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Shock and Vibration
Volume 2017 (2017), Article ID 2408234, 18 pages
Research Article

Seismic Failure Probability of a Curved Bridge Based on Analytical and Neural Network Approaches

1Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2International Institute of Earthquake Engineering and Seismology (IIEES) and Iranian Earthquake Engineering Association, Tehran, Iran

Correspondence should be addressed to P. Zarfam;

Received 4 February 2017; Revised 28 April 2017; Accepted 2 May 2017; Published 17 August 2017

Academic Editor: Carlo Rainieri

Copyright © 2017 K. Karimi-Moridani 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.


This study focuses on seismic fragility assessment of horizontal curved bridge, which has been derived by neural network prediction. The objective is the optimization of structural responses of metaheuristic solutions. A regression model for the responses of the horizontal curved bridge with variable coefficients is built in the neural networks simulation environment based on the existing NTHA data. In order to achieve accurate results in a neural network, 1677 seismic analysis was performed in OpenSees. To achieve better performance of neural network and reduce the dimensionality of input data, dimensionality reduction techniques such as factor analysis approach were applied. Different types of neural network training algorithm were used and the best algorithm was adopted. The developed ANN approach is then used to verify the fragility curves of NTHA. The obtained results indicated that neural network approach could be used for predicting the seismic behavior of bridge elements and fragility, with enough feature extraction of ground motion records and response of structure according to the statistical works. Fragility curves extracted from the two approaches generally show proper compliance.