Predictive model for site specific simulation of ground motions based on earthquake scenarios

Abstract

SummaryA predictive stochastic model is developed based on regression relations that inputs a given earthquake scenario description and outputs seismic ground acceleration time histories at a site of interest. A bimodal parametric nonstationary Kanai-Tajimi (K-T) ground motion model lies at the core of the proposed predictive model. The functional forms that describe the temporal evolution of the K-T model parameters can effectively represent strong non-stationarities of the ground motion. Fully non-stationary ground motion time histories can be generated through the powerful Spectral Representation Method. A Californian subset of the available NGA-West2 database is used to develop and calibrate the predictive model. Samples of the model parameters are obtained by fitting the K-T model to the database records, and the resulting marginal distributions of the model parameters are efficiently described by standard probability models. The samples are translated to the standard normal space and linear random-effect regression models are established relating the transformed normal parameters to the commonly used earthquake scenario defining predictors: moment magnitude M w , closest-to-site distance R rup , and average shear-wave velocity V S 30 at a site of interest. The random-effect terms in the developed regression models can effectively model the correlation among ground motions of the same earthquake event, in parallel to taking into account the location-dependent effects of each site. For validation purposes, simulated acceleration time histories based on the proposed predictive model are compared with recorded ground motions. In addition, the median and median plus/minus one standard deviation elastic response spectra of synthetic ground motions, pertaining to a variety of different earthquake scenarios, are compared to the associated response spectra computed by the NGA-West2 ground motion prediction equations and found to be in excellent agreement. KEYWORDSpredictive stochastic ground motion model, analytical evolutionary power spectrum, ground motion parametrization, random-effect regression, NGA-West2 database

Performance‐based seismic design of tuned inerter dampers

“…Figure 4b shows time-history samples of A(t) in Equation 3 for the three pairs of (m, r) in (a), with their corresponding frequency contents and lengths t f . Other ground-motion stochastic simulation models such as the ones proposed by Rezaeian and Kiureghian 31,72 or Vlachos et al, 74 or more complex physics-based models such as Goda et al, 75 can be used to simulate earthquakes as functions of (m, r), and so can be substitutes of the model adopted in this study, within our proposed framework. Irrespective of whether the substitute model is fully defined or just its samples are available, it suffice to just build the respective SROM based on sample statistics.…”

Validation of stochastic ground motion model modification by comparison to seismic demand of recorded ground motions

“…An alternative approach for ground motion modelling that has been receiving increasing interest during the past decade is the use of simulated ground motions derived by stochastic ground motion models (e.g., Boore 2003, Rezaeian and Der Kiureghian 2010, Vlachos et al 2018. These models are able to produce acceleration time-histories by modulating a white-noise sequence through functions that address spectral and temporal features of the ground shaking.…”

Non-stationary property in frequency content of horizontal ground motion vectors

“…In both time and frequency domains, ground motions are typical non-stationary signals. The non-stationary property has been widely studied, 1 methods to generate non-stationary ground motions, 2,3 especially non-stationary ground motion in two orthogonal horizontal directions, 4,5 have gained increasing attention recently. The non-stationary frequency content is harder to describe than amplitude content; therefore, some methods are put forward, such as Fourier transform, physical spectrum, 6 Hilbert transform and Hilbert-Huang transform, 7,8 wavelet transform, 9,10 zero-crossing rate, 11 and univariate phase spectrum model.…”