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Intelligent delineation of rock discontinuity data using fuzzy cluster analysis

grantor: University of TorontoDiscontinuities affect the behaviour of rock masses in many important ways and superimpose properties on them. Discontinuity analysis, which in the main deals with the delineation of discontinuities into sets, plays a crucial role in helping rock mechanics and rock engineering experts make important design and construction decisions. The conventional approach to analysing discontinuity data is subjective and does not utilize many of the important properties (other than orientations) in delineating discontinuities into sets. The traditional approach is also slow, can be very boring, and poorly handles the uncertainty present in discontinuity data. In this thesis the application of intelligent computer methods that can inject objectivity into the delineation process in discontinuity analysis were examined. These methods included a non-parametric density estimation method for clustering discontinuity orientations and fuzzy clustering techniques. As a result, a fuzzy cluster algorithm was developed for separating discontinuity data into sets or clusters. The algorithm uses fuzzy and statistical methods that introduce objectivity into discontinuity analysis and account for the uncertainty present in both discontinuity data and the data analysis process. The practical application of the fuzzy 'K'-means algorithm is limited unless clustering issues such as distance metrics, variable weighting, variable standardisation and cluster validity are meaningfully addressed. The thesis provides insight into the importance of resolving these issues in clustering and develops solutions to them.Ph.D