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

Abstract

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.

Towards Multiclass Damage Detection and Localization using Limited Vibration Measurements

“…bases such as Morlet, Daubechies, etc.). For example, in Cauchy CWT (CCWT), ψ(t) is defined as follows [109]:…”

“…In [155], a least-square SVM and time-dependent AR model was proposed to identify structural parameters of time-varying systems. However, the CWT and CCWT need significant improvement for frequency resolution to identify the minor damage as it is relatively less sensitive to minor frequency change in the structures [109].…”

A Comparison of Time-Frequency Methods for Real-Time Application to High-Rate Dynamic Systems

“…The application of these methods results in a trade-off between time and frequency resolutions [11]. An adaptive non-parametric method have also been proposed, including the Hilbert-Huang transform (HHT) [12][13][14], the Cauchy continuous wavelet transform (CCWT) [15], the instantaneous frequencies (IF) re-assignment methods, synchrosqueezing transform (SST) [16], and the multi-SST (MSST) [17].…”

Entropy algorithms for detecting incipient damage in high-rise buildings subjected to dynamic vibrations

“…the autoregressive moving average model) are used for modeling the behavior of stationary signals, yet time-series models cannot model noisy signals with nonstationary properties as acquired/measured in the civil structures, especially in large civil structures (Perez-Ramirez et al, 2019). As an alternative to problems encountered in the aforementioned methods, powerful signal processing techniques such as wavelet transform and its variant called the synchrosqueezed wavelet transform (Amezquita- Sanchez and Adeli, 2015;Sadhu et al, 2019;Yang and Oyadiji, 2017) and the empirical mode decomposition and its variants such as ensemble empirical mode decomposition (EEMD) and complete EEMD (Moreno-Gomez et al, 2018;Vazirizade et al, 2018) have been used for evaluating the current state of a civil structure. The wavelet transform-based methods are techniques capable of decomposing signals with nonstationary properties in a set of frequency bands; regrettably, for obtaining suitable results by using the wavelet transform-based methods, an adequate selection of the wavelet mother and the decomposition level is of paramount importance to evaluate appropriately the health condition of a civil structure (Mejia-Barron et al, 2018).…”