The Utilization of Slag, Steel Slag, and Desulfurization Gypsum as Binder Systems in UHPC with Iron Tailings and Steel Fibers—A Review
Bibliographic record
Abstract
Ultra-high-performance concrete (UHPC) is known for its outstanding strength and durability but is often limited by the high cost of traditional materials, like cement, fine aggregates, and silica fume. This review examines the use of industrial by-products—specifically, iron tailings, steel slag, and desulfurization gypsum—as sustainable alternatives in UHPC mix design. These materials serve as supplementary cementitious components and fine aggregates, helping reduce environmental impacts and production costs. This study highlights the synergistic hydration mechanisms between Portland cement and waste-based materials, leading to improved microstructure and long-term strength. The role of steel fibers in enhancing crack resistance is also discussed. Challenges related to workability, cost, and lack of standardization are addressed, along with opportunities for innovative mix designs, low-carbon binders, and 3D printing. Overall, this paper underscores the potential of industrial by-products to advance sustainable, high-performance UHPC solutions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".