Abstract
With the Peak Ground Velocity 283 records in three dimensions, the velocity attenuation relationship with distance was discussed by neural network in this paper. The earthquake magnitude, epicenter distance, site intensity and site condition were considered as basic input element for the network. By using Bayesian Regularization Back Propagation Neural Networks (BRBPNN), the over-fitting phenomenon was reduced to some extent. The horizontal velocity was discussed. The PGV predicted by neural networks can simulate the detail difference with distance, while the PGV given by other traditional attenuation relationship only give a reduction relation with distance. The importance of each input factor was compared by the square weight of the input layer of the network. The order may be earthquake magnitude, epicenter distance and soil condition.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zhen, G.F., Tao, X.X.: Construct the Intensity Attenuation Relation Using ANN Method. Earthquake Engineering and Engineering Vibration 13(1), 60–66 (1993)
Wang, H.S.: Intelligent Prediction of the Peak Seismic Parameters Based On ANN. Journal of Seismology 15(2), 208–216 (1993)
Cui, J.W., Fan, Y.X., Wen, R.Z.: Establishment of Attenuation Law of Acceleration Peak Value by Using Neural Network. Earthquake Research 20(3), 296–306 (1997)
Hu, Y.X., Zhang, Y.M., Shi, Z.L.: Training Material for the Code of Evaluation of Seismic Safety for Engineering Sites. Engineering Earthquake Research Center (1994)
Joyner, W.B., Boore, D.M.: Peak Attenuation and Velocity from Strong-Motion Records Including Records from 1979 Imperial Valley, California, Earthquake. Bulletin of Seismology Society of America 71(6), 2011–2038 (1981)
Huo, J.R.: Near Field Ground Motion Attenuation Research. Ph. D. Thesis of the Institute Of Engineering Mechanics, China Earthquake Administration (1989)
MacKay, D.J.C.: Bayesian Interpolation. Neural Computation 4(3), 415–447 (1992)
MacKay, D.J.C.: A practical Bayesian framework for back propagation networks. Neural Computation 4(3), 448–472 (1992)
Foresee, F.D., Hagan, M.T.: Gauss-Newton approximation to Bayesian regularization. In: Proceedings of the 1997 International Joint Conference on Neural Networks, pp. 1930–1935 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, By., Ye, Ly., Xiao, Ml., Miao, S. (2006). Peak Ground Velocity Evaluation by Artificial Neural Network for West America Region. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_104
Download citation
DOI: https://doi.org/10.1007/11893257_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
eBook Packages: Computer ScienceComputer Science (R0)