Modelling the thermo-mechanical behaviour of a rock joint
Bibliographic record
Abstract
The CNSC, the Canadian regulator for the nuclear industry, participated in DECOVALEX-2023 Task G that focuses on the thermo (T) - hydro (H)- mechanical (M) behaviour of rock joints. Joints are omnipresent in rock masses and are planes of weakness in the host rock. When deep geological repositories (DGRs) for radioactive waste are being considered in areas where rock joints are present, the joints could be preferential pathways for radionuclide migration. Therefore, their THM behaviour must be better understood to assess the safety of the DGR. Under different possible internal and external perturbations, a joint can move by shear and dilation. If the joint crosses the emplacement area of a waste container, the heat generated from the waste can itself induce shearing of the joint. Excessive shear movement can in turn lead to failure of the container, resulting in earlier release of radionuclides. Furthermore, dilation that might accompany shear, results in an increase in the joint aperture creating a faster flow path for radionuclide transport. Mathematical models are important tools that need to be developed and employed, in order to assess joint shear and dilation under different loading conditions, such as the heat generated from the emplaced waste. The authors have developed such a mathematical model based on a macroscopic formulation within the framework of elasto-plasticity. It is verified against analytical solutions and validated against shear under constant normal load tests and thermal shearing tests of joints in granite.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".