Mining Wastes as Road Construction Material: A Review
Bibliographic record
Abstract
The mining industry manages large volumes of tailings, sludge, and residues that represent a huge environmental issue. This fact has prompted research into valorization of these wastes as alternative aggregates for concrete production, embankments, pavement material, etc. The use of mining wastes as a resource for construction presents two benefits: conserving natural resources and reducing the environmental impacts of mining. In the case of road construction, the use of mining wastes has not yet been developed on a large scale and there is a major lack of specific legislation. This gap is due to the variety of exploited rocks, the diversity of tailings, mine residues, or valuable by-products slated for valorization, and the environmental specifics. This paper presents a review on recycling mine wastes as road construction material, including waste rock and mine tailings. Those materials were mostly used in infrastructure where soils had initially poor geotechnical properties (low bearing capacity, frost susceptibility, swelling risk, etc.). Different mining wastes were used directly or stabilized by a hydraulic binder through geopolymerization or, in some cases, with bituminous treatment. Overall, the use of mine wastes for road construction will have a considerable environmental impact by reducing the volume of waste and offering sustainable raw materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".