Effects of sulfur by-products on the durability and sustainability of asphalt pavement construction: A systematic review
Bibliographic record
Abstract
There has been growing interest in utilizing sulfur by-products in asphalt binders and mixtures for pavement construction. Historically explored in the 1970s and shelved in the 1980s due to economic constraints, sulfur-based technologies are now being reconsidered considering current environmental and economic demands. This systematic review aims to assess the current research on incorporating sulfur into asphalt binders and mixtures and its effects on pavement performance, environmental sustainability, and economic feasibility. It conducts a detailed analysis of the existing literature and synthesizes the key findings on the engineering properties, long-term performance, environmental impacts, and safety considerations of sulfur-modified asphalt (SMA) or Sulfur-Extended Asphalt (SEA). The key findings revealed that incorporating sulfur enhanced the Marshall stability, stiffness, and ductility of asphalt mixtures while reducing flow and permanent deformation. However, concerns remain over sulfur’s environmental and health impacts, particularly hazardous gas emissions, underscoring the need for clear guidelines, deeper insight into chemical and mechanical interactions, optimized mixing procedures, and effective mitigation strategies. This review outlines these challenges alongside motivations and future directions to guide further research and development. The paper summaries the findings that highlight the potential use of sulfur by-products to modify asphalt binders and mixtures for robust and sustainable pavement road construction.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".