2011/08/01 by S. Pezeshk, Shahram Pezeshk, A. Zandieh +3 · 6 citations
Engineering · Earth and Planetary Sciences · #Seismic Performance and Analysis #Structural Health Monitoring Techniques #Seismic Waves and Analysis
paper · doi:10.1785/0120100144
In the field of earthquake engineering, ground-motion prediction models are frequently used to estimate the peak ground acceleration (PGA) and the pseudos- pectral acceleration (PSA). In regions of the world where ground-motion recordings are plentiful, such as western North America (WNA), the ground-motion prediction equations are obtained using empirical methods. In other regions, such as eastern North America (ENA), with insufficient ground-motion data, alternative methods must be used to develop ground-motion prediction equations (GMPEs). The hybrid empiri- cal method is one such method used to develop ground-motion prediction equations in areas with sparse ground motions. This method employs the stochastic simulation method to adjust empirical GMPEs developed for a region with abundant strong- motion recordings in order to estimate strong-motion parameters in a region with a sparse database. The adjustments take into account differences in the earthquake source, wave propagation, and site-response characteristics between the two regions. In this study, a hybrid empirical method is used to develop a new GMPE for ENA, using five new ground-motion prediction models developed by the Pacific Earthquake Engineering Research Center (PEER) for WNA. A new ENA GMPE is derived for a magnitude range of 5 to 8 and closest distances to the fault rupture up to 1000 km. Ground-motion prediction equations are developed for the response spectra (pseudoacceleration, 5% damped) and the PGA for hard-rock sites in ENA. The resulting ground-motion prediction model developed in this study is compared with two ENA ground-motion models used in the 2008 national seismic hazard maps as well as with available observed data for ENA.