Modeling of self‐desiccation in a cemented backfill structure
Bibliographic record
Abstract
Summary After placement of cemented tailings backfill (CTB), which is a mixture of tailings (man‐made soil), water, and binder, into underground mined‐out voids (stopes), the hydration reaction of the binder converts the capillary water into chemically bound water, which results in the reduction of the water content in the pores of the CTB, thereby causing a reduction in the pore‐water pressure in the CTB (self‐desiccation). Self‐desiccation has a significant impact on the pore‐water pressure and effective stress development in CTB and paramount and practical importance for the stability assessment and design of CTB structures and barricades. However, self‐desiccation in CTB structures is complex because it is a function of the multiphysics or coupled (i.e., thermal, hydraulic, mechanical, and chemical) processes that occur in CTB. To understand the self‐desiccation behavior of CTB, an integrated multiphysics model of self‐desiccation is developed in this study, which fully considers the coupled thermal, hydraulic, mechanical, and chemical processes and the consolidation process in CTB. All model coefficients are determined in measurable parameters. Moreover, the predictive ability of the model is verified with extensive case studies. A series of engineering issues are examined with the validated model to investigate the self‐desiccation process in CTB structures with respect to the changes in the mixture recipe, backfilling, and the surrounding rock and curing conditions. The obtained results provide in‐depth insight into the self‐desiccation behavior of CTB structures. The developed multiphysics model is therefore a potential tool for assessing and predicting self‐desiccation in CTB structures.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".