Improving Pavement Resiliency to Flooding: A Case for Concrete Pavement
Bibliographic record
Abstract
Future climate conditions are not going to resemble the past. Temperatures will be hotter, and storms will be stronger. However, pavements are still being designed assuming that past conditions will resemble the future. This is a bad assumption. When climate change and pavement resilience are discussed, the focus is often on the immediate impacts of the natural disaster. While this is important, pavement damage also occurs after the natural disaster when rescue, emergency response, recovery, and rebuilding activities are taking place, and the pavement, often in a weakened state, is subjected to increased volume of heavier traffic. This paper will show how current resiliency concepts and framework can be applied to pavements and how two cement-based solutions (concrete overlays and full depth reclamation with cement) can be used to improve pavement resilience and mitigate damage both during and after a natural disaster, using flooding as an example.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".