Rock Engineering Systems are a collection of ideas, mathematical tools and computer technology all of which are designed to solve problems in rock engineering with interacting components. The interactions between components can be complex and the rock engineering problems themselves contain a high degree of uncertainty. The research described in this thesis investigates the incorporation of computational techniques known as parallel distributed processing methods into the disciplines of rock mechanics and rock engineering. Two main applications of parallel distributed processing methods in rock engineering are investigated in this thesis.

1. Multilayered perceptron artificial neural networks are used successfully to encapsulate the laboratory behaviour of rocks under triaxial compression. Trained artificial neural networks are then used to replace conventional constitutive models within finite difference geomechanical numerical modelling codes.
2. Two multilayered perceptron artificial neural networks are developed to assist in the task of discrimination of rock fracture presence within digital imagery of rock exposures. The first is trained using samples of the image that contain fracture image content and samples that do not, and provides a probability-like measure of fracture presence. It was sufficiently successful to permit estimation of fracture intensity parameter P_2,2. The second was developed specifically to identify fracture termination condition by matching samples to a set of fracture termination condition templates.

Seven original contributions to the rock mechanics and rock engineering disciplines have resulted across the three application areas. These contributions are itemised, with details, at the beginning of the final Chapter of the thesis.