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. 1. Introduction As an effective structural health monitoring technique, electromechanical impedance (EMI) technique has gained widespread attention for its traits of high local sensitivity, easy installation of sensors, and nonparametric model analysis [1–3]. EMI technique has unique advantages, especially in the field of real-time health monitoring of some complex and irregular structures, such as truss structures  and connection bolts . The influence of the added mass and shape of PZT patches was also reported . Sun et al.  first proposed EMI technique to monitor the health state of truss structures and validated the effectiveness and potential of this technique to characterize such complex and irregular structures. Yan et al.  combined EMI technique with reverberation matrix method to quantitatively evaluate structural damage in Timoshenko beams. To solve the difficulty in the evaluation of damage severity and location, Yang and Divsholi  and Divsholi and Yang  divided the whole testing frequency range into multiple subbands. Annamdas and Yang  adopted EMI technique to monitor the health state of excavation support structures after experimental study. The result showed that, although the variations in signatures were not
V. Giurgiutiu, K. Harries, M. Petrou, J. Bost, and J. B. Quattlebaum, “Disbond detection with piezoelectric wafer active sensors in RC structures strengthened with FRP composite overlays,” Earthquake Engineering and Engineering Vibration, vol. 2, no. 2, pp. 213–223, 2003.
H. A. Sodano, G. Park, and D. J. Inman, “An investigation into the performance of macro-fiber composites for sensing and structural vibration applications,” Mechanical Systems and Signal Processing, vol. 18, no. 3, pp. 683–697, 2004.
F. P. Sun, Z. Chaudhry, C. Liang, and C. A. Rogers, “Truss structure integrity identification using PZT sensor-actuator,” Journal of Intelligent Material Systems and Structures, vol. 6, no. 1, pp. 134–139, 1995.
S. Ritdumrongkul and Y. Fujino, “Identification of the location and level of damage in multiple-bolted-joint structures by PZT actuator-sensors,” Journal of Structural Engineering, vol. 132, no. 2, pp. 304–311, 2006.
L. V. Palomino, K. M. Tsuruta, J. R. V. Mour Jr., D. A. Radea, V. Steffen Jr., and D. J. Inman, “Evaluation of the influence of sensor geometry and physical parameters on impedance-based structural health monitoring,” Shock and Vibration, vol. 19, no. 5, pp. 811–823, 2012.
W. Yan, C. W. Lim, J. B. Cai, and W. Q. Chen, “An electromechanical impedance approach for quantitative damage detection in Timoshenko beams with piezoelectric patches,” Smart Materials and Structures, vol. 16, no. 4, pp. 1390–1400, 2007.
V. G. M. Annamdas and Y. Yang, “Practical implementation of piezo-impedance sensors in monitoring of excavation support structures,” Structural Control & Health Monitoring, vol. 19, no. 2, pp. 231–245, 2012.
R. Shanker, S. Bhalla, and A. Gupta, “Integration of electro-mechanical impedance and global dynamic techniques for improved structural health monitoring,” Journal of Intelligent Material Systems and Structures, vol. 21, no. 3, pp. 285–295, 2010.
V. Lopes Jr., G. Park, H. H. Cudney, and D. J. Inman, “Impedance-based structural health monitoring with artificial neural networks,” Journal of Intelligent Material Systems and Structures, vol. 11, no. 3, pp. 206–214, 2000.
C. Liang, F. P. Sun, and C. A. Rogers, “Coupled electro-mechanical analysis of adaptive material systems—determination of the actuator power consumption and system energy transfer,” Journal of Intelligent Material Systems and Structures, vol. 5, no. 1, pp. 12–20, 1997.
S. Na and H. K. Lee, “Resonant frequency range utilized electro-mechanical impedance method for damage detection performance enhancement on composite structures,” Composite Structures, vol. 94, no. 8, pp. 2383–2389, 2012.
S. Na and H. K. Lee, “A technique for improving the damage detection ability of the electro-mechanical impedance method on concrete structures,” Smart Materials and Structures, vol. 21, no. 8, Article ID 085024, 2012.
S. Park, J. Lee, C. Yun, and D. J. Inman, “A built-in active sensing system-based structural health monitoring technique using statistical pattern recognition,” Journal of Mechanical Science and Technology, vol. 21, no. 6, pp. 896–902, 2007.
J. L. Rickli and J. A. Camelio, “Damage detection in assembly fixtures using non-destructive electromechanical impedance sensors and multivariate statistics,” International Journal of Advanced Manufacturing Technology, vol. 42, no. 9-10, pp. 1005–1015, 2009.
A. Hai, A. Weiguang, Y. Duohe, and W. Binsheng, “Static force reliability analysis of truss structure with piezoelectric patches affixed to its surface,” Chinese Journal of Aeronautics, vol. 22, no. 1, pp. 22–31, 2009.
J. Min, S. Park, and C. Yun, “Impedance-based structural health monitoring using neural networks for autonomous frequency range selection,” Smart Materials and Structures, vol. 19, no. 12, Article ID 125011, 2010.
J. Min, S. Park, C. Yun, C. Lee, and C. Lee, “Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity,” Engineering Structures, vol. 39, pp. 210–220, 2012.