Mechanistic Design and Nondestructive Structural Validation of a “Green Street” Test Section Using Recycled Rubble Materials
Bibliographic record
Abstract
The City of Saskatoon, Saskatchewan, Canada, commissioned the “Green Street” Infrastructure Program to investigate the potential of using recycled reclaimed asphalt pavement (RAP) and portland cement concrete (PCC) rubble as structural road layers. This study validated the mechanistic materials characterization and structural design of a field test section constructed using recycled RAP and PCC materials in addition to in situ reclaimed and recycled road materials. This paper presents a summary case study of the Green Street test section on 8th Street in Saskatoon. The rehabilitation of 8th Street consisted of two pavement rehabilitation systems: one incorporated a drainage layer and the other did not. The rehabilitation of the right-turn lane included a drainage system incorporating City of Saskatoon offsite impact-crushed PCC rubble material. The entire right-hand-turn lane and sections of the median lane and the driving lane were rehabilitated by rotomixing hot-mix asphalt concrete (HMAC) and granular base layers and adding offsite impact-crushed RAP to top up the remixed base layer. The top 200 mm of this remixed base layer was stabilized with cement–emulsion. The entire 8th Street test section was surfaced with typical City of Saskatoon HMAC. When subjected to mechanistic triaxial frequency sweep characterization, both the cement–emulsion-stabilized in situ remix material (utilized as a black base course) and the HMAC surfacing materials showed good mechanistic structural material constitutive behavior. The stabilized in situ remix material yielded end-product mechanistic material behavior that exceeded that of the HMAC surfacing.
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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 | 0.000 |
| 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".