[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.12989/sem.2008.28.2.153

Application of wavelet multiresolution analysis and artificial intelligence for generation of artificial earthquake accelerograms

Amiri, G. Ghodrati (Center of Excellence for Fundamental Studies in Structural Engineering, College of Civil Engineering,Iran University of Science & Technology)
Bagheri, A. (Center of Excellence for Fundamental Studies in Structural Engineering, College of Civil Engineering,Iran University of Science & Technology)

Publication Information

Structural Engineering and Mechanics / v.28, no.2, 2008 , pp. 153-166 More about this Journal

Abstract

This paper suggests the use of wavelet multiresolution analysis (WMRA) and neural network for generation of artificial earthquake accelerograms from target spectrum. This procedure uses the learning capabilities of radial basis function (RBF) neural network to expand the knowledge of the inverse mapping from response spectrum to earthquake accelerogram. In the first step, WMRA is used to decompose earthquake accelerograms to several levels that each level covers a special range of frequencies, and then for every level a RBF neural network is trained to learn to relate the response spectrum to wavelet coefficients. Finally the generated accelerogram using inverse discrete wavelet transform is obtained. An example is presented to demonstrate the effectiveness of the method.

Keywords

artificial accelerogram; wavelet transform; RBF neural network; target spectrum;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)
Times Cited By Web Of Science : 6 (Related Records In Web of Science)
Times Cited By SCOPUS : 4

1	Ghodrati Amiri, G., Ashtari, P. and Rahami, H. (2006), 'New development of artificial record generation by wavelet theory', Struct. Eng. Mech., 22(2), 185-195 DOI ScienceOn
2	Iyama, J. and Kuwamura, H. (1999), 'Application of wavelets to analysis and simulation of earthquake motions', Earthq. Eng. Struct. D., 28, 255-272 DOI
3	MATLAB Reference Guide (1999), The Math Works Inc.
4	Mukherjee, S. and Gupta, K. (2002), 'Wavelet-based characterization of design ground motions', Earthq. Eng. Struct. D., 31, 1173-1190 DOI ScienceOn
5	Naeim, F. (1999), The Seismic Design Handbook, Van Nostrand
6	Zhou, Z. and Adeli, H. (2003), 'Wavelet energy spectrum for time-frequency localization of earthquake energy', Comput. Aided Civil Infrastruct. Eng., 13, 133-140
7	Ghaboussi, J. and Lin., C.J. (1998), 'New method of generating spectrum compatible accelerograms using neural networks', Earthq. Eng. Struct. D., 27, 377-396 DOI
8	Lin, C.J. and Ghaboussi, J. (2000), 'Recent progress on neural network based methodology for generating artificial earthquake accelerograms', Proc. of the 12th World Conf. on Earthquake Engineering, Auckland, New Zealand, 29 Jan.-5 Feb
9	MacCann, W.M. and Shah, H.C. (1979), 'Determining strong-motion duration of earthquake', B. Seismol. Soc. Am., 69, 1253-1265
10	Lee, S.C. and Han, S.W. (2002), 'Neural-network-based models for generating artificial earthquakes and response spectra', Comput. Struct., 80, 1627-1638 DOI ScienceOn
11	Fan, F.G. and Ahmadi, G. (1990), 'Nonstationary Kanai-Tajimi models for El Centro 1940 and Mexico City 1985 earthquake', Probab. Eng. Mech., 5, 171-181
12	Benedetto, J.J. and Frazier, M.W. (1994), WAVELETS: Mathematics and Applications, CRC Press, Boca Raton
13	Daubechies, I. (1992), 'Ten lectures on wavelets', CBMS-NSF Conference Series in Applied Mathematics, Montpelier, Vermont
14	Newland, D.E. (1994), Random Vibrations, Spectral and Wavelet Analysis, 3rd Edition, Longman Singapore Publishers
15	Ramezi, H. (1997), Base Accelerogram Data of Iranian Accelerograph Network, Building and Housing Research Center, BHRC-PN S 253, Tehran, Iran
16	Rajasekaran, S., Latha, V. and Lee, S.C. (2006), 'Generation of artificial earthquake motion records using wavelets and principal component analysis', J. Earthq. Eng., 10(5), 665-691 DOI
17	Park, J. and Sandberg, J.W. (1991), 'Universal approximation using radial basis functions network', Neural Comput. 3, 246-257 DOI

