Framework for Evaluation of Seismic Damage of Water Distribution Networks
Bibliographic record
Abstract
ABSTRACT Seismic damage evaluation of urban water distribution networks (WDNs) is essential for improving infrastructure resilience and emergency response. However, conventional fragility models coupled with probabilistic seismic hazard analysis often fail to capture local failure mechanisms at pipeline intersections, and cannot address the complex soil–structure interaction and ground motion propagation effects. This study bridges this gap by developing a numerical framework that integrates spatially correlated ground motions, detailed finite‐element models of segmented pipelines with various intersection types, and GIS‐based visualization of seismic damage distribution. The framework explicitly accounts for axial and rotational joint failures, intersection‐induced deformation amplification, and spatial heterogeneity in site conditions. Numerical results show that cross‐shaped pipeline intersections, including T‐shaped, 45°‐crossed, and 90°‐crossed configurations, exhibit peak joint openings approximately 1.4–2 times greater than those in straight pipelines. Moderate‐to‐severe seismic damage is observed for small‐diameter pipelines in soft‐soil areas near fault sources. These findings underscore the importance of capturing site‐specific ground motion variability and joint‐level mechanical behavior for realistic damage prediction of WDNs. The proposed approach provides a practical decision‐support tool for engineering design, seismic retrofit, and risk mitigation planning of urban WDNs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".