A multiphysics-viscoplastic cap model for simulating blast response of cemented tailings backfill
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.
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 | 0.000 | 0.000 |
| 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".