Abstract: This talk addresses the design of monitoring and inspection strategies to improve infrastructure resilience, with focus on urban water and gas networks facing natural or adversarial disruptions. Firstly, we address the problem of finding a monitoring strategy utilizing the minimum number of sensors necessary to ensure a desired detection performance against multiple adversarial disruptions. This problem can be formulated as a mathematical program with constraints involving Nash equilibria of a large bimatrix game. To overcome the computational issues in solving this problem for real-world networks, we develop an approach that computes randomized strategies based on solutions of a minimum set cover problem and a maximum set packing problem. Game-theoretic and combinatorial arguments enable us to derive optimality guarantees of the resulting strategies. Secondly, we present a stochastic orienteering problem for finding an optimal network inspection strategy in the aftermath of an earthquake. Specifically, the objective is to maximize failure localization performance under timing, sensing, and routing constraints. To calibrate the orienteering problem, we develop a predictive failure model using data from SF Bay area’s gas pipeline inspection operations. We propose non-adaptive algorithms based on integer programming, and obtain efficient solutions that capture the key exploration/exploitation trade-off faced by utility crews in choosing their inspection strategy. These results demonstrate the value of utilizing the real- world failure data and network properties for improving response operations.

Bio: Saurabh Amin is Robert N. Noyce Career Development Associate Professor in the Department of Civil and Environmental Engineering at MIT. He is also affiliated with the Institute of Data, Systems and Society and the Operations Research Center at MIT. His research focuses on the design of network inspection and control algorithms for infrastructure systems resilience. He studies the effects of security attacks and natural events on the survivability of cyber-physical systems, and designs incentive mechanisms to reduce network risks. Dr. Amin received his Ph.D. from the University of California, Berkeley in 2011. His research is supported by NSF CPS FORCES Frontiers project, NSF CAREER award, Google Faculty Research award, DoD-Science of Security Program, and Siebel Energy Institute Grant.