[1]薛 强,等.基于RBF 神经网络的钢框架梁端节点损伤识别[J].西安建筑科技大学学报:自然科学版,2011,43(02):192-197.[doi:DOI :10.15986/j .1006-7930.2011.02.020]
 XUE Qiang,HAO J i-ping,et al.Research on steel frame parameters identification based on RBF neural networks[J].J.Xi’an Univ. of Arch. & Tech.:Natural Science Edition,2011,43(02):192-197.[doi:DOI :10.15986/j .1006-7930.2011.02.020]
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基于RBF 神经网络的钢框架梁端节点损伤识别()
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西安建筑科技大学学报:自然科学版[ISSN:1006-7930/CN:61-1295/TU]

卷:
43
期数:
2011年02期
页码:
192-197
栏目:
出版日期:
2011-04-30

文章信息/Info

Title:
Research on steel frame parameters identification based on RBF neural networks
文章编号:
1006-7930(2011)02-0192-06
作者:
薛 强1 2 郝际平1 郑 粤2
(1 .西安建筑科技大学土木工程学院, 陕西西安710055 ;2 .西安建筑科技大学建筑设计研究院, 陕西西安710055)
Author(s):
XUE Qiang 1 2 HAO J i-ping 1 Z HEN GY ue2
(1 .Scho ol of Civil Eng ., Xi′an Univ .o f Arch .& Tech., Xi′an 710055, China ;
2.Institute of Architecture ., Xi′an Univ .o f Arch.& Tech ., Xi′a n 710055 , China)
关键词:
RBF 神经网络钢框架节点损伤识别
Keywords:
RBF neural network ssteel f rame f rame detection .
分类号:
TU375
DOI:
DOI :10.15986/j .1006-7930.2011.02.020
文献标志码:
A
摘要:
为有效识别钢框架梁端节点损伤程度及半刚性节点刚度参数, 提出采用钢梁位移模态和曲率模态指
标作为神经网络的输入参数, 基于RBF 神经网络对刚框架梁端节点损伤程度进行参数识别研究.结果证明,
位移模态识别损伤位置的准确度高于曲率模态, 对损伤程度的识别曲率模态优于位移模态.其中位移模态损
伤识别误差小于10 %, 曲率模态识别误差小于5%, 得出基于RBF 神经网络可以较好的识别节点损伤及半刚
性刚度参数
Abstract:
To effectively ide ntify the post-ear thquake level o f steel frame jo int damage and semi-rigid joint stiff ness paramete
rs , and mode shapes, cur vature mode a re used as RBF neura l ne tw o rks impo rt vecto r to identify steel po rtal fr ame co nstr
uction .Curva ture mo de is mo re sensitive than mode shapes fo r str uctural parame te rs identificatio n.Mo dal identification
results show tha t the displacement damage locatio n accuracy is higher than curvature mo de and the damag e deg ree identificatio
n curv ature mode is superior to displacement mode .Modal displacement damag e identifica tion e rro r is less than
10 % and the cur vature modal identifica tion er ror is less than 5%.Based o n RBF neura l ne tw o rks joint damag e and semirig
id parameter s can be better identified .

参考文献/References:

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备注/Memo

备注/Memo:
 收稿日期:2010-03-10   修改稿日期:2011-03-26
基金项目:国家自然科学基金资助项目(50878181)
作者简介:薛 强(1982-), 男, 陕西延安人, 博士研究生, 工程师, 主要从事钢结构稳定设计及损伤识别方法研究.
更新日期/Last Update: 2015-11-01