Algorithmic Geology: Tackling Methodological Challenges in Applying Machine Learning to Rock Engineering
Bibliographic record
Abstract
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".