Durability Evaluation of Polyurethane-Bound Porous Rubber Pavement for Sustainable Urban Infrastructure
Bibliographic record
Abstract
Permeable pavements are vital in sustainable urban water management, addressing critical challenges while enhancing environmental resilience. This study focuses on the innovative polyurethane-bound Porous Rubber Pavement (PRP), which possesses high permeability and elasticity due to its unique composition of stone and crumb rubber aggregates with polyurethane binders. PRP’s useful benefits, such as noise reduction, efficient snow/ice management, and others, enhance its appeal, emphasizing the necessity for a thorough investigation into its performance and characteristics, especially in North America. To address these gaps, this paper comprehensively analyzes PRP’s durability and performance, including its strength range, failure criteria, and susceptibility to moisture-induced damage. Various testing methods are utilized, such as evaluating the abrasion loss of the stone aggregate, rutting, stripping due to moisture susceptibility, resistance to degradation from impact and abrasion, and permeability tests. This study evaluates five distinct mix compositions with varied proportions of aggregates and binders. Further, it investigates the effects of different binder types on PRP performance, such as aromatic and aliphatic binders obtained from various sources. Upon the analysis of the comprehensive test results, it was found that the mix characterized by increased rubber aggregates and a high binder content demonstrated a superior performance across various tests for PRP applications. This mix exhibited an enhanced resistance to abrasion, raveling, rutting, and permanent deformation, showcasing its durability and functionality. Additionally, when combined with an aliphatic binder, it displayed an optimal performance even in challenging freeze–thaw conditions, making it a recommended choice for long-term pavement solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".