Laboratory Study on the Effect of Kraft Lignin and Sasobit on Construction Temperatures, Compactability and Physical Properties of Hot and Warm Mix Asphalt
Bibliographic record
Abstract
This study investigates the feasibility of using Kraft lignin in Hot and Warm Mix Asphalt (HMA and WMA), with a particular focus on its integration alongside Sasobit®. The research aims to evaluate the impact of Kraft lignin and Sasobit, individually and in combination, on the construction temperatures, compactability, and physical properties of asphalt mixtures. The experimental program included a reference HMA and modified mixes with 20% Kraft lignin, 3% Sasobit, and their combinations. These mixes were designed and subjected to tests to assess their volumetric and mass properties and to determine the construction temperatures using the Superpave Gyratory Compactor (SGC). The results demonstrated that adding Kraft lignin increased construction temperatures, while Sasobit effectively reduced these temperatures by lowering binder viscosity. When used together, Sasobit offset the increase in construction temperatures caused by Kraft lignin, resulting in compaction temperatures similar to the reference HMA mix. Additionally, Kraft lignin increased air voids, leading to reduced compactability at higher gyration levels. It also exhibited indications of a dual role, functioning as both a binder replacement and a filler. In conclusion, the combination of 20% Kraft lignin with 3% Sasobit offers a promising solution for enhancing the sustainability of asphalt mixtures.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".