Damage detection in beams by using artificial neural networks and dynamical parameters
DOI:
https://doi.org/10.17533/udea.redin.12493Keywords:
neural networks, dynamical parameter, damage detectionAbstract
In this paper is presented a multilayer perceptron neural network combined with the Nelder-Mead Simplex method to detect damage in multiple support beams. The input parameters are based on natural frequencies and modal flexibility. It was considered that only a number of modes were available and that only vertical degrees of freedom were measured. The reliability of the proposed methodology is assessed from the generation of random damages scenarios and the definition of three types of errors, which can be found during the damage identification process. Results show that the methodology can reliably determine the damage scenarios. However, its application to large beams may be limited by the high computational cost of training the neural network.
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S. Doebling, C. Farrar, M. Prime. “A Summary Review of Vibration-Based Damage Identification Methods”. The shock and vibration digest. Vol. 30. 1998. pp. 91- 105. DOI: https://doi.org/10.1177/058310249803000201
C. Chang, T. Chang, Y. Xu, M. Wang. “Structural Damage Detection Using an Iterative Neural Network” J. of Intelligent Material Systems and Structures. Vol. 11. 2000. pp. 32-42. DOI: https://doi.org/10.1106/XU88-UW1T-A6AM-X7EA
Y. Ni, B. Wang, J. Ko. “Constructing Input Vectors to Neural Networks for Structural Damage Identification”. Smart Materials and Structures. Vol. 11. 2002. pp. 825-833. DOI: https://doi.org/10.1088/0964-1726/11/6/301
X. Fang, H. Luo, J. Tang. “Structural Damage Detection using neural network with learning rate improvement”. Computers and Structures. Vol. 83. 2005. pp. 2150-2161. DOI: https://doi.org/10.1016/j.compstruc.2005.02.029
J. Lee, S. Kim. “Structural Damage Detection in the Frequency Domain using Neural Networks”. J. of Intelligent Material Systems and Structures. Vol. 18. 2007. pp. 785-792. DOI: https://doi.org/10.1177/1045389X06073640
G. Cybenko. “Approximation by superposition of a Sigmoidal Function”. Mathematics of Control, Signals and Systems. Vol. 2. 1989. pp. 303-314. DOI: https://doi.org/10.1007/BF02551274
B. Koh, S. Dyke. “Structural health monitoring for flexible bridge structures using correlation and sensitivity of modal data”. Computers and Structures. Vol. 85. 2007. pp. 117-130. DOI: https://doi.org/10.1016/j.compstruc.2006.09.005
O. Begambre. Algoritmo Híbrido para a Avaliação da Integridade Estrutural: Uma Abordagem Heurística. Tesis Doctoral. Universidad de São Paulo. São Paulo (Brasil). 2007. pp 152.
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