Toward sustainable construction: Comprehensive utilization of coal gangue in building materials
Bibliographic record
Abstract
Coal gangue (CG), as a major solid waste byproduct of the coal industry, has attracted widespread attention due to its potential in construction materials. This review systematically evaluates the activation mechanisms and engineering performance of CG, exploring the impact of various activation methods such as thermal, mechanical, chemical, microwave, and synergistic activation on its hydration potential. The findings suggest that activated CG can significantly enhance the cementitious performance of cement-based materials, reduce cement consumption, and contribute to sustainable construction practices. Thermal activation enhances CG reactivity by removing combustible components and transforming mineral phases, while mechanical activation increases the specific surface area through fine milling. Chemical activation, using alkaline or acidic activators, promotes the formation of hydration products by depolymerizing silica-aluminate networks, and microwave activation rapidly enhances reactivity by converting electromagnetic energy into thermal energy . When used as a supplementary cementitious material , activated CG improves durability, but may also affect workability and mechanical properties. When used as an aggregate, CG’s high water absorption and low elastic modulus can lead to increased shrinkage and reduced freeze-thaw resistance, requiring strict control of the substitution ratio. Future research should focus on optimizing activation methods to enhance CG’s reactivity and performance, while addressing challenges related to its high water absorption and variable mineral composition. Additionally, a life cycle assessment of CG utilization in construction materials should be conducted to comprehensively evaluate its environmental impact.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".