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Peak Ground Acceleration Prediction Using Artificial Neural Networks Approach: Application to the Kik-Net Data


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DOI: https://doi.org/10.15866/irehm.v2i4.7121

Abstract


The aim of this work is to propose a prediction equation of the PGA using the Multi-Layer Perceptron Artificial Neural Network method (ANN) with a Levenberg–Marquardt backpropagation algorithm for the training. The inputs are the magnitude, the focal depth, the epicentral distance, the thickness and the mean frequency at up to a shear wave velocity equal to 800m/s, while the target result is the PGA. To establish this network, data collected from the KIK-NET seismic data base in Japan area have been used. 102 sites and 1850 records are used in the training phase while 326 events were not used in the training are kept for the test phase. The obtained results show that PGA computed using the ANN method are close to those recorded. Finally, a example is presented, in which, 55 records are used to compared the ANN method with two Ground Motion Prediction Equations (GMPEs). This example demonstrates how the ANN is more robust than classical models.
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Keywords


Artificial Neural Networks; KIK-NET Recorder Network; Peak Ground Acceleration; Seismic Parameters; Site Parameters

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References


N.N. Ambraseys, and J. Douglas, Near-field horizontal and vertical earthquake ground motions, Soil Dynamics and Earthquake Engineering, vol.23(Issue 1):1 – 18, January 2003.
http://dx.doi.org/10.1016/s0267-7261(02)00153-7

T. Takahashi, S. Kobayashi, Y. Fukushima, J.X. Zhao, H. Nakamura, and P.G. Somerville, A spectral attenuation model for Japan using strong-motion data base. 6th International Conference on Seismic Zonation (6ICSZ): Managing Earthquake Risk in the 21st Century, Earthquake, Oakland, CA, USA. November 2000.

P. Lussou, P.Y. Bard, and F. Cotton, Site design regulation codes: Contribution of K-NET DATA to site effect evaluation, Journal of Earthquake, Vol.5(Issue 1):13-33, 2001.
http://dx.doi.org/10.1080/13632460109350384

J. Douglas, Earthquake ground motion estimation using strong-motion records: a review of equations for the estimation of peak ground acceleration and response spectral ordinates, Earth-Science Reviews, vol.61(Issue1-2): 43–104, April 2003.
http://dx.doi.org/10.1016/s0012-8252(02)00112-5

G. Giacinto, R. Paolucci, and F. Roli, Application of neural networks and statistical pattern recognition algorithms to earthquake risk evaluation, Pattern recognition letters vol.18(Issue11-13) :1353-1362. November 1997.
http://dx.doi.org/10.1016/s0167-8655(97)00088-3

R. Paolucci, P. Colli, and G. Giacinto, Assessment of seismic site effect in 2-D alluvial Valleys Using Neural Networks, Eartquake Spectra, Vol.16, (Issue 3):661-680, August 2000.
http://dx.doi.org/10.1193/1.1586133

J. Ghaboussi J, and C.J. Lin, New method of generating spectrum compatible accelerograms using neural networks, Earthquake Engineering and Structural Dynamics, Vol.27(Issue 4):377-396, April 1998.
http://dx.doi.org/10.1002/(sici)1096-9845(199804)27:4%3C377::aid-eqe735%3E3.3.co;2-u

C-C.J. Li and J. Ghaboussi, Generating multiple spectrum compatible accelerograms using stochastic neural networks, Engineering and Structural Dynamics, Vol.30(Issue 7):1021–1042, July 2001.
http://dx.doi.org/10.1002/eqe.50

J.E. Hurtado, J.M. Londono and M.A. Meza, On the applicability of neural networks for soil dynamic amplification analysis, Soil dynamics and earthquake engineering, Vol. 21(Issue 7):579-591, Octobre 2001.
http://dx.doi.org/10.1016/s0267-7261(01)00037-9

B. Derras, A. Bekkouche, and D. Zendagui, neuronal approach and the use of kik-net network to generate response spectrum on the surface. Jordan Journal of Civil Engineering, Vol.4(Issue 1):12-21, January 2010.

