Calculating earthquake damage building by building: the case of the city of Cologne, Germany
Auteur(s): |
Cecilia I. Nievas
Marco Pilz Karsten Prehn Danijel Schorlemmer Graeme Weatherill Fabrice Cotton |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Bulletin of Earthquake Engineering, 28 janvier 2022, n. 3, v. 20 |
Page(s): | 1519-1565 |
DOI: | 10.1007/s10518-021-01303-w |
Abstrait: |
The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties. |
Copyright: | © The Author(s) 2022 |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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10656629 - Publié(e) le:
17.02.2022 - Modifié(e) le:
01.06.2022