2020 Joan Bath Bursary Recipient
Josephine Morgenroth
PhD Candidate, York University
Mining operations are motivated to identify underground rock mass instabilities. However, geotechnical professionals often have insufficient time to investigate complex rock mass phenomena in detail, meanwhile more geomechanical instrumentation data is collected than ever before. The developing field of machine learning has been shown to ease the burden of data manipulation and reduce the bias introduced in a manual process.
Josephine’s research lies at the intersection of rock mechanics and emerging machine learning algorithms.
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She is developing data driven methods to predict rock mass behaviour in underground mining excavations. Machine learning algorithms have been found to be highly useful in ore prospecting, and for autonomous mine vehicles. The challenge in applying these algorithms to rock engineering is combining the various input data formats, developing algorithms to be interpretable, and validating the predictions using conventional methods. Josephine’s research objective is to produce a framework for applying machine learning to geomechanical datasets so that there is a blueprint for how these may be applied in mining practice. She is working with a collection of operating mines, instrumentation manufacturers, and mine consultants to improve the prediction of rock mass stability hazards, and to improve the efficiency of the rock support rehabilitation scheduling.