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Intelligent prediction of the stress-strain response of intact and jointed rocks

Garaga, Arunakumari and Latha, Gali Madhavi (2010) Intelligent prediction of the stress-strain response of intact and jointed rocks. In: Computers and Geotechnics, 37 (5). pp. 629-637.

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Official URL: http://dx.doi.org/10.1016/j.compgeo.2010.04.001


An application of Artificial Neural Networks for predicting the stress-strain response of jointed rocks under different confining pressures is presented in this paper. Rocks of different compressive strength with different joint properties (frequency, orientation and strength of joints) are considered in this study. The database for training the neural network is formed from the results of triaxial compression tests on different intact and jointed rocks with different joint properties tested at different confining pressures reported by various researchers in the literature. The network was trained using a three-layered network with the feed-forward back propagation algorithm.About 85% of the data was used for training and the remaining 15% was used for testing the network. Results from the analyses demonstrated that the neural network approach is effective in capturing the stress-strain behaviour of intact rocks and the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different jointed rocks, whose intact strength varies from 11.32 MPa to 123 MPa, spacing of joints varies from 10 cm to 100 cm. and confining pressures range from 0 to 13.8 MPa. (C) 2010 Elsevier Ltd. All rights reserved.

Item Type: Journal Article
Publication: Computers and Geotechnics
Publisher: Elsevier science
Additional Information: Copyright of this article belongs to Elsevier science.
Keywords: Intact rocks;Jointed rocks;Stress-strain behaviour;ANN; Triaxial compression;Prediction.
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Date Deposited: 09 Aug 2010 10:25
Last Modified: 19 Sep 2010 06:13
URI: http://eprints.iisc.ac.in/id/eprint/31218

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