A New Approach for Characterisation of Densely Fractured Rock Masses Using Artificial Neural Networks and Principal Component Analysis

One fundamental requirement for surface and underground rock engineering is the ability to characterise the rock mass. An accurate characterisation of the rock mass allows rock engineers to optimize designs safely and economically. A number of classification systems have been developed in the past 50 years to help characterise rock masses such as RMR, Q system, Rmi, and several others. The challenge in developing rock characterisation schemes is often practicality, i.e. creating systems with relatively simple inputs for the everyday practitioner. A second challenge is the degree of subjectivity often inherent in commonly used classification systems. These challenges are especially exacerbated in weak rock or densely jointed rock masses. This paper introduces a new approach to characterise densely jointed rock mass using artificial neural networks and principal component analysis, based on principles commonly used in the geotechnical engineering of rock fills and waste rock dumps. Thirteen input parameters are dimensionally compressed using principal component analysis. Using a global database with more than 100 case histories, an artificial neural network is developed to conduct the rock mass characterisation. The advantage of this approach is less subjectivity as commonly used engineering (quantitative) terms are put to use. A further advantage is that given that the method is site-data dependent, it allows for ‘fine tuning’ of the model for a specific site.