Követés
Josephine Morgenroth
Josephine Morgenroth
Director of Innovation & Applied Research, RockMass Technologies
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Hivatkozott rá
Év
An overview of opportunities for machine learning methods in underground rock engineering design
J Morgenroth, UT Khan, MA Perras
Geosciences 9 (12), 504, 2019
592019
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
312017
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
8*2021
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
52020
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
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
32022
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
32020
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
22023
Cigar Lake Mine Convolutional Neural Network
J Morgenroth
https://doi.org/10.5281/zenodo.5755063, 2021
22021
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
22019
Elastic stress modelling and prediction of ground class using a Bayesian Belief Network at the Kemano tunnels
JS Morgenroth
University of British Columbia, 2016
22016
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
12022
Kemano Project–70 Years of Development
DD Tannant, J Morgenroth
12020
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
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
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
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
Machine Learning and Underground Geomechanics-data needs, algorithm development, uncertainty, and engineering verification
J Morgenroth, UT Khan, MA Perras
EGU General Assembly Conference Abstracts, EGU22-799, 2022
2022
Garson Mine Long Short-Term Memory Network
J Morgenroth
https://doi.org/10.5281/zenodo.6606521, 2022
2022
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