Earthquakes and Structures

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

A neural-attenuation model before Mexican extreme events

  • Garcia, Silvia R. (Geotechnical Department, Instituto de Ingenieria, Universidad Nacional Autonoma de Mexico) ;
  • Alcantara, Leonardo (Seismology Department, Instituto de Ingenieria, Universidad Nacional Autonoma de Mexico)
  • Received : 2018.07.31
  • Accepted : 2019.10.22
  • Published : 2019.12.25
⟨ Previous Next ⟩

Abstract

The most recent shaking experiences have demonstrated that the predictions of the seismic models are not always in agreement with the registered responses, especially in the face of extreme earthquakes. Records collected from 1960 to 2011 at a rock-like site are used to develop a neural network that permits to estimate peak ground accelerations via the magnitude, the focal depth, the site-source distance and a seismogenic zone. The neural model is applied to the 8th and 19th September 2017 events that hit Mexican territory and the obtained results show that the network is flexible enough to work appropriately to various conditions of intensity and sites-sources with remarkably predictive capacity. The neural-attenuation curves are compared with those obtained from Ground Motion Prediction Equations and their performance is assessed for events, in addition to the devastating Mexican events, from Japan, Taiwan, Chile and USA.

Keywords

References

  1. Ambraseys, N.N. and Douglas, J. (2003), "Near-field horizontal and vertical earthquake ground motions", Soil Dyn. Earthq. Eng., 23(1), 1-18. https://doi.org/10.1016/S0267-7261(02)00153-7.
  2. Arroyo, D., Garcia, D., Ordaz, M., Mora, M.A. and Singh, S.K. (2010), "Strong ground-motion relations for Mexican interplate earthquakes", J. Seismol., 14, 769-785. https://doi.org/10.1007/s10950-010-9200-0.
  3. Atkinson, G.M. (2008), "Ground-motion prediction equations for eastern North America from a referenced empirical approach: Implications for epistemic uncertainty", Bull. Seismol. Soc. Am., 98(3), 1304-1318. https://doi.org/10.1785/0120070199.
  4. Atkinson, G.M. and Boore, D.M. (2011), "Modifications to existing ground-motion prediction equations in light of new data", Bull. Seismol. Soc. Am., 101(3), 1121-1135. https://doi.org/10.1785/0120100270.
  5. Atkinson,, G.M. and Boore, D.M. (2006), "Earthquake ground-motion prediction equations for eastern North America", Bull. Seismol. Soc. Am., 96(6), 2181-2205. https://doi.org/10.1785/0120050245.
  6. BMDSF-SMIS (2000), Mexican Strong Motion DataBase, CD-ROM vol.2, Sociedad Mexicana de Ingenieria Sismica-SMIS, Mexico.
  7. Bose, N.K. and Liang, P. (1996), Neural Nnetwork Fundamentals with Graphs, Algorithms, and Applications, McGraw-Hill.
  8. Campbell, K.W. (1981), "Near-source attenuation of peak horizontal acceleration", Bull. Seismol. Soc. Am., 71, 2039-2070.
  9. Cruz, M., Ferrari, L., Krishna, S. and Ordaz, M. (2017), "El sismo del 19 de septiembre 2017", Academia de Ciencias de Morelos, A.C., La Union de Morelos, Mexico.
  10. Csaji, B.C. (2001), "Approximation with artificial neural networks", Faculty of Sciences, Etvs Lornd University, Hungary.
  11. Cybenko, G. (1989), "Approximations by superpositions of sigmoidal functions", Math. Control, Signal. Syst., 2, 303-314. https://doi.org/10.1007/BF02551274
  12. Fasel, B. (2003), "An introduction to Bio-Inspired Artificial Neural Network Architectures", Acta Neurol. Belg., 103, 6-12.
  13. Garcia, D., Singh, S.K., Herraiz, M., Ordaz, M. and Pacheco, F.J. (2005), "Inslab earthquakes of central Mexico: Peak Ground-Motion parameters and response spectra", Bull. Seismol. Soc. Am., 95(6), 2272-2282. https://doi.org/10.1785/0120050072.
  14. Garcia, S., Alcantara, L. and Manjarrez, L. (2017), "Neural-Attenuation Laws for the Mexican Subduction: An Updating Effort", 16th World Conference on Earthquake, 16WCEE 2017, Santiago Chile.
  15. Gupta, I.D. (2006), "Defining source-to-site distances for evaluation of design earthquake ground motion", Thirteenth Symp. On Earthq. Engineering, At IIT Roorkee, India.
  16. Hassoun, M. (1995), Fundamentals of Artificial Neural Networks, MIT Press.
  17. Haykin, S. (1999), Neural Networks: A Comprehensive Foundation, Prentice Hall, 2nd Edition, Upper Saddle River, New Jersey.
