Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks

Petrographic features of a rock are intrinsic properties, which control the mechanical behaviour of the rock mass at the fundamental level. This paper deals with the application of neural networks for the prediction of uniaxial compressive strength, tensile strength and axial point load strength simultaneously from the mineral composition and textural properties. Statistical analysis has also been conducted for prediction of the same strength properties and compared with the predicted values by neural networks to investigate the authenticity of this approach. The network was trained to predict the uniaxial compressive strength, tensile strength and axial point load strength from the mineralogical composition, grain size, aspect ratio, form factor, area weighting and orientation of foliation planes (planes of weakness). A data set having 112 test results of the four schistose rocks were used to train the network with the back-propagation learning algorithm. Another data set of 28 test results of the four schistose rocks were used to validate the generalization and prediction capabilities of the network.