Graph-Theory Metrics for the Prioritization of Water Distribution Network Assets of Tyndall AFB, Florida

Water distribution networks, like other large infrastructure systems, must consider reliability and resilience efforts to resist and recover from failure with limited resources. Management of these assets requires a plan to prioritize the maintenance of system components, which are key to its reliability. Many current asset management practices for water distribution networks include only reactive strategies, such as fixing components after breaking, or on predetermined maintenance schedules, which might not correlate with a component’s condition or need. Asset management and graph theory principles are utilized in this study to evaluate a water distribution network and its vulnerabilities at Tyndall Air Force Base (AFB). We begin by estimating pipe condition indices and establishing the importance of each pipe through a set of graph-theoretic measures (e.g., pipe betweenness centrality and network efficiency). We next simulate two pipe failure scenarios: (1) a single pipe failure scenario, and (2) a cascading failure scenario. The outcomes of the second scenario reveal that a larger betweenness centrality equates to being a critical pipe in relation to overall system performance. Further, we estimate the risk associated with each pipe by combining pipe condition and network properties. We find that $\sim 7\mathrm{km}$ (or $\sim 6\%$) of pipes are at high risk, while the majority of the network’s pipes ($\sim 79\mathrm{km}$ or $\sim 62\%$) are low risk. This study demonstrates how integrating condition indices with graph theory metrics could be employed to promote best practices in pipe maintenance and create a risk-informed framework for asset management.