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A Substructure Approach For Damage Detection of Large Size Structures Under Limited Input and Output Measurements

机译:在有限输入和输出测量下用于大型结构损伤检测的子结构方法

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摘要

Recently, damage detection of large size structures based on the system identification, through the changes of structural dynamic parameters (mainly stiffness parameters), has attracted great attention. Due to practical limitations, it may not be possible to install enough sensors in the health monitoring system to measure either all the external excitations (inputs) or the responses (outputs) at all degree of freedoms. In this paper, a substructure approach for damage detection of large size structure is proposed. A large structure is decomposed into smaller substructures based on its finite element formulation. Interaction effect between adjacent substructures is accounted by considering the interaction forces at substructural interfaces as the 'unknown inputs' to the substructures concerned. Two cases that measurements at the substructure interfaces are available or not available are considered. Base on sequential application of the extended Kalman estimator for the extended state vector and the least squares estimation for the unknown inputs, the approach can identify structural parameters, such as the stiffness, damping, and the unmeasured inputs in the substructures. Numerical simulation results of a 20 story shear building show that the proposed approach is capable of identifying structural parameters and unknown excitations with good accuracy. Thus, structural local damage can be detected through the degradation of the stiffness at structural element level.
机译:近年来,基于系统识别的大型结构损伤检测,通过改变结构动力学参数(主要是刚度参数),引起了人们的广泛关注。由于实际限制,可能无法在健康监控系统中安装足够的传感器来测量所有自由度下的所有外部激励(输入)或响应(输出)。本文提出了一种用于大型结构损伤检测的子结构方法。根据其有限元公式,大型结构可分解为较小的子结构。通过将子结构界面处的相互作用力视为相关子结构的“未知输入”,来解释相邻子结构之间的相互作用效果。考虑在子结构接口处的测量可用或不可用的两种情况。基于对扩展状态向量的扩展卡尔曼估计器和未知输入的最小二乘估计的顺序应用,该方法可以识别结构参数,例如刚度,阻尼和子结构中未测量的输入。一座20层高的剪力房的数值模拟结果表明,该方法能够以较高的精度识别结构参数和未知的激励。因此,可以通过在结构元件水平上降低刚度来检测结构局部损坏。

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