Probabilistic Resilience-Guided Infrastructure Risk Management
Bibliographic record
Abstract
The increased frequency and magnitude of natural and anthropogenic hazard events that affected infrastructure systems over the past two decades have highlighted the need for more effective risk management strategies. Such strategies are expected to not only manage the immediate disruption to system’s functionality following hazard realization, but to also mitigate the latter’s extended-term consequences (e.g., recovery cost and restoration time), which would otherwise be disastrous. To yield realistic managerial insights, such resilience-guided risk management necessitates accounting for the different sources of uncertainties associated with both the hazard quantification and the response of the infrastructure being considered. Through considering such uncertainties, the probabilistic resilience quantification framework developed in this study is expected to provide valuable managerial insights to guide resource allocations for both pre- and posthazard realization. The applicability of the framework is demonstrated on a simplified system subjected to different anthropogenic hazard scenarios. Beyond the presented case study, the developed framework lays the foundation for adopting probabilistic resilience quantification to guide the next-generation risk management processes of infrastructure systems under different forms of natural and anthropogenic hazards.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".