Experimental study on the performance of light and dense backfills
Bibliographic record
Abstract
Numerical modeling is a cost-effective and important approach to examine the long-term performance of engineered materials. However, to identify the appropriate constitutive model for a particular material it is necessary to measure physical properties in a laboratory. Laboratory experiments provide the data from which the input parameters for the selected model can be interpreted. This paper studies the mechanical behaviour of two clay-based sealing materials — light backfill and dense backfill — recommended for use in disposal of nuclear waste in a proposed Canadian repository. Test specimens of light and dense backfills were saturated in a triaxial cell and then subjected to specified isotropic and shearing stress paths to measure consolidation and shear characteristics. The triaxial results are interpreted in a critical state context. The light backfill results suggest that it has similar stress–strain behaviour to a bentonite–sand buffer, which has a similar composition, but different preparation procedures and design requirements. Results from testing of dense backfill indicate that it is a much stiffer and stronger material than light backfill. Dense backfill is expected to provide mechanical support to the used-fuel container and other sealing components of the Canadian repository, and the mechanical behaviour of dense backfill satisfies these requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".