Optimization of Mechanical and Durability Properties of Manganese Slag Hybrid Fiber Concrete Using an L9 Orthogonal Design and Grey Relational Analysis
Bibliographic record
Abstract
Studies have highlighted manganese slag (MnS) mixed-fiber concrete as a construction material, in view of the effects of varying proportions of MnS, steel fibers (SF), and polypropylene fibers (PPF) on the mechanical properties and durability of C30 MnS hybrid fiber-reinforced concrete (MHFC).Using an L9 orthogonal design, ten mix ratios were tested for compressive strength, flexural strength, and chloride ion resistance at 7, 28, 56, and 91 days.A grey relational analysis (GRA) method was employed to comprehensively evaluate nine mix proportioning schemes across four curing ages.By analyzing the grey relational degrees, the optimal mix proportioning scheme was identified.Results indicated that SF had the greatest positive impact on both compressive and flexural strength, followed by MnS, while PPF had a limited effect.The optimal mix-20% MnS, 1.0% SF, and 0.5% PPF-achieved a 23% increase in compressive strength and 33% in flexural strength at 28 days.In terms of the durability of concrete in corrosive environments, the optimal performance was achieved with a mix proportion of 10% MnS, 1.0% SF, and 1.0% PPF.These findings provide guidance for optimizing MHFC and highlight the potential of industrial by-products in enhancing concrete durability.Further research is recommended to refine mix designs and assess long-term field performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".