Fly ash for sustainable roads: A comprehensive review on mechanisms and performance optimization
Bibliographic record
Abstract
Fly ash (FA), a major solid waste from coal-fired industries, represents a critical pathway for the green disposal of bulk solid waste and the low-carbon construction of transportation infrastructure, holding significant strategic importance. In recent years, extensive research has been conducted on the mechanisms, optimization, and applications of FA-based road engineering materials. This review focuses on FA-based road engineering material systems, synthesizing previous studies from three perspectives: cement concrete pavement materials, asphalt mixture pavement materials, and stabilized soil road materials. The findings reveal that FA significantly enhances road material performance through its pozzolanic activity, micro-nano filling effects, and interfacial strengthening. In cement concrete systems, the synergistic interaction of pozzolanic reactions and microsphere filling achieves microstructural densification. Integrating particle size optimization, nano-modification, and alkali activation techniques can overcome the mechanical strength and durability limitations of traditional FA systems. For asphalt mixtures, leveraging FA’s porous adsorption characteristics and chemical bonding effects optimizes the asphalt-aggregate interface adhesion, while alkali activation further extends its application scope. In stabilized soil systems, FA enhances soil integrity by forming cementitious networks, with structural reinforcement achievable through alkali activation and composite stabilization. However, current research still faces unresolved challenges. These include elemental imbalances and performance limitations in single-FA-based road engineering materials, insufficient high-value applications of FA carbon sequestration technology in road materials, lack of systematic frameworks for environmental risk assessments of FA-based road systems, and constrained application scenarios for FA in road engineering. Future research should focus on innovating activation technologies to enhance the reactivity of FA, co-utilizing multi-source solid wastes, researching carbon sequestration technologies for FA-based road engineering materials, establishing environmental monitoring and evaluation systems, and promoting the application of FA in subgrade filling. The conclusions provide comprehensive insights for industrial solid waste recycling and low-carbon road construction, supporting sustainable, low-carbon, and high-quality development in road engineering.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 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".