Compressive and shear response of fibre-reinforced backfill: impact of field temperature
Bibliographic record
Abstract
Fibre-reinforced cemented paste backfill (F-CPB; a mixture of tailings, fibre, cement, and water) is exposed to non-isothermal curing environments in the field. However, no investigations have been conducted on the impact of non-isothermal curing environments on F-CPB compressive strength, shear characteristics and stress–strain behaviour, although these are important design parameters for F-CPB structures. Therefore, a series of mechanical and microstructural tests were performed on samples with and without fibres exposed to isothermal and non-isothermal conditions. It was found that the non-isothermal curing condition substantially enhances the F-CPB compressive strength and strength gain ratio. The shear strength, cohesion and internal friction angle of the F-CPB samples cured under non-isothermal conditions were higher than those cured under isothermal conditions. These impacts of non-isothermal curing on the compressive and shear characteristics of F-CPBs are related to the temperature-induced change in the microstructure of these samples. The F-CPBs cured under non-isothermal conditions had more hydration products than those cured under isothermal conditions. Moreover, mercury instrusion porosimetry (MIP) tests showed that the F-CPBs subjected to non-isothermal conditions had a finer pore structure than those exposed to isothermal conditions, which contributed to an enhancement of the mechanical characteristics of the F-CPBs subjected to non-isothermal curing.
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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) | 0.001 | 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) | 0.002 | 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".