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

Amiri, Gholamreza Ghodrati; Khorasani, Mahsa; Aghajari, Sara; Tabrizian, Zahra

doi:10.3233/ifs-120746

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Ghaboussi and Lin (1998) solved this problem using artificial neural networks by considering the problem as reverse mapping and improved their work with a stochastic neural network approach (Lin & Ghaboussi, 2001). Moreover, wavelet transforms (Zhou & Adeli, 2003) in combination with neural networks or independently have been employed for generating artificial accelerograms (Amiri, Rad, & Hazaveh, 2014; Amiri et al., 2012; Sirca & Adeli, 2004). Furthermore, wavelet transforms in combination with the Hilbert transform have been used in modal parameters identification and health monitoring of large‐scale structures (Amezquita‐Sanchez et al., 2017; Z. Li et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, wavelet transforms in combination with the Hilbert transform have been used in modal parameters identification and health monitoring of large‐scale structures (Amezquita‐Sanchez et al., 2017; Z. Li et al., 2017). In addition, fuzzy systems (Amiri, Khorasani, et al., 2014; Heidari & Khorasani, 2012; H. Li et al., 2013) and genetic algorithm‐based methods (Naeim et al., 2004; Shahjouei & Ghodrati Amiri, 2011) have played crucial roles in the generation of artificial earthquake time histories. Park et al.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al, 2017). In addition, fuzzy systems (Amiri, Khorasani, et al, 2014;Heidari & Khorasani, 2012;H. Li et al, 2013) and genetic algorithm-based methods (Naeim et al, 2004;Shahjouei & Ghodrati Amiri, 2011) have played crucial roles in the generation of artificial earthquake time histories.…”

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confidence: 99%

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Deep convolutional generative adversarial networks for the generation of numerous artificial spectrum‐compatible earthquake accelerograms using a limited number of ground motion records

Matinfar

Khaji

Ahmadi

2022

Computer aided Civil Eng

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Deep learning (DL) methodologies have been recently employed to solve various civil and earthquake engineering problems. Nevertheless, due to the limited number of reliable data in the field of earthquake engineering, it is not convenient to obtain accurate results using DL. To tackle this challenge, the generative adversarial network (GAN) approach may be considered a reliable possible candidate. GANs have been introduced as an efficient way to train generative models. GANs exhibited their capabilities as well as versatility in the field of image production. For nonlinear dynamic analyses of structures, artificial ground accelerograms that are compatible with a target response spectrum are usually generated. In this paper, an efficient algorithm is proposed by which numerous artificial spectrum-compatible earthquake accelerograms are generated using a few ground motion records. For this purpose, a specific wellestablished generative model, namely, the deep convolutional GAN (DCGAN), is adopted for the first time and used. It is shown that DCGAN can easily generate desirable artificial ground accelerograms by having a limited number of seismic records as input to train the network. To quantitatively demonstrate the quality of the artificial ground accelerograms generated by the DCGAN, several computer experiments are presented, among which the robustness and feasibility of the proposed method are examined by using only four earthquake accelerograms as the worst scenario. Moreover, the efficiency of the DCGAN is illustrated by comparing various seismic parameters and the spectral response of the generated accelerograms with those of the actual accelerograms. The outcomes illustrate the efficiency and robustness of the presented DCGAN. INTRODUCTIONFor the aseismic design of structural systems, response spectra may be employed as a convenient tool to evaluate the maximum responses of these systems. A useful © 2022 Computer-Aided Civil and Infrastructure Engineering.response spectrum is the design spectrum provided by seismic codes. Time-history dynamic analysis is frequently employed to design critical/important structures like high-rise buildings, for which several ground accelerations should be available in conducting nonlinear dynamic

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep convolutional generative adversarial networks for the generation of numerous artificial spectrum‐compatible earthquake accelerograms using a limited number of ground motion records

Matinfar

Khaji

Ahmadi

2022

Computer aided Civil Eng

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Generative adversarial network to produce numerous artificial accelerograms with pseudo-spectral acceleration as conditional input

Kim,

Kim

2024

Computers and Geotechnics

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Generation of critical aftershocks using stochastic neural networks and wavelet packet transform

Rajabi

Amiri

2019

Journal of Vibration and Control

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This paper proposes a methodology using wavelet packet transform, principal component analysis, and neural networks in order to generate artificial critical aftershock accelerograms which are compatible with the response spectra. This procedure uses the learning abilities of neural networks, principal component analysis as a dimension reduction technique, and decomposing capabilities of wavelet packet transform on consecutive earthquakes. In fact, the proposed methodology consists of two steps and expands the knowledge of the inverse mapping from mainshock response spectrum to aftershock response spectrum and aftershock response spectrum to wavelet packet transform coefficients of the aftershocks. This procedure results in a stochastic ensemble of response spectra of aftershock (first step) and corresponding wavelet packet transform coefficients (second step) which are then used to generate the aftershocks through applying the inverse wavelet packet transform. Finally, in order to demonstrate the effectiveness of the proposed method, three examples are presented in which recorded critical successive ground motions are used to train and test the neural networks.

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

Cited by 3 publications

References 19 publications

Deep convolutional generative adversarial networks for the generation of numerous artificial spectrum‐compatible earthquake accelerograms using a limited number of ground motion records

Deep convolutional generative adversarial networks for the generation of numerous artificial spectrum‐compatible earthquake accelerograms using a limited number of ground motion records

Generative adversarial network to produce numerous artificial accelerograms with pseudo-spectral acceleration as conditional input

Generation of critical aftershocks using stochastic neural networks and wavelet packet transform

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