Structural Applications of a Predictive Stochastic Ground Motion Model: Assessment and Use

Abstract

This paper presents a novel stochastic method for simulation of ground motions. The input is a user-specified earthquake scenario description, and the output consists of fully nonstationary acceleration time histories at a site of interest. A bimodal analytical evolutionary Kanai–Tajimi (K–T) model lies at the core of the predictive stochastic model. The K–T model parameters are linked through mixed-effects regression models to three commonly used ground motion physical predictors, moment magnitude Mw, closest distance Rrup, and average soil shear-wave velocity VS3 at the site of interest. An extensive Californian subset of the next-generation attenuation NGA-West2 database is used to develop and calibrate the regression models. The random effect terms in the developed regression models effectively describe the correlation among ground motions of the same earthquake event, while also accounting for the location dependent effects at each site. The simulation of sample ground motion realizations based on each specified earthquake scenario is facilitated by the spectral representation method (SRM). In order to evaluate the performance, assess the versatility and validate the proposed predictive model, the simulation-based attenuation of important scalar ground motion engineering metrics is studied and compared with results from well-established ground motion prediction equation (GMPE) models. The statistics of elastic response spectra of simulated time histories are also compared with the associated predictions of the NGA-West2 GMPE models based on a variety of earthquake scenarios. Nonlinear response-history analyses for representative single-degree-of-freedom and multiple-degree-of-freedom structural systems compare the seismically induced inelastic structural demand of the considered systems when subjected to sets of both recorded and corresponding simulated ground motions.