Spatial variation of seismic ground motions : modeling and by Aspasia Zerva

By Aspasia Zerva

The spatial version of seismic flooring motions denotes the variations within the seismic time histories at quite a few destinations at the flooring floor. this article specializes in the spatial variability of the motions that's because of the propagation of the waveforms from the earthquake resource in the course of the earth strata to the floor floor, and it brings jointly a few of the facets underlying this advanced phenomenon.

Topics coated contain:

  • Evaluation of the spatial variability from seismic info recorded at dense tool arrays through sign processing techniques
  • Presentation of the main widespread parametric coherency versions, besides short descriptions in their derivation
  • Illustration of the motives underlying the spatial edition of the motions and its actual interpretation
  • Estimation of seismic ground-surface traces from unmarried station facts, spatial array files, and analytical methods
  • Introduction of the concept that of random vibrations as utilized to discrete-parameter and non-stop structural structures on a number of supports
  • Generation of simulations and conditional simulations of spatially variable seismic flooring motions
  • Overview of the results of the spatial variability of seismic motions at the reaction of lengthy constructions, akin to pipelines, bridges and dams, with short descriptions of opt for seismic codes that include spatial variability concerns of their layout thoughts

This booklet might function an academic and/or reference for graduate scholars, researchers and practising engineers drawn to advancing the present kingdom of information within the research and modeling of the spatial version of the seismic floor motions, or using spatially variable excitations within the seismic reaction assessment of lengthy constructions.

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Extra resources for Spatial variation of seismic ground motions : modeling and engineering applications

Example text

15, the fluctuation due to the random phase variability occurs around the linear trend of the phase spectra. It is noted that, again, the scale of the phase spectra in Figs. 16(b) is different. The phase spectra of the aligned data fluctuate fairly “smoothly” around the zero axis, which is somewhat counterintuitive with the notion of random variability of the phases at the individual stations. This “smoothness” of the phase spectra results exactly from the fact that the cross spectral estimates have been smoothed over 11 nearneighbor frequencies.

4 PHASE CHARACTERISTICS OF (SINGLE) RECORDED ACCELEROGRAMS The power spectral density of recorded data (Figs. 6) provides information about the scaled square Fourier amplitude of the motions, but does not retain information about their Fourier phase. Ohsaki [380], in analyzing phases and phase differences of recorded accelerograms, noted that the probability distribution of the Fourier phases of the series appears to be uniform. 5 Effect of smoothing by means of the frequency domain Hamming window (Fig.

The corresponding results for the non aligned data are presented in Fig. 6. amplitude spectra with significant differences in their frequency content. 14 provide the reference figures for the evaluation of the lagged coherency in example applications of the following section. The phase spectra of the cross spectral estimates (Eqs. 1. 14 Smoothed cross amplitude spectra, derived from Eq. 64 and the M = 5 Hamming window, of the aligned time histories of Fig. 8 at the selected SMART 1 array stations: Part (a) presents the results between C00 and the inner ring stations I03, I06, I09 and I12, at a separation distance of 200 m, and part (b) those between C00 and the middle ring stations M03, M06, M09 and M12, at a separation distance of 1000 m.

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