Mechanical response and pore pressure evolution of cemented paste backfill under deep mine-like multiaxial stress and temperature conditions
Bibliographic record
Abstract
As underground mining advances to greater depths, cemented paste backfill (CPB) is increasingly subjected to complex thermo-mechanical loading conditions, including multiaxial stress states and elevated temperatures. This study investigates the coupled effects of field-representative vertical self-weight and horizontal rockwall closure stresses, along with in-situ temperatures, on the mechanical behavior and pore water pressure (PWP) evolution of CPB. Experiments were conducted using a novel apparatus capable of controlling multiaxial stress and temperature during curing, replicating in-situ stress paths and thermal profiles typical of deep mine environments. Results show that multiaxial stress enhances CPB strength and stiffness by promoting denser particle packing, reducing porosity, and increasing frictional resistance. Elevated temperatures independently accelerate early-age cement hydration, further improving bond strength and stiffness. When combined, multiaxial stress and elevated temperature produce a synergistic enhancement in unconfined compressive strength (UCS) and elastic modulus, as confirmed by two-way ANOVA and synergy index analysis. PWP responses were also highly sensitive to thermo-mechanical conditions. The evolution of positive and negative PWP was governed by the interplay of thermal expansion, hydration-induced desaturation, and mechanical compaction. Multiaxial stress amplified early positive PWP and delayed its dissipation, whereas elevated temperature accelerated hydration and reduced pore pressure, leading to enhanced suction at later ages. A transient “stress-induced resaturation” effect was observed under late-stage excessive horizontal stress but was mitigated by elevated temperatures. These findings provide critical insights into the coupled mechanical and hydraulic behavior of CPB under realistic field conditions and offer guidance for optimizing backfill design, binder content, and barricade stability in deep mining 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 | 0.000 |
| 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) | 0.001 | 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".