Robust Risk-based Decision Analytics for Enhancing Seismic Resilience of Water Pipe Networks

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Water utilities in the US need to make difficult rehabilitation decisions about the water infrastructure system. There are deep uncertainties in these decisions because of aging and typically buried water pipe networks and their ability to sustain disasters, such as earthquakes. These decisions are further complicated with the enormous lack of funding in many areas for infrastructure rehabilitation. This research will create and empirically examine a robust decision-making framework for managing the maintenance and remediation of water pipelines subject to these uncertainties. This framework is expected to assist water utilities to achieve the most value out of every dollar that they spend on their water infrastructure system. This project will engage industry stakeholders, such as water utilities and disseminate research results widely through the creation of easy-to-understand and practical educational materials. Undergraduate and graduate students-especially minority students-at the Hispanic Serving University of Texas at Arlington (UTA) will participate in every step of this project. This research will also engage K-12 students in research through hands-on activities and make them familiar with the fundamentals of making engineering decisions under uncertainty. This research will address fundamental limitations of existing models for making robust risk-based decisions about water pipe networks by (1) characterizing the network uncertainties and integrating them with seismic vulnerability assessment, and (2) designing and integrating a risk-averse stochastic combinatorial optimization algorithm with the seismic vulnerability assessment to identify vulnerable locations in a pipeline. This project is at the nexus of two critical disciplines: decision science and infrastructure vulnerability assessment. The integration of network uncertainties in seismic vulnerability assessment of water pipe networks is critically needed in a highly uncertain environment. The project will transform existing models for selecting rehabilitation and maintenance policies by integrating mechanisms borrowed from quantitative finance to assure that selection risks are not too high. The novel introduction of risk-averse stochastic combinatorial optimization models to the civil engineering community equips infrastructure decision makers with proper scalable models to optimize rehabilitation decisions with a controllable risk aversion level in a highly uncertain environment. This project will create computationally cost-effective algorithms to solve the formulated optimization problems for large-scale city water networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.