Fuzzy Neural Network-Based Damage Assessment of Bridge under Temperature Effect
Fuzzy Neural Network-Based Damage Assessment of Bridge under Temperature Effect
复制标题
基于模糊神经网络的温度效应下桥梁损伤评估
DOI：
10.1155/2014/418040
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发表时间：
2014-03
影响因子：
--
通讯作者：
Song Gang
中科院分区：
文献类型：
--
作者：
Jiao Yubo;Liu Hanbing;Cheng Yongchun;Wang Xianqiang;Gong Yafeng;Song Gang
基于振动的损伤识别方法在桥梁损伤识别中得到了广泛的应用。固有频率、振型及其导数是对损伤敏感的参数。然而，这些参数不仅会受到结构健康状况的影响，而且还会受到温度变化的影响。在实际应用中，消除温度的影响是十分必要的.因此，本文提出了一种基于模糊神经网络的损伤评估方法。均匀载荷表面曲率被用作损伤指标。在桥梁模型的数值模拟中，假定混凝土的弹性模量与温度有关。通过选取温度和均布载荷面曲率作为模糊神经网络的输入变量，该算法可以区分损伤和温度效应。通过模糊神经网络与BP网络的对比分析，说明了该方法的优越性.
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.
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影响因子：
1.4
作者：
Zhang, Z;Aktan, AE
通讯作者：
Aktan, AE
影响因子：
5.5
作者：
Xia, Y;Hao, H;Deeks, A
通讯作者：
Deeks, A
影响因子：
1.1
作者：
M. Wahab;G. Roeck
通讯作者：
M. Wahab;G. Roeck
影响因子：
5.5
作者：
Huapeng Chen
通讯作者：
Huapeng Chen
影响因子：
5.8
作者：
Orcesi, Andre D.;Frangopol, Dan M.
通讯作者：
Frangopol, Dan M.