Abstract
Tensegrity systems are a special class of spatial reticulated structures that are composed of struts in compression and cables in tension. In this paper, the performance of stochastic subspace algorithms for modal identification of complex tensegrity systems is investigated. A sub-class algorithm of the Stochastic Subspace Identification family: the Balanced Realization Algorithm is investigated for modal identification of a tripod simplex structure and a Geiger dome. The presented algorithm is combined with a stabilization diagram with combined criteria (frequency, damping and mode shapes). It is shown that although the studied structures present closely spaced modes, the Balanced Realization Algorithm performs well and guarantees separation between closely-spaced natural frequencies. Modal identification results are validated through comparisons of the correlations (empirical vs. model based) showing effectiveness of the proposed methodology.
Highlights
Tensegrity systems are a special class of spatial reticulated structures that are composed of struts and tendons
Since it is difficult to separate these close modes based only on frequencies, we have introduced in this work the use of the mode shape vectors (MAC) value in the stabilization diagram in order to separate the modes
For tensegrity systems the structure modal signature is directly related to its state of self-stress
Summary
Tensegrity systems are a special class of spatial reticulated structures that are composed of struts and tendons. The structure modal parameters are not identified especially when the natural frequencies are closely spaced which is generally the case for tensegrity structures (Bel Hadj Ali, Smith 2009) This arises the challenge of choosing the appropriate identification technique for such systems. To obviate difficulties of traditional techniques, methods of extracting modal parameters from structural response data only have been deeply investigated during the past few decades (Mrabet et al 2014; Reynders et al 2007) Several identification techniques such as Natural Excitation Technique (), Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI) are employed to identify dynamic characteristics when structures are excited by unknown input. Modal identification results are validated through comparisons of the correlations (empirical vs. model based) showing effectiveness of the proposed methodology
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