基于挠度的铁路双线简支钢桁梁桥杆件损伤程度识别研究
发布时间：2018-08-01 16:13
【摘要】：以梁桥节点最大位移改变率作为损伤程度伤识别指标,分别采用广义回归神经网络(GRNN)算法和ε-支持向量回归机(ε-SVR)算法,进行损伤程度识别研究。通过对一座铁路双线简支钢桁梁桥某杆件的损伤程度识别研究发现:(1)GRNN损伤程度识别模型具有一定的抗噪能力,不具有泛化性。(2)SVR损伤程度识别模型具有很强的抗噪能力和很好的泛化性。(3)以桥梁节点最大位移改变率作为损伤程度识别指标时,数据回归算法不能采用GRNN算法,应采用ε-SVR算法。
[Abstract]:The maximum displacement change rate of beam bridge joint is used as the index of damage degree identification, and the generalized regression neural network (GRNN) algorithm and 蔚-support vector regression machine (蔚 -SVR) algorithm are used to study the damage degree identification. Based on the research on the damage degree of a member of a steel truss bridge with two railway lines simply supported, it is found that: (1) the GRNN damage degree recognition model has a certain anti-noise ability. (2) SVR damage recognition model has strong anti-noise ability and good generalization. (3) when the maximum displacement change rate of bridge node is taken as the index of damage degree identification, the data regression algorithm can not adopt GRNN algorithm, but 蔚 -SVR algorithm should be used.
【作者单位】： 河北省电力勘测设计研究院土建部;石家庄铁道大学工程力学系;石家庄铁道大学大型结构健康诊断与控制研究所;
【基金】：国家自然科学基金(51278315) 河北省自然科学基金(E2012210061) 河北省教育厅基金(Z2013034)
【分类号】：U441.4
[Abstract]:The maximum displacement change rate of beam bridge joint is used as the index of damage degree identification, and the generalized regression neural network (GRNN) algorithm and 蔚-support vector regression machine (蔚 -SVR) algorithm are used to study the damage degree identification. Based on the research on the damage degree of a member of a steel truss bridge with two railway lines simply supported, it is found that: (1) the GRNN damage degree recognition model has a certain anti-noise ability. (2) SVR damage recognition model has strong anti-noise ability and good generalization. (3) when the maximum displacement change rate of bridge node is taken as the index of damage degree identification, the data regression algorithm can not adopt GRNN algorithm, but 蔚 -SVR algorithm should be used.
【作者单位】： 河北省电力勘测设计研究院土建部;石家庄铁道大学工程力学系;石家庄铁道大学大型结构健康诊断与控制研究所;
【基金】：国家自然科学基金(51278315) 河北省自然科学基金(E2012210061) 河北省教育厅基金(Z2013034)
【分类号】：U441.4
【参考文献】
相关期刊论文 前10条
1 刘涛;李爱群;丁幼亮;;基于小波包能量谱的大跨斜拉桥拉索损伤预警方法[J];东南大学学报(自然科学版);2007年02期
2 姜绍飞;张帅;;基于模糊神经网络的数据融合结构损伤识别方法[J];工程力学;2008年02期
3 朱宏平;余t，
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