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Record W4410500560 · doi:10.3390/min15050538

The Utilization of Slag, Steel Slag, and Desulfurization Gypsum as Binder Systems in UHPC with Iron Tailings and Steel Fibers—A Review

2025· article· en· W4410500560 on OpenAlexaff
Hocine Heraiz, Jiajie Li, Dongdong Zhang, Yabo Hu, Xinli Mu, Amer Baras, Jinhai Liu, Wen Ni, Michael Hitch

Bibliographic record

VenueMinerals · 2025
Typearticle
Languageen
FieldEngineering
TopicTailings Management and Properties
Canadian institutionsUniversity of the Fraser Valley
FundersHigher Education Discipline Innovation ProjectNational Social Science Fund of ChinaNational Office for Philosophy and Social Sciences
KeywordsFlue-gas desulfurizationTailingsSlag (welding)GypsumMetallurgyMaterials scienceSteelmakingGround granulated blast-furnace slagWaste managementCementEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.226
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2025
Admission routes1
Has abstractyes

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