Disrupting rock engineering concepts: is there such a thing as a rock mass digital twin and are machines capable of learning rock mechanics?
Bibliographic record
Abstract
Introduced by NASA engineers in the early 1960s during the Apollo space program, the term ‘digital twin’ has recently gained more visibility and exposure thanks to the development of the related new concept of the Internet of Things, IoT. A digital twin is not merely a model of a real physical asset but represents the actual connection between the physical world and the virtual (digital) reality. In this context, numerical models of a rock mass are virtual prototypes of that rock mass, which can be used to guide the design process and to estimate possible ground behaviour; they cannot however be considered true rock mass digital twins. Rock engineering differs from other engineering disciplines since often design must be completed prior to developing access to rock exposures, forcing engineers and practitioners to rely on information obtained from severely limited 1D sampling methods. Even if engineers were to have unlimited resources, the natural variability of the rock mass, combined with the limited knowledge of the rock mass in the early phases of a project, would be such that the design outcome would still be influenced by what we do not know rather than by what we effectively know. The authors strongly believe that if initial 1D information to be complemented by 2D sampling during slope excavation, and smart sensors embedded within the rock mass, the opportunity exists to update virtual models and to compare models to the data from the smart sensors on a periodic basis. Note that the smart sensors would have to provide more information than deformation alone, since failure of intact rock bridges in engineered slopes is a progressive damage process and as such, requires location of the source where damage is accumulating. This paper outlines the challenges facing the rock engineering community if we really want to truly transform and improve virtual (digital) geological and geomechanically rock mass models. The authors also provide a critical discussion on the potential use of machine learning algorithms based on empirical methods and the use of qualitative to semi-quantitative scales of measurements that are inappropriate for a full statistical analysis.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".