Rocks, Data, Algorithms: A Roadmap for Practical Machine Learning Adoption in Rock Engineering

Recent advancements in sensor technologies, computing and data storage, and accessibility to commercial AI tools has resulted in an increase in machine learning in rock engineering research. Many researchers have been able to demonstrate data-driven algorithms that have the potential to transform rock engineering paradigms. These studies include developing ML for predicting geology with high accuracy, forecasting rock bursts in high stress environments, and decreasing the computational time needed to update complex numerical models. Despite the potential shown in the research literature, machine learning (ML) has not yet crossed the threshold into practical use in rock engineering. This delay in adoption can be seen as an opportunity to standardize ML development in rock engineering to avoid some of the common pitfalls that can result in erroneous or nonsensical predictions, prior to widespread adoption. Namely, typical rock engineering projects suffer from three primary problems that will affect the trust rock engineers have in using ML: 1) Biased/poor quality data resulting in biased ML models 2) Insufficient data quantity resulting in unreliable ML models 3) Misunderstanding the trained ML model resulting in opaque rock engineering decisions This presentation will highlight the source of these methodological issues by illustrating them with examples and will discuss possible strategies to avoid them when applying ML to rock engineering.