Abstract
The aim of the paper is to explore the application of Parallel Neuro Simulator for the generation of artificial earthquake. Parallel Neuro Simulator is a neural network code developed on PARAM 10000 using ‘C’ language and MPI library subroutines. In this study, two artificial neural network (ANN) models have been proposed to replace the auto-regressive moving average (ARMA) model. First ANN model substitutes the polynomial model that represents the relation of initial site information and coefficients of polynomial and the second ANN based model substitutes the estimated parameters of the ARMA model. Several Indian earthquake records have been used for present study on PARAM 10000. The variation in computational time with increasing number of processors has also been studied.
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References
Box, G.E.P., Cox, D.R.: An analysis of transformations. J. R. Statist. Soc. B 26, 211–252 (1964)
Cheng, M., Popplewell, N.: Neural Network for Earthquake Selection in Structural Time History Analysis. Earthquake Engineering and Structural Dynamics 23, 303–319 (1994)
Chang, M.K., Kwaitkowski, J.W., Nau, R.F., Oliver, R.M., Pister, K.S.: ARMA models for earthquake ground motions. Earthquake Eng. Struct. Dyn. 10, 651–662 (1982)
Ghaboussi, J., Lin, C.: New method of generating spectrum compatible accelerograms using neural networks. Earthquake Eng. Struct. Dyn. 27, 377–396 (1998)
Haykin, S.: Neural networks: a comprehensive foundation. Prentice-Hall International, Inc., Englewood Cliffs (1994)
Kerh, T., Chu, D.: Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion. Advances in Engineering Software 33, 733–742 (2002)
Lee, S.C., Han, S.W.: Neural network based models for generating artificial earth-quakes and response spectra. Computers and Structures 80, 1627–1638 (2002)
Lin, Y.K., Young, Y.: Evolutionary Kanai-Tajimi earthquake models. J. Eng. Mech., ASCE 113(8), 1119–1137 (1987)
MSMPI, The Mississippi State MPI web page, http://www.erc.msstate.edu/mpi
Pacheco, P.S.: A User Guide to MPI, Dept. of Mathematics, University of San Francisco, San Francisco, March 30 (1998), http://lavica.fesb.hr/~slap/upute/mpi.guide.ps
Polhemus, N.W., Cakmak, A.S.: Simulation of earthquake ground motions using autoregressive moving average (ARMA) models. Earthquake Engg. Struct. Dyn. 9, 343–354 (1981)
Roverso, D.: Neural Ensembles for Event Identification. In: Proceedings of Safeprocess 2000, the 4th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (2000)
Tung, A.T.Y., Wang, Y.Y., Wong, F.S.: Prediction of the Spatial Distribution of the Modified Mercalli Intensity using Neural Networks. Earthquake Engineering and Structural Dynamics 23(1), 49–62 (1994)
Wen, Y.K., Yeh, C.-H.: Biaxial and torsional responses of inelastic structures under random excitations. Structural Safety 6(2-4), 137–152 (1989)
Yeh, C.-H., Wen, Y.K.: Modeling of nonstationary ground motion and analysis of inelastic structural response. Structural Safety 8(1-4), 281–298 (1990)
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Singh, S., Barai, S.V. (2004). Earthquake Engineering Problems in Parallel Neuro Environment. In: Bougé, L., Prasanna, V.K. (eds) High Performance Computing - HiPC 2004. HiPC 2004. Lecture Notes in Computer Science, vol 3296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30474-6_25
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DOI: https://doi.org/10.1007/978-3-540-30474-6_25
Publisher Name: Springer, Berlin, Heidelberg
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