Studies on the Valorization of Aluminum Production Residues into Bituminous Materials at Different Scales: A Review
Bibliographic record
Abstract
To conserve natural resources and reduce waste generation, the effective valorization of industrial waste and byproducts in engineering applications is becoming increasingly important. Among these materials, aluminum production residues (APRs) offer a promising and sustainable solution for road pavement applications. Unlike previous reviews, this paper uniquely examines recent research on the use of various APRs in bituminous materials across multiple scales, with particular attention to their roles as additives and fillers. The APRs examined included red mud (RM), aluminum dross (AD), and spent pot lining (SPL) residues, as well as secondary aluminum waste (SAW). These materials have been employed as additives in asphalt binders (microscale), as fillers in asphalt mastics (mesoscale), and as additives or fillers in asphalt mixtures (macroscale). Overall, this review indicates that adopting appropriate treatment approaches for APRs as asphalt modifiers can enhance their dispersion, thermal stability, rheological behavior, and leaching performance. In particular, the use of RM has been shown to improve thermal stability, tensile strength, intermediate-temperature cracking resistance, and rutting resistance, largely due to the increased stiffness it imparts to asphalt mastic and mixture phases. However, there is no clear consensus among researchers regarding other properties, as performance outcomes depend strongly on multiple factors, particularly the physicochemical characteristics of the RM, filler–binder ratios, testing methods, and reference filler types. Other APRs—such as AD, SPL, and SAW—have also shown beneficial effects on the performance of asphalt mixtures. There is still limited research on the influence of APRs physicochemical variability on asphalt–filler interactions and the performance of bituminous materials. For the safe and large-scale adoption of APRs, it is essential to establish standardized characterization procedures, testing methods, and application guidelines while considering diverse climatic conditions. Comprehensive assessments of cost and environmental impacts should also be incorporated to support informed decision-making by engineers and industrial stakeholders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".