Algorithmic Geology: Tackling Methodological Challenges in Applying Machine Learning to Rock Engineering
department or school: Applied Science, Faculty of
department or school: Science, Faculty of
department or school: Earth, Ocean and Atmospheric Sciences, Department of
department or school: Mining Engineering, Keevil Institute of
data provider: DSpace
identifier citation: Geosciences 14 (3): 67 (2024)
creator: Yang, Beverly
creator: Heagy, Lindsey
creator: Morgenroth, Josephine
creator: Elmo, Davide
issued: 2024-04-10T21:11:44Z
issued: 2024-03-04
description: Technological advancements have made rock engineering more data-driven, leading to increased use of machine learning (ML). While the use of ML in rock engineering has the potential to transform the industry, several methodological issues should first be addressed: (i) rock engineering’s use of biased (poor quality) data, resulting in biased ML models and (ii) limited rock mass classification and characterization data. If these issues are not addressed, rock engineering risks using unreliable ML models that can have potential real-life adverse impacts. This paper aims to provide an overview of these methodological issues and demonstrate their impact on the reliability of ML models using surrogate models. To take full advantage of the benefits of ML, rock engineers should make sure that their ML models are reliable by ensuring that there are sufficient unbiased data to develop reliable ML models. In the context of this paper, the term sufficient retains a relative meaning since the amount of data that is sufficient to develop reliable a ML models depends on the problem under consideration and the application of the ML model (e.g., pre-feasibility, feasibility, design stage).
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/87747
note: Citation: Yang, B.; Heagy, L.J.;Morgenroth, J.; Elmo, D. AlgorithmicGeology: Tackling MethodologicalChallenges in Applying MachineLearning to Rock Engineering.Geosciences 2024, 14, 67.https://doi.org/10.3390/geosciences14030067Academic Editors: Anika Braun,Stratis Karantanellis andJesus Martinez-FriasReceived: 14 January 2024Revised: 25 February 2024Accepted: 28 February 2024Published: 4 March 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).geosciencesArticleAlgorithmic Geology: Tackling Methodological Challenges inApplying Machine Learning to Rock EngineeringBeverly Yang 1, Lindsey J. Heagy 2, Josephine Morgenroth 1 and Davide Elmo 1,*1 NBK Institute of Mining Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;jsmorgen@mail.ubc.ca (J.M.)2 Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia,Vancouver, BC V6T 1Z4, Canada; lheagy@eoas.ubc.ca* Correspondence: davide.elmo@ubc.caAbstract: Technological advancements have made rock engineering more data-driven, leading toincreased use of machine learning (ML). While the use of ML in rock engineering has the potential totransform the industry, several methodological issues should first be addressed: (i) rock engineer-ing’s use of biased (poor quality) data, resulting in biased ML models and (ii) limited rock massclassification and characterization data. If these issues are not addressed, rock engineering risksusing unreliable ML models that can have potential real-life adverse impacts. This paper aims toprovide an overview of these methodological issues and demonstrate their impact on the reliabilityof ML models using surrogate models. To take full advantage of the benefits of ML, rock engineersshould make sure that their ML models are reliable by ensuring that there are …
has type: Article
is shown at: 10.14288/1.0441277
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/geosciences14030067
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
scholar level: Researcher
subject: rock engineering
subject: machine learning
subject: rock mass characterization
subject: rock mass classification
type: Text
identifier URI: http://hdl.handle.net/2429/87747