NDLI: Study on RS and GIS based comprehensive analysis of multi-factors affecting the earthquake risk: An application of Monte Carlo simulation

Content Provider	IEEE Xplore Digital Library
Author	Xiaoqing Wang Aixia Dou Xiang Ding Xiaoxiang Yuan
Copyright Year	2014
Description	Author affiliation: Inst. of Earthquake Sci., China Earthquake Adm., Beijing, China (Xiaoqing Wang; Aixia Dou; Xiang Ding; Xiaoxiang Yuan)
Abstract	The seismic risk, as the product of seismic hazard and vulnerability, is widely assessed based on the traditional inventory method with a detailed classified facilities and structures. The losses caused by historical earthquakes, show great variation in the same seismic intensity. Thus the study of uncertainty of building vulnerability is important to improve the accuracy of seismic risk assessment. Because of the complexity of the combined factors affecting the building vulnerability, it is hard to estimate the effects by using the simple models. The paper introduces Monte Carlo random testing method to simulate the seismic vulnerability and estimate its uncertainty in the case that complex affecting factors exist. By using the building damage data of 2008 Ms=8.0 Wenchuan earthquake in Sichuan, China, acquired through interpretation of post-earthquake RS images and field survey, the exemplary quantitative structural vulnerabilities are estimated. The results show that the vulnerability uncertainty with affecting factors can be accurately simulated.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	3117
Ending Page	3120
File Size	838675
Page Count	4
File Format	PDF
ISBN	9781479957750
DOI	10.1109/IGARSS.2014.6947137
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Buildings Earthquakes Uncertainty Monte Carlo methods Testing Cities and towns Sociology RS and GIS Earthquake risk vulnerability Monte Carlo simulation multi-factors influence
Content Type	Text
Resource Type	Article

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