Hydration Mechanisms, Microstructure, and Mechanical Properties of Mortars Prepared with Mixed Binder Cement Slurry-Asphalt Emulsion
Bibliographic record
Abstract
Nowadays, deep cold recycling techniques are increasingly considered an effective method for the preventive and corrective maintenance of existing pavements. Not only are these techniques quickly performed and cost effective, but they also allow one to recycle in-place materials, thus limiting the cost of transportation. Various binders can be used for the cold recycling process, including asphalt emulsion, asphalt foam, hydraulic binders, or mixed binders. Of the latter, asphalt emulsion is most often used with the addition of a small quantity of cement (less than 2% with respect to the total mass of aggregates) to accelerate the breaking of the emulsion. This paper aims at understanding the hydration process, the microstructure, and the mechanical properties of mortars prepared with a new mixed binder made of a cement slurry and a small quantity of asphalt emulsion (SS-1 and CSS-1). Conduction calorimetry data reveal that the cement hydration process is nominally influenced by the presence of a small quantity of emulsion. Scanning electron microscope observations show the good dispersion of the asphalt droplets inside the hydrated cement paste. A cationic emulsion tends to entrain less air than anionic emulsion. Test results also indicate that the introduction of asphalt droplets inside a cement mortar matrix leads to a significant reduction in compressive strength and elastic modulus as well as a slight decrease in flexural strength. Mortars made with the cationic emulsion (CSS-1) show higher strengths and elastic modulus than mortars made with anionic emulsion (SS-1).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".