Optimization of Ultra-High-Performance Concrete Using a Clinker-Free Binder and Iron Mine Tailings Aggregate
Bibliographic record
Abstract
This study investigates the use of iron mine tailings (ITs) as a fine aggregate and a clinker-free binder composed of ground granulated blast-furnace slag (GBFS), desulfurization gypsum (DG), and basic oxygen furnace slag (BOFS) to produce low-cost ultra-high-performance concrete (UHPC). The research optimizes the UHPC base by evaluating the impact of key parameters, including the BOFS to GBFS ratio, DG content, BOFS fineness, and binder-to-sand ratio on compressive strength. The study also compares the use of iron mine tailings and silica sand as fine aggregates, demonstrating that tailings are a viable substitute. The results show that the optimal mix, consisting of a 1:1 BOFS to GBFS ratio, 15% DG, and 400 m2/kg BOFS fineness, achieves a compressive strength of 113.7 MPa after 28 days when using iron mine tailings as fine aggregate. Microstructural analysis through X-ray diffraction (XRD), thermogravimetry (TG), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) reveal that the primary hydration products—C-S-H gel and AFt—contribute to the dense and strong microstructure of the UHPC. This research offers a sustainable approach to producing cost-effective UHPC by utilizing industrial waste materials, providing a promising solution for reducing both environmental impact and production costs in construction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".