Incorporating Flood Hazards into Pavement Sustainability Assessment
Bibliographic record
Abstract
The functional and structural performance of pavement infrastructures are at risk from climate change impacts. However, past sustainability assessment studies do not consider how the performance of infrastructure will be affected by a changing climate. The goal of this research is to investigate the impacts of flooding in a unified assessment of the resilience and sustainability of pavement. A case study of concrete pavement was evaluated for scenarios with and without flooding. The AASHTOWare Pavement ME design program was used to simulate the structural performance of typical jointed plain concrete pavement (JPCP) designs for collector roads in Canada. Predicted performance was used in the resilience and sustainability assessment under scenarios with and without flooding. The damage ratio and cost of damage were assessed to quantify the resilience of concrete pavement to floods. Life cycle costs and environmental impacts were compared across a climate change scenario with consideration of maintenance and rehabilitation activities. Results reveal that a typical Ontario JPCP design with structural slab thickness < 200 mm is less resilient than the Manitoba design for collector roads, but the 25 mm slab thickness difference in Manitoba design can lead to 50% change in damage ratio and damage cost. When estimating the social cost associated with greenhouse gas and air pollutant emissions, the additional environmental impact resulting from flooding for the Ontario road is almost seven times that of the Manitoba road. However, the life cycle economic cost and environmental impact do not yield an overall benefit with increased slab thickness.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".