Wavelet entropy based structural damage identification under seismic excitation

Abstract

In this work, an entropy based two-step structural damage identification method under seismic excitation is proposed. The measured signals are decomposed by means of wavelet packet transform, and the wavelet entropies are obtained on the basis of the information entropy theory. In the first step, the damage alarming indices, calculated with the wavelet entropies in undamaged and damaged conditions, are used as samples for Shewhart individuals control chart to alarm the structural damage. In the second step, the damage localization indices, constructed by calculating the differences of curvature of wavelet entropies in undamaged and damaged conditions, are fed to the back-propagation neural network to identify structure damage location. The numerical simulation and shaking table model test of an offshore platform under seismic excitation are implemented to verify the feasibility of the proposed two-step structural damage identification method. The results show that the proposed method needs only the non-stationary output data and has low sensitivity to signal noise.

Energy dissipation‐based material deterioration assessment using random decrement technique and convolutional neural network: A case study of Saigon bridge in Ho Chi Minh City, Vietnam

“…Second, frequency‐domain methods use the Fourier, and inverse Fourier transforms to extract features in a given time window, such as frequency response functions (FRFs), 10 power spectral density (PSD), 11 and cepstrum 12 . Frequency‐domain methods only analyze stationary events localized in the time domain; on the other hand, time‐scale domain methods can explore any nonstationary possibility localized in the time domain by employing wavelet transforms to extract the signal features, including wavelet coefficients, 13 wavelet energies, 14 and wavelet entropy 15 . The main advantage of time‐domain methods over frequency‐domain methods is the ability to control nonlinear responses.…”

“…12 Frequency-domain methods only analyze stationary events localized in the time domain; on the other hand, time-scale domain methods can explore any nonstationary possibility localized in the time domain by employing wavelet transforms to extract the signal features, including wavelet coefficients, 13 wavelet energies, 14 and wavelet entropy. 15 The main advantage of time-domain methods over frequency-domain methods is the ability to control nonlinear responses. However, most time-domain methods require the undamaged state of the structure as the baseline state to compare with damage states.…”

Seismic Damage Identification Method for Curved Beam Bridges Based on Wavelet Packet Norm Entropy

“…Ren et al [ 38 ] defined wavelet entropy, relative wavelet entropy, and wavelet time entropy, and numerical simulation and laboratory experiments showed that these three kinds of wavelet entropy could locate and identify damage; moreover, relative wavelet entropy did not require pre-damage response data. Diao et al [ 39 ] constructed a kind of wavelet entropy to identify structural damage under seismic excitation. They verified the feasibility of wavelet entropy on the basis of model experiments using a numerical simulation of an offshore platform structure and a vibration table.…”

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

“…In the last few years, entropy, a powerful nonlinear measurement, has been introduced in different fields such as neuroscience (Li et al, 2018a(Li et al, , 2018bWang et al, 2018aWang et al, , 2018b, monitoring of electrical machines (Wu et al, 2018), monitoring of gearboxes (Figlus, 2019), cardiac diseases (Acharya et al, 2018;Kumar et al, 2016), and the health monitoring of simple beams of few structural elements (Diao et al, 2018;Moreno-Gomez et al, 2018), among other fields. Entropy provides a measure of data randomness encountered in a nonlinear and nonstationary time signal (Shannon, 1948), making it an appropriate tool for detecting damage in civil structures because the measured signals can vary according to damage and its severity encountered in the civil structure, thus producing a change in the entropy value.…”