A Convolutional Neural Network Approach for Predicting Tunnel Liner Yield at Cigar Lake Mine

As underground instrumentation improves and the storage of large volumes of data becomes more cost effective, the rock engineering community has access to larger datasets than ever before. Machine learning algorithms (MLAs) present an opportunity to uncover nuanced rock mass deformation mechanics more efficiently than conventional data analysis tools, resulting in increased reliability of underground excavations. MLAs require appropriate pre-processing of inputs as well as ground truth validation of outputs. Convolutional Neural Networks (CNNs) are an MLA that allow for the preservation of spatial and temporal dependencies within a dataset. CNNs were developed for image recognition and segmentation, such as video processing, and are efficient at analyzing sequential snapshots of an excavation as the environmental and in-situ factors change. Herein a CNN is developed for Cigar Lake Mine, Saskatchewan, Canada, to predict tunnel liner yield. The mine experiences a complex time-dependent ground squeezing behaviour resulting from the poor geological conditions and the artificial ground freezing implemented to stabilize the ore cavities and to control ground water during the ore extraction process. A sensitivity analysis of the CNN training parameters, called hyperparameters, is completed to optimize the final CNN performance. Hyperparameters analyzed include: the amount of training data, the convolutional filter size, and the error weighting scheme. Two final models are developed, one balanced model able to accurately predict tunnel liner yield across all classes of severity, and one targeted model that is calibrated to predict the higher classes of tunnel liner yield particularly well. Model results demonstrate that the CNN is a promising tool for preserving the spatial and temporal dependencies between input variables, and for predicting tunnel liner yield. This is a novel approach for geomechanical datasets. In combination, the two final CNNs achieve a prediction precision of > 87% across all classes and a recall of up to 99.9% for the higher yield classes. The activation strengths of the inputs were studied, and it was determined that the primary installed support class is the most dominant predictor of tunnel liner yield.

Automated summary

Advancements in underground instrumentation and cost-effective data storage have provided the rock engineering community with larger datasets than ever before. Machine learning algorithms, specifically Convolutional Neural Networks, offer a more efficient way to analyze rock mass deformation mechanics, ultimately increasing the reliability of underground excavations.