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Sensor optimization using an evolutionary strategy for structural health monitoring in high temperature environments
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Paper Abstract

In a high temperature environment, it is challenging to perform structural health monitoring (SHM), which has become a required task for many important civil structures in harsh environments. A SHM system in high temperature environments requires a large number of sensors for different data resource measurements, for example, strain and temperature. The accuracy of the measurement is highly dependent on the trade-off between the number of sensors of each type and the associated cost of the system. This paper introduces a sensor optimization approach based on an evolutionary strategy for the multi-objective sensor placement of structural health monitoring in high temperature environments. A single-bay steel frame with localized high temperature environment validates the multi-objective function of the evolutionary strategy. The variance between the theoretical and the experimental analysis was within 5 %, indicating an effective sensor placement optimization using the developed genetic algorithm, which can be further applied to general sensor optimization for SHM system applications in high temperature environments.

Paper Details

Date Published: 27 March 2018
PDF: 8 pages
Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105981G (27 March 2018); doi: 10.1117/12.2296565
Show Author Affiliations
Ying Huang, North Dakota State Univ. (United States)
Simone A. Ludwig, North Dakota State Univ. (United States)


Published in SPIE Proceedings Vol. 10598:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018
Hoon Sohn, Editor(s)

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