Effects of sulfur by-products on the durability and sustainability of asphalt pavement construction: A systematic review
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it