Can new technologies shake the empirical foundations of rock engineering?
Bibliographic record
Abstract
The past decade has witnessed an increasing interest in applications of machine learning (ML) to solve mining and geotechnical problems. This is largely due to an increased use of high-level programming languages, development of user-friendly and open source ML libraries, improved computational power, and increased cloud storage capacity to handle large and complex data sets. The benefit of incorporating ML in rock engineering design are apparent, including the reduction in the time required to sort and characterise field data and the capability to find mathematical correlations between overly complex sets of input data. However, when applied to geotechnical engineering, the question arises as to whether ML can truly provide objective results. In geotechnical engineering, where the medium considered is typically heterogenous and only limited information is spatially available, experience and engineering judgement dominate the early stage of the design process. However, experience and engineering judgement alone cannot reduce data uncertainty. It is also true that the inherent variability of natural materials cannot be truly captured unless sufficient field data is collected in an objective manner. This paper investigates the readiness of the technical community to integrate ML in rock engineering design at this time. To fully realise the potential and benefits of ML tools, the technical community must be willing to accept a paradigm shift in the data collection process and, if required, abandon empirical systems that are considered ‘industry standards’ by virtue of being commonly accepted despite acknowledging their limitations.
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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 | 0.035 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.012 | 0.041 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".