1Acoustic Remote Monitoring of Rock and Concrete Structures for Nuclear Waste Repositories
Bibliographic record
Abstract
Abstract: Excavation and thermally induced damage is of significance for many types of engineering structures but no more so than in the case of nuclear waste repository design. My research and that of my group, formally at Queen’s University Canada and Keele University UK and now at the University of Liverpool UK, has focused on the development of acoustic techniques for the in situ detection and quantification of induced damage and fracturing. The application of earthquake seismology to this problem has provided the opportunity to study the micromechanics of damage mechanisms in situ and provide validation data for predictive geomechanical models used for engineering design. Since 1987 I have been a principal investigator at Atomic Energy of Canada’s Underground Research Laboratory (URL), responsible for the development of acoustic emission techniques (AE). In the last twelve years, the application of acoustic techniques to rock damage assessment has been pioneered by my group at the URL and successfully applied in several other major international projects including the ZEDEX, Retrieval and Prototype repository experiments at the Aspo Hard Rock Laboratory (HRL) of SKB Sweden. In this paper I describe what information is available by remote acoustic monitoring of rock and concrete structures and demonstrate this with reference to two international scientific experiments carried out at the URL Canada and the HRL Sweden.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".