CAREER: Infrastructure Management under Model Uncertainty: Adaptive Sequential Learning and Decision Making

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Maintenance and operation of interconnected infrastructures, including transportation and energy systems, are critical to provide adequate levels of safety and functionality to society. Critical decisions must be made about aging infrastructures under high uncertainty about the current condition of their components, their evolution, and the results of maintenance actions. As sensors, robotic inspectors, communication and computation become cheaper and more reliable, their use can be integrated into infrastructure management to improve maintenance policies and reduce uncertainty, risk and overall management cost. Full exploitation of this potential requires a change in paradigm in how we model and approach infrastructure decision making, and how novel computational methods may be adopted and extended in statistical inference and sequential optimization for managing infrastructure systems. This CAREER award exploits the interdependency among sensor deployment, probabilistic inference and decision making on maintenance and operation by developing advanced models and algorithms that would ultimately result in enhanced decision making for maintenance and operation purposes and reduced costs. The research will test the developed framework on three case studies including wind farms, networks of gas pipelines, and networks of bridges, in collaboration with companies that manage systems and develop software. The same framework underpinning the project research also forms the basis for a pedagogic approach on infrastructure management based on active, situated-learning activities, in which games will be developed to involve K-12 and college students with assigned roles as virtual infrastructure managers in an interactive environment. The PI will work closely with Carnegie Mellon's SEE (Summer Engineering Experience for Girls) program to inspire interest in risk analysis and infrastructure systems decision making. This project's goal is to develop scalable algorithms for long-term decision-making under persistent model uncertainty. These algorithms will target decisions about not only operation and maintenance of infrastructure systems, but also about the gathering of further information: placing sensors, scheduling inspections, planning tests and experimenting with new technologies for operation and sensing. By sequential iterations of learning and acting, managers can adaptively optimize resource allocation. Optimization becomes more complex, but with higher pay-off, when focusing on large interconnected systems: when numerous infrastructure components are statistically interdependent, because of spatial proximity, common stressors, or common dynamic models, information can propagate across them. This allows observations collected in one location to also be useful for inferring the conditions of others and, more importantly, the higher-level system evolution model. By identifying locations and components that deserve inspections, the agent can sequentially adapt the exploration of the system, avoid costly over-instrumentation, and guide management towards a sustainable use of the limited resources. To develop these approaches, this project will make use of Bayesian hierarchical modeling, random field modeling, pre-posterior and value-of-information analysis, sub-modularity analysis, approximate dynamic programming for Partially Observable Markov Decision Processes (POMDP), on-line optimization and network analysis.