ALBERT

Description: The relation between the response of an elastic single degree of freedom oscillator subject to seismic shaking and the seismological characteristics of the input waveforms represented in terms of the Fourier amplitude spectrum (FAS) has become a topic of growing interest in the engineering seismological community in recent years. As simulations of ground motions assume a greater prominence in engineering design and databases of both strong and weak motion records expand exponentially, the need to reconcile the influence of the controlling seismological properties of the motions with their potential impacts on structural response is becoming ever more important. Prediction of ground motion in the Fourier amplitude domain has several key advantages when compared to that of the response spectrum, namely a closer relationship to the physical seismological properties of the source, path and site that can be inferred from more abundant small magnitude and weak motion data, as well as maintained linearity of site response at all frequencies. Recognising this, new empirical ground motion models have been developed in terms of FAS [1, 2], in addition to an inter-frequency correlation model that can facilitate the definition of conditional spectrum compatible empirical and simulated ground motion records for design [3]. In spite of these advantages, the usage of FAS in seismic hazard and risk analysis remains limited to the scaling of simulated ground motions in the development of median ground motion models using random vibration theory (RVT). One major factor behind this is that translation from FAS to response spectra via RVT requires joint characterization not only of ground motions across a range of frequencies but also of strong motion duration. However, this limitation could potentially be overcome if fragility functions were to be derived directly in terms of FAS. It is for this purpose that a comparison is made in this paper between the efficiency of intensity measures based on the FAS and those based on conventional response spectra for a set of simple building fragility models of the type commonly used in seismic risk analysis. The feasibility of achieving end-to-end loss estimation exclusively in the Fourier amplitude domain is subsequently explored. While its full range of benefits and limitations will require further study, the potential for embedding seismological theory and data more deeply into engineering applications is appealing for the future practice of seismic design and risk analysis.

Description: In risk assessment, the exposure component describes the elements exposed to the natural hazards and susceptible to damage or loss, while the vulnerability component defines the likelihood to incur damage or loss conditional on a given level of hazard intensity. In this article, we propose a novel adaptive approach to exposure modeling which exploits Dirichlet-Multinomial Bayesian updating to implement the incremental assimilation of sparse in situ survey data into probabilistic models described by compositions (proportions). This methodology is complemented by the introduction of a custom spatial aggregation support based on variable-resolution Central Voronoidal Tessellations. The proposed methodology allows for a more consistent integration of empirical observations, typically from engineering surveys, into large-scale models that can also efficiently exploit expert-elicited knowledge. The resulting models are described in a probabilistic framework, and as such allow for a more thorough analysis of the underlying uncertainty. The proposed approach is applied and discussed in five countries in Central Asia.

Description: The spatial resolution of exposure data has a substantial impact on the accuracy and reliability of seismic risk estimates. While several studies have investigated the influence of the geographical detail of urban exposure data in earthquake loss models, there is also a need to understand its implications at the regional scale. This study investigates the effects of exposure resolution on the European loss model and its influence on the resulting loss estimates by simulating dozens of exposure and site models (630 models) representing a wide range of assumptions related to the geo-resolution of the exposed asset locations and the associated site conditions. Losses are examined in terms of portfolio average annual loss (AAL) and return period losses at national and sub-national levels. The results indicate that neglecting the uncertainty related to asset locations and their associated site conditions within an exposure model can introduce significant bias to the risk results. The results also demonstrate that disaggregating exposure to a grid or weighting/relocating exposure locations and site properties using a density map of the built areas can improve the accuracy of the estimated losses.

Description: Multi-resolution exposure model for seismic risk assessment in Turkmenistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Turkmenistan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process) For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Description: Since 2015, the Global Earthquake Model (GEM) Foundation and its partners have been supporting regional programs and bilateral collaborations to develop an open global earthquake risk model. These efforts led to the development of a repository of probabilistic seismic hazard models, a global exposure dataset comprising structural and occupancy information regarding the residential, commercial and industrial buildings, and a comprehensive set of fragility and vulnerability functions for the most common building classes. These components were used to estimate probabilistic earthquake risk globally using the OpenQuake-engine, an open-source software for seismic hazard and risk analysis. This model allows estimating a number of risk metrics such as annualized average losses or aggregated losses for particular return periods, which are fundamental to the development and implementation of earthquake risk mitigation measures.

Description: Multi-resolution exposure model for seismic risk assessment in the Kyrgyz Republic. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of 1'175 geo-cells covering the territory of the Kyrgyz Republic. The model integrates around 6'000 building observations (see related dataset Pittore et al. 2019). The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Description: Multi-resolution exposure model for seismic risk assessment in Kazakhstan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Kazakhstan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Description: Multi-resolution exposure model for seismic risk assessment in Uzbekistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Uzbekistan (provided as a separate file). The model prior is based on empirical observations in Kyrgyzstan and Tajikistan as well as user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Description: The significant potential for human and economic losses arising from earthquakes affecting urban infrastructure has been demonstrated by many recent events such as, for example, L’Aquila (2009), Christchurch (2011) and Tohoku (2012). Within the current practice of seismic loss estimation in both academic and industry models, the modelling of spatial variability of the earthquake ground motion input across a region, and its corresponding influence upon portfolios of heterogeneous building types, may be oversimplified. In particular, the correlation properties that are well-known in observations of ground motion intensity measures (IMs) may not always be fully represented within the probabilistic modelling of seismic loss. Using a case study based on the Tuscany region of Italy, the impacts of including spatially cross-correlated random fields of different ground motion IMs are appraised at varying spatial resolutions. This case study illustrates the impact on the resulting seismic loss when considering synthetic aggregated portfolios over different spatial scales. Inclusion of spatial cross-correlation of IMs into the seismic risk analysis may often result in the likelihood of observing larger (and in certain cases smaller) losses for a portfolio distributed over a typical city scale, when compared against simulations in which the cross-correlation is neglected. It can also be seen that the degree to which the spatial correlations and cross-correlations can impact upon the loss estimates is sensitive to the conditions of the portfolio, particularly with respect to the spatial scale, the engineering properties of the different building types within the portfolio and the heterogeneity of the portfolio with respect to the types.