Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two-layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it