  18. Hornik, K. (1991), "Approximation capabilities of multilayer feedforward networks", Neur. Network., 4(2), 251-257. https://doi.org/10.1016/0893-6080(91)90009-T.
  19. Jeremias, L., Bobby, G. and Yung-Cheol, B. (2014), "An adaptive stopping criterion for backpropagation learning in feedforward neural network", Int. J. Multim. Ubiq. Eng., 9(8), 149-156. https://doi.org/10.14257/ijmue.2014.9.8.13.
  20. Lussou, P., Bard, P.Y. and Cotton, F. (2001), "Site design regulation codes: contribution of KNET DATA to site effect evaluation", J. Earthq., 5(1), 13-33.
  21. Mc Culloch, W.S. and Pitts, W.H. (1943), "A logical calculus of the ideas immanent in nervous activity", Bull. Math. Biophys., 5, 115-133. https://doi.org/10.1007/BF02478259.
  22. Moreshwar, Z.P. (2013), "Applications of artificial neural networks in civil engineering", Bachelor Thesis, Department of Civil Engineering, University of Pune, India.
  23. Nunez-Cornu, F.J., Ortiz, M. and Sanchez, J.J. (2008), "The great 1787 Mexican tsunami", Natur. Hazard.. 47(3), 569-576. https://doi.org/10.1007/s11069-008-9239-1.
  24. Ordaz, M. and Reyes, C. (1999), "Earthquake hazard in Mexico City: Observations versus computations", Bull. Seismol. Soc. Am., 89, 1379-1383.
  25. Ordaz, M., Jara, J.M. and Singh, S.K. (1989), "Riesgo sismico y espectros de diseno en el estado de Guerrero", VIII Congr. Nac. Ing., Sismica, Acapulco, Mexico, D40-D56.
  26. Rodriguez-Perez, Q. (2014), "Ground-Motion prediction equations for near-trench interplate and normal-faulting inslab subduction zone earthquakes in Mexico", Bull. Seismol. Soc. Am., 104(1), 427-438. https://doi.org/10.1785/0120130032.
  27. Rumelhart, D.E. and McClelland, J.L. (1986), "On learning the past tense of English verbs", Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 2, Psychological and Biological Models, MIT Press, Cambridge, MA.
  28. Saragoni, G.R. and Concha, P. (2004), "Damaging of cascadia subduction earthquakes compared with chilean subduction", Proceedings of the 13th World Conference on Earthquake Engineering, Vancouver, Canada.
  29. Scordilis, E.M. (2006), "Empirical global relations converting MS and mb to moment magnitude", J. Seismol., 10(2), 225-236. https://doi.org/10.1007/s10950-006-912-4.
  30. Seung, C.L. and Sang, W.H. (2002), "Neural-network-based models for generating artificial earthquakes and response spectra", Comput. Struct., 80, 1627-1638. https://doi.org/10.1016/S0045-7949(02)00112-8.
  31. Shahin, M.A., Jaksa, M.B. and Maier, H.R. (2008), "State of the Art of artificial neural networks in geotechnical engineering", Elec. J. Geotech. Eng., 8(1), 1-26.
  32. Shahin, M.A., Jaksa, M.B. and Maier, H.R. (2009), "Recent advances and future challenges for artificial neural systems in geotechnical engineering applications", J. Adv. Artif. Neur. Syst., ID 308239. https://doi.org/10.1155/2009/308239.
  33. SSN-UNAM (2017), Special Reports Web Page for the 19 September 2017 Puebla-Morelos (M7.1) Event, http://www.ssn.unam.mx/ (last accessed 15 March 2018).
  34. Stewart, J.P., Seyhan, E., Boore, D.M., Campbell, K.W., Erdik, M., Silva, W.J., Di Alessandro, C. and Bozorgnia, Y. (2012), "Site effects in parametric ground motion models for the GEM-PEER global GMPEs project", Proceedings of the Fifteenth World Conference on Earthquake Engineering, Lisbon, Portugal.
  35. USGS (2018), "Earthquake data Web page for the 8 September 2017 magnitude 8.2 event of Tres Picos", Mexico, http://earthquakes.usgs.gov/earthquakes/search.
  36. Vladimir, C. and Pacheco, F.J. (1999), "Cien anos de sismicidad en Mexico", Instituto de Geofisica, UNAM, Mexico.
  37. Werbos, P.J. (1994), The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting, John Wiley & Sons, New York.
  38. Youngs, R., Chiou, S., Silva, W. and Humphrey, J. (1997), "Strong ground motion attenuation relationships for subduction zone earthquakes", Seismol. Res. Lett., 68(1), 58-73. https://doi.org/10.1785/gssrl.68.1.58.
  39. Zhao, J.X., Zhang, J., Asano, A., Ohno, Y., Oouchi, T., Takahashi, T., Ogawa, H., Irikura, K., Thio, H.K., Somerville, P.G. and Fukushima, Y. (2006), "Attenuation relations of strong ground motion in Japan using site classification based on predominant period", Bull. Seismol. Soc. Am., 96(3), 898-913. https://doi.org/10.1785/0120050122.