New method for generation of artificial ground motion by a nonstationary Kanai-Tajimi model and wavelet transform

Amiri, Gholamreza Ghodrati; Bagheri, Abdollah; Fadavi, Manouchehr

doi:10.12989/sem.2007.26.6.709

Cited by 15 publications

(4 citation statements)

References 5 publications

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“…As a compression to the overall assessment of the proposed method with other methods of doing so now, the results of this study is compared with the other results conducted by researchers such as Ghodrati and Bagheri [23], Ghodrati et al [14], Namiranian [24] and Mehrabi [25] both in terms of quantity and quality. For this purpose, Newmark and Hall design spectrum is given to the trained network as input and the output of the training methods.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

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Assessment of ANFIS networks on wavelet packet levels in generating artificial accelerograms

Amiri

Khorasani

Aghajari

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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Based on Adaptive Neural Network Fuzzy Inference System (ANFIS) networks, this paper presents a novel approach to generate artificial earthquake accelerograms from available data, which are compatible with specified design or response spectra. The proposed procedure uses the learning abilities of ANFIS networks as a powerful tool to develop the knowledge of the inverse mapping from response spectrum to earthquake records. Furthermore, to obtain better simulation results, Wavelet Packet Transform (WPT) and Principle Component Analysis (PCA) are used to convert records and response spectra from real to transformed spaces. Then, ANFISs are trained to relate response spectrum of records to their wavelet packet coefficients. In this process, the same results of different training levels of ANFIS method are obtained. In order to clarify the efficiency and accuracy of the proposed method, the results have been compared with the outcomes of previous artificial earthquake accelerograms generation methods. Finally, several interpretive examples are provided to demonstrate success of the suggested method.

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Section: Comparison Of Resultsmentioning

confidence: 99%

“…Iyama and Kuwamura [8], Mukherjee and Gapta [9], Zhou and Adeli [10], Suarez and Montejo [11,12], Hancock et al [13] and Ghodrati Amiri et al [14] developed the wavelet Packet Transform to generate artificial earthquake records.…”

Section: Introductionmentioning

confidence: 99%

Assessment of ANFIS networks on wavelet packet levels in generating artificial accelerograms

Amiri

Khorasani

Aghajari

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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“…However, the consciousness of the real nature of an earthquake has made some generation of spectrum compatible nonstationary earthquakes strategies be formulated [13][14][15] although these are not still suggested by technical codes.…”

Section: Introductionmentioning

confidence: 99%

Artificial Ground Motions and Nonlinear Response of RC Structures

Ferrotto

Basone

Asteris

et al. 2020

Advances in Civil Engineering

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The selection of seismic inputs for nonlinear dynamic analysis is widely debated, mainly focusing on the advantages and disadvantages provided by the choice of natural, simulated, or artificial records. This work proves the differences in the structural behavior of RC buildings when using accelerograms with different levels of stationarity. Initially, nonlinear response under three sets of accelerograms equivalent in terms of pseudo acceleration spectrum is evaluated and compared. Then, the results of incremental dynamic analyses are compared by the statistical point of view considering different levels of irregularity for the reference structure.

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“…A number of methods have been proposed for generalizing and simulating the nonstationary behavior of earthquake ground motions (Mukherjee and Gupta, 2002a,b). Boore (2003) used the point source stochastic simulation model to simulate ground‐motion records. Amiri and Bagheri (2009) used the generalized nonstationary Kanai–Tajimi model to simulate the ground motion time histories.…”

Section: Introductionmentioning

confidence: 99%

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

Amiri

Rad

Aghajari

et al. 2012

Computer aided Civil Eng

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The principal purpose of this article is to present a novel method based on particle swarm optimization (PSO) and wavelet packet transform (WPT) techniques and multilayer feed‐forward (MLFF) neural networks, in order to generate spectrum‐compatible near‐field artificial earthquake accelerograms. PSO is employed to optimize the weight values of the networks. Moreover, to improve the training efficiency principal component analysis (PCA) is used, as a reduction technique. The proposed PSO‐based MLFF (PSOBMLFF) neural network develops an inverse mapping from compacted and reduced spectrum coefficients into metamorphosed accelerogram wavelet packet coefficients. In this research, to produce compatible synthetic long‐period near‐field ground motions with median predicted spectra, the attenuation of peak ground velocity (PGV) with the close horizontal distance (R), moment magnitude (M), and time‐average shear‐wave velocity from the surface to 30 m (Vs30) has been developed using nonlinear regression analysis.

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New method for generation of artificial ground motion by a nonstationary Kanai-Tajimi model and wavelet transform

Cited by 15 publications

References 5 publications

Assessment of ANFIS networks on wavelet packet levels in generating artificial accelerograms

Assessment of ANFIS networks on wavelet packet levels in generating artificial accelerograms

Artificial Ground Motions and Nonlinear Response of RC Structures

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

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