基于加速度时程的结构参数直接识别方法及验证
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摘要
提出了一种直接运用加速度响应测量的结构参数识别方法。该方法通过两个神经网络对结构刚度和阻尼比进行直接识别。通过结构运动微分方程的离散解,阐明了该方法的理论基础以及构建两个神经网络的依据。首先,依据对目标结构参数的初步估计,假定一个参考结构,构建一个神经网络来描述该参考结构的加速度响应时间序列之间的映射关系,即建立该参考结构的非参数模型。然后,定义加速度响应预测差值均方根向量作为结构参数识别指标,并构建一个参数识别用神经网络来描述该指标与结构参数之间的关系。最后,基于一个框架结构模型振动台试验的加速度响应时间序列实测值,运用以上两个神经网络,在假定结构质量已知的情况下,对该框架模型的刚度和阻尼比进行了识别,并与扫频试验的结果进行了比较。结果表明该方法的结构参数识别结果可靠,所提出的准实时结构参数直接识别方法可行。
A novel time domain neural networks based structural stiffness and damping parameters identification methodology with the direct use of structural acceleration time series is proposed, which is called direct soft parametric identification (DSPI). The theoretical fundamentals of the methodology is explained and the architecture of the two neural networks is described according to the discrete time solution of the state space equation of the structural vibration differential equation. An evaluation index called the root mean square of the acceleration prediction difference vector (RMSAPDV) is defined and employed to identify structural parameters. Based on an acceleration-based neural network modeling (ANNM) for the reference structure which parameters are determined by an estimation of an object structure, and a parameter evaluation neural network(PENN) that describes the relation between structural parameters and the components of the corresponding RMSAPDVs, the inter-storey stiffness and damping ratio of a frame model structure excited by a shaking table with known mass distribution, are identified by the direct use of acceleration measurements. Compared with the identified structural parameters from sweep frequency tests, results show that structural parameters can be identified with acceptable accuracy and the proposed method is an applicable approach closing to real-time direct identifications.
A novel time domain neural networks based structural stiffness and damping parameters identification methodology with the direct use of structural acceleration time series is proposed, which is called direct soft parametric identification (DSPI). The theoretical fundamentals of the methodology is explained and the architecture of the two neural networks is described according to the discrete time solution of the state space equation of the structural vibration differential equation. An evaluation index called the root mean square of the acceleration prediction difference vector (RMSAPDV) is defined and employed to identify structural parameters. Based on an acceleration-based neural network modeling (ANNM) for the reference structure which parameters are determined by an estimation of an object structure, and a parameter evaluation neural network(PENN) that describes the relation between structural parameters and the components of the corresponding RMSAPDVs, the inter-storey stiffness and damping ratio of a frame model structure excited by a shaking table with known mass distribution, are identified by the direct use of acceleration measurements. Compared with the identified structural parameters from sweep frequency tests, results show that structural parameters can be identified with acceptable accuracy and the proposed method is an applicable approach closing to real-time direct identifications.
引文
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[2]Doebling S W,Farrar C R,Prime M B.A summary review of vibration-based damage identification methods[J].Shock and Vibration Digest,1998,30(2):91―105.
[3]Chang C C,Roschke P.Neural network modeling of a magnetorheological damper[J].Journal of Intelligent Material Systems and Structures,1998,9:755―764.
[4]Ghaboussi J,Joghataie A.Active control of structures using neural networks[J].Journal of Engineering Mechanics,ASCE,1995,121(4):555―567.
[5]Feng M Q,Kim D K,Yi J-H,Chen Y.Baseline models for bridge performance monitoring[J].Journal of Engineering Mechanics,ASCE,2004,130(5):562―569.
[6]Nakamura M,Masri S F,Chassiakos A G.A method fornon-parametric damage detection through the use of neural networks[J].Earthquake Engineering and Structural Dynamics,1998,27(6):997―1010.
[7]Worden K.Structural fault detection using a novelty measure[J].Journal of Sound and Vibration,1997,201(1):85―101.
[8]Yun C B,Bahng E Y.Substructural identification using neural networks[J].Computers and Structures,2000,77:41―52.
[9]Xu B,Wu Z S,Chen G.Direct identification of structural parameters from dynamic responses with neural networks[J].Engineering Applications of Artificial Intelligence,2004,17(8):931―943.
[10]Xu B,Wu Z S,Yokoyama K.A soft post-earthquake damage identification methodology using vibration time series[J].Smart Materials and Structures,2005,14(3):s116―s124.
[11]Xu B,Chen G,Wu Z S.Parametric identification for a truss structure using axial strain[J].Computer-Aided Civil and Infrastructure Engineering,2007,22:34―46.
[12]Wu Z S,Xu B,Yokoyama K.Decentralized parametric damage based on neural networks[J].Computer-Aided Civil and Infrastructure Engineering,2002,17:175―184.
[13]Xu B,Wu Z S,Yokoyama K.A localized identification method with neural networks and its application to structural health monitoring[J].Journal of Structural Engineering,JSCE,2002,48A:419―427.
[14]Lee C G,Yun C B.Parameter identification of linear structural dynamic systems[J].Computers and Structures,1991,40(6):1475―1487.
[15]爱德华·L·威尔逊.结构静力与动力分析-强调地震工程学的物理方法[M].第4版.北京金土木软件技术有限公司,中国建筑标准设计研究院译.北京:中国建筑工业出版社,2006:201―208.Wilson E L.Static&dynamic analysis of structures-A physical approach with emphasis on earthquake engineering[M].4th ed.Translated by Civi King Software Technology Co.,Ltd.,and China Institute of Building Standard Design and Research.Beijing:China Architecture&Building Press,2006:201―208.(in Chinese)
[16]克拉夫R,彭津J.结构动力学[M].第2版(修订版).王光远,译校.北京:高等教育出版社,2006:183―192.Clough R,Penzien J.Dynamics of structures[M].2nd ed(Revised).Translated by Wang Guangyuan.Beijing:Higher Education Press,2006:183―192.(in Chinese)
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