An overview of opportunities for machine learning methods in underground rock engineering design J Morgenroth, UT Khan, MA Perras Geosciences 9 (12), 504, 2019 | 74 | 2019 |
Characterization and analysis of a translational rockslide on a stepped-planar slip surface DD Tannant, D Giordan, J Morgenroth Engineering Geology 220, 144-151, 2017 | 33 | 2017 |
A Convolutional Neural Network approach for predicting tunnel liner yield at Cigar Lake Mine. UT Morgenroth, J., Perras M. A., & Khan Rock Mechanics Rock Engineering 55, 2821–2843, 2021 | 10* | 2021 |
A novel long-short term memory network approach for stress model updating for excavations in high stress environments J Morgenroth, K Kalenchuk, L Moreau-Verlaan, MA Perras, UT Khan Georisk: Assessment and Management of Risk for Engineered Systems and …, 2023 | 6 | 2023 |
Convolutional Neural Networks for predicting tunnel support and liner performance: Cigar Lake Mine case study J Morgenroth, MA Perras, UT Khan ARMA US Rock Mechanics/Geomechanics Symposium, ARMA-2020-1513, 2020 | 6 | 2020 |
Practical recommendations for machine learning in underground rock engineering: on algorithm development, data balancing, & input variable selection T Morgenroth, J., Unterlaß, P.J., Sapronova, A., Khan, U. T., Perras, M. A ... Geomechanics & Tunnelling 15 (5), 2022 | 4 | 2022 |
Forecasting principal stresses using microseismic data and a Long-Short Term Memory network at Garson Mine. L Morgenroth, J., Perras, M. A., Khan, U.T., Kalenchuk, K., & Moreau-Verlaan GEO Niagara 2021 – Celebrating a Sustainable and Smart Future, 2021 | 4* | 2021 |
An artificial neural network approach for predicting rock support damage at Cigar Lake Mine: A case study J Morgenroth, MA Perras, UT Khan, A Vasileiou ISRM EUROCK, ISRM-EUROCK-2020-021, 2020 | 3 | 2020 |
Generation of a Discrete Fracture Network from Digital Discontinuity Data Captured Using the 3D Axis Mapping Method J Morgenroth, J Hazzard, S Yee, D Elmo ISRM Congress, ISRM-15CONGRESS-2023-119, 2023 | 2 | 2023 |
On the Interpretability of Machine Learning Using Input Variable Selection: Forecasting Tunnel Liner Yield J Morgenroth, MA Perras, UT Khan Rock Mechanics and Rock Engineering 55 (11), 6779-6804, 2022 | 2 | 2022 |
Cigar Lake Mine Convolutional Neural Network J Morgenroth https://doi.org/10.5281/zenodo.5755063, 2021 | 2 | 2021 |
Comparison of Bayesian Belief Networks and Artificial Neural Networks for prediction of tunnel ground class J Morgenroth, E Snieder, M Perras, UT Khan ISRM Congress, ISRM-14CONGRESS-2019-291, 2019 | 2 | 2019 |
Elastic stress modelling and prediction of ground class using a Bayesian Belief Network at the Kemano tunnels JS Morgenroth University of British Columbia, 2016 | 2 | 2016 |
Kemano Project–70 Years of Development DD Tannant, J Morgenroth | 1 | 2020 |
Rocks, Data, Algorithms: A Roadmap for Practical Machine Learning Adoption in Rock Engineering J Morgenroth ARMA US Rock Mechanics/Geomechanics Symposium, D021S011R003, 2024 | | 2024 |
Algorithmic Geology: Tackling Methodological Challenges in Applying Machine Learning to Rock Engineering B Yang, LJ Heagy, J Morgenroth, D Elmo Geosciences 14 (3), 67, 2024 | | 2024 |
To remove, or not to remove outliers, that is the question B Yang, J Morgenroth, L Heagy, D Elmo New Challenges in Rock Mechanics and Rock Engineering, 117-122, 2024 | | 2024 |
High-resolution ground-deformation and support monitoring using a portable handheld LiDAR approach S Mercer, J Morgenroth, B Simser Ground Support 2023: Proceedings of the 10th International Conference on …, 2023 | | 2023 |
A novel method for automated trace discontinuity mapping at the Kemano hydroelectric tunnels in Western Canada J Morgenroth, S Taylor, S Yee IOP Conference Series: Earth and Environmental Science 1124 (1), 012025, 2023 | | 2023 |
Practical Applications of Machine Learning to Underground Rock Engineering J Morgenroth | | 2022 |