The purpose of the project is to develop a Bayesian network-based decision making framework for effective utilization of structural health monitoring systems with inherent uncertainties. The information gained from health monitoring systems is often highly uncertain, leading to decisions about inspection schedules, maintenance, repair, retrofit, or closure/continued operation of a facility that are not completely clear. The Bayesian network, extended with utility and decision nodes, will be used to provide quantitative and graphical representation for near-real time information processing and decision making about monitored structural systems. The proposed framework will offer transparency in modeling and interaction with non-experts, modeling of complex interdependent systems, and ability to learn from data to provide near-real time decision making. The methodology will be applied to three demonstrative applications: a monitored building structure subjected to a severe earthquake for near-real time post-earthquake decision making; a continuously monitored bridge for inspection and maintenance decisions to minimize life-cycle cost; and selection of the best among a set of candidate structural health monitoring systems based on the value of information they provide. The project is expected to facilitate effective and objective use of health monitoring systems in practice, leading to mitigate losses due to natural and man-made hazards. The methodology will improve the ability of emergency responders to make optimal decisions after a major hazard, such as a severe earthquake, or managers of constructed facilities to implement inspection and maintenance plans in order to minimize life-cycle cost, thereby benefiting society. The project will provide multidisciplinary training to a graduate student and to undergraduate summer interns, improve existing undergraduate and graduate courses, and enhance international research collaboration with a European country.

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University of California Berkeley
United States
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