Integrated Mechanistic-Based Framework for Sustainable “Green Street” Rehabilitation of Urban Low-Volume Roads
Bibliographic record
Abstract
This research developed a mechanistic-based framework for recycling rubble materials into high-value-added engineered road structural materials for use in urban road rehabilitation. Scientific-based engineering methods were integrated with advanced materials processing, road construction, and nondestructive asset management techniques to explicitly quantify the benefits of recycled material systems using reclaimed asphalt pavement (RAP) and portland cement concrete (PCC) rubble generated within the city of Saskatoon, Saskatchewan, Canada. The ability to process RAP and PCC rubble to meet or exceed conventional granular aggregate specifications with minimal waste was demonstrated. It was found that RAP and PCC aggregates can exceed the mechanistic material constitutive properties of conventional city of Saskatoon granular base aggregates by at least 30%. The mechanistic material property value of unstabilized RAP and PCC was demonstrated in addition to the benefits of various cold stabilization systems using cement and emulsion. Recycled RAP was used as a black base layer and PCC was used as a subbase course or a drainage and stress-dissipation layer, or both, in rehabilitated road structures of nine “Green Street” test sections constructed in Saskatoon. These test sections met or exceeded target structural designs and were validated by using nondestructive heavy-weight deflectometer testing. The use of recycled RAP and PCC rubble materials for urban road rehabilitation had economic, social, environmental, and energy benefits for the city of Saskatoon. Recycled rubble materials were found to provide a technically viable and cost-effective solution for rehabilitating urban low-volume roads relative to conventional granular aggregates.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".