Evolutive phase field modelling of fracture behavior of cemented paste backfill under Mode-I loading condition
Bibliographic record
Abstract
An evolutive phase field model (PFM) that incorporates cement hydration process is developed to predict Mode-I fracture behavior in cemented paste backfill (CPB). The model seamlessly integrates hydration-dependent stiffness and toughness evolution into a standard finite element framework, regularizing sharp cracks through a diffusive phase field variable. In all three case studies, the PFM reproduces key experimental observations-narrow crack bands and stable propagation in single-edge notched bending (SENB) test, mode-I dominated crack paths and post-peak softening in semi-circular bending (SCB) test, and wide, diffuse fracture zones in splitting tensile tests. The coupled hydration damage formulation captures the time- and temperature-sensitive strengthening of CPB, successfully matching the delayed crack initiation and gradual load-bearing increases seen across varied curing ages and temperatures. The robustness of a single parameter set across multiple test configurations underscores the model potential for generalized CPB fracture prediction without geometry-specific calibration. By accurately forecasting crack patterns and load-displacement responses, this evolutive PFM offers a powerful tool for optimizing CPB mix design, service-life prediction, and curing protocol development in deep-mine applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".