Coupled chemo-mechanical modeling of early-age pastefill material under cyclic loading
Bibliographic record
Abstract
Cemented paste backfill (CPB), a mixture of mine tailings, water, and binder, is widely used to provide structural support in underground mines. However, early-age CPB is particularly vulnerable to dynamic loading events, such as earthquakes and rockbursts, which can compromise mine safety and increase the risk of failure. This paper presents a novel coupled chemo-mechanical constitutive model that captures both the time-dependent enhancement of structure during binder hydration and its subsequent degradation under cyclic loading. The model introduces several original features: (i) a hydration-sensitive Phase Transformation Line (PTL) to characterize dilatancy behavior, evolving with curing and degrading with destructuration; (ii) internal variables for bonding and structural strengths, which degrade with accumulated plastic strain to simulate progressive debonding; (iii) a generalized bounding surface and plastic potential formulation that extends into both compression and extension stress states; and (iv) direct chemo-mechanical coupling through hydration-dependent evolution of key mechanical parameters (e.g., structural strength, PTL slope). These advancements are embedded in a unified, bounding surface plasticity framework, specifically designed to simulate early-age CPB under cyclic loading conditions. The model is successfully validated against a series of laboratory cyclic triaxial tests, demonstrating strong predictive capabilities. By capturing the coupled chemical and mechanical processes that govern early-age CPB behavior, this model provides a robust and physically meaningful tool to assess the performance of backfill structures under dynamic conditions. The proposed framework offers new insights into liquefaction susceptibility and structural reliability of CPB under cyclic conditions, contributing to safer and more cost-effective designs of CPB structures in underground mines.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".