A stochastic ground motion model with separable temporal and spectral nonstationarities

Rezaeian, Sanaz; Kiureghian, Armen Der

doi:10.1002/eqe.831

Cited by 246 publications

(193 citation statements)

References 23 publications

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“…15 To assure zero residual velocity, the simulated process is eventually high-pass filtered. 51 This filter corresponds to a critically damped oscillator, and the corrected acceleration record is obtained as the solution of the differential equation…”

Section: Appendix a Details For Stochastic Ground Motion Model Considmentioning

confidence: 99%

Modification of stochastic ground motion models for matching target intensity measures

Tsioulou

Taflanidis

Galasso

2017

Earthq Engng Struct Dyn

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SummaryStochastic ground motion models produce synthetic time-histories by modulating a white noise sequence through functions that address spectral and temporal properties of the excitation. The resultant ground motions can be then used in simulation-based seismic risk assessment applications. This is established by relating the parameters of the aforementioned functions to earthquake and site characteristics through predictive relationships. An important concern related to the use of these models is the fact that through current approaches in selecting these predictive relationships, compatibility to the seismic hazard is not guaranteed. This work offers a computationally efficient framework for the modification of stochastic ground motion models to match target intensity measures (IMs) for a specific site and structure of interest. This is set as an optimization problem with a dual objective. The first objective minimizes the discrepancy between the target IMs and the predictions established through the stochastic ground motion model for a chosen earthquake scenario. The second objective constraints the deviation from the model characteristics suggested by existing predictive relationships, guaranteeing that the resultant ground motions not only match the target IMs but are also compatible with regional trends. A framework leveraging kriging surrogate modeling is formulated for performing the resultant multi-objective optimization, and different computational aspects related to this optimization are discussed in detail. The illustrative implementation shows that the proposed framework can provide ground motions with high compatibility to target IMs with small only deviation from existing predictive relationships and discusses approaches for selecting a final compromise between these two competing objectives. | INTRODUCTIONThe growing interest in performance-based earthquake engineering 1,2 and in simulation-based, seismic risk assessment approaches 3,4 has increased in the past decades the relevance of ground motion modeling techniques. These techniques describe the entire time-series of seismic excitations, providing a characterization appropriate for dynamic time-history analysis. UndoubtedlyThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Appendix a Details For Stochastic Ground Motion Model Considmentioning

confidence: 99%

Modification of stochastic ground motion models for matching target intensity measures

Tsioulou

Taflanidis

Galasso

2017

Earthq Engng Struct Dyn

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show abstract

“…Incorporating a high-pass filter is deemed essential since it suitably diminishes the spectral energy levels in the lower-frequency range of the simulated ground motions, eg, see other studies, [40][41][42] alleviating the typically arising problem of stochastic ground motion that contains excessive amounts of long-period spectral amplitudes. The proposed model is thus expressed as:…”

Section: Evolutionary Spectral K-t Modelmentioning

confidence: 99%

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

Vlachos

Παπακωνσταντίνου

Deodatis

2017

Earthq Engng Struct Dyn

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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

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“…These two parameters are related to the two physical parameters that describe the recorded GM in the frequency domain and which are respectively the predominant frequency and the bandwidth of the GM. For more details about the identification procedure between the recorded GM and the stochastic model described previously, the reader may refer to [14][15].…”

Section: D Tmentioning

confidence: 99%

Probabilistic Dynamic Analysis of a Soil-Structure System Subjected to a Stochastic Earthquake Ground-Motion

Al-Bittar¹,

Kotronis²,

Grange³

et al. 2014

Proceedings of 10th World Congress on Computational Mechanics

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Abstract. In this paper, a probabilistic dynamic analysis of a five-storey building subjected to a stochastic earthquake Ground-Motion (GM) is presented. The entire soil-structure system is considered in the analysis. The stochastic GM time-histories are based on a real recorded time history. The probabilistic dynamic analysis is performed using the classical Monte Carlo Simulation (MCS) methodology. As is well known, this method requires a great number of calls of the deterministic model. To overcome the inconvenience of the time cost, a simple deterministic model based on the "macro-element" concept is used. The main advantage of the macro-element is that the time cost for a single deterministic calculation is relatively small and thus, this model is suitable for the probabilistic dynamic analysis. The simulation of the stochastic GM time-histories is done using a fully nonstationary stochastic model in both the time and the frequency domains. This model employs filtering of a discretized white-noise process. Non-stationarity is achieved by modulating the intensity and by varying the filter properties in time. As for the probabilistic analysis, a large number of samples (say 100,000) of the stochastic GM time-histories is generated using Monte Carlo technique. For each sample, a dynamic calculation using the macro-element is performed and the following responses are retained for the probabilistic analysis: (i) the maximum horizontal displacement at the top of the building, (ii) the three maximum displacements of the footing centre, and finally (iii) the three maximum reaction forces at the contact of the soil and the footing. These results are used to compute the statistical moments of the different system responses together with the probability of exceeding of predefined thresholds for these responses.

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A stochastic ground motion model with separable temporal and spectral nonstationarities

Cited by 246 publications

References 23 publications

Modification of stochastic ground motion models for matching target intensity measures

Modification of stochastic ground motion models for matching target intensity measures

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

Probabilistic Dynamic Analysis of a Soil-Structure System Subjected to a Stochastic Earthquake Ground-Motion

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