On Assessing the Robustness of Structural Health Monitoring Technologies

Conference · Fri Aug 24 04:00:00 UTC 2012

DOI:https://doi.org/10.1007/978-1-4614-2431-4_1· OSTI ID:1049983

Stull, Christopher J ^[1]; Hemez, Francois M ^[1]; Farrar, Charles R ^[1]

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1. Los Alamos National Laboratory

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. This paper offers a framework that aims to provide the engineer with a quantitative 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 the presentation will focus on applying the framework to two commonly encountered problems: (1) selection of damage-sensitive features and (2) selection of a damage classifier. The data employed for these problems will be drawn from a study that examined the feasibility of applying SHM to the RAPid Telescopes for Optical Response observatory network.

• Research Organization: Los Alamos National Laboratory (LANL)
• Sponsoring Organization: DOE/LANL
• DOE Contract Number: AC52-06NA25396
• OSTI ID: 1049983
• Report Number(s): LA-UR-12-24308
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
97 MATHEMATICS AND COMPUTING
ENGINEERS
LEARNING
MANAGEMENT
MONITORING
TELESCOPES