The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis.

Water distribution network (WDN) models are an essential tool used by water utilities for hydraulic analysis. Unfortunately, missing data and insufficient resources often make creating and maintaining these models unfeasible. Existing methods to address missing pipe properties, like sequential imputation for missing values and reconstruction using graph metrics, are designed to accommodate random patterns of missing information and require a significant percentage of the system's attributes to be known. However, these data completeness assumptions do not always align with real‐world scenarios where large sections of the WDN model have missing data. To address this challenge, this study proposes a data‐driven approach for estimating pipe diameter when considering different spatial patterns and degrees of data completeness (i.e., 0%–90%). Using data from 16 WDNs in Kentucky, this study compares the use of machine learning (ML) using topological and geospatial features against an existing deterministic approach. Results demonstrate that WDN models with pipe diameters predicted by the proposed ML method had comparable hydraulic performance to the ground truth models. Moreover, results showed that ML method performance varies between WDNs of differing topological classification. Insights from this study help advance the ability to leverage partial data to create and maintain WDN models amid uncertainty and inadequate resources.

Supplying potable water and other services to critical facilities such as hospitals and evacuation shelters is crucial following disasters. Identifying areas at high risk for service loss and optimal shelter locations is challenging due to complex infrastructure networks, uncertain damage, and dynamic populations. This study presents a framework for analyzing the resilience of water infrastructure networks while considering the interdependencies between human behavior, evacuation shelter location, and infrastructure service availability. The framework provides opportunities to determine the likelihood of water service availability at shelter sites, how shelter location impacts services at other critical locations, and how evacuation patterns change which infrastructure components are most critical. The value of the framework is demonstrated with an analysis of probabilistic damage scenarios from a 250-year seismic event on a water distribution network for a small coastal town. Hydraulic performance is measured over 72 hours assuming 10 different possible shelter sites. Results show that water availability varied widely among evacuation sites, where average availability differed by as much as a factor of 2.6 between sites. Sites not reliant on pumps, along larger diameter pipes in looped sections, and closer to water sources had more consistent water availability. Evacuation location can influence water availability at other critical locations, particularly when evacuation-driven demand is concentrated upstream of critical facilities in capacity- or redundancy-limited portions of the network. This research highlights the advantages of evacuation scenario analysis as a decision-support tool and offers valuable insights into enhancing infrastructure network resilience through both network design and evacuation strategy.