China Probabilistic Seismic Risk Model - Part 1 Hazard and Exposure

Abstract eng:
The potential financial and social loss due to earthquakes in China has increased significantly over the past decades due to the country’s rapid growth and increase in exposure concentration in major cities. In order to quantify the dynamically changing seismic risk, a probabilistic seismic risk model of China has been developed as a joined effort between the Institute of Engineering Mechanics (IEM) in Harbin and Risk Management Solutions, Inc. (RMS) in California. This paper is the first of two papers presenting the study and its results and focuses on the hazard quantification and the exposure development. The second paper focuses on vulnerability curves development and probabilistic loss estimation. This paper presents the parameters describing the seismic risk: the delineation of seismic sources, the magnitude distribution, rate of earthquake occurrence and upper magnitude truncation. Soil classification and liquefaction potential are estimated from high resolution geologic maps. Western and Eastern Chinese elliptical spectra based attenuation functions are adopted and calibrated against historical events. Over 85,000 simulated events are included based on the seismic source model to quantify the seismicity. The outputs of this effort are peak ground acceleration and spectral acceleration maps for selected return periods. The paper further presents the development of the exposure for the whole country at the district/county level by lines of businesses based on national census, industry statistics and market information. For each province, a detailed review of the exposure was performed to reach this high level of resolution. In order to properly estimate the vulnerability of the exposure a detailed inventory classification was developed including the building class, construction material and design philosophy, age of structure, number of stories and seismic zone requirements. More than ten such inventories were derived going from “central business district” to “rural” environment. This information is used as the input for the second paper that addresses the loss estimation.

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