Towards resilient roads to storm-surge flooding: case study of Bangladesh
Bibliographic record
Abstract
Operating roads are critical during emergency operations at a disaster area. Prolonged inundation of pavements accelerates rapid deterioration of pavements and increases maintenance cost. The upgrade of vulnerable pavements with a raised subgrade and gabion walls is proposed as the means to increase the resiliency of strategic roads vital during the emergency attention in the aftermath of a cyclone. Hence, optimal pavement management can be used to allocate upgrade and maintenance and rehabilitation (M&R) operations to reduce the damage and mitigate the geo-physical risk and community vulnerability before the disaster even occurs. A case study is presented for regional highways, arterial and collector roads of Barguna district in Bangladesh that is frequently affected by cyclones and storm surges. The geo-physical risk and vulnerability (GEOPHRIV) index of each road segments is estimated by integrating the geo-physical risk; community, structure and infrastructure vulnerabilities; and damage indices. Dynamic linear programming is applied to optimise M&R strategies and the conversion of strategic roads into resilient perpetual pavements. The same budget required to optimise roads condition is also used to guide the conversion of roads into perpetual pavements, therefore increasing the overall network resiliency. As expected, the results show that most of the annual budget is equally expended into the conversion or the resurfacing of pavements. The decision-making approach herein proposed is very useful to roads agencies around the world, because it provides them with the ability to increase the resiliency of their strategic network ex-ante any flooding disaster.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".