Enhanced Prediction of Urban Road Pavement Performance under Climate Change with Machine Learning
Bibliographic record
Abstract
In light of climate change, increasing traffic demands, and aging infrastructures, flexible pavements face escalating challenges in terms of resilience and longevity. This paper highlights the potential of Machine Learning (ML) to integrate with Mechanistic-Empirical pavement design, aiming to facilitate proactive maintenance and rehabilitation and ultimately enhanced resilience of urban road pavements. A comprehensive analysis comprising 4800 case studies across 10 major Canadian cities was conducted, encompassing various scenarios reflecting climate change pathways, pavement structures, and traffic levels. The findings indicate an increased risk of failure, particularly rutting, under projected future climate conditions. The study demonstrates that developed artificial neural network models exhibit high accuracy in predicting fatigue cracking (R2: 0.96) and rutting (R2: 0.98). Furthermore, it emphasizes the potential of ML techniques in conducting impact assessments and devising strategies for climate change adaptation, considering the evolving landscape of urban complexities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".