Mechanistic Laboratory Evaluation and Field Construction of Recycled Concrete Materials for Use in Road Substructures
Bibliographic record
Abstract
Given the renewal of urban infrastructure and the increased costs of landfilling concrete rubble materials, opportunities exist to optimize the reclamation and recycling of portland cement concrete (PCC) and hot-mix asphalt concrete (HMAC) rubble through their innovative use in road rehabilitation. The primary objective of this study was to demonstrate the ability to reclaim, process, and recycle stockpiled concrete materials to provide improved structural mechanistic–climatic material properties and to meet or exceed the mechanical properties of conventional granular road materials. This research was based on advancements made in 2009 as part of the Green Streets Infrastructure Program in the city of Saskatoon, Saskatchewan, Canada. A second objective of this research was to pilot the field application of reclaimed and recycled HMAC and PCC rubble in typical urban road reconstruction applications. Recycled HMAC and PCC materials were used in the pilot reconstruction of a road that was exhibiting substructure moisture problems and structural failure. This study showed that recycled HMAC and PCC rubble materials could be processed to achieve mechanistic laboratory properties that exceeded those of conventional granular-based materials. This study also demonstrated efficient constructability and high end-product structural asset value of a typical rehabilitated urban road structure test section in the city of Saskatoon by using recycled HMAC and PCC rubble. On the basis on these findings, the use of quality processed HMAC and PCC rubble materials for road reconstruction was found to be a technical and environmentally sustainable solution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".