On the Practical Use of a Neural Network Strategy for the Modelling of the Deformability Behaviour of Croslands Hill Sandstone Rock

Evaluation of previous studies of multi-layer perceptron type artificial neural networks in the modelling of the deformability behaviour of rock have suggested that a deviation from the standard method of laboratory testing of rock under triaxial loading at constant confining stresses and constant axial strain rates may be inappropriate when the data produced are used to train an artificial neural network. In this paper, preliminary results of adopting such a strategy are reported. Of specific concern is the establishment of a suitable schedule for the stress path during testing. Furthermore, the training element of back propagation optimisation was directed towards prediction of new stresses given old stresses and old strain increments. Adoption of this method of supervisation in artificial neural network training facilitates the integration of the trained neural network within existing numerical methods used in rock mechanics. The target code was a finite difference Lagrangian formulation for the analysis of continua. Sandstone rock taken from a quarry in the UK was used in the study which showed that the method has great potential.