Application of Neural Networks in Vibrational Signature Analysis
Publication: Journal of Engineering Mechanics
Volume 120, Issue 2
Abstract
To identify the pattern of changes in vibrational signatures of a structure is a promising approach for on‐line structure monitoring. Artificial neural networks, developed by researchers in cognitive sciences and artificial intelligence, can be used for this purpose. The main benefit of using artificial neural networks is their ability to diagnose signals that are fuzzy or imprecise. In this paper, neural networks were used to analyze the changes in vibrational signatures of a five‐story, three dimensional, steel frame. The neural networks were first trained with a set of experimental data obtained from shake‐table test results of the model. The capability of the neural networks to diagnose new signals was then tested against a separate set of experimental data. The results show that application of artificial neural networks in analyzing the changes in vibrational signatures of structures has considerable potential to structural damage diagnosis and condition monitoring.
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References
1.
Adeli, H., and Yeh, C. (1989). “Perceptron learning in engineering design.” Microcomp. Civ. Engrg., 4(4), 247–256.
2.
Chang, K. C., Yao, G. C., and Lee, G. C. (1990). “Intelligent structures through automatic dynamic monitoring.” Int. Workshop on Intelligent Structures, Elsevier Appl. Sci., 95–108.
3.
Chen, S., and Shah, K. (1992). “Neural networks in dynamic analysis of bridges.” Proc., 8th Annual Conf. of Computing in Civil Engineering, ASCE, New York, N.Y.
4.
Garrett, J. H. Jr. (1992). “Neural networks and their applicability within civil engineering.” Proc., 8th Annual Conf. of Computing in Civil Engineering, ASCE, New York, N.Y.
5.
Ghaboussi, J., Garrett, J. H. Jr., and Wu, X. (1991). “Knowledge‐based modeling of material behavior with neural networks.” J. Engrg. Mech., ASCE, 117(1), 132–153.
6.
Hopfield, J. J. (1982). “Neural networks and physical systems with emergent collective computational abilities.” Proc. National Academy of Science, 79, 2554–2558.
7.
Huang, S. L., and Adeli, H. (1991). “A model of perceptron learning with a hidden layer for engineering design.” Neurocomput., 3, 3–14.
8.
Hwang, J. S., Chang, K. C., and Lee, G. C. (1987). “The system characteristics and performance of a shaking table.” Tech. Rep. NCEER‐87‐0004, Nat. Ctr. of Earthquake Engrg. Res., State Univ. of New York, Buffalo, N.Y.
9.
Kohonen, T. (1988). Self‐organization and associative memory. Springer‐Verlag, Berlin, Germany.
10.
Lee, G. C., Chang, K. C., Yao, G. C., Hao, D. S., and Yeh, Y. Y. “Dynamic behavior of a prototype and a 2/5 scale steel frame structure.” Proc., 4th Nat. Conf. on Earthquake Engineering, vol. 2, Nat. Ctr. of Earthquake Engrg. Res., State Univ. of New York, Buffalo, N.Y., 605–613.
11.
Mazurek, D. F., and DeWolf, J. T. “Experimental study of bridge monitoring technique.” J. Struct. Engrg., ASCE, 116(9), 2532–2549.
12.
Rodeman, R., and Yao, J. T. P. (1973). “Structural identification—Literature review.” Tech. Rep. No. CE‐STR‐73‐3, Purdue Univ.
13.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986a). “A general frame work for parallel distributed processing.” Parallel distributed processing, Vol. 1: Foundations, D. E. Rumelhart, and J. L. McClelland, eds., The MIT Press, Cambridge, Mass.
14.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986b). “Learning internal representation by error propagation.” Parallel distributed processing, Vol. 1: Foundations, D. E. Rumelhart, and J. L. McClelland, eds., The MIT Press, Cambridge, Mass.
15.
Salane, H. J., Baldwin, J. W., and Duffield, R. C. (1981). “Dynamic approach for monitoring bridge deterioration.” Transp. Res. Record 832.
16.
Shen, Z. (1992). “Local fault diagnosis using modal analysis technique,” MS thesis, State Univ. of New York at Buffalo, Buffalo, N.Y.
17.
Wu, X., Ghaboussi, J., and Garrett, J. H. Jr. (1992). “Use of neural networks in detection of structural damage.” Comput. Struct., 42(4), 649–659.
18.
Yao, G. C. (1991). “Diagnostic studies of steel structures through vibrational signature analysis,” PhD dissertation, State Univ. of New York at Buffalo, Buffalo, N.Y.
Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 120 • Issue 2 • February 1994
Pages: 250 - 265
Copyright
Copyright © 1994 American Society of Civil Engineers.
History
Received: Mar 2, 1992
Published online: Feb 1, 1994
Published in print: Feb 1994
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