Experimental study of structural change detection using data-driven reduced-order models
by Miguel R. Hernandez-Garcia; Sami F. Masri
International Journal of Sustainable Materials and Structural Systems (IJSMSS), Vol. 4, No. 2/3/4, 2020

Abstract: This paper presents a comparison of the effectiveness of three different data-driven vibration-based approaches in detecting and locating structural changes in a 1/4 scale six-storey single-bay steel frame laboratory structure from measured experimental input-output data obtained from band-limited white-noise base-excitation tests. The implemented methodologies are based on reduced-order models obtained using three input-output system identification approaches: a system realisation algorithm using information matrices, a general time-domain least-squares identification method, and a non-parametric chain-like system identification approach. Variations in the estimated reduced-order models are then used to indicate the presence and infer the location of actual structural changes in the test structure. The results of this experimental study show that even though the changes introduced by the various levels of damage in the structure were robustly detected in the presence of modelling, measurement, and data processing errors using reduced-order representations, the identified change locations in the reduced-order model could not be, in some cases, reliably correlated with the actual damage location in the structure.

Online publication date: Wed, 19-Aug-2020

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