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Sensor network optimization using Bayesian networks, decision graphs, and value of information

International Conference on Applications of Statistics and Probability (12th : 2015 : Vancouver, B.C.)

Bayesian Networks (BNs) and decision graphs provide a useful framework for modeling the uncertain behavior of civil engineering infrastructures subjected to various risks, as well as the potential outcomes of risk mitigation actions undertaken by managing agents. These graphs can also guide optimal sensing and inspection of infrastructure by maximizing the value of information of sensing efforts. This paper presents a general framework for modeling infrastructure systems using BNs and for evaluating sensor placement metrics within this model. An example application of the use of the value of information metric in guiding optimal sensing in a system of infrastructure assets in the San Francisco Bay area subjected to seismic risk is then presented. A parametric study also investigates the sensitivity of the value of information metric to various parameters of the BN system model.

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This collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.

Vancouver : University of British Columbia Library

10.14288/1.0076269

Non UBC

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/