Multi-dataset OMA of a Sightseeing Tower with the New SpCF Method

When performing multi-dataset OMA, the main challenge is to extract the global modal properties of the tested structure from the various datasets acquired in the vibration test in a robust and clear manner. In this paper, the novel Subspace-based poly-reference Complex Frequency (SpCF) technique is applied to the vibration responses of a sightseeing tower to evaluate its robustness and accuracy when applied to multi-dataset identification. The underlying idea in the formulation of the SpCF technique is to apply the concepts of controllability and observability from the control theory to the pCF technique which is formulated in the frequency domain modal model. This approach is accomplished by factoring the system matrices formed, by means of the singular value decomposition, into the multiplication of the observability, discrete-time state-space and the frequency-domain controllability matrices. In order to assess the robustness of the identification achieved with the new SpCF, its modal properties estimates for the sightseeing tower are compared to those obtained with a state-of-the-art identification technique regarded as standard in modal analysis.