@article{NievasPilzPrehnetal.2022,
  author    = {Nievas, Cecilia and Pilz, Marco and Prehn, Karsten and Schorlemmer, Danijel and Weatherill, Graeme and Cotton, Fabrice},
  title     = {Calculating earthquake damage building by building},
  series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  volume    = {20},
  journal   = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  number    = {3},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1570-761X},
  doi       = {10.1007/s10518-021-01303-w},
  pages     = {1519 -- 1565},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{ZhuCottonKawaseetal.2022,
  author    = {Zhu, Chuanbin and Cotton, Fabrice and Kawase, Hiroshi and H{\"a}ndel, Annabel and Pilz, Marco and Nakano, Kenichi},
  title     = {How well can we predict earthquake site response so far?},
  series = {Earthquake spectra : the professional journal of the Earthquake Engineering Research Institute},
  volume    = {38},
  journal   = {Earthquake spectra : the professional journal of the Earthquake Engineering Research Institute},
  number    = {2},
  publisher = {Sage Publ.},
  address   = {Thousand Oaks},
  issn      = {8755-2930},
  doi       = {10.1177/87552930211060859},
  pages     = {1047 -- 1075},
  year      = {2022},
  abstract  = {Earthquake site responses or site effects are the modifications of surface geology to seismic waves. How well can we predict the site effects (average over many earthquakes) at individual sites so far? To address this question, we tested and compared the effectiveness of different estimation techniques in predicting the outcrop Fourier site responses separated using the general inversion technique (GIT) from recordings. Techniques being evaluated are (a) the empirical correction to the horizontal-to-vertical spectral ratio of earthquakes (c-HVSR), (b) one-dimensional ground response analysis (GRA), and (c) the square-root-impedance (SRI) method (also called the quarter-wavelength approach). Our results show that c-HVSR can capture significantly more site-specific features in site responses than both GRA and SRI in the aggregate, especially at relatively high frequencies. c-HVSR achieves a "good match" in spectral shape at similar to 80\%-90\% of 145 testing sites, whereas GRA and SRI fail at most sites. GRA and SRI results have a high level of parametric and/or modeling errors which can be constrained, to some extent, by collecting on-site recordings.},
  language  = {en}
}