Abstract

Campaigns to determine the state of in situ stress form an integral part of almost all underground rock engineering projects, but stress measurements obtained at different locations on a project site generally display spatial variability. However, there is no international consensus on techniques to objectively quantify such spatial variability, and the nebulous description of “stress heterogeneity” is often used. Our review shows that there are no consistent and universally agreed definitions for stress heterogeneity: the existing definitions are overly simplistic and lack a robust statistical treatment of variability in stress tensors. We demonstrate that stress data can be partitioned into homogeneous stress domains using the k-means algorithm in multivariate stress space, and the resulting clusters characterised using multivariate statistics. This allows us to propose a clear and unambiguous definition of stress heterogeneity. Our analyses also suggest that completely meaningful partitioning of stress data requires development of new algorithms and cluster validity indices.

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