Innovative Pavement Design: Are Solar Roads Feasible?
Bibliographic record
Abstract
Sustainability is critical in current engineering designs, particularly in the field of pavement engineering, and is based on having only limited resources while trying to maximize designs for performance. To this end, developing infrastructure that can meet multiple needs is highly beneficial to society’s will to live at our current standard of living. One such project is the proposal to build roads that have been integrated with photovoltaic cells in order to provide a high performance driving surface while generating renewable electricity. This electricity could then be used by local infrastructure, adjacent buildings, or sold to the electrical grid. In order to do this there are many challenges that need to be overcome, as these roads cannot be made from traditional road surface materials, and a thorough analysis of many design aspects needs to be considered. This paper looks to determine, based on existing pavement materials research, how such a road panel can be constructed and manufactured. Specific elements investigated include the design of each layer of the solar road panel, how the panel can be integrated with photovoltaic electronics, how such a design can be weatherproofed, and how to optimize between solar capture area and structural integrity. The analysis will be influenced by designing around available materials and engineering calculations of the panel under loading. The end result of this paper is a detailed solar road panel design that will be built and evaluated through physical and finite element testing in future work.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".