A multiphysics-viscoplastic cap model for simulating blast response of cemented tailings backfill
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Bibliographic record
Abstract
Although a large number of previous researches have significantly contributed to the understanding of the quasi-static mechanical behavior of cemented tailings backfill, an evolutive porous medium used in underground mine cavities, very few efforts have been made to improve the knowledge on its response under sudden dynamic loading during the curing process. In fact, there is a great need for such information given that cemented backfill structures are often subjected to blast loadings due to mine exploitations. In this study, a coupled thermo-hydro-mechanical-chemical (THMC)-viscoplastic cap model is developed to describe the behavior of cementing mine backfill material under blast loading. A THMC model for cemented backfill is adopted to evaluate its behavior and evolution of its properties in curing processes with coupled thermal, hydraulic, mechanical and chemical factors. Then, the model is coupled to a Perzyna type of viscoplastic model with a modified smooth surface cap envelope and a variable bulk modulus, in order to reasonably capture the nonlinear and rate-dependent behaviors of the cemented tailings backfill under blast loading. All of the parameters required for the variable-modulus viscoplastic cap model were obtained by applying the THMC model to reproducing evolution of cemented paste backfill (CPB) properties in the curing process. Thus, the behavior of hydrating cemented backfill under high-rate impacts can be evaluated under any curing time of concern. The validation results of the proposed model indicate a good agreement between the experimental and the simulated results. The authors believe that the proposed model will contribute to a better understanding of the performance of hydrating cemented backfill under blasting, and also to practical risk management of backfill structures associated with such a dynamic condition.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it