Restoration modelling of Water Network under seismic hazard: Role ofElectrical and Power Network
Bibliographic record
Abstract
The socio-economic wellbeing of a community depends on the proper functioning of its critical infrastructure system.Two most important critical infrastructure systems i.e., Water Network (WN) system, and Electrical and Power Network (EPN) system are crucial for the full functioning of the households, and socio-economic wellbeing of its citizens.After an extreme event, the functionality of the households will be reduced due to the damage in the WN and EPN.The functionality of the WN may also depend on the recovery of the EPN and should be considered for better estimates of the functionality recovery of interdependent critical infrastructure system.In this paper, the restoration modelling of WN is assessed under a maximum considered seismic hazard scenario, considering the dependence on the EPN network.The fragility and consequence functions are utilized for the damage and repair time assessment of each component.The repair times for each component are evaluated and the total repair time of the WN and EPN is determined.The WN and EPN restoration modelling is assessed through network models where the nodes represent the sub-components of a network and edges represent the connection restoration.In the considered example, the WN is fully repaired in 8 days after an earthquake event, while the EPN is fully repaired in 10 days.The WN will therefore be fully functional after 10 days since the power plant component of WN requires electricity from EPN network to pump water form reservoir to the water tank.It is therefore important to consider the dependencies of WN on the EPN for better estimates of functionality recovery of critical utilities to the households.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".