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Detection of structural damages by model updating based on singular value decomposition of transfer function subsets

机译:基于转移函数子集的奇异值分解模型更新检测结构损坏

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摘要

In this paper, a sensitivity-based finite element (FE) model updating method using singular value decomposition (SVD) of frequency response function (FRF) is introduced. An exact sensitivity equation is proposed by incorporating measured responses of a damaged structure in the mathematical formulations. A set of incompletely measured natural frequencies of a damaged structure and mode shapes of the intact structure are used to deal with incomplete measurement without the implementation of FE model reduction or data expansion algorithms. The insights provided from the variation of SVD of transfer functions are used for the selection of proper updating frequency ranges. The appropriate arrangement and assembly of SVD-based sensitivity equations are discussed to achieve more accurate model updating results. The solution of the developed sensitivity equation is obtained by the linear least-square (LS) method and imposing unbiased side constraints on the design variables. The proposed method was examined numerically on the FE model of a 2D truss model and experimentally on a concrete beam. Low-frequency ranges, including ranges around the first sixth vibration modes for numerical cases and ranges around the first three vibration modes for experimental cases, are successfully implemented for damage detection. The numerical and experimental results prove its high sensitivity for the cases of low severity and distributed damages and its robustness against high levels of measurement, natural frequency, and mass modeling errors.
机译:本文介绍了使用频率响应函数(FRF)的奇异值分解(SVD)的灵敏度的有限元(FE)模型更新方法。提出了一种精确的灵敏度方程,通过在数学制剂中包含受损结构的测量响应来提出。完整结构的损坏结构和模式形状的一组未完全测量的自然频率用于处理不完全的测量,而无需实现FE模型减少或数据扩展算法。从转移函数SVD的变化提供的见解用于选择适当的更新频率范围。讨论了SVD基灵敏度方程的适当布置和组装以实现更准确的模型更新结果。开发的灵敏度方程的解决方案由线性最小二乘(LS)方法获得,并在设计变量上施加非偏见的侧约束。该方法在2D桁架模型的FE模型上进行了数值检查,实验在混凝土梁上。低频范围,包括第一个第六次振动模式的范围,用于数值案例和围绕前三种振动模式进行实验情况的范围,用于损坏检测。数值和实验结果证明了对低严重程度和分布式损害造成的案例的高灵敏度及其对高水平测量,自然频率和质量建模误差的鲁棒性。

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