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Understanding Civil Structures and Infrastructure Systems through Probabilistic Modeling and Data Analytics

Abstract:

Civil engineering systems, including structures and infrastructure lifelines, are critical to the functioning of society. These systems are complex, comprised of many interconnected components, and subject to hazards of increasing frequency and severity. In this seminar, I will present novel approaches for the probabilistic modeling and assessment of civil structures and infrastructure, and the use of data analytics to better understand and predict the behavior of these systems. Among the approaches I will discuss are methods to model interdependent critical infrastructure systems using Bayesian networks, predict structural risk and reliability using sensor monitoring data, and integrate varying data sources for infrastructure monitoring and assessment. I will discuss both theoretical developments in these areas and applications to real-world systems.

Bio:

Dr. Iris Tien joined the faculty in the School of Civil and Environmental Engineering at the Georgia Institute of Technology in 2014. She received her Ph.D. in Civil Systems Engineering in 2014 from the University of California, Berkeley. Dr. Tien’s research interests are in probabilistic methods for modeling and reliability assessment of civil infrastructure systems. She has a unique interdisciplinary background that encompasses traditional topics of civil engineering, sensing and data analytics, stochastic processes, and decision making under uncertainty. A previous recipient of the NSF Engineering Innovation Fellowship, Tien was recently selected for the NSF Early Career Investigators Workshop in Smart Cities and the National Academy of Engineering U.S. Frontiers of Engineering Symposium.

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