Evaluation of progressive damage in structures using tensor decomposition-based wavelet analysis

Effective monitoring and retrofitting of large-scale infrastructure subjected to natural hazards such as strong wind, severe earthquakes or man-made excitation are critical to ensure structural integrity and prevent any premature failure. With the aid of structural health monitoring, it is now possible to acquire rich vibration data, estimate the hidden structural information, and evaluate the existing structural performance. The nonstationary component of vibration response resulting from natural hazards poses difficulty in analysis using traditional modal identification methods that are based on the stationarity assumption of vibration response. Apart from the excitation-induced nonstationarity, inherent damages in the structure also cause frequency-dependent nonstationarity in the response. With such a combination of both amplitude and frequency-dependent nonstationary response, the modal identification becomes a significantly challenging task. In this paper, Cauchy continuous wavelet transform is integrated with the tensor decomposition to track time-varying characteristics of modal responses and detect any progressive damage. The proposed technique is validated using a suite of numerical studies as well as a laboratory experiment where the progressive damage is simulated in the members by heating them using a butane torch. Unlike detection of discrete damage, the proposed method is one of introductory approaches to assess progressive damage in structures.

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