Simulation-Based Evaluation of Resilience-Enhancing Measures for Transportation Systems Subject to Hydrometeorological Hazard Events
Bibliographic record
Abstract
This paper identifies the essential requirements for simulation-based approaches such that these approaches serve as effective decision support tools for evaluating the effectiveness of climate-adaptation measures that enhance the resilience of transport systems against hydrometeorological events. These requirements include the ability to capture the effect of different types of measures, the spatial and temporal possibilities of their execution, their aggregate effect when executed together, and the effect of uncertainties in their evaluation. A novel simulation-based approach that meets the identified requirements is presented, and its application in a case study is showcased. The presented approach uses a set of interacting probabilistic models to generate numerous scenarios, each representing chains of cascading events from the occurrence of a possible hazard event, the impact on the assets and the network, restoration of the infrastructure, and the temporal evolution of its service. The models enable capturing the effect of resilience-enhancing measures on the intensity of hazard events and their ensuing consequences. The case study includes a road system in Switzerland comprising 605 km of roads and 121 bridges and subject to rainfall events leading to flooding and landslide. Twenty-one portfolios of measures combining four specific types are considered, and their effect on resilience was evaluated. Those include flood protection walls, stormwater retention basins, raising road embankments, and temporary flood barriers. The proposed approach enables infrastructure managers to engage in an appropriate quantitative evaluation to better devise and plan measures with the aim of cost efficiently improving resilience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".