Machine Learning and Underground Geomechanics – data needs, algorithm development, uncertainty, and engineering verification

Machine learning algorithms (MLAs) are emerging as a powerful tool for forecasting complex and nuanced rock mass behaviour, particularly when large, multivariate datasets are available. In engineering practice, it is often difficult for geomechanical professionals to investigate all available data in detail, and simplifications are necessary to streamline the engineering design process. An MLA is capable of processing large volumes of data quickly and may uncover relationships that are not immediately evident when manually processing data. This research compares two algorithms developed for two mines representing end member behaviours of rock failure mechanisms: squeezing ground with high radial convergence, and spalling ground with high in situ stresses and seismicity. For the squeezing ground case study, a Convolutional Neural Network is used to forecast the yield of the tunnel liner elements using tunnel mapping images as the input. For the high stress case study, a Long Short Term Memory network is used to forecast the in-situ stresses that takes time series microseismic events and geomechanical properties as inputs. The two case studies are used to compare input data requirements and pre-processing techniques. Ensemble modelling techniques used to quantify MLA uncertainty for both case studies are presented. The development of the two MLAs is discussed in terms of their complexity, generalizability, performance evaluation, verification, and practical applications to underground rock engineering. Finally, best practices for MLA development are proposed based on the two case studies to ensure model interpretability and use in engineering applications.