A township-level exposure model of residential buildings for mainland China

Ma, Jingyun; Rao, Anirudh; Silva, Vítor; Liu, Kai; Wang, Ming

doi:10.1007/s11069-021-04689-7

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…This study argues that most large-area portfolios within an exposure model are affected by epistemic uncertainties, resulting in a range of possible total building counts and class compositions. Expert-elicited models used by top-down approaches, even when carefully crafted (e.g., Ma et al 2021) may often provide only a partial perspective of the real composition of the building stock, while bottom-up approaches based on field-surveys are usually resource-intensive and are seldom carried out systematically.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models

Zapata

Pittore

Cotton

et al. 2022

Bull Earthquake Eng

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In seismic risk assessment, the sources of uncertainty associated with building exposure modelling have not received as much attention as other components related to hazard and vulnerability. Conventional practices such as assuming absolute portfolio compositions (i.e., proportions per building class) from expert-based assumptions over aggregated data crudely disregard the contribution of uncertainty of the exposure upon earthquake loss models. In this work, we introduce the concept that the degree of knowledge of a building stock can be described within a Bayesian probabilistic approach that integrates both expert-based prior distributions and data collection on individual buildings. We investigate the impact of the epistemic uncertainty in the portfolio composition on scenario-based earthquake loss models through an exposure-oriented logic tree arrangement based on synthetic building portfolios. For illustrative purposes, we consider the residential building stock of Valparaíso (Chile) subjected to seismic ground-shaking from one subduction earthquake. We have found that building class reconnaissance, either from prior assumptions by desktop studies with aggregated data (top–down approach), or from building-by-building data collection (bottom–up approach), plays a fundamental role in the statistical modelling of exposure. To model the vulnerability of such a heterogeneous building stock, we require that their associated set of structural fragility functions handle multiple spectral periods. Thereby, we also discuss the relevance and specific uncertainty upon generating either uncorrelated or spatially cross-correlated ground motion fields within this framework. We successively show how various epistemic uncertainties embedded within these probabilistic exposure models are differently propagated throughout the computed direct financial losses. This work calls for further efforts to redesign desktop exposure studies, while also highlighting the importance of exposure data collection with standardized and iterative approaches.

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Section: Conclusion and Recommendationsmentioning

confidence: 99%

Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models

Zapata

Pittore

Cotton

et al. 2022

Bull Earthquake Eng

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“…(3) Provide a mapping scheme, building a relationship between the building attribute information from the For more detail, please reference Ma et al (2021).…”

Section: Exposure Modelmentioning

confidence: 99%

“…Chinese residential building taxonomy is based on Ma et al (2021) and the parameters were matched with the global building capacity parameter database proposed by the Global Earthquake Model (Martins and Silva 2021) (Table 3). ( 2 (3) Select ground motion records based on regional seismic hazard disaggregation results (Fig.…”

Section: Vulnerability Modelmentioning

confidence: 99%

Seismic Risk Model for the Beijing–Tianjin–Hebei Region, China: Considering Epistemic Uncertainty from the Seismic Hazard Models

Ma,

Goda,

Liu

et al. 2024

Int J Disaster Risk Sci

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This study presents a probabilistic seismic risk model for the Beijing–Tianjin–Hebei region in China. The model comprises a township-level residential building exposure model, a vulnerability model derived from the Chinese building taxonomy, and a regional probabilistic seismic hazard model. The three components are integrated by a stochastic event-based method of the OpenQuake engine to assess the regional seismic risk in terms of average annual loss and exceedance probability curve at the city, province, and regional levels. The novelty and uniqueness of this study are that a probabilistic seismic risk model for the Beijing–Tianjin–Hebei region in China is developed by considering the impact of site conditions and epistemic uncertainty from the seismic hazard model.

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“…The improvements of the exposure models in comparison with the reference publications include the incorporation of additional information for the definition of the building classes, such as land use, seismic codes, cost manuals, and contributions or reviews from local experts (e.g. China, Ma et al, 2021).…”

Section: Model Coveragementioning

confidence: 99%

Global building exposure model for earthquake risk assessment

Yepes-Estrada,

Calderon,

Costa

et al. 2023

Earthquake Spectra

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The global building exposure model is a mosaic of local and regional models with information regarding the residential, commercial, and industrial building stock at the smallest available administrative division of each country and includes details about the number of buildings, number of occupants, vulnerability characteristics, average built-up area, and average replacement cost. We aimed for a bottom-up approach at the global scale, using national statistics, socio-economic data, and local datasets. This model allows the identification of the most common types of construction worldwide, regions with large fractions of informal construction, and areas prone to earthquakes with a high concentration of population and building stock. The mosaic of exposure models presented herein can be used for the assessment of probabilistic seismic risk and earthquake scenarios. Information at the global, regional, and national levels is available through a public repository ( https://github.com/gem/global_exposure_model ), which will be used to maintain, update and improve the models.

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A township-level exposure model of residential buildings for mainland China

Cited by 7 publications

References 18 publications

Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models

Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models

Seismic Risk Model for the Beijing–Tianjin–Hebei Region, China: Considering Epistemic Uncertainty from the Seismic Hazard Models

Global building exposure model for earthquake risk assessment

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