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Record W4415685013 · doi:10.3390/su17219634

Studies on the Valorization of Aluminum Production Residues into Bituminous Materials at Different Scales: A Review

2025· article· en· W4415685013 on OpenAlex
Reza Salehfard, Reza Jafari

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSustainability · 2025
Typearticle
Languageen
FieldEngineering
TopicBauxite Residue and Utilization
Canadian institutionsUniversité du Québec à Chicoutimi
FundersRio Tinto
KeywordsAsphaltCrackingFiller (materials)RutRheologyAluminiumThermal treatmentLeaching (pedology)

Abstract

fetched live from OpenAlex

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.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.283
Teacher spread0.269 · 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