基于RBF神经网络的钢框架梁端节点损伤识别
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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.
To effectively identify the post-earthquake level of steel frame joint damage and semi-rigid joint stiffness parameters,and mode shapes,curvature mode are used as RBF neural networks import vector to identify steel portal frame construction.Curvature mode is more sensitive than mode shapes for structural parameters identification.Modal identification results show that the displacement damage location accuracy is higher than curvature mode and the damage degree identification curvature mode is superior to displacement mode.Modal displacement damage identification error is less than 10% and the curvature modal identification error is less than 5%.Based on RBF neural networks joint damage and semi-rigid parameters can be better identified.
To effectively identify the post-earthquake level of steel frame joint damage and semi-rigid joint stiffness parameters,and mode shapes,curvature mode are used as RBF neural networks import vector to identify steel portal frame construction.Curvature mode is more sensitive than mode shapes for structural parameters identification.Modal identification results show that the displacement damage location accuracy is higher than curvature mode and the damage degree identification curvature mode is superior to displacement mode.Modal displacement damage identification error is less than 10% and the curvature modal identification error is less than 5%.Based on RBF neural networks joint damage and semi-rigid parameters can be better identified.
引文
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[2]陈伟,瞿伟廉,姜正国.人工神经网络技术在结构损伤诊断中的应用[J].世界地震工程,2002,18(1):146-149.CHEN Wei,QU Wei-lian,JIANG Zheng-gou.The application of antificial neural networks to structural damage de-tection.[J].World earthquake engineering,2002,18(1):146-149.
[3]陈绍蕃.钢结构设计原理[M].3版.北京:科学出版社,2005:180-214.CHEN Shao-fan.Principles of steel structure design[M].3nd ed.Beijing:Science Press,2005:180-214.
[4]陈惠发,周绥平,译.钢框架稳定设计[M].上海:世界图书出版公司,1999:161-221CHEN Hui-fa,ZHOUSui-ping,Translated Stability Design of Steel Frame[M].Shanghai:World Book Publishing Com-pany,1999.
[5]沈祖炎.钢结构学[M].北京:中国建筑工业出版社,2006:55-80.SHEN Zu-yan.Research on steel structure[M].Beijing:China Architecture and Building Press,2006:55-80.
[6]瞿伟廉,陈伟,李秋胜.基于神经网络技术的复杂框架结构节点损伤的两步诊断法[J].土木工程学报,2003,36(5):37-42.QU Wei-lian,CHEN Wei,LI Qiu-sheng.Two-step approach for joints damage diagnosis of framed structures byartificial neural networks[J].China Civil Engineering Journal,2003,36(5):37-45.
[7]周建中,赵鸿铁,薛建阳.粘弹性阻尼结构的递归神经网络优化[J].西安建筑科技大学学报:自然科学版,2005,37(1):35-39.ZHOUJian-zhong,ZHAO Hong-tie,XUE Jian-yang.Opti mum of the viscoelastic structure based on recurrentneural network[J].J.Xi′an Univ.of Arch.&Tech.:Natural Science Edition,2005,37(1):35-39
[8]陈骥.钢结构稳定与理论设计[M].2版.北京:科学出版社,2003:188-201.CHENJi.Stability of steel structures theory and design[M].2nd ed.Beijing:Science Press,2003:180-214.
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