Pavement Risk Assessment for Future Extreme Precipitation Events under Climate Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it