Machine Learning and Underground Geomechanics – data needs, algorithm development, uncertainty, and engineering verification
Bibliographic record
Abstract
Machine learning algorithms (MLAs) are emerging as a powerful tool for forecasting complex and nuanced rock mass behaviour, particularly when large, multivariate datasets are available. In engineering practice, it is often difficult for geomechanical professionals to investigate all available data in detail, and simplifications are necessary to streamline the engineering design process. An MLA is capable of processing large volumes of data quickly and may uncover relationships that are not immediately evident when manually processing data. This research compares two algorithms developed for two mines representing end member behaviours of rock failure mechanisms: squeezing ground with high radial convergence, and spalling ground with high in situ stresses and seismicity. For the squeezing ground case study, a Convolutional Neural Network is used to forecast the yield of the tunnel liner elements using tunnel mapping images as the input. For the high stress case study, a Long Short Term Memory network is used to forecast the in-situ stresses that takes time series microseismic events and geomechanical properties as inputs. The two case studies are used to compare input data requirements and pre-processing techniques. Ensemble modelling techniques used to quantify MLA uncertainty for both case studies are presented. The development of the two MLAs is discussed in terms of their complexity, generalizability, performance evaluation, verification, and practical applications to underground rock engineering. Finally, best practices for MLA development are proposed based on the two case studies to ensure model interpretability and use in engineering applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".