Understanding pore water pressure responses to sulphate in cemented tailings backfill with superplasticizers under thermo-hydro-mechanical-chemical field conditions
Bibliographic record
Abstract
This research examines the impact of sulphate on pore water pressure (PWP) development in cement paste backfill (CPB) containing polycarboxylate ether (PES) superplasticizers under thermal-hydraulic-mechanical-chemical (THMC) conditions that imitate actual field curing scenarios. PWP in CPB-PES, with and without sulphate, was assessed under non-isothermal field curing temperatures, varied drainage conditions, and curing stresses using a specially experimental setup. Key findings indicate that PWP behavior in CPB with PES under field conditions diverges markedly from standard laboratory conditions due to the significant effects of field curing temperatures, drainage conditions, and backfill self-weight. The study establishes that high sulphate ion concentrations notably increase initial PWP and slow its dissipation by interfering with the cement hydration process. This interference delays hydration, reduces pore water consumption, and lowers capillary pressure. Moreover, the results show that THMC conditions, especially non-isothermal field temperatures and varied drainage scenarios, considerably accelerate cement hydration compared to standard laboratory conditions, resulting in a more rapid decrease in PWP. Furthermore, improved drainage under THMC conditions mitigates the adverse effects of sulphates by facilitating sulphate ion removal, thus supporting more efficient cement hydration and CPB self-desiccation. The insights gained from this research are essential for understanding PWP behavior in sulphate-bearing CPB-PES in the field, developing predictive THMC models for backfill performance assessment, and enhancing the safety and effectiveness of mining backfills.
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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 | 0.000 |
| 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 | 0.001 |
| 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".