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This study presents a structural health monitoring (SHM) method for self-sensing CFRPs based on the “probabilistic sensing cloud” with the aim of minimizing the number of electrodes. Electrical resistances measured within various electrode pairs provide the information on potential damaged areas. Subsequently, the most overlapped probabilistic clouds localize the damaged location, which