S.C. Lee, and S.W. Han, Neural-network-based models for generating artificial earthquakes and response spectra. Computers and Structures, Vol.80(Issue 20-21):1627–1638, August 2002.
http://dx.doi.org/10.1016/s0045-7949(02)00112-8

A.T.C. Goh, Seismic liquefaction potential assessed by neural networks », ASCE Journal of Geotechnical Engineering, Vol. 120(Issue9): 1467-1480, September 1994.
http://dx.doi.org/10.1061/(asce)0733-9410(1994)120:9(1467)

M.H. Basiar, and A. Ghorbani, Evaluation of lateral spreading using artificial neural networks, Soil Dynamics and Earthquake Engineering Vol.25(Issue 5):1–9, January 2005.
http://dx.doi.org/10.1016/j.soildyn.2004.09.001

B. Vincenzo, C. Matteo, D. Sebastiano, G. Antonino, C.M. Francesco, and P. Francesco, Radial Basis Function Neural Networks to Foresee Aftershocks in Seismic Sequences Related to Large Earthquakes, 13th international conference on Neural Information Processing, Hong Kong, China, Vol.4234, pp.909-916, October 2006.
http://dx.doi.org/10.1007/11893257_100

E.L. Alves, Earthquake Forecasting Using Neural Networks: Results and Future Work, Nonlinear Dynamics, Vol.44(Issue 1-4 ): 341–349, June 2006.
http://dx.doi.org/10.1007/s11071-006-2018-1

B-Y. Liu, L.Y YE, ye, M-L. Xiao and M. Sheng, Peak Ground Velocity Evaluation by Artificial Neural Network for West America Region, The 13th International Conference on Neural Information Processing, Hong Kong, China,Vol.4234, pp.942-951, October 2006.
http://dx.doi.org/10.1007/11893257_104

T. Kerh, and S.B. Ting, « Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system ». Engineering Applications of Artificial Intelligence, vol.18(Issue 7): 857–866. October 2005.
http://dx.doi.org/10.1016/j.engappai.2005.02.003

Kemal Günaydın, and Ayten Günaydın, 2008. “Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey”. Hindawi Publishing Corporation Mathematical Problems in Engineering Vol.2008, Article ID 919420, 20 pages.
http://dx.doi.org/10.1155/2008/919420

Y.H. hu, and J-N. Hwang, Handbook of neural network signal processing, (CRC press, 2002).

K. Kuźniar, E. Maciągb, and Z. Waszczyszyn, Computation of response spectra from mining tremors using neural networks, Soil Dynamics and Earthquake Engineering, vol.25(Issue4):331–339, June 2005.
http://dx.doi.org/10.1016/j.soildyn.2005.02.001

J. Wang and Ta-L. Teng, Artificial Neural Network-Based Seismic Detector, Bulletin of the Seismological Society of America, Vol.85, (Issue1):308-319, February 1995.

S. Rajasekaran , V. latha, and S.C. Lee, Generation of artificial earthquake motion records Using wavelets and principal component analysis, Journal of earthquake engineering, vol.10(Issue5):665-691, September 2006
http://dx.doi.org/10.1080/13632460609350614

T. Kerh, and Da. Chu, Neural networks approach and microtremor measurements in estimating peak ground acceleration due to strong motion, Advances in Engineering Software, Vol.33(Issue11-12):733-742, December 2002.
http://dx.doi.org/10.1016/s0965-9978(02)00081-9

J. J. Moré, The Levenberg-Marquardt algorithm: Implementation and theory (Lecture Notes in Mathematics 630, G. A. Watson, ed., Springer-Verlag, Berlin-Heidelberg-New York, 1978, pp.105-116).
http://dx.doi.org/10.1007/bfb0067700

N.S. Klimis, B.N. Margaris, and P.K. Koliopoulos, Response spectra estimation according to the EC8 and NEHRP soil classification provisions: a comparison study based on Hellenic data, CDROM paper. Proc. 11th European Conference on Earthquake Engineering, Paris, France.

P. Lussou, Calcul du mouvement sismique associé à un séisme de référence pour un site donné avec prise en compte de l’effet de site. Méthode empirique linéaire et modélisation de l’effet de site non-linéaire, Thèse de doctorat, Université Joseph Fourier, Grenoble, France, 2001.

DTR-C 2-4.8, Règles parasismiques algériennes- R.P.A.99 /version 2003 », Document technique réglementaire, centre national de recherche appliquée en génie parasismique, Ministère de l’habitat, Algérie, 2003.

G. Pousse, Analyse des données accélérometriques de K-NET et KIK-NET: implications pour la prédiction du mouvement sismique-accélérogrammes et spectres de réponse et la prise en compte des effets de site nonlinéaire, Thèse de doctorat, Université Joseph Fourier, Grenoble, France, 2005.

T. Kashima, ViewWave Help, (IISEE, BRI, 2002).

NeuroDimension. Inc, NeuroSolutions Getting Started Manual Version 5.07. www.nd.com.

A.S. Elnashai, and L.D. Sarno, Fundamentals of earthquake engineering. (Wiley, Publication United Kingdom, 2008).
http://dx.doi.org/10.1002/9780470024867


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