Structural Damage Detection Using Parameters Combined with Changes in Flexibility Based on BP Neural Networks

Abstract:

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Modals of BP neural networks with different inputs and outputs are presented for different damage detecting schemes. To identify locations of structural damages, the regular vectors of changes in modal flexibility are looked on as inputs of the networks, and the state of localized damage are as outputs. To identify extents of structural damage, parameters combined with changes in flexibility and the square changes in frequency are as inputs of the networks, and the state of damage extents are as outputs. Examples of a simply supported beam and a plate show that the BP neural network modal can detect damage of structures in quantitative terms.

Info:

Periodical:

Advanced Materials Research (Volumes 243-249)

Edited by:

Chaohe Chen, Yong Huang and Guangfan Li

Pages:

5475-5480

DOI:

10.4028/www.scientific.net/AMR.243-249.5475

Citation:

Z. Jun "Structural Damage Detection Using Parameters Combined with Changes in Flexibility Based on BP Neural Networks", Advanced Materials Research, Vols. 243-249, pp. 5475-5480, 2011

Online since:

May 2011

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Price:

$35.00

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