UC Berkeley

Cities and people who live in them require clean water to thrive. However, the conditions of water infrastructure in the United States are concerning. Many water distribution networks (WDNs) are serving beyond their intended design life. The aging water infrastructure is reflected by the frequent water main break events across the country in recent years. Using data mining and natural language processing (NLP) techniques, this study validates the commonly-hold opinions that water main breaks cause severe societal troubles, including repair cost, local traffic disturbance, and water quality related health issues.

Hazard events often damage the already in-risk aging WDNs. To estimate the hazard impact (e.g. earthquake) to a WDN, this study developed a WDN hydraulics simulator, HydrauSim, which can quantify the WDN hydraulics (e.g., flow rate, etc.) under normal or damaged states. HydrauSim is highly optimized to be computationally efficient, making it feasible for large-scale networks and tasks that require repeated simulation runs (e.g., Monte Carlo simulation). Using HydrauSim, the post-earthquake response of a WDN in the San Francisco Bay area is simulated. The simulation was performed on East Bay Municipal Utility District's (EBMUD) main gravity feed zone. Around 200--800 pipes were estimated to break during the simulated earthquake events. On average, 25\% of demand nodes may experience insufficient water pressure levels, which can rise to 78\% for the worst-case scenario.

In real-life situations, failed pipes need to be isolated from the main network by closing the corresponding isolation valves to prevent the effects of individual events from spreading throughout the system. However, most utilities do not have sufficient valves installed, and the installed ones may malfunction at the time of usage. This study proposed an analysis framework for WDN pipe isolation risk considering valve condition uncertainties. It is found that the magnitude of the risk depends on the mean and variance of isolation segment sizes and demand distribution across the network.

Using dynamic programming, an optimal valve placement algorithm is developed to find the best place to install isolation valves to minimize the system risk. The proposed method is tested on two real-life WDNs. Comparing to the existing valve placement configuration, the proposed configuration significantly reduces the pipe isolation risk of the system. Furthermore, the proposed valve placement strategy produces a more robust network than the original one regarding valve failure scenarios. Pipe isolation risks are significantly reduced at all tested failure rates, and the risk-increasing trend (as the valve failure rate increases) is effectively restrained.

Due to resource constraints, it is impractical for water utilities to maintain all the isolation valves in a system. This study proposes a method to rank the isolation valves based on their potential failure consequences. The valve ranking algorithm utilizes network analysis methods and machine learning techniques to label valve maintenance priorities automatically. Simulation on real-life WDNs shows that applying the proposed valve maintenance strategy effectively reduces both the direct and indirect risk for the tested networks, especially under high valve failure rate cases.