Numerical Simulation of Consolidation Behavior of Large Hydrating Fill Mass
Bibliographic record
Abstract
Abstract Underground mined-out voids need to be backfilled for the stability of the surrounding rock and also to increase ore extraction from adjacent pillars. One of the relatively newer means is cemented paste backfilling, which has been extensively adopted in underground mining operations around the world. During and after the placement of cemented paste backfill (CPB) into stopes, complex multiphysics (thermal, hydraulic, mechanical and chemical) processes take place in the large mass of CPB and could affect its consolidation behavior. An analysis of the consolidation process in CPB mass is essential for the assessment of CPB behavior and cost-effective designs in practice. In this paper, multiphysics simulation of the consolidation behavior of CPB mass is performed under different conditions, including the mixture recipe, and backfilling, drainage, surrounding rock and curing conditions. It is found that the in situ consolidation behavior of CPB structures is a function of the multiphysics processes that occur in the cementing backfill mass. Moreover, the rock mass conditions, including the geometry and rock wall roughness, influence the consolidation process of CPB structures. Cement content, curing time and backfilling rate play a crucial role in the consolidation process of CPB. The obtained results can facilitate a better understanding of the consolidation behaviour of CPB for backfill designers and engineers, and thus contribute to enhance the engineering of CPB structures.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".