Modeling pore-pressure evolution in hydrating minefill subjected to coupled thermo-mechanical loading
Bibliographic record
Abstract
Despite the long-standing success of waste tailings recycling at shallower lithosphere, heat influx from the stifling environment of deep strata and ground squeezing due to aggravating deposit rheology have posed substantial challenges to traditional mine backfilling. To explore the minefill behavior in difficult geology settings, a new thermo-poroelastic model is developed in this study for characterizing the pore-pressure evolution in hydrating backfill subjected to coupled thermal perturbation and mechanical deformation. By scrutinizing the undrained pressure responses to a hierarchy of thermo-mechanical loading configurations, we investigated the relative contribution of heat exchange and wall convergence to the macroscopic minefill behavior. The result suggests that the backfill response could exhibit unique rate and path-dependence upon thermal loading due to the competing processes that govern the pressure evolution. Moreover, it shows that while rapid wall convergence would always induce significant pressure, thermal straining via heat exchange might still modulate substantially the pressure distribution when the temperature catalyzes considerable water expansivity. These findings could facilitate understanding of the complex backfill behavior in difficult geology settings, and thus have significant implications for adaptive tailings management to deep mining development.
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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 | 0.001 |
| 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".