5	Hossein Ghaffarzadeh. (2013) Earthquakes and Structures Neural network-based generation of artificial spatially variable earthquakes ground motions / 4 (5) , 509
02	A. A. FATEMI. (2012) Journal of Earthquake and Tsunami GENERATION OF UNIFORM HAZARD EARTHQUAKE ACCELEROGRAMS AND NEAR-FIELD GROUND MOTIONS / 06 (02) , 1250013
7	G. Ghodrati Amiri. (2009) Journal of Earthquake Engineering Generation of Multiple Earthquake Accelerograms Compatible with Spectrum Via the Wavelet Packet Transform and Stochastic Neural Networks / 13 (7) , 899
	Tienfuan Kerh. (2013) Abstract and Applied Analysis Seismic Design Value Evaluation Based on Checking Records and Site Geological Conditions Using Artificial Neural Networks / 2013 , 1
8	Hossein Bakhshi. (2014) Proceedings of the Institution of Civil Engineers - Structures and Buildings Estimation of spectral acceleration based on neural networks / 167 (8) , 457
3	Majid Mohammadi. (2016) Journal of Vibroengineering Simulation of spectrum-correspondent accelerogram by using artificial neural networks / 18 (3) , 1617
9	G. Ghodrati Amiri. (2012) Computer-Aided Civil and Infrastructure Engineering Generation of Near-Field Artificial Ground Motions Compatible with Median-Predicted Spectra Using PSO-Based Neural Network and Wavelet Analysis / 27 (9) , 711
	Kemal Günaydın. (2008) Mathematical Problems in Engineering Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey / 2008 , 1
5	Abdollah Bagheri. (2014) KSCE Journal of Civil Engineering New method for the estimation of strong ground motions based on the colonial competitive algorithm / 18 (5) , 1403
2	(2008) Structural engineering and mechanics : An international journal Simulation of earthquake records using combination of wavelet analysis and non-stationary Kanai-Tajimi model / 33 (2) , 179
6	(2008) Structural engineering and mechanics : An international journal Application of neural networks and an adapted wavelet packet for generating artificial ground motion / 37 (6) , 575
3	(2008) Iranian journal of science and technology. Transactions of civil engineering Simulation of Endurance Time Excitations via Wavelet Transform / 43 (3) , 429
5	(2020) Journal of vibration and control : JVC Generation of critical aftershocks using stochastic neural networks and wavelet packet transform / 26 (5) , 331

1	Simulation of earthquake records using combination of wavelet analysis and non-stationary Kanai-Tajimi model / [Amiri, G. Ghodrati;Bagheri, A.;] / Structural Engineering and Mechanics
2	Application of neural networks and an adapted wavelet packet for generating artificial ground motion / [Asadi, A.;Fadavi, M.;Bagheri, A.;Ghodrati Amiri, G.;] / Structural Engineering and Mechanics
3	Neural network-based generation of artificial spatially variable earthquakes ground motions / [Ghaffarzadeh, Hossein;Izadi, Mohammad Mahdi;Talebian, Nima;] / Earthquakes and Structures
4	New Method for the Estimation of Strong Ground Motions based on the Colonial Competitive Algorithm / [Bagheri, Abdollah;Amiri, Gholamreza Ghodrati;Haghdoust, Jamshid;] / KSCE Journal of Civil Engineering

1	/ Mathematical Problems in Engineering
2	/ Structural Engineering and Mechanics
3	/ Structural Engineering and Mechanics
4	/ Journal of Earthquake Engineering