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Shock and Vibration
Volume 2015, Article ID 789384, 11 pages
http://dx.doi.org/10.1155/2015/789384
Research Article

Bridge Damage Severity Quantification Using Multipoint Acceleration Measurement and Artificial Neural Networks

1Department of Civil and Environmental Engineering, Ehime University, Matsuyama, Ehime 790-8577, Japan
2West Nippon Expressway Engineering Shikoku Company Ltd., Takamatsu, Kagawa 760-0072, Japan

Received 1 March 2015; Revised 6 April 2015; Accepted 7 April 2015

Academic Editor: Jiawei Xiang

Copyright © 2015 Pang-jo Chun 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

The deterioration of bridges as a result of ageing is a serious problem in many countries. To prevent the failure of these deficient bridges, early damage detection which helps us to evaluate the safety of bridges is important. Therefore, the present research proposed a method to quantify damage severity by use of multipoint acceleration measurement and artificial neural networks. In addition to developing the method, we developed a cheap and easy-to-make measurement device which can be made by bridge owners at low cost and without the need for advance technical skills since the method is mainly intended to apply to small to midsized bridges. In addition, the paper gives an example application of the method to a weathering steel bridge in Japan. It can be shown from the analysis results that the method is accurate in its damage identification and mechanical behavior prediction ability.