Investigation of back-propagation artificial neural networks in modelling the stress-strain behaviour of sandstone rock

Author

Millar, David ; Clarici, E.

Author_Institution

Dept. of Mineral Resources Eng., Imperial Coll. of Sci., Technol. & Med., London, UK

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

3326

Abstract

Highlights the current principal application areas of artificial neural networks in mineral engineering. An investigation into the suitability of a multilayer perceptron architecture using the generalised delta training rule with backpropagation of error for synthesising deformability models for Crosland Sandstone is discussed. The results of the investigation indicate that this method is effective in achieving this objective for engineering purposes

Keywords

backpropagation; digital simulation; geophysics computing; mechanical engineering; mechanical engineering computing; multilayer perceptrons; rocks; stress-strain relations; Crosland Sandstone; backpropagation artificial neural networks; deformability model synthesis; elastic property; elasticity; error backpropagation; generalised delta training rule; geophysics computing; mineral engineering; model; multilayer perceptron architecture; rock mechanics; sandstone rock; stress-strain behaviour; stress-strain behaviour modelling; Artificial neural networks; Deformable models; Educational institutions; Geologic measurements; Geology; Intelligent networks; Mineral resources; Multilayer perceptrons; Neural networks; Ores;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374770

Filename

374770