- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 937480, 15 pages
Prediction of Inelastic Response Spectra Using Artificial Neural Networks
1Facultad de Ingeniería, Universidad Autónoma de Sinaloa, Calzada de las Américas y Boulevard Universitarios S/N, Ciudad Universitaria, 80040 Culiacán Rosales, SI, Mexico
2Coordinación de Mecánica Aplicada, Instituto de Ingeniería, Universidad Nacional Autónoma de México, Ciudad Universitaria, 04510 Coyoacán, DF, Mexico
Received 4 July 2012; Revised 20 August 2012; Accepted 21 August 2012
Academic Editor: Xu Zhang
Copyright © 2012 Edén Bojórquez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- H. Benioff, “The physical evaluation on seismic destructiveness,” Bulletin of the Seismological Society of America, vol. 24, pp. 398–403, 1934.
- G. Housner, An investigation of the effects of earthquakes on buildings [Ph.D. thesis], California Institute of Technology, Pasadena, Calif, USA, 1941.
- M. A. Biot, “A mechanical analyzer for the prediction of earthquake stresses,” Bulletin of the Seismological Society of America, vol. 31, no. 2, pp. 151–171, 1941.
- E. I. Alves, “Earthquake forecasting using neural networks: results and future work,” Nonlinear Dynamics, vol. 44, no. 1–4, pp. 341–349, 2006.
- S. C. Lee and S. W. Han, “Neural-network-based models for generating artificial earthquakes and response spectra,” Computers and Structures, vol. 80, no. 20-21, pp. 1627–1638, 2002.
- 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, no. 7, pp. 857–866, 2005.
- V. Barrile, V. Cacciola, S. D'Amico, A. Greco, F. C. Morabito, and F. Parrillo, “Radial basis function neural networks to foresee aftershocks in seismic sequences related to large earthquakes,” in Proceedings of the 13th International Conference on Neural Information Processing, vol. 4233, pp. 909–916, 2006.
- S. R. García, M. P. Romo, and J. M. Mayoral, “Estimation of peak ground accelerations for Mexican subduction zone earthquakes using neural networks,” Geofisica Internacional, vol. 46, no. 1, pp. 51–63, 2007.
- K. Günaydn and A. Günaydn, “Peak ground acceleration prediction by artificial neural networks for northwestern Turkey,” Mathematical Problems in Engineering, vol. 2008, Article ID 919420, 20 pages, 2008.
- C. R. Arjun and A. Kumar, “Artificial neural network-based estimation of peak ground acceleration,” Journal of Earthquake Technology, vol. 46, pp. 19–28, 2009.
- M. Papadrakakis and N. D. Lagaros, “Reliability-based structural optimization using neural networks and Monte Carlo simulation,” Computer Methods in Applied Mechanics and Engineering, vol. 191, no. 32, pp. 3491–3507, 2002.
- J. Cheng and Q. S. Li, “Reliability analysis of structures using artificial neural network based genetic algorithms,” Computer Methods in Applied Mechanics and Engineering, vol. 197, no. 45–48, pp. 3742–3750, 2008.
- L. Alcántara, E. Ovando, and M. A. Macías, “Estimación de espectros de respuesta en la ciudad de puebla utilizando redes neuronales artificiales,” in XVI Congreso Nacional de Ingeniería Sísmica, Puebla, México, 2009.
- E. Bojórquez and I. Iervolino, “Spectral shape proxies and nonlinear structural response,” Soil Dynamics and Earthquake Engineering, vol. 31, no. 7, pp. 996–1008, 2011.
- A. K. Chopra, Dynamics of Structures, Theory and Applications to Earthquake Engineering, Prentice-Hall, Upper Saddle River, NJ, USA, 2nd edition, 2001.
- G. M. Shepherd, The Synaptic Organization of the Brain, Oxford University Press, 4th edition, 1997.
- D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing. Foundations, MIT Press, 1986.
- W. B. Joyner and D. M. Boore, “Methods for regression analysis of strong-motion data,” Bulletin of Seismological Society of America, vol. 83, no. 2, pp. 469–487, 1993.
- H. Demuth, M. Beale, and M. Hagan, Neural Network Toolbox: For Use With Matlab, Mathworks, Natick, Mass, USA, 2009.
- E. Bojórquez, I. Iervolino, and G. Manfredi, “Evaluating a new proxy for spectral shape to be used as an intensity measure,” in Proceedings of the Seismic Engineering Conference Commemorating the 1908 Messina and Reggio Calabria Earthquake, vol. 1020, pp. 1599–1606, 2008.
- E. Bojórquez, A. Terán-Gilmore, S. E. Ruiz, and A. Reyes-Salazar, “Evaluation of structural reliability of steel frames: inter-story drifts versus plastic hysteretic energy,” Earthquake Spectra, vol. 27, no. 3, pp. 661–682, 2011.
- E. Bojórquez, I. Iervolino, A. Reyes-Salazar, and S. E. Ruiz, “Comparing vector-valued intensity measures for fragility analysis of steel frames in the case of narrow-band ground motions,” Engineering Structures, vol. 45, pp. 472–480, 2012.
- A. Teran-Gilmore and J. O. Jirsa, “Energy demands for seismic design against low-cycle fatigue,” Earthquake Engineering and Structural Dynamics, vol. 36, no. 3, pp. 383–404, 2007.
- E. Bojorquez, S. E. Ruiz, and A. Teran-Gilmore, “Reliability-based evaluation of steel structures using energy concepts,” Engineering Structures, vol. 30, no. 6, pp. 1745–1759, 2008.