A theoretical framework for sensor placement, structural identification and damage detection in tensegrity structures

被引:11
作者
Aloui, Omar [1 ]
Lin, Jan [1 ]
Rhode-Barbarigos, Landolf [1 ]
机构
[1] Univ Miami, Coll Engn, Dept Civil Architectural & Environm Engn, 1251 Mem Dr,McArthur Engn Bldg, Coral Gables, FL 33146 USA
基金
美国国家科学基金会;
关键词
tensegrity; sensor configuration; self-stress identification; structural identification; damage identification; cellular decomposition;
D O I
10.1088/1361-665X/ab3d21
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
Tensegrity structures are systems composed of elements in compression and tension in a stable self-equilibrium state that provides stability and stiffness to the structure. Tensegrity finds its root in contemporary art with Kenneth Snelson?s sculptures, yet it quickly evolved into a structural paradigm employed in a wide spectrum of science and engineering applications. Tensegrity structures being lightweight, and capable of combining sensors and actuators with structural elements, they are advantageous for active applications. However, in most active applications, sensor placement is based on engineering judgement and not a systematic approach based on the analysis of tensegrity structures. This paper addresses sensor placement for structural identification and damage detection in tensegrity structures using cellular decomposition. By decomposing tensegrity structures into the minimum number of constitutive unicellular sub-structures (cells and stable sub-structures resulting from their interaction), the minimum number of sensors required for their self-stress identification can be defined along with a set of edge solutions for sensor placement. Moreover, under the assumptions of a known deformed geometry and loading, it is shown that the resulting sensor configurations can be extended for structural identification as well as damage detection providing a theoretical framework for active and sensory tensegrity structures based on their cellular composition.
引用
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页数:11
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