Paper Title:
Damage Identification of Rock Mass with Artificial Neural Networks
  Abstract

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, S. J. Li, M. T. Luan, "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
$32.00
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