This paper deals with the well-known problem of generating spectrum-compatible synthetic accelerograms for the linear and non-linear time-history analyses of structural systems. A wavelet-based procedure has been used to decompose a recorded accelerogram into a desired number of time-histories with non-overlapping frequency contents, and then each of the time-histories has been suitably scaled ...

A new methodology is proposed to simulate accelerogram which is compatible to both linear and nonlinear design spectra. For this purpose, a wavelet-based parametric characterization of earthquake accelerogram is adopted and the parameters are optimized to tune its non-stationary features. A modified form of L-P basis is considered for wavelet transform which has the unique feature to decompose ...

The paper is devoted on the problem of strong motion waves classification and real-time prognoses with neural network. As input information for the neural network are given the parameters of recorded part of accelerogram, principle axis transform and spectral characteristics of the wave. With the help of stochastic long-range dependence time series analyses are determined the separated phases o...

In this paper, the use of wavelet analysis and artificial neural networks for generating artificial earthquake accelerograms from target spectrum is suggested as a new method. This procedure uses the learning capabilities of neural network to expand the knowledge of the inverse mapping from response spectrum to earthquake accelerogram. In the first step, wavelet analysis is used to decompose ea...

A recently developed nonstationary stochastic model of strong earthquake ground motion is described. The model employs filtering of a white-noise process, where nonstationarity is achieved by modulating the intensity and varying the filter parameters in time. The model is fitted to a target accelerogram by matching a set of statistical measures that characterize the evolution of the intensity, ...

The research addressed here is devoted to the generation of seismic accelerograms compatible with a given response spectrum and other associated properties. The time sampling of the stochastic accelerogram yields a time series represented by a random vector in high stochastic dimension. The probability density function (pdf) of this random vector is constructed using the Maximum Entropy (MaxEnt...