Shear Strength Criterion for Rock Discontinuities: A Comparative Study of Regression Approaches

Fathipour‐Azar, Hadi

doi:10.1007/s00603-023-03302-6

Cited by 7 publications

(1 citation statement)

References 45 publications

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“…This can make acquiring good quality data for ML challenging, resulting in smaller datasets relative to other industries. Recent examples of rock engineering ML models have been trained on only a few hundred data points (e.g., [12,13,[16][17][18][19], etc. ), with some on as few as 80 data points [20].…”

Section: Data Quantity Issuesmentioning

confidence: 99%

Algorithmic Geology: Tackling Methodological Challenges in Applying Machine Learning to Rock Engineering

Yang,

Heagy,

Morgenroth

et al. 2024

Geosciences

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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).

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