Practical recommendations for machine learning in underground rock engineering – On algorithm development, data balancing, and input variable selection

Research has demonstrated that machine learning algorithms (MLAs) are a powerful addition to the rock engineering toolbox, and yet they remain a largely untapped resource in engineering practice. The reluctance to adopt MLAs as part of standard practice is often attributed to the ‘opaque’ nature of the algorithms, the complexity in developing them, and the difficulty in determining how the algorithms use the datasets. This article presents tools and processes for developing MLAs, input selection, and data balancing for practical underground rock engineering. MLAs for classification and regression – two main machine learning applications – are presented in terms of developing MLA to extract information from the dataset to obtain the desired output. Engineering verification metrics are selected based on their suitability for specific output. Methods for input selection and data balancing are discussed with a focus on selecting appropriate input data for the problem without introducing bias or excess complexity. Each tool and process for algorithm development, data preparation, and input selection is illustrated with a case study. This article demonstrates that geotechnical practitioners can extract additional value by applying MLAs to rock engineering problems. Once an understanding of the functions of MLAs is reached, the building blocks and open‐source code are available to be adapted to suit the rock mass behaviour of interest.