Construction and Performance Evaluation of Polyurethane-Bound Porous Rubber Pavement (PRP) Trial Section in the Cold Climate
Bibliographic record
Abstract
Porous pavements are designed and used in current construction practices to address environmental and safety issues related to wet weather. Porous rubber pavement (PRP) is a novel porous pavement material consisting of recycled crumb tire rubbers, stone aggregates, and polyurethane binders. The higher permeability (up to 45% of air voids) of PRP and its composition offers excellent benefits to the urban hydrological system and environment. Due to its recent outset in the Canadian climate, its properties and performance are not yet investigated. This research investigates PRP’s properties and performance as pavement material through the construction of two trial sections incorporating three newly developed PRP mixes along with a Control Mix. Samples were obtained from the field and tested in the laboratory to determine the mechanical and durability properties, including indirect tensile strength, moisture-induced damage due to freeze-thaw cycles and permanent deformation. A field evaluation was also performed three times: right after construction, three weeks later and after seven months to determine stiffness, frictional properties, roughness and permeability. The results revealed that all PRP mixes exhibited excellent permeability and retained more than 68% of tensile strength after five freeze-thaw cycles. Although PRP showed significantly lower initial elastic modulus than conventional pavement material, ranging between 28 MPa to 59 MPa, in the springtime none of them went below 23 MPa. Material composition, site geometry and subgrade conditions were found to be the main factors influencing the field performance of PRP pavement.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".