Mathematical Problems in Engineering
Volume 2011 (2011), Article ID 464353, 15 pages
Research Article

Neural Network Approach for Analyzing Seismic Data to Identify Potentially Hazardous Bridges

1Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
2Faculty of Architecture, Design, and Planning, The University of Sydney, NSW 2006, Australia

Received 11 December 2010; Revised 4 March 2011; Accepted 16 March 2011

Academic Editor: Massimo Scalia

Copyright © 2011 Tienfuan Kerh 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.


Examining the effect of strong ground motions on civil engineering structures is important as it concerns public safety. The present study initially selects twenty-one bridges with lengths over 500 m in the Formosa freeway of Taiwan and collects a series of recorded seismic data from checking stations near these bridges. Then, three seismic parameters including focal depth, epicenter distance, and local magnitude are used as the input data sets, and a model for estimating the key seismic parameter—peak ground acceleration—for each of bridge site is developed by using the neural network approach. This model is finally combined with a simple distribution method and a new weight-based method to estimate peak ground acceleration at each of the bridges along the freeway. Based on the seismic design value in the current building code as the evaluation criteria, the model identifies five bridges, out of all the bridges investigated, as having the potential to be subjected to significantly higher horizontal peak ground accelerations than that recommended for design in the building code. The method presented in this study hence provides a valuable reference for dealing with nonlinear seismic data by developing neural network model, and the approach presented is also applicable to other areas of interest around the world.