Applications of Information Theory in Rock Engineering

Rock engineering relies heavily on empirical systems to identify significant parameters influencing rock mass behaviour. The empirical and inductive nature of rock engineering design is such that it is not possible to eliminate uncertainty. One way of managing uncertainty during the design process is by collecting good quality data in a standardized and objective manner. However, difficulties arise when defining and determining what constitutes good quality data. We believe that information theory and the concept of Shannon’s entropy could be effectively used to better audit rock engineering data. This paper builds on established concepts by expanding and refining the application of information theory to rock mass classification systems, specifically the rock mass rating and the Q-system. One of the objectives is to provide and showcase a method whereby information auditing is used to flag uncertain (or poor quality) data. In the future it is not difficult to envision data collection processes that include improved core logging and data processing where imaging technologies are coupled with machine learning processing capability. Such an approach requires more quantitative and objective rock mass descriptions; in this context it easy to appreciate the role that information theory might have in the future in rock engineering.