Time domain probabilistic seismic risk analysis using ground motion prediction equations of Fourier amplitude spectra

• DOI: 10.1016/j.soildyn.2022.107218
• Publication Date: 2022-03-25T18:09:30Z
• Authors: Hexiang Wang; Fangbo Wang; Han Yang; Yuan Feng; Boris Jeremic
• Keywords: Time domain approach; Fourier amplitude spectra; 02 engineering and technology; Intrusive stochastic framework; Civil Engineering; 0201 civil engineering; Defence & Security Studies; Engineering; Geophysics; Seismic risk; Ground motion prediction equation; Stochastic finite element method; Civil engineering; Strategic

Modeling of Fourier amplitude spectra (FAS) of seismic motions has gained much attention in engineering seismology. In the past few years, several ground motion prediction equations (GMPEs) and inter-frequency correlation structure of FAS have been established. Due to many preferable characteristics of FAS, probabilistic seismic hazard/risk analysis is rapidly changing from ergodic, spectrum acceleration Sa(T0)-based approach to non-ergodic, site-specific, FAS-based approach. This paper presents time domain intrusive framework for probabilistic seismic risk analysis using GMPE of FAS. Methodology for time domain stochastic ground motion modeling based on GMPEs of FAS is presented in some detail. The simulated uncertain motions are modeled as a random process and represented by polynomial chaos Karhunen-Loève expansion. The random process excitations are further propagated into the uncertain structural system using Galerkin stochastic finite element method (SFEM). Probabilistic evolution of structural response is solved, and such solution is used to develop seismic risk for any damage state. The presented framework is illustrated through seismic risk analysis of a four-story building subjected to possible earthquakes from two strike slip faults. The influences of the epistemic uncertainties in source stress drop Δσ and site attenuation κ0 on seismic risk are investigated. The need for non-ergodic seismic risk analysis with source-specific and site specific characterizations is emphasized.