Urban water supply systems’ resilience under earthquake scenario
Bibliographic record
Abstract
Abstract Threats to water supply systems have increased in number and intensity. Natural disasters such as earthquakes have caused different types of damage to water distribution networks (WDN), particularly for those with aged infrastructure. This paper investigates the resilience of an existing water distribution network under seismic hazard. An earthquake generation model coupled with a probabilistic flow-based pressure driven demand hydraulic model is investigated and applied to an existing WDN. A total of 27 earthquake scenarios and 2 repair strategies were simulated. The analysis examined hydraulic resilience metrics such as pressure, leak demand, water serviceability, and population impacted. The results show that nodal pressure drops below nominal pressure and reaches zero in some earthquake scenarios. Leak demand could reach to more than 10 m 3 /s within hours following an earthquake. Water serviceability drops to a low of 40% and population impacted reaches up to 90% for a 6.5 M earthquake, for example. This study highlights and quantifies vulnerabilities within the simulated WDN. The tools outlined here illustrate an approach that can: (1) ultimately help to better inform utility water safety plans, and (2) prepare proactive strategies to mitigate/repair before a hazard of this nature occurs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".