Prediction of Elastic Modulus of Jointed Rock Mass Using Artificial Neural Networks

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.