基于受控结构振型的损伤定位分步识别方法
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摘要
提出了基于神经网络技术和受控结构振型数据的损伤定位分步识别方法。首先采用状态反馈控制的方法,有目的地对结构进行极点配置,得到设置的受控结构的动力特性数据。将结构划分为若干个子区域,以受控结构损伤前后振型差作为输入参数,利用概率神经网络确定结构损伤所在的子区域,然后用多损伤定位确信准则对结构损伤子区域中的具体损伤位置进行识别。数值仿真表明,利用概率神经网络能有效地确定结构损伤子区域,采用分步识别的策略能大大缩小具体损伤单元的识别范围,而利用受控结构的动力特性参数可提高识别指标对损伤的敏感度,进而提高损伤识别的准确性。
The paper proposes a two-step structural damage localization method based on a neural network and the mode shapes of structures under control. In the study, the state feedback control method is first employed to place the poles of the structure intentionally and the prescribed characteristic frequencies and mode shapes of the controlled structure are obtained accordingly. The structure is divided into several sub-domains and damaged sub-domain is identified using a probabilistic neural network, in which the mode differences between undamaged and damaged controlled structures are chosen as the input vectors. Then the multiple damage location assurance criterion is taken as the damage indicator to locate the specific damaged element. Numerical results show that the damaged sub-domain can be identified using the probabilistic neural network and the search domain for locating the specific damaged element is greatly reduced by the adoption of the proposed two-step identification strategy. The use of dynamic characteristic data of the structure under control can improve the sensitivity of the damage indicator and consequently the accuracy of damage identification.
The paper proposes a two-step structural damage localization method based on a neural network and the mode shapes of structures under control. In the study, the state feedback control method is first employed to place the poles of the structure intentionally and the prescribed characteristic frequencies and mode shapes of the controlled structure are obtained accordingly. The structure is divided into several sub-domains and damaged sub-domain is identified using a probabilistic neural network, in which the mode differences between undamaged and damaged controlled structures are chosen as the input vectors. Then the multiple damage location assurance criterion is taken as the damage indicator to locate the specific damaged element. Numerical results show that the damaged sub-domain can be identified using the probabilistic neural network and the search domain for locating the specific damaged element is greatly reduced by the adoption of the proposed two-step identification strategy. The use of dynamic characteristic data of the structure under control can improve the sensitivity of the damage indicator and consequently the accuracy of damage identification.
引文
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[2]马宏伟,杨桂通.结构损伤探测的基本方法和研究进展[J].力学进展,1999,29(4):513~527.Ma Hongwei,Yang Guitong.Methods and advances of structural damage detection[J].Advances in Mechanics,1999,29(4):513~527.(in Chinese)
[3]谢强,薛松涛.土木工程结构健康监测的研究状况与进展[J].中国科学基金,2001,5:285~288.Xie Qiang,Xue Songtao.Research state and advances on structural health monitoring in civil engineering[J].Bulletin of National Natural Science Foundation of China,2001,5:285~288.(in Chinese)
[4]孙鸿敏,李宏男.土木工程结构健康监测研究进展[J].防灾减灾工程学报,2003,23(3):92~98.Sun Hongmin,Li Hongnan.State-of-the-art review of the structural health monitoring in civil engineering[J].Journal of Disaster Prevention and Mitigation Engineering,2003,23(3):92~98.(in Chinese)
[5]瞿伟廉,黄东梅.大型复杂结构的两阶段损伤诊断方法[J].世界地震工程,2003,19(2):72~78.Qu Weilian,Huang Dongmei.Two-step damage diagnosis methods of large and complex structures[J].World Earthquake Engineering,2003,19(2):72~78.(in Chinese)
[6]Kim H,Young M,Barkowicz T J.Two-step structural damage detection approach with limited instrumentation[J].Journal of Vibration and Acoustics,1997,119(2):258~264.
[7]王柏生,倪一清,高赞明.用概率神经网络进行结构损伤位置识别[J].振动工程学报,2001,14(1):60~64.Wang Baisheng,Ni Yiqing,Ko Janming.Structure damage localization using probabilistic neural network[J].Journal of Vibration Engineering,2001,14(1):60~64.(in Chinese)
[8]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):37~45.Qu Weilian,Chen Wei,Li Qiusheng.Two-step approach for joints damage diagnosis of framed structures by artificial nueral networks[J].China Civil Engineering Journal,2003,36(5):37~45.(in Chinese)
[9]Ray L R,Tian L.Damage detection in smart structures through sensitivity enhancing feedback control[J].Journal of Sound and Vibration,1999,227(5):987~1002.
[10]Lew J S,Juang J N.Structural damage detection using virtual passive controllers[J].Journal of Guidance,Control,and Dynamics,2002,25(3):419~424.
[11]Koh B H,Ray L R.Localization of damage in smart structures though sensitivity enhancing feedback control[J].Mechanical System and Signal Processing,2003,17(4):837~855.
[12]Shi Z Y,Law S S,Zhang L M.Damage detection by directly using incomplete mode shapes[J].Journal of Engineering Mechanics,2000,126(6):656~660.
[13]Specht D F.Probabilistic neural networks[J].Neural Networks,1990,1(3):109~118.
[14]顾仲权,马扣根,陈卫东.振动主动控制[M].北京:国防工业出版社,1997.Gu Zhongquan,Ma Kougen,Chen Weidong.Active vibration control[M].Beijing:China Defence Industry Press,1997.(in Chinese)
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