Application of wavelets to analysis and simulation of earthquake motions

Abstract

SUMMARYA method of applying wavelet transform to earthquake motion analysis is developed from the viewpoint of energy input structures, in which relationships between wavelet coefficients and energy input, namely energy principles in wavelet analysis are derived. By using the principles, time-frequency characteristics of the 1995 Hyogoken-Nanbu earthquake ground motions are analysed and time histories of energy input for various ranges of frequencies and epicentral distances are identified. Furthermore, a technique to simulate earthquake ground accelerations by wavelet inverse transform is developed on the condition that target time-frequency characteristics are specified. Structural responses to the simulated accelerations are compared with the target time-frequency characteristics, which shows satisfactory correlations between wavelet coefficients and energy responses in both time and frequency domains.

Wavelet domain analysis for identification of vehicle axles from bridge measurements

“…Wavelet analysis can be used to provide an enhanced time-frequency resolution desirable for several applications. It can be effectively used to generate random processes and fields [15,16], simulate earthquake ground motions [17], predict seismic response of storage tanks [18,19], solve partial differential equations [20], and identify damping in multi-degree-of-freedom systems based on time-scale decomposition 6 [21] and characterise the nonlinear systems [22]. A new representation scheme for random fields based upon the projection onto a biorthogonal wavelet basis was developed [23].…”

Nonstationary seismic response of a tank on a bilinear hysteretic soil using wavelet transform

“…The above Eqs. (22)- (24) can be solved to obtain the following two expressions relating the wavelet coefficients of the impulsive liquid mass displacements and the foundation displacements, respectively, to those of the ground accelerations…”

“…Considerable research has been carried out to develop several wavelet functions with specific characteristics to suit different purposes [16][17][18][19][20][21]. Wavelet analysis can be used to provide an enhanced time-frequency resolution desirable for several applications and can be effectively used to generate random processes and fields [22,23], simulate earthquake ground motions [24] and model stochastic dynamic systems [25]. Wavelet based system identification techniques for linear and nonlinear systems and solution of time-varying differential equations have been developed by [26,27], Ghanem and Romeo [48] and Ghanem and Romeo [45].…”

Synthesis of accelerograms compatible with the Chinese GB 50011-2001 design spectrum via harmonic wavelets: artificial and historic records

“…Lam et al, 2000;Bommer and Ruggeri, 2002). Apart from the above standard probabilistic approaches for obtaining artificial/synthetic accelerograms to be used in time-history analysis applications, Iyama and Kuwamura (1999) employed the wavelet transform, Lin and Ghaboussi (2001) considered properly trained stochastic neural networks, Wang et al (2002) utilized the adaptive chirplet transform and Wen and Gu (2004) incorporated the empirical mode decomposition in conjunction with the Hilbert transform to capture the time-varying frequency content of recorded earthquakes and to simulate non-stationary strong ground motion signals.…”