Pavement Risk Assessment for Future Extreme Precipitation Events under Climate Change
Bibliographic record
Abstract
Pavement infrastructure is experiencing unanticipated climate conditions caused by global warming. Extreme weather events, such as extreme precipitations, are increasing in intensity and frequency, creating rising concern in pavement vulnerability and resilience analysis. Previous design approaches based on historical climate data may no longer be adequate for addressing future conditions. To promote pavement resilience under climate change, assessing pavement risk for extreme events is essential for prioritizing vulnerable infrastructure and developing adaptation strategies. The objective of this study is to develop a quantitative evaluation methodology for assessing pavement risk from extreme precipitations under climate change. Hazard analysis, fragility modeling, and cost estimation are the three major components for risk evaluation. An ensemble of 24 global climate models is used for predicting future extreme precipitations under various climate-forcing scenarios. The Mechanistic-Empirical Pavement Design Guide is employed to simulate performance change for performing fragility modeling. Risk assessment models considering a full range of hazards were used to quantify risk of asset value loss over specified analysis periods. Results indicate that future extreme precipitation events are expected to cause an increased medium risk of asset value loss. However, high uncertainties are involved in the estimation owing to variations in predicted climates. Major pavement damages do not necessarily equate with highest risk because the probability of occurrence of major damage is relatively lower. The proposed approach provides a practical tool for analyzing the interaction among extreme precipitation levels, pavement designs, damage states, occurrence probability, and asset value at risk.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".