Integration of Recycled Industrial Wastes into Pavement Design and Construction for a Sustainable Future
Bibliographic record
Abstract
Transportation Infrastructure has remained a key element for the economic and social development. Especially in developing countries, the demand for new roads and maintenance of existing roads is very high as they depend on the overall economic development. These calls for transforming the methods the roads are being constructed. Recent studies have shed light on the concept of making transportation system greener and more sustainable, which can be a fast track to achievie the goals of saving the planet from further generations. One of the effective ways of addressing this issue is by substituting or replacing pavements layers by sustainable alternate materials. Promising alternate materials have been investigated in road construction-specifically using recycled wastes. The purpose of this study is to analyze the environmental significances of alternate materials such as recycled tires, recycled glass and waste plastics in road construction and delineate their economic and environmental importance. This is addressed by comparing various parameters such as global warming potential, carbon footprint, cost, and other environmental impact factors. The results of this investigation showed that the use of these materials in pavement construction has substantial environmental and economic benefits. These results will assist in developing revised pavement design and construction methods that are more efficient and economically feasible.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".