In this paper, a method for generating samples of a fully non-stationary zero-mean Gaussian process, having a target acceleration time-history as one of its own samples, is presented. The proposed method requires the following steps: i) divide the time axis of the target accelerogram in contiguous time intervals in which a uniformly modulated process is introduced as the product of a deterministic modulating function per a stationary zero-mean Gaussian sub-process, whose power spectral density (PSD) function is filtered by two Butterworth filters; ii) estimate, in the various time intervals, the parameters of modulating functions by least-square fitting the expected energy of the proposed model to the energy of the target accelerogram; iii) estimate the parameters of the PSD function of the stationary sub-process, once the occurrences of maxima and of zero-level up-crossings of the target accelerogram, in the various intervals, are counted; iv) obtain the evolutionary spectral representation of the fully non-stationary process by adding the various contribution evaluated in the various intervals.

Generation of fully non-stationary random processes consistent with target accelerograms

Federica Genovese
;
2021-01-01

Abstract

In this paper, a method for generating samples of a fully non-stationary zero-mean Gaussian process, having a target acceleration time-history as one of its own samples, is presented. The proposed method requires the following steps: i) divide the time axis of the target accelerogram in contiguous time intervals in which a uniformly modulated process is introduced as the product of a deterministic modulating function per a stationary zero-mean Gaussian sub-process, whose power spectral density (PSD) function is filtered by two Butterworth filters; ii) estimate, in the various time intervals, the parameters of modulating functions by least-square fitting the expected energy of the proposed model to the energy of the target accelerogram; iii) estimate the parameters of the PSD function of the stationary sub-process, once the occurrences of maxima and of zero-level up-crossings of the target accelerogram, in the various intervals, are counted; iv) obtain the evolutionary spectral representation of the fully non-stationary process by adding the various contribution evaluated in the various intervals.
2021
Artificial accelerograms
Fully non-stationary stochastic process
Evolutionary Power spectral density function
Real ground motion records
Simulation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/138146
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