Global Perspectives on Recycled Concrete Aggregates (RCA): A Comprehensive Review of Worldwide Applications and Best Practices
Bibliographic record
Abstract
Given the high consumption of natural resources, sidewalks are considered one of the most cost-intensive pieces of infrastructure. The use of recycled concrete aggregates in road pavements can provide a beneficial approach to improving economic, social and environmental sustainability. RCA is considered a multi-layered solution that protects natural resources, minimizes road construction costs, and limits harmful emissions. Research has found that RCA can be effectively applied to a variety of pavement layers, including base and surface layers. It was also observed that the mechanical and physical properties of RCA can be studied and compared with those of natural aggregates. Additionally, several technologies that can be used to improve RCA performance on pavement are revealed. Although RCA may be considered a viable and sustainable alternative to NA in pavement applications, recommendations regarding replacement rates in asphalt mixtures still vary widely. To ensure a balance between sustainability, functionality and quality of pavements, the continued rollout of RCA implementation relies on in-depth research leading to a unified and homogeneous set of standards and specifications. This article highlights the role of RCA as an important driver of sustainable road construction as well as circular economy strategies. It may also call for advanced research and shared decision-making to realize the full potential of RCA.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".