Abstract
As Structural Health Monitoring (SHM) continues to gain popularity, both as an area of research and as a tool for use in industrial applications, the number of technologies associated with SHM will also continue to grow. As a result, the engineer tasked with developing a SHM system is faced with myriad hardware and software technologies from which to choose, often adopting an ad hoc qualitative approach based on physical intuition or past experience to making such decisions, and offering little in the way of justification for a particular decision. The present paper offers a framework that aims to provide the engineer with a qualitative approach for choosing from among a suite of candidate SHM technologies. The framework is outlined for the general case, where a supervised learning approach to SHM is adopted, and is then demonstrated on a problem commonly encountered when developing SHM systems: selection of a damage classifier, where the engineer must select from among a suite of candidate classifiers, the one most appropriate for the task at hand. The data employed for these problems are taken from a preliminary study that examined the feasibility of applying SHM technologies to the RAPid Telescopes for Optical Response observatory network. (Approved for unlimited public release on September 20, 2011, LA-UR 11-05398, Unclassified)
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Acknowledgements
The first author is grateful to Yakov Ben-Haim for his guidance during the initial phases of development of this work. The algorithmic development supporting this work was facilitated, in part, by the use of functions available in SHMTools, a software package developed at the Los Alamos National Laboratory//University of California San Diego Engineering Institute to aid in the construction of SHM processes. Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is operated by Los Alamos National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy under contract DE-AC52-06NA25396.
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© 2012 The Society for Experimental Mechanics, Inc. 2012
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Stull, C.J., Hemez, F.M., Farrar, C.R. (2012). On Assessing the Robustness of Structural Health Monitoring Technologies. In: Simmermacher, T., Cogan, S., Horta, L., Barthorpe, R. (eds) Topics in Model Validation and Uncertainty Quantification, Volume 4. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2431-4_1
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DOI: https://doi.org/10.1007/978-1-4614-2431-4_1
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