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Shock and Vibration
Volume 2014, Article ID 727404, 9 pages
Research Article

Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural Network

College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Pingleyuan 100, Chaoyang District, Beijing 100124, China

Received 18 December 2013; Revised 26 May 2014; Accepted 5 June 2014; Published 29 June 2014

Academic Editor: Gyuhae Park

Copyright © 2014 Cunfu He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Truss structure is widely used in civil engineering. However, it is difficult to quantitatively monitor the state of truss structures because of the connection diversity and complexity of truss structures. In this paper, electromechanical impedance (EMI) technique was proposed to measure impedance spectra by using PZT elements and backpropagation (BP) neural network was used as an effective nonlinear conversion tool to quantify the health state of truss structures. Firstly, frequency band of the spectrum was experimentally determined by the trial-and-error approach. Then four connection rods of this truss structure were selected for experimental research. These connection rods were loosened gradually with a small angle increment and the impedance spectra were recorded. Then, the measured data were compressed through dividing the frequency range into multiple subbands. And RMSD values of these bands showed that data points were reduced while damage features remained. Finally, one four-layered BP neural network model was constructed based on these compressed data. The research results showed that compressed impedance data could retain their damage features. After the training, the developed neural network model could not only determine the location of loosened rod, but also quantify the loosening levels.