Unsupervised fuzzy neural networks for damage detection of structures
2005; Wiley; Volume: 14; Issue: 1 Linguagem: Inglês
10.1002/stc.116
ISSN1545-2263
AutoresC. M. Wen, Shih‐Lin Hung, Chiung‐Shiann Huang, J. C. Jan,
Tópico(s)Infrastructure Maintenance and Monitoring
ResumoStructural Control and Health MonitoringVolume 14, Issue 1 p. 144-161 Research Article Unsupervised fuzzy neural networks for damage detection of structures C. M. Wen, C. M. Wen Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Search for more papers by this authorS. L. Hung, Corresponding Author S. L. Hung [email protected] Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Search for more papers by this authorC. S. Huang, C. S. Huang Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Search for more papers by this authorJ. C. Jan, J. C. Jan Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Search for more papers by this author C. M. Wen, C. M. Wen Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Search for more papers by this authorS. L. Hung, Corresponding Author S. L. Hung [email protected] Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Search for more papers by this authorC. S. Huang, C. S. Huang Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Search for more papers by this authorJ. C. Jan, J. C. Jan Department of Civil Engineering, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300, R.O.C.Search for more papers by this author First published: 12 October 2005 https://doi.org/10.1002/stc.116Citations: 32AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract This work presents an artificial neural network (ANN) approach for detecting structural damage. In place of the commonly used supervised neural network, this work adopts an unsupervised neural network which incorporates the fuzzy concept (named the unsupervised fuzzy neural network, UFN) to detect localized damage. The structural damage is assumed to take the form of reduced elemental stiffness. The damage site is demonstrated to correlate with the changes in the modal parameters of the structure. Therefore, a feature representing the damage location, termed the damage localization feature (DLF) is presented. When the structure experiences damage or change in the structural member, the measured DLF is obtained by analyzing the recorded dynamic responses of the structure. The location of the structural damage then can be identified using the UFN according to the measured DLF information. This study verifies the proposed model using an example involving a five-storey frame building. Both single- and multiple-damaged sites are considered. The effects of measured noise and the use of incomplete modal data are introduced to inspect the capability of the proposed detection approach. Additionally, the simulation results of well-known back-propagation network (BPN) and UFN are compared. The analysis results indicated that the use of fuzzy relationship in UFN made the detection of structural damage more robust and flexible than the BPN. Copyright © 2005 John Wiley & Sons, Ltd. REFERENCES 1 Kim JT, Stubbs N. Damage detection in offshore jacket structures from limited modal information. International Journal of Offshore and Polar Engineering 1995; 5(1): 58– 66. 2 Topole KG, Stubbs N. Nondestructive damage evaluation in complex structures from a minimum of modal parameters. The International Journal of Analytical and Experimental Modal Analysis 1995; 10(2): 95– 104. 3 Stubbs N, Topole KG. A damage localization algorithm for nonlinear structures. In: Natke GR, Yao JTP (eds) Safety Evaluation Based on Identification Approach Related to Time-variant and Nonlinear Structures. Vieweg 1993; 93– 106. 4 Wen YK. Intelligent structures 2: monitoring and control. Proceedings of the International Workshop on Intelligent Systems, New York, 1992; 1– 10. 5 Cawley P, Adams RD. The location of defects in structures from measurements of natural frequencies. Journal of Strain Analysis 1979; 14(2): 49– 57. 6 Penny JET, Wilson D, Friswell MI. Damage location in structures using vibration data. Proceedings of the 11th International Modal Analysis Conference, Kissimee, 1993; 861– 867. 