Bridge damage identification based on a VMD-PCA-GPR method under the influence of seasonal environment

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (24) : 332-342.

HUANG Jiezhong1,2, YUAN Sijie1, LI Dongsheng1,2

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Abstract

The influence of the changing environments on dynamic feature may completely mask the dynamic feature changes caused by damage, making vibration-based methods challenging to effectively detect structural damage. To address this issue, this paper proposes a structural damage detection method based on the Variational Mode Decomposition (VMD), Principal Component Analysis (PCA) and Gaussian Process Regression (GPR). First, the VMD algorithm is used to preprocess the frequency signal to obtain the IMF1 after separating the seasonal environmental patterns; Secondly, the PCA method is used to analyze the IMF1 and calculate the Euclidean distance of the PCA residual; Then, the IMF1 signal and the corresponding PCA residual Euclidean distance are used as input and output, and the GPR model is used to learn the calculation rules between input and output; Finally, the trained GPR model is used to predict the PCA Euclidean distance of the remaining IMF1, the prediction residual between the predicted value and the true value is calculated, and statistical control chart is used for damage warning. Monitoring data from a laboratory wooden bridge and the Z24 bridge are used to verify the effectiveness of this method.

Key words

damage identification / environmental variations / variational mode decomposition / principal component analysis / gaussian process regression

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HUANG Jiezhong1, 2, YUAN Sijie1, LI Dongsheng1, 2. Bridge damage identification based on a VMD-PCA-GPR method under the influence of seasonal environment[J]. Journal of Vibration and Shock, 2024, 43(24): 332-342

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