Development and assessment of lightweight walkable pavement slabs with integrated solar panels
Bibliographic record
Abstract
Sustainability plays a vital role in modern engineering, particularly in urban pavement design. This research explores the integration of solar energy with recycled materials to develop a durable and eco-friendly pavement solution. Existing solar pavement systems often struggle with durability and inefficient energy storage, limiting their practical use. To address these challenges, a novel solar pavement slab was developed, incorporating photovoltaic panels, batteries, and LED lighting within a lightweight concrete base enhanced with recycled materials. A comprehensive experimental study was conducted to optimise the mechanical properties of foamed concrete (FC) for walkable pavement slabs. Various recycled materials, including waste foundry sand, recycled tyre steel fibres, and polystyrene, were incorporated to improve strength, reduce self-weight, and enhance sustainability. Waste foundry sand was partially replaced for normal sand, creating a more environmentally friendly concrete mix while minimising dependence on virgin materials. Results indicate that replacing 25% of fine aggregates with waste foundry sand increased compressive strength by 100% while reducing overall weight. Additionally, incorporating steel fibres and controlled quantities of recycled materials further enhanced structural performance. The final prototype, measuring 400 × 400 × 50 mm, featured a tempered glass or acrylic optical layer over a photovoltaic module and was successfully tested under different environmental conditions. The findings confirm the feasibility of using lightweight foamed concrete with recycled materials to develop efficient and resilient solar pavements. This approach not only enhances the sustainability of construction materials but also supports environmental conservation by reducing carbon emissions. In situ testing demonstrated reliable LED lighting and battery charging performance during summer, with battery voltages averaging 4.19 volts overnight. However, reduced sunlight in winter led to minor declines in performance, highlighting the need for further optimisation. This study contributes to sustainable infrastructure development by integrating renewable energy into urban paving solutions and encouraging the use of recycled materials. Future research could focus on improving battery efficiency and extending applications to snow-melting systems and street lighting.
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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 | 0.001 | 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".