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在结构损伤识别中,如何充分利用整体和局部传感器测试得到的信息来增加结果的准确性是一个值得研究的问题。该文提出基于Bayesian理论的结构整体局部信息融合的损伤识别方法。首先根据Bayesian理论建立了关于频率、位移模态和应变模态结构损伤的概率模型,静态应变信息提供了Bayesian理论的先验信息,使该概率模型充分利用了各种传感器信息;然后为了减少计算量,采用分步损伤识别的方法,在采用模态应变能指标初步定位损伤范围的基础上,用该文提出的逐个单元消去法定位损伤单元。最后对20跨刚桁架模型进行试验研究证明了该方法的有效性,并且比较考虑与不考虑应变传感器信息的损伤识别结果。
In the structural damage identification, how to make full use of the information obtained from the whole and partial sensor testing to increase the accuracy of the results is a problem worthy of study. This paper presents a method of damage identification based on Bayesian theory for global local information fusion. Firstly, based on the Bayesian theory, a probabilistic model of structural damage in frequency, displacement and strain modes is established. The static strain information provides the a priori information of Bayesian theory, which makes full use of various sensor information. Then, in order to reduce the computation Based on the method of stepwise damage identification, based on the initial location damage range using modal strain energy index, the damage unit is located by the unit-by-unit elimination method proposed in this paper. Finally, the experimental study of the 20-span rigid-truss model shows the effectiveness of the proposed method and compares the damage identification results with or without considering the strain sensor information.