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Prognosis of Structural Health: Non-Destructive Methods

机译:结构健康的预后:非破坏性方法

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For the prognosis of structural health, non-destructive defect assessment procedures are under active development by the profession. Two such procedures now under development by the research team at the University of Arizona, are MILS-UI and GILS-EKF-UI. They indicate a considerable application potential. The unique feature of the algorithms is that they can identify members' properties and in the process access the health of a structural system using only dynamic responses completely ignoring the excitation information. Although mathematically elegant, their practical implemental potential to identify defect-free and defective (single or multiple defects) states need critical evaluation and is discussed in the paper. With the help of an illustrative example, it was shown that both the MILS-UI and GILS-EKF-UI methods can identify defect-free and defective states of a structure very well. Both methods successfully identified the presence of multiple defects. Ignoring responses at vertical dynamic degrees of freedoms did not alter the outcomes of the nondestructive evaluation for the problem under consideration. Both methods also correctly identified less severe defect in terms of loss of area over a finite length. It can be concluded that the methods are capable of identifying small and large defects.
机译:对于结构健康的预后,专业人员正在积极开发无损缺陷评估程序。亚利桑那大学研究小组目前正在开发的两个这样的程序是MILS-UI和GILS-EKF-UI。它们表明了巨大的应用潜力。该算法的独特之处在于它们可以识别成员的属性,并且在此过程中仅使用动态响应即可完全忽略激励信息,从而访问结构系统的运行状况。尽管从数学上讲是优雅的,但是它们在识别无缺陷和有缺陷(单个或多个缺陷)状态方面的实际实现潜力需要进行严格评估,并在本文中进行了讨论。借助于说明性示例,表明MILS-UI和GILS-EKF-UI方法都可以很好地识别结构的无缺陷和有缺陷状态。两种方法都成功地确定了多个缺陷的存在。忽略垂直动态自由度上的响应不会改变所考虑问题的无损评估结果。两种方法都可以在有限的长度上正确地识别出不太严重的缺陷。可以得出结论,该方法能够识别大小缺陷。

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