4.7 Article

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

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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.

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