Performance Evaluation of an Asphalt Mix Containing Non-Metallic Fractions of Recycled Printed Circuit Boards
Bibliographic record
Abstract
The growing volume of non-metallic fractions extracted from recycled printed circuit boards necessitates sustainable solutions. Integrating these fractions into road paving asphalt mix presents potential opportunities to bolster environmental sustainability and resource conservation. While previous studies have predominantly focused on the wet process of incorporating electronic waste into asphalt binder, challenges persist, including but not limited to the requirement for specialized machinery and concerns regarding the storage stability of the modified binder. In contrast, this paper explored designing a Superpave mix via the dry process followed by execution of fundamental performance tests including Hamburg wheel-tracking and the Illinois flexibility index test. The results highlighted that the asphalt mix substantially surpassed the minimum requirement of 80% for Tensile Strength Ratio (TSR), indicating excellent moisture resistance. Additionally, its resistance to rutting and fatigue cracking remained well within acceptable limits, underscoring the overall durability of the mix. Finally, structural analysis of four pavement models was conducted using AASHTOWare Pavement M-E Design (PMED). The findings indicated that predicted distresses consistently remained below the failure threshold, even when faced with varying subgrade conditions, traffic volumes, and climate factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".