Damage Identification of Rock Mass with Artificial Neural Networks

Abstract:

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The inverse problem of rock damage detection is formulated as an optimization problem, which is then solved by using artificial neural networks. Convergence measurements of displacements at a few of positions are used to determine the location and magnitude of the damaged rock in the excavation disturbed zones. Unlike the classical optimum methods, ANN is able to globally converge. However, the most frequently used Back-Propagation neural networks have a set of problems: dependence on initial parameters, long training time, lack of problemindependent way to choose appropriate network topology and incomprehensive nature of ANNs. To identify the location and magnitude of the damaged rock using an artificial neural network is feasible and a well trained artificial neural network by Levenberg-Marquardt algorithm reveals an extremely fast convergence and a high degree of accuracy.

Info:

Periodical:

Key Engineering Materials (Volumes 353-358)

Edited by:

Yu Zhou, Shan-Tung Tu and Xishan Xie

Pages:

2325-2328

DOI:

10.4028/www.scientific.net/KEM.353-358.2325

Citation:

Z. C. Shangguan et al., "Damage Identification of Rock Mass with Artificial Neural Networks ", Key Engineering Materials, Vols. 353-358, pp. 2325-2328, 2007

Online since:

September 2007

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Price:

$35.00

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