Microwave Heating Characteristics of Emulsified Asphalt Repair Materials Incorporated with Steel Slag
Bibliographic record
Abstract
Emulsified asphalt needs to be cured for a certain age after demulsification to produce strength, which seriously affects the traffic opening time. In this work, microwave heating technology was applied for emulsified asphalt repair materials. Steel slag with high microwave activity was adopted to improve the performance of emulsified asphalt repair materials by microwave heating. Effects of steel slag sizes and contents on the heating rate, temperature distribution, and thermal performance of emulsified asphalt repair materials were analyzed by close microwave heating, open microwave heating, and repair simulation tests. Results show that the temperature of emulsified asphalt repair materials presents three different heating stages under microwave irradiation. The “critical point of phase transition” in the three stages is gradually advanced with the increase in steel slag content. The core temperature and maximum temperature of emulsified asphalt repair materials with different steel slag sizes are basically the same; however, the heat distribution of emulsified asphalt repair materials is significantly different. In contrast to conventional asphalt mixture, there exists a smaller temperature difference. The temperature of repairing materials can reach above 80°C. The interface area can form an embedded interface structure. Incorporation of steel slag and adoption of microwave heating are effective to improve the performance of emulsified asphalt repair materials.
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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".