A coupled chemo‐viscoplastic cap model for simulating the behavior of hydrating cemented tailings backfill under blast loading
Bibliographic record
Abstract
Summary Although the use of blasting has become a routine in contemporary mine operations, there is a lack of knowledge on the response of cement tailings backfills subjected to sudden dynamic loading. To rationally describe such a phenomenon, a new coupled chemo‐viscoplastic cap model is proposed in the present study to describe the behavior of hydrating cemented tailings backfill under blast loading. A modified Perzyna type of visco‐plasticity model is adopted to represent the rate‐dependent behavior of the cemented tailings backfill under blast loading. A modified smooth surface cap model is consequently developed to characterize the yield of the material, which also facilitates hysteresis and full compaction as well as dilation control. Then, the viscoplastic formulation is further augmented with a variable bulk modulus derived from a Mie–Gruneisen equation of state, in order to capture the nonlinear hydrostatic response of cemented backfills subjected to high pressure. Subsequently, the material properties required in the viscoplastic cap model are coupled with a chemical model, which captures and quantifies the degree of cement hydration. Thus, the behavior of hydrating cemented backfills under the impact of blast loading can be evaluated under any curing time of interest. The validation results of the developed model show a good agreement between the experimental and the predicted results. The authors believe that the proposed model will contribute to a better understanding of the performance of cemented backfills under mine blasting and contribute to evaluating and managing the risk of failure of backfill structures under such a dynamic condition. Copyright © 2015 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".