The reliability of urban water distribution networks (WDNs) is critical for ensuring sustainable infrastructure management. However, traditional failure prediction models often overlook the complex interdependencies between water pipelines and road networks, leading to suboptimal predictive accuracy. This study introduces a novel pipeline failure prediction framework that leverages Graph Neural Networks (GNNs) to incorporate coupled road–pipeline network features. By integrating traffic-related indicators, such as intersection proximity, pipeline–road angles, and network topology, this approach systematically assesses their impact on failure risk. A comparative evaluation of various GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE, demonstrates that GraphSAGE achieves the highest predictive performance, significantly surpassing traditional machine learning methods. The findings underscore the necessity of incorporating network topology into predictive models, validating the role of spatial dependencies in accurately assessing pipeline failure risks. This study contributes to advancing infrastructure resilience modeling by providing a robust predictive framework that supports proactive maintenance strategies and enhances risk mitigation in urban water distribution systems.

Urban infrastructure systems, such as water supply and transportation networks, are highly interdependent, making them susceptible to cascading disruptions. This paper introduces a bi-level optimization framework designed to coordinate water supply network repairs while minimizing traffic impacts. The framework integrates a dynamic traffic assignment (DTA) model to evaluate the interplay between repair schedules and traffic conditions. The upper-level model generates and adjusts repair schedules, focusing on timing and location, while the lower-level model simulates the resulting traffic flow and travel time changes. Five optimization algorithms—adaptive differential evolution (ADE), genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and ant colony optimization (ACO)—are employed to identify repair plans that reduce traffic disruptions effectively. A case study in the Yangpu District of Shanghai demonstrates that the timing and spatial distribution of repairs significantly influence traffic flow. Among the tested algorithms, ADE achieves the lowest traffic impact, whereas SA excels in computational efficiency. The results highlight the importance of strategic scheduling in mitigating traffic disruptions by optimizing repair activities and leveraging traffic rerouting. This study provides a practical framework for urban planners to improve repair scheduling and minimize disruptions, contributing to more efficient infrastructure management. Future work could incorporate real-time data for adaptive scheduling and explore broader applications of the framework.