Fuzzy Neural Network-Based Damage Assessment of Bridge under Temperature EffectReport as inadecuate

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Mathematical Problems in Engineering - Volume 2014 2014, Article ID 418040, 9 pages -

Research ArticleCollege of Transportation, Jilin University, No. 5988 Renmin Street, Changchun 130025, China

Received 24 July 2013; Revised 16 December 2013; Accepted 30 December 2013; Published 5 March 2014

Academic Editor: Yuri Petryna

Copyright © 2014 Yubo Jiao 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.


Vibration-based method has been widely applied for damage identification of bridge. Natural frequency, mode shape, and their derivatives are sensitive parameters to damage. However, these parameters can be affected not only by the health of structure, but also by the changing temperature. It is essential to eliminate the influence of temperature in practice. Therefore, a fuzzy neural network-based damage assessment method is proposed in this paper. Uniform load surface curvature is used as damage indicator. Elasticity modulus of concrete is assumed to be temperature dependent in the numerical simulation of bridge model. Through selecting temperature and uniform load surface curvature as input variables of fuzzy neural network, the algorithm can distinguish the damage from temperature effect. Comparative analysis between fuzzy neural network and BP network illustrates the superiority of the proposed method.

Author: Yubo Jiao, Hanbing Liu, Yongchun Cheng, Xianqiang Wang, Yafeng Gong, and Gang Song

Source: https://www.hindawi.com/


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