Analyzing the Environmental Impact of Recycled Concrete Aggregates for Road Base Construction in Mauritius
Bibliographic record
Abstract
The depletion of natural aggregates and the increasing volume of construction and demolition (C&D) waste demand innovative solutions for sustainable construction practices.This study evaluates the feasibility and environmental benefits of incorporating Recycled Concrete Aggregates (RCA) into road base construction in Mauritius.Laboratory analyses were conducted on seven design mixes of RCA blended with conventional crushed aggregates (CRU), assessing key properties such as compaction, durability, and compliance with Road Development Authority (RDA) standards.The optimal mix of 30-40% RCA with 60-70% CRU demonstrated reliable performance for high-traffic roads while achieving a 3.8-6.3%reduction in energy consumption and a 1.9-3.5% decrease in CO2 emissions.Additionally, the research highlights the scalability of RCA in addressing natural resource scarcity and reducing landfill contributions in small island states.A lifecycle assessment, supported by SEVE software, quantified the environmental gains, emphasizing reduced energy demands and minimized carbon footprints compared to traditional practices.The study underscores the role of policy, industry investment, and standardized guidelines in mainstreaming RCA adoption in infrastructure projects.By combining environmental stewardship with technical reliability, this work advances sustainable development goals and sets a precedent for integrating recycled materials into construction sectors worldwide.This research offers a transformative approach to road construction, balancing performance and sustainability while contributing to a circular economy in regions with limited aggregate resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".