Multi-stage damage identification method for PC structures based on machine learning driven by piezoelectric singular feature

Abstract

Efficient and accurate damage identification methods are great significant for ensuring the structural safety. This study proposes a multi-stage damage identification method for prestressed concrete (PC) structures based on machine learning driven by piezoelectric singular feature to improve the identification accuracy. This method extracts the singular value vector in the piezoelectric signal as the damage feature by multi-layer wavelet packet transform and singular value decomposition. The singular value vectors are put into the improved back propagation neural network (BPNN) model to achieve complete damage identification. This method is verified by analyzing the piezoelectric signals collected by PZT patches based on the static load failure test. The results show that the amplitude and energy of the signal gradually decreases and the frequency subject gradually develops from high frequency to low frequency with the development of damage. The extracted singular values effectively retain the main information of the piezoelectric signal as damage feature. The peak singular value decreases from fast to slow with the development of damage, which finally tends to be stable. The improved BPNN model has strong flexibility and adaptability, which enhances its ability of damage identification. The proposed method can achieve high-precision damage identification using piezoelectric signals, which has great application potential in the field of damage identification for PC structures.