City of Saskatoon's Green Streets Program—A Case Study for the Implementation of Sustainable Roadway Rehabilitation with the Reuse of Concrete and Asphalt Rubble Materials
Bibliographic record
Abstract
In 2006 the City of Saskatoon recognized that the large amounts of asphalt and concrete rubble materials stockpiled in its yards must be addressed. After experimenting with the material as a replacement backfill material with City forces, the City determined that improved engineering specifications and structural design processes needed to be implemented to facilitate the standard use of the recycled aggregates. In 2009, the City of Saskatoon developed the Green Streets Program to pilot the use of advanced mechanistic engineering and state of the art impact crushing of rubble materials for roadway construction. For the program to be considered successful value added processing, mechanistic-climatic characterization and sustainable holistic life cycle analysis had to be considered. Based on the findings of the City of Saskatoon Green Streets Program, the City is now producing five types of high value specified crushed concrete materials, and three types of specified asphalt aggregate materials. These materials are being used as subbase, base course, drainage rock, stress dissipation layers and structural black base. From a mechanistic engineering characterization perspective, this research shows the crushed concrete and crushed asphalt aggregates are technically superior to conventional locally available aggregates. The City of Saskatoon Green Streets Program identified many benefits achieved through the use of recycled materials in road construction on a technical, social, environmental and economic basis. Residents are provided a roadway with superior structural performance and waste rubble generated from aging city infrastructure is diverted from landfills. In addition, the cost savings generated by the Green Streets Project was determined to be approximately 55 percent through the structurally equivalent substitution of recycled aggregates for virgin sourced aggregates. To further address the key aspects of road infrastructure sustainability the quantification of the energy used to rehabilitate roads and carbon generation during roadway rehabilitation will assist in quantifying the benefits of sustainable construction solutions. The future implementation of mechanistic based End Product Specifications will also ensure that the City will have a reliable engineering framework from which to employ innovative and sustainable road infrastructure solutions utilizing recycled road materials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".