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.
Uncertainty in rock engineering is unavoidable, whether geological, parameter, model, and/or human uncertainty. As such, it is imperative that rock engineers develop methods to manage uncertainty during the design process, especially as the digitalization trends increase. One such method is to collect data and then quantitatively determine what constitutes good quality data. Information theory, such as the concept of Shannon’s entropy [1], can be applied to rock engineering as a way to better audit rock engineering data and determine the quality of data. The field of information theory was originally developed for communications and has been extended to other fields, such as computer science and machine learning; however, its use in rock engineering has been limited. This paper will (i) provide a review of information theory concepts relevant to rock engineering and (ii) build upon the concepts and examples introduced in [2] by providing and showcasing a method whereby information auditing and assessment are used to flag uncertain (or poor quality) rock mass classification values, specifically the rock mass rating (RMR) and Q-system.