Prediction of Elastic Modulus of Jointed Rock Mass Using Artificial Neural Networks |
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| Authors: | Vidya Bhushan Maji T G Sitharam |
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| Institution: | (1) Department of Civil Engineering, Indian Institute of Science, Bangalore, Karnataka, 560012, India |
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| Abstract: | Two artificial neural network models for the prediction of elastic modulus of jointed rock mass from the elastic modulus of
corresponding intact rock and joint parameters have been demonstrated in this paper. The data collected from uniaxial and
triaxial compression tests on different rocks with different joint configurations and different confining pressure conditions,
reported in the literature are used as input for training the networks. Important joint properties like joint frequency, joint
inclination and roughness of joints are considered separately for making the network more versatile. Two different techniques
of artificial neural networks namely feed forward back propagation (FFBP) and radial basis function (RBF) are used to predict
the elastic modulus ratio. |
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| Keywords: | Neural networks Jointed rock mass Joint factor Back propagation Radial basis function |
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