The Use of Geopolymers in Warm Mix Asphalt Technology – A Review
Bibliographic record
Abstract
The utilization of geopolymers as additives in warm mix asphalt has recently gained significant attention in the field of asphalt technology.This paper provides an in-depth review of the current literature on using geopolymers in warm-mix asphalt.Warm mix asphalt has captured the attention of many researchers due to its production temperature being lower than Hot Mix Asphalt, which can result in a reduced number of gases released during production, allow for better handling of the binder on-site during the application, reduced energy consumption, and allow for quick opening to traffic.The review focuses on the effects of geopolymers on the physical and mechanical properties, moisture susceptibility, and rutting resistance of warm mix asphalt.This review's findings show that adding geopolymers can improve the mechanical properties of warm mix asphalt, such as stiffness and fatigue resistance.Moreover, including geopolymers in warm mix asphalt can improve their resistance to moisture damage and rutting.Additionally, the review highlights the need for further research on optimizing the geopolymer's dosage and their interaction with the other constituents of warm mix asphalt.Overall, this review provides valuable insights into the potential benefits and challenges of using geopolymers as additives in warm mix asphalt and highlights the need for further research in this area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".