Back-Analysis of Rock Mass Strength at a Radioactive Waste Disposal Site Using Acoustic Emission Monitoring Data and 3D Numerical Modelling
Bibliographic record
Abstract
In this study, a new method was examined that used acoustic emission (AE) monitoring data, in combination with a primary Boundary Element stress analysis, to back-calculate rock mass strength. The presented AE data came from the National Radioactive Waste Repository (NRWR) for low- and intermediate-level waste (LLW/ILW). AE monitoring is able to detect the pre-peak, peak, and post-peak stress changes in rock mass. The presented method used AE monitoring data to back-calculate parameters, such as uniaxial compressive strength and the rock mass deformation modulus. The AE initiation threshold was used to develop an objective function that considered the stress in the rock mass and the rock mass strength. The findings of this research propose that most AE events can be related to the crack initiation threshold, and in space, most of them are located at points away from the excavated walls. In the vicinity of the excavation damage zones around the cavities, the stress conditions beyond the crack damage boundary of the rock mass occur in many areas, leading to significant irreversible deformations. This novel method was demonstrated to aid in the prediction of rock mass strength and is a valuable, non-invasive method for improving the spatial prediction of rock mass parameters, which will lead to safer underground storage facilities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".