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• W2754136313 abstract "Residential buildings and associated population distribution modeling, is an integral part of analysis of the direct and indirect risks related to natural disasters, including earthquakes in urban environments. In this study, the distribution of residential population along with the distribution of residential buildings is estimated. The study offers a more comprehensive and accurate model for Iran in accordance with local conditions and with higher degree of details as available and accessible databases. Dasymetric mapping method as an alternative to a choropleth map is an efficient method for the spatial distribution of data benefitting from spatial information and statistical modeling. This research intends to integrate Dasymetric mapping method and GIS (Geographical Information System) to bring spatial, locational and statistical dataset together in order to analyze vulnerability of the exposed elements at risk in an urban context considering different seismic scenarios. In this study, statistical data at urban, block and parcel level as well as digital aerial and satellite images were compiled together. Building related features were extracted from remotely sensed images and integrated with the statistical information in order to generate spatially distributed dataset. This study develops a scenario-based seismic loss estimation model for residential building in city of Sari as a case study. The data was processed according to the following steps. At first, object extraction from satellite imagery was accomplished to acquire building boundaries within urban blocks. Then, the structural and demographic information were spatially distributed using Dasymetric mapping method. Next, ground motions map for different scenarios were generated for the entire metropolitan area. In the final step, the estimation of structural damage and loss of life were calculated for different scenarios. The results from this study indicate that, in comparison with Gridding method, Dasymetric mapping method would allow for a more realistic and careful assessment of seismic vulnerability of buildings at an urban scale due to more accurate classification and distribution of underlying data." @default.
• W2754136313 created "2017-09-25" @default.
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• W2754136313 date "2017-01-01" @default.
• W2754136313 modified "2023-09-25" @default.
• W2754136313 title "Vulnerability Analysis of the Urban Environments to Different Seismic Scenarios: Residential Buildings and Associated Population Distribution Modelling through Integrating Dasymetric Mapping Method and GIS" @default.
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• W2754136313 doi "https://doi.org/10.1016/j.proeng.2017.07.100" @default.
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