Sustainable Induction-Heatable Cold Patching Using Microwave and Reclaimed Asphalt Pavement
Bibliographic record
Abstract
Patching is a common pavement treatment, and it is implemented using hot and cold process patching. Hot process patching is not an efficient implementation method because keeping asphalt temperature in long hauls is difficult, on-site mixing equipment is required, and transport costs are generally high. Moreover, hot process patching requires a significant amount of energy, so it is not an eco-friendly process. Although cold process patching significantly reduces the energy consumption of patching, its implementation reduces the patching quality. To this end, this study aimed to propose a new cold process patching using induction heating. Furthermore, this study attempted to apply waste materials in the proposed mixtures to preserve the environment. Accordingly, high percentages of reclaimed asphalt pavement (RAP) were used in the proposed mixture. Since induction heating was applied in the introduced patching method, two waste materials, including steel slag and electronic waste, were utilized in the mixtures to enhance the sensitivity to electromagnetic radiation. Moreover, different experimental tests were conducted to evaluate the mixtures’ mechanical properties. Ultimately, gray relational analysis was performed to assess the proposed mixtures’ sustainability. The results indicated that using steel slag and electronic waste as conductive materials could considerably reduce the heating time to raise the fabricated mixtures’ temperature. Moreover, replacing the Neat mixture with the proposed asphalt mixtures containing the waste materials significantly reduced the unit price, greenhouse gas emission, energy consumption, and raw material utilization.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".