Self-Organizing Maps for Structural Damage Detection: A Novel Unsupervised Vibration-Based Algorithm
Publication: Journal of Performance of Constructed Facilities
Volume 30, Issue 3
Abstract
The study presented in this paper is arguably the first study to use a self-organizing map (SOM) for global structural damage detection. A novel unsupervised vibration-based damage detection algorithm is introduced using SOMs in order to quantify structural damage. In this algorithm, SOMs are used to extract a number of damage indices from the random acceleration response of the monitored structure in the time domain. The summation of the indices is used as an indicator which reflects the overall condition of the structure. The ability of the algorithm to quantify the overall structural damage is demonstrated using experimental data of Phase II experimental benchmark problem of structural health monitoring.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The financial support for this research was provided by Qatar National Research Fund [QNRF (a member of Qatar Foundation)] via the National Priorities Research Program (NPRP), Project Number: NPRP 6-526-2-218. The statements made herein are solely the responsibility of the authors.
References
Arangio, S., and Beck, J. L. (2012). “Bayesian neural networks for bridge integrity assessment.” Struct. Control Health Monitoring, 19(1), 3–21.
Arredondo, M. A. T., et al. (2014). “A pattern recognition approach for damage detection and temperature compensation in acousto-ultrasonics.” EWSHM—7th European Workshop on Structural Health Monitoring.
Bakhary, N., Hao, H., and Deeks, A. J. (2010). “Structure damage detection using neural network with multi-stage substructuring.” Adv. Struct. Eng., 13(1), 95–110.
Bandara, R. P., Chan, T. H. T., and Thambiratnam, D. P. (2013). “The three-stage artificial neural network method for damage assessment of building structures.” Aust. J. Struct. Eng., 14(1), 13–25.
Bani-Hani, K., Ghaboussi, J., and Schneider, S. P. (1999). “Experimental study of identification and control of structures using neural network. Part 1: Identification.” Earthquake Eng. Struct. Dyn., 28(9), 995–1018.
Catbas, F. N., Kijewski-Correa, T., and Aktan, A. E. (2013). “Structural identification of constructed systems: Approaches, methods, and technologies for effective proactive of structural identification.” American Society of Civil Engineers (ASCE) Structural Engineering Institute (SEI), Reston, VA.
Chang, C. C., Chang, T. Y. P., and Xu, Y. G. (2000). “Structural damage detection using an iterative neural network.” J. Intell. Mater. Syst. Struct., 11(1), 32–42.
Dackermann, U., Li, J., and Samali, B. (2010). “Dynamic-based damage identification using neural network ensembles and damage index method.” Adv. Struct. Eng., 13(6), 1001–1016.
Dyke, S. J., Bernal, D., Beck, J., and Ventura, C. (2003). “Phase II experimental structural health monitoring benchmark problem.” Proc., Engineering Mechanics Conf.
Efstathiades, C., Baniotopoulos, C. C., Nazarko, P., Ziemianski, L., and Stavroulakis, G. E. (2007). “Application of neural networks for the structural health monitoring in curtain-wall systems.” Eng. Struct., 29(12), 3475–3484.
Elshafey, A. A., Haddara, M. R., and Marzouk, H. (2010). “Damage detection in offshore structures using neural networks.” Mar. Struct., 23(1), 131–145.
Ettouney, M., and Alampalli, S. (2011). Infrastructure health in civil engineering, CRC Press, Boca Raton, FL.
Fang, X., Luo, H., and Tang, J. (2005). “Structural damage detection using neural network with learning rate improvement.” Comput. Struct., 83(25–26), 2150–2161.
Goh, L. D., Bakhary, N., Rahman, A. A., and Ahmad, B. H. (2013). “Application of neural network for prediction of unmeasured mode shape in damage detection.” Adv. Struct. Eng., 16(1), 99–114.
Gul, M., and Catbas, F. N. (2011). “Damage assessment with ambient vibration data using a novel time series analysis methodology.” J. Struct. Eng., 1518–1526.
Hong, A., Shuai, G., and Weisong, C. (2013). “Application of the optimal BP neural network in bridge health assessment.” 2nd Int. Conf. on Measurement, Information and Control, Vol. 2, IEEE, New York, 921–925.
Jiang, S. F., Zhang, C.-M., and Zhang, S. (2011a). “Two-stage structural damage detection using fuzzy neural networks and data fusion techniques.” Expert Syst. Appl., 38(1), 511–519.
Jiang, S.-F., Fu, C., and Zhang, C. (2011b). “A hybrid data-fusion system using modal data and probabilistic neural network for damage detection.” Adv. Eng. Software, 42(6), 368–374.
Kao, C. Y., and Hung, S.-L. (2003). “Detection of structural damage via free vibration responses generated by approximating artificial neural networks.” Comput. Struct., 81(28-29), 2631–2644.
Kohonen, T. (1982). “Self-organized formation of topologically correct feature maps.” Biol. Cybern., 43(1), 59–69.
Kohonen, T. (2001). Self-organizing maps, Springer, Berlin.
Lee, J., and Kim, S. (2007). “Structural damage detection in the frequency domain using neural networks.” J. Intell. Mater. Syst. Struct., 18(8), 785–792.
Lee, J. J., Lee, J. W., Yi, J. H., Yun, C. B., and Jung, H. Y. (2005). “Neural networks-based damage detection for bridges considering errors in baseline finite element models.” J. Sound Vibr., 280(3–5), 555–578.
Liu, Y.-Y., Ju, Y.-F., Duan, C.-D., and Zhao, X.-F. (2011). “Structure damage diagnosis using neural network and feature fusion.” Eng. Appl. Artif. Intell., 24(1), 87–92.
Lopes, V., Park, G., Cudney, H. H., and Inman, D. J. (2000). “Impedance-based structural health monitoring with artificial neural networks.” J. Intell. Mater. Syst. Struct., 11(3), 206–214.
Machavaram, R., and Krishnapillai, S. (2013). “Identification of crack in a structural member using improved radial basis function (IRBF) neural networks.” Int. J. Intell. Comput. Cybern., 6(2), 182–211.
MATLAB version 8.1.0.604 [Computer software]. Natick, MA, MathWorks.
Mehrjoo, M., Khaji, N., Moharrami, H., and Bahreininejad, A. (2008). “Damage detection of truss bridge joints using artificial neural networks.” Expert Syst. Appl., 35(3), 1122–1131.
Min, J., Park, S., Yun, C.-B., Lee, C.-G., and Lee, C. (2012). “Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity.” Eng. Struct., 39(2012), 210–220.
Nazarko, P., and Ziemiański, L. (2011). “Application of artificial neural networks in the damage identification of structural elements.” Comput. Assisted Mech. Eng. Sci., 18(3), 175–189.
Ng, C.-T. (2014). “Application of Bayesian-designed artificial neural networks in Phase II structural health monitoring benchmark studies.” Aust. J. Struct. Eng., 15(1), 27–36.
Pawar, P. M., Reddy, K. V., and Ganguli, R. (2007). “Damage detection in beams using spatial Fourier analysis and neural networks.” J. Intell. Mater. Syst. Struct., 18(4), 347–359.
Pierce, S. G., Worden, K., and Manson, G. (2006). “A novel information-gap technique to assess reliability of neural network-based damage detection.” J. Sound Vibr., 293(1-2), 96–111.
Rucka, M., and Wilde, K. (2010). “Neuro-wavelet damage detection technique in beam, plate and shell structures with experimental validation.” J. Theor. Appl. Mech., 48(3), 579–604.
Tibaduiza, D. A., Mujica, L. E., and Rodellar, J. (2013). “Damage classification in structural health monitoring using principal component analysis and self-organizing maps.” Struct. Control Health Monitoring, 20(10), 1303–1316.
Vafaei, M., Adnan, A. B., and Abdrahman, A. B. (2013). “Real-time seismic damage detection of concrete shear walls using artificial neural networks.” J. Earthquake Eng., 17(1), 137–154.
Wen, C. M., Hung, S. L., Huang, C. S., and Jan, C. (2007). “Unsupervised fuzzy neural networks for damage detection of structures.” Struct. Control Health Monitoring, 14(1), 144–161.
Worden, K., Manson, G., Hilson, G., and Pierce, S. G. (2008). “Genetic optimisation of a neural damage locator.” J. Sound Vibr., 309(3–5), 529–544.
Yam, L. H., Yan, Y. J., and Jiang, J. S. (2003). “Vibration-based damage detection for composite structures using wavelet transform and neural network identification.” Compos. Struct., 60(4), 403–412.
Yan, L., Elgamal, A., and Cottrell, G. W. (2013). “Substructure vibration NARX neural network approach for statistical damage inference.” J. Eng. Mech., 737–747.
Yeung, W. T., and Smith, J. W. (2005). “Damage detection in bridges using neural networks for pattern recognition of vibration signatures.” Eng. Struct., 27(5), 685–698.
Zheng, S.-J., Li, Z.-Q., and Wang, H.-T. (2011). “A genetic fuzzy radial basis function neural network for structural health monitoring of composite laminated beams.” Expert Syst. Appl., 38(9), 11837–11842.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 30 • Issue 3 • June 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Jan 23, 2015
Accepted: May 13, 2015
Published online: Jun 12, 2015
Discussion open until: Nov 12, 2015
Published in print: Jun 1, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.