Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural Network
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 s...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2014/727404 |
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| _version_ | 1849403093987360768 |
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| author | Cunfu He Shen Yang Zenghua Liu Bin Wu |
| author_facet | Cunfu He Shen Yang Zenghua Liu Bin Wu |
| author_sort | Cunfu He |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-989c187a225b4a7c8d9695b81da41f8f |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-989c187a225b4a7c8d9695b81da41f8f2025-08-20T03:37:22ZengWileyShock and Vibration1070-96221875-92032014-01-01201410.1155/2014/727404727404Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural NetworkCunfu He0Shen Yang1Zenghua Liu2Bin Wu3College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Pingleyuan 100, Chaoyang District, Beijing 100124, ChinaCollege of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Pingleyuan 100, Chaoyang District, Beijing 100124, ChinaCollege of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Pingleyuan 100, Chaoyang District, Beijing 100124, ChinaCollege of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Pingleyuan 100, Chaoyang District, Beijing 100124, ChinaTruss 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.http://dx.doi.org/10.1155/2014/727404 |
| spellingShingle | Cunfu He Shen Yang Zenghua Liu Bin Wu Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural Network Shock and Vibration |
| title | Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural Network |
| title_full | Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural Network |
| title_fullStr | Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural Network |
| title_full_unstemmed | Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural Network |
| title_short | Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural Network |
| title_sort | damage localization and quantification of truss structure based on electromechanical impedance technique and neural network |
| url | http://dx.doi.org/10.1155/2014/727404 |
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