper, databases of building damage are simulated using ground-motion fields that take account of spatial variability and a known fragility curve. These databases are then inverted, applying a standard approach for the derivation of empirical fragility curves, and the difference with the known curve is studied. A parametric analysis is conducted to investigate the impact of various assumptions on the results. By this approach, it is concluded that ground-motion variability leads to flatter fragility curves and that the epistemic uncertainty in the ground-motion prediction equation used can have a dramatic impact on the derived curves. Without dense ground-motion recording networks in the epicentral area empirical curves will remain highly uncertain. Moreover, the use of aggregated damage observations appears to substantially increase uncertainty in the empirical fragility assessment. In contrast, the use of limited randomly-chosen un-aggregated samples in the affected area can result in good predictions of fragility.
Highlights We examine the impact of ground-motion variability on empirical fragility curves. The absence of accelerograms leads to flatter curves with wide confidence intervals. Only a very dense network of stations can reduce the uncertainty in these curves. Relatively few random samples of buildings can lead to accurate empirical curves. Aggregated data leads to curves with large uncertainties.
Assessing the impact of ground-motion variability and uncertainty on empirical fragility curves
https://orcid.org/0000-0003-3822-0060
Abstract Empirical fragility curves, constructed from databases of thousands of building-damage observations, are commonly used for earthquake risk assessments, particularly in Europe and Japan, where building stocks are often difficult to model analytically (e.g. old masonry structures or timber dwellings). Curves from different studies, however, display considerable differences, which lead to high uncertainty in the assessed seismic risk. One potential reason for this dispersion is the almost universal neglect of the spatial variability in ground motions and the epistemic uncertainty in ground-motion prediction. In this paper, databases of building damage are simulated using ground-motion fields that take account of spatial variability and a known fragility curve. These databases are then inverted, applying a standard approach for the derivation of empirical fragility curves, and the difference with the known curve is studied. A parametric analysis is conducted to investigate the impact of various assumptions on the results. By this approach, it is concluded that ground-motion variability leads to flatter fragility curves and that the epistemic uncertainty in the ground-motion prediction equation used can have a dramatic impact on the derived curves. Without dense ground-motion recording networks in the epicentral area empirical curves will remain highly uncertain. Moreover, the use of aggregated damage observations appears to substantially increase uncertainty in the empirical fragility assessment. In contrast, the use of limited randomly-chosen un-aggregated samples in the affected area can result in good predictions of fragility.
Highlights We examine the impact of ground-motion variability on empirical fragility curves. The absence of accelerograms leads to flatter curves with wide confidence intervals. Only a very dense network of stations can reduce the uncertainty in these curves. Relatively few random samples of buildings can lead to accurate empirical curves. Aggregated data leads to curves with large uncertainties.
Assessing the impact of ground-motion variability and uncertainty on empirical fragility curves
Ioannou, Ioanna (author) / Douglas, John (author) / Rossetto, Tiziana (author)
Soil Dynamics and Earthquake Engineering ; 69 ; 83-92
2014-10-25
10 pages
Article (Journal)
Electronic Resource
English
Assessing the impact of ground-motion variability and uncertainty on empirical fragility curves
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