7 Contursi T, Messina A, Williams EJ. A multiple-damage location assurance criterion based on natural frequency changes. Journal of Vibration and Control 1998; 4(5): 619– 663. 8 Messina A, Jones IA, Williams EJ. Damage detection and localisation using natural frequency changes. Proceedings of the Conference on Identification in Engineering Systems, Swansea, 1996; 67– 76. 9 Messina A, Williams EJ, Contursi T. Structural damage detection by a sensitivity and statistical-based method. Journal of Sound and Vibration 1998; 216(5): 791– 808. 10 West WM. Illustration of the use of modal assurance criterion to detect structural changes in an orbiter test specimen. Proceedings of the 4th International Modal Analysis Conference, 1986; 1– 5. 11 Lieven NAJ, Ewins DJ. Spatial correlation of mode shapes, the coordinate modal assurance criterion (COMAC). Proceedings of the 5th International Modal Analysis Conference, 1988; 690– 695. 12 Biswas M, Pandey AK, Samman MM. Diagnosis experiment spectral/modal analysis of highway bridges. The International Journal of Analytical and Experimental Modal Analysis 1990; 5(1): 33– 42. 13 Topole KG, Stubbs N. Non-destructive damage evaluation of a structure from limited modal parameters. Earthquake Engineering and Structural Dynamics 1995; 24(12): 1427– 1436. 14 Shi ZY, Law SS, Zhang LM. Damage localization by directly using incomplete mode shapes. Journal of Engineering Mechanics (ASCE) 2000; 126(6): 656– 660. 15 Hearn G, Testa RB. Modal analysis for damage detection in structures. Journal of Structural Engineering (ASCE) 1991; 117(10): 3042– 3063. 16 Shi ZY, Law SS, Zhang LM. Structural damage localization from modal strain energy change. Journal of Sound and Vibration 1998; 218(5): 825– 844. 17 Masri SF, Chassiakos AG, Caughey TK. Identification of nonlinear dynamic systems using neural networks. Journal of Applied Mechanics 1993; 60: 123– 133. 18 Ghaboussi J, Garrett JH, Wu X. Knowledge-based modeling of material behavior with neural networks. Journal of Engineering Mechanics (ASCE) 1991; 117(1): 132– 153. 19 Wu X, Ghaboussi J, Garrett Jr JH. Use of neural networks in detection of structural damage. Computers and Structures 1992; 42(4): 649– 659. 20 Elkordy MF. Application of Neural Networks in Structural Damage Diagnosis and Condition Monitoring. UMI Dissertation Services, A Bell & Howell Company, 1992. 21 Zhao J, Ivan JN, DeWolf JT. Structural damage detection using artificial neural networks. Journal of Infrastructure Systems (ASCE) 1998; 4(2): 93– 101. 22 Masri SF, Nakamura M, Chassiakos AG, Caughey TK. Neural network approach to the detection of changes in structural parameters. Journal of Engineering Mechanics (ASCE) 1996; 122(4): 350– 360. 23 Masri SF, Smyth AW, Chassiakos AG, Caughey TK, Hunter NF. Application of neural networks for detection of changes in nonlinear systems. Journal of Engineering Mechanics (ASCE) 2000; 126(7): 666– 676. 24 Huang CS, Hung SL, Wen CM, Tu TT. A neural network approach for structural identification and diagnosis of a building from seismic response data. Earthquake Engineering and Structural Dynamics 2003; 32(2): 187– 206. 25 Lam HF, Ko JM, Wong CW. Localization of damaged structural connections based on experimental modal and sensitivity analysis. Journal of Sound and Vibration 1998; 210(1): 91– 115. 26 Hung SL, Jan JC. Machine learning in engineering design: an unsupervised fuzzy neural network learning model. Proceedings of the Conference on Intelligent Information Systems, IEEE Computer Society, California, 1997; 156– 160. 27 Hung SL, Jan JC. Machine learning in engineering analysis and design: an integrated fuzzy neural network learning model. Computer-Aided Civil and Infrastructure Engineering 1999; 14: 207– 219. 28 Hung SL, Jan JC. Augmented IFN Learning Model. Journal of Computing in Civil Engineering (ASCE) 2000; 14(1): 15– 22. 29 Shi ZY, Law SS, Zhang LM. Structural damage detection from modal strain energy change. Journal of Engineering Mechanics (ASCE) 2000; 126(12): 1216– 1223. Citing Literature Volume14, Issue1February 2007Pages 144-161 ReferencesRelatedInformation
Referência(s)