Case Study Evaluation of the Sustainability of Recycled Aggregate Road Structure
Bibliographic record
Abstract
Many jurisdictions are facing an infrastructure crisis while working towards becoming more sustainable. Alternative methods of road rehabilitation are being used and it is important that the sustainability aspects of these alternative methods be reviewed and compared to traditional methods. The objective of this research was to examine the sustainability of using recycled asphalt materials in road rehabilitation. A holistic sustainability review was completed within four key aspects of sustainability including economic, social, environmental, and technical aspects. A case study using the rehabilitation of 8th Street in Saskatoon, Saskatchewan conducted under the City’s “Green Street” Program found that from an economic perspective, a significant cost saving was realized. Socially, residents received an improved level of service on 8th Street. Non-destructive heavy weight deflection measurements of the recycled structure were at the same level or better than the adjacent conventionally constructed section, indicating residents received a comparable ride quality to the conventional structure. Technically, the recycled materials had equal or superior mechanistic properties when compared to conventional road building materials. The use of a low concentration of stabilization materials greatly enhanced the mechanical properties of the recycled materials, which may further improve their life cycle performance and reduce the need for frequent rehabilitation treatments. This study illustrated that recycled materials, when applied within a framework of engineering mechanics, can be used effectively in sustainable road construction. Furthermore, laboratory and field measurements indicated that under higher stress environments, the recycled materials exhibited performance that exceeded that of conventional granular materials.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".