Time–Frequency Spectral Representation Models to Simulate Nonstationary Processes and Their Use to Generate Ground Motions

Hong, H.P.; Cui, X.Z.

doi:10.1061/(asce)em.1943-7889.0001827

Cited by 27 publications

(18 citation statements)

References 34 publications

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“…The time decay of F b and F c is similar, so that their ratio can be considered time independent, while a weak dependence on magnitude and V S30 is observed. Laurendeau (2013) and Hong and Cui (2020) also showed that F b and F c data are affected by a large dispersion in the case of Japanese and NGA-West2 records, respectively, so that the predicting equations as a function of M W , R JB and V S30 suffer relevant uncertainties. For this reason, we performed several trial calibrations to define the optimal expressions.…”

Section: Figmentioning

confidence: 99%

“…Among the above listed techniques for generating accelerograms, in this study we focused on methods based on stochastic simulations, considering the time-frequency decomposition of the seismic ground motion (Laurendeau 2013;Causse et al 2014;Hong and Cui 2020). We have chosen to update the non-stationary stochastic model initially developed by Sabetta and Pugliese (1996) and modified by Pousse et al (2006) and by Laurendeau et al (2012).…”

Section: Introductionmentioning

confidence: 99%

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Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes

Sabetta

Pugliese²,

Fiorentino

et al. 2021

Bull Earthquake Eng

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This work presents an up-to-date model for the simulation of non-stationary ground motions, including several novelties compared to the original study of Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996). The selection of the input motion in the framework of earthquake engineering has become progressively more important with the growing use of nonlinear dynamic analyses. Regardless of the increasing availability of large strong motion databases, ground motion records are not always available for a given earthquake scenario and site condition, requiring the adoption of simulated time series. Among the different techniques for the generation of ground motion records, we focused on the methods based on stochastic simulations, considering the time- frequency decomposition of the seismic ground motion. We updated the non-stationary stochastic model initially developed in Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996) and later modified by Pousse et al. (Bull Seism Soc Am 96:2103–2117, 2006) and Laurendeau et al. (Nonstationary stochastic simulation of strong ground-motion time histories: application to the Japanese database. 15 WCEE Lisbon, 2012). The model is based on the S-transform that implicitly considers both the amplitude and frequency modulation. The four model parameters required for the simulation are: Arias intensity, significant duration, central frequency, and frequency bandwidth. They were obtained from an empirical ground motion model calibrated using the accelerometric records included in the updated Italian strong-motion database ITACA. The simulated accelerograms show a good match with the ground motion model prediction of several amplitude and frequency measures, such as Arias intensity, peak acceleration, peak velocity, Fourier spectra, and response spectra.

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Section: Figmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes

Sabetta

Pugliese²,

Fiorentino

et al. 2021

Bull Earthquake Eng

View full text Add to dashboard Cite

show abstract

“…Given the target TFPSD and the coherence functions for n points, the ground motion records at multiple points can be simulated using a simulation model (i.e., time-frequency spectral representation method [TFSRM]) in Hong and Cui 28 that was developed based on the ST and DOST (Stockwell 2007). 26 More specifically, let M ST (f,τ) denote the n × n TFPSD matrix with the (n 1 , n 2 )-th element denoted as 1 2 ( , ) given by where n 1 , n 2 = 1, .…”

Section: Methods To Simulate Ground Motions For Given Target Tfpsd Functionsmentioning

confidence: 99%

“…where 1 denotes the (n 1 , m)-th element of L(j), θ m (j) are independent and uniformly distributed between 0 and 2π, and 1 ( ; ) are the basis functions used in DOST 26 in a single-index form. 28 A total of N T sets of { , 1 ( )} is sampled, and the average of TFPSD of , 1 ( ), 1 1 ( , ), is calculated. Finally, the records at the n 1 -th point, 1 ( ), is then obtained using…”

Section: Methods To Simulate Ground Motions For Given Target Tfpsd Functionsmentioning

confidence: 99%

“…Section 3 describes the estimation of TF-dependent coherence and the proposed empirical TF-dependent lagged coherence model. In section 4, the use of the proposed TF-dependent coherence model to simulate the ground motions at multiple sites by using a recently developed ground motion simulation technique 27,28 based on the ST and discrete orthonormal S-transform (DOST) 26 is shown. Finally, the conclusions are presented in Section 5.…”

Section: Introductionmentioning

confidence: 99%

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A time‐frequency dependent coherence model for seismic ground motions

Cui

Hong

2020

Earthq Engng Struct Dyn

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There are several well‐known empirical lagged spatial coherence models for seismic ground motions proposed in the literature. The models are often developed based on the ordinary Fourier transform. None of the parametric models depend on time and frequency. The present study is focused on the development of the time‐frequency dependent (TF‐dependent) lagged coherence model for the seismic ground motions. The estimation of the TF‐dependent lagged coherence is carried out using the records obtained from dense arrays in Taiwan by applying the S‐transform—a TF‐dependent windowed Fourier transform. The spectral analysis results show that the TF‐dependent lagged coherence decreases with increasing separation or increasing frequency. Most importantly, it is shown that the TF‐dependent lagged coherence varies with the time‐varying intensity within the duration of the records; a higher normalized intensity corresponds to a higher lagged coherence. This feature is included in the developed empirical parametric TF‐dependent lagged coherence model, which is a function of the frequency, the separation between recording sites, and the normalized intensity. A numerical example illustrating its application to simulate nonstationary ground motions at multiple points is presented by using the time‐frequency spectral representation method that was developed based on the S‐transform and discrete orthonormal S‐transform.

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Simulating nonstationary and non‐Gaussian vector ground motions with time‐ and frequency‐dependent lagged coherence

Cui

Hong

2021

Earthq Engng Struct Dyn

Self Cite

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Seismic ground motions at multiple sites are nonstationary and non‐Gaussian with potentially time‐ and frequency‐dependent coherence, although the nonstationarity or non‐Gaussian or time‐dependent coherence aspects are often neglected because of lack of algorithm or method to take all these aspects into account to simulate synthetic ground motion records at multiple sites. In the present study, an iterative power and amplitude correction algorithm is proposed to simulate the nonstationary and non‐Gaussian vector process and takes into account time‐ and frequency‐dependent coherence. The algorithm usually converges within 10 iterations. It can be viewed as the extension of the well‐known iterative amplitude adjust Fourier transform algorithm for generating a vector of surrogates. The proposed algorithm uses the S‐transform and discrete orthonormal S‐transform rather than the ordinary Fourier transform. The adequacy of the proposed algorithm is validated numerically by using simulated ground motions.

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Time–Frequency Spectral Representation Models to Simulate Nonstationary Processes and Their Use to Generate Ground Motions

Cited by 27 publications

References 34 publications

Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes

Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes

A time‐frequency dependent coherence model for seismic ground motions

Simulating nonstationary and non‐Gaussian vector ground motions with time‐ and frequency‐dependent lagged coherence

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