Abstract
This paper presents the application of a novel Artificial Neural Net-work (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cawley, P., Adams, R.D.: The Location of Defects in Structures from Measurements of Natural Frequencies. Journal of Vibration and Acoustics 14(2), 49–57 (1979)
Lam, H.F., Ko, J.M., Wong, C.W.: Localization of Damaged Structural Connections based on Experimental Modal and Sensitivity Analysis. Journal of Sound and Vibration 210(1), 91–115 (1998)
Lee, E.W.M., Lim, C.P., Yuen, R.K.K., Lo, S.M.: A hybrid neural network for noisy data regression. IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernetics 34(2), 951–960 (2004)
Lee, E.W.M., Yuen, R.K.K., Lo, S.M., Lam, K.C., Yeoh, G.H.: A Novel Artificial Neural Network Fire Model for Prediction of Thermal Interface Location in Single Compartment Fire. Fire Safety Journal 39, 67–87 (2004)
Yuen, R.K.K., Yuen, K.K.Y., Lee, E.W.M., Cheng, G.W.Y.: A Novel Artificial Neural Network Fire Model for Determination of Thermal Interface in Compartment Fire. In: Proceedings of the International conference on building fire safety, Brisbane, Australia, November 2003, pp. 25–32 (2003)
Lee, E.W.M., Lim, C.P., Yuen, R.K.K.: A Novel Neural Network Model for Online Noisy Data Regression and Classification. In: Proceedings of International Conference on Computational Intelligence for Modelling Control and Automation, Vienna, Austria, February 2003, pp. 95–105 (2003)
Lee, E.W.M., Yuen, R.K.K., Cheung, S.C.P., Cheng, C.C.K.: Application of Artificial Neural Network for Determination of Thermal Interface in Single Compartment Fire. In: Proceedings of the International Conference on Robotics, Vision, Information and Signal Processing, Penang, Malaysia, January 2003, pp. 588–593 (2003)
Carpenter, G.A., Grossberg, S., David, B.R.: Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System. Neural Network 4, 759–771 (1991)
Specht, D.F.: A general regression neural network. IEEE Transaction on Neural Networks 2(6), 568–576 (1991)
Tomandl, D., Schober, A.: A modified general regression neural network (MGRNN) with new, efficient training algorithms as a robust ‘black box’ –tool for data analysis. Neural Networks 14, 1023–1034 (2001)
Lim, C.P., Harrison, R.F.: Modified Fuzzy ARTMAP approaches Bayes Optimal Classification Rates: An Empirical Demonstration. Neural Network 10(4), 755–774 (1997)
Efron, B.: Bootstrap Methods: Another Look at the Jackknife. The Annals Of Statistics 7, 1–26 (1979)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, E.W.M., Lam, H.F. (2004). ANN-Based Structural Damage Diagnosis Using Measured Vibration Data. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_50
Download citation
DOI: https://doi.org/10.1007/978-3-540-30134-9_50
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23205-6
Online ISBN: 978-3-540-30134-9
eBook Packages: Springer Book Archive