Wavelet‐based simulation of spectrum‐compatible aftershock accelerograms

Abstract

SUMMARYIn damage-based seismic design it is desirable to account for the ability of aftershocks to cause further damage to an already damaged structure due to the main shock. Availability of recorded or simulated aftershock accelerograms is a critical component in the non-linear time-history analyses required for this purpose, and simulation of realistic accelerograms is therefore going to be the need of the profession for a long time to come. This paper attempts wavelet-based simulation of aftershock accelerograms for two scenarios. In the first scenario, recorded main shock and aftershock accelerograms are available along with the pseudo-spectral acceleration (PSA) spectrum of the anticipated main shock motion, and an accelerogram has been simulated for the anticipated aftershock motion such that it incorporates temporal features of the recorded aftershock accelerogram. In the second scenario, a recorded main shock accelerogram is available along with the PSA spectrum of the anticipated main shock motion and PSA spectrum and strong motion duration of the anticipated aftershock motion. Here, the accelerogram for the anticipated aftershock motion has been simulated assuming that temporal features of the main shock accelerogram are replicated in the aftershock accelerograms at the same site. The proposed algorithms have been illustrated with the help of the main shock and aftershock accelerograms recorded for the 1999 Chi-Chi earthquake. It has been shown that the proposed algorithm for the second scenario leads to useful results even when the main shock and aftershock accelerograms do not share the same temporal features, as long as strong motion duration of the anticipated aftershock motion is properly estimated.

Spectrum-compatible accelerograms with harmonic wavelets

“…[2,3]), derived from seismological and geotechnical data, or on recorded ground motions (e.g. [4][5][6][7][8][9]). In both cases, the compatibility with the EDS is achieved while retaining some direct or indirect information on the expected non-stationary features in terms of amplitude and frequency content of the seismic action.…”

Generation of Near‐Field Artificial Ground Motions Compatible with Median‐Predicted Spectra Using PSO‐Based Neural Network and Wavelet Analysis

“…Neural networks have been used as an effective method for solving engineering problems in a wide range of application areas such as biomedical engineering and medical diagnosis (Adeli et al, 2008; Ghosh‐Dastidar et al, 2007; Wu et al, 2010), neurosciences (Ghosh‐Dastidar and Adeli, 2007; Vejmelka et al, 2010; Cruz‐Barbosa and Vellido, 2011; Jumutc et al, 2011), structural engineering (Hung and Adeli, 1994; Park and Adeli, 1997; Karim and Adeli, 1999; Senouci and Adeli, 2001; Adeli and Karim, 1997; Jiang and Adeli, 2005, 2008a,b; Graf et al, 2010; Reuter and Moeller, 2010), transportation engineering (Karim and Adeli, 2002, 2003a,b), image analysis and recognition (Hung and Adeli, 1993; Gopych, 2008), and speech recognition (Yau et al, 2007). Determining an accelerogram from its spectrum is an inverse problem for which biologically inspired soft computing methods such as neural networks are appropriate (Ghaboussi and Lin, 1997). Ghaboussi and Lin (1997) utilized the multilayer feed‐forward (MLFF) neural network and fast Fourier transform (FFT) to generate spectrum‐compatible earthquake time histories.…”

“…Determining an accelerogram from its spectrum is an inverse problem for which biologically inspired soft computing methods such as neural networks are appropriate (Ghaboussi and Lin, 1997). Ghaboussi and Lin (1997) utilized the multilayer feed‐forward (MLFF) neural network and fast Fourier transform (FFT) to generate spectrum‐compatible earthquake time histories. Lee and Han (2002) developed a new method via neural networks to generate artificial earthquake accelerograms.…”

“…Jiang and Adeli (2004) introduced and developed a wavelet packet-autocorrelation function method for traffic flow pattern analysis. Mukherjee and Gupta (2002a,b), Das and Gupta (2008), Suarez and Montejo (2005), Rajasekaran et al (2006), Giaralis and Spanos (2009), and Amiri et al (2006, 2007) applied wavelet analysis to generate artificial ground motions. Sirca and Adeli (2004) developed a neural network-wavelet model for generating artificial accelerograms.…”

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

“…or in the frequency domain (e.g. Karabalis et al, 2000;Mukherjee and Gupta, 2002;Suarez and Montejo, 2005;Suarez and Montejo, 2007;Das and Gupta, 2008). A different approach involves casting the problem at hand on a probabilistic basis by interpreting the accelerograms as realizations of a "mother" stochastic process characterized in the frequency domain by its power spectrum.…”