Experimental investigation of fiber content and length on curing time‐dependent mode‐I fracture behavior and properties of cemented paste backfill and implication to engineering design
Bibliographic record
Abstract
Abstract The inclusion of fiber in cemented paste backfill (CPB) can significantly alter the mechanical response of the CPB body. The intrinsic defects in CPB and the potential dynamic loading condition make it necessary to investigate the fracture properties of fiber‐reinforced CPB (FR‐CPB). The mode‐I fracture behavior and properties are crucial to the successful engineering application of FR‐CPB technology used in underground mines. The contribution of fiber length and content to the evolutive mode‐I fracture behavior and properties of FR‐CPB was examined in this study. The results show that the addition of fiber reduces the prepeak stiffness but improves the mode I fracture toughness (KIc), and the improvement in KIc increases with fiber length. In contrast, the initial increase in fiber content (from 0% to 0.5%) benefits the KIc acquisition, while a further increase in fiber content from 0.5% to 0.75% poses a negative influence on the KIc development. Moreover, with the adoption of the cement hydration model, four predictive functions are proposed to describe the contribution of fiber length and content to the development of fracture properties. In addition, KIc is identified as a more reliable fracture property for assessing the immediate ground support role played by the FR‐CPB structure. The findings are helpful when it comes to the determination of the fiber length and content in FR‐CPB design.
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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 | 0.001 | 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 | 0.000 |
| 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".