Self-healing responses of cementitious tailings materials to changing drainage conditions
Bibliographic record
Abstract
Cemented paste backfill (CPB) is an innovative mine backfilling method widely used in underground mining operations around the world. In field applications, CPB structures can experience a range of drainage conditions, varying from undrained to fully drained states. However, the influence of these varying drainage conditions on the self-healing behavior and performance of CPB remains unknown, as no studies to date have addressed this critical knowledge gap. This study addresses this gap by evaluating the self-healing efficiency of CPB under three drainage scenarios: full drainage, partial drainage, and no drainage. Results show that drainage conditions significantly influence self-healing performance, with specimens under partial or full drainage demonstrating superior crack closure and recovery of mechanical and hydraulic properties compared to undrained specimens. These findings enhance understanding of CPB's self-healing mechanisms and offer practical insights for improving the durability and stability of CPB structures in mining applications. • Examines the influence of drainage conditions on self-healing of cemented paste backfill. • Demonstrates enhanced self-healing efficiency under full and half drainage scenarios. • Quantifies crack closure, strength recovery, and hydraulic conductivity improvement. • Reveals microstructural refinement as key to accelerated self-healing mechanisms. • Provides insights to improve mechanical stability of CPB in underground mining.
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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 | 0.000 | 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) | 0.001 | 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".