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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 937480, 15 pages
http://dx.doi.org/10.1155/2012/937480
Research Article

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.

Abstract

Several studies have been oriented to develop methodologies for estimating inelastic response of structures; however, the estimation of inelastic seismic response spectra requires complex analyses, in such a way that traditional methods can hardly get an acceptable error. In this paper, an Artificial Neural Network (ANN) model is presented as an alternative to estimate inelastic response spectra for earthquake ground motion records. The moment magnitude (), fault mechanism (), Joyner-Boore distance (), shear-wave velocity (), fundamental period of the structure (), and the maximum ductility () were selected as inputs of the ANN model. Fifty earthquake ground motions taken from the NGA database and recorded at sites with different types of soils are used during the training phase of the Feedforward Multilayer Perceptron model. The Backpropagation algorithm was selected to train the network. The ANN results present an acceptable concordance with the real seismic response spectra preserving the spectral shape between the actual and the estimated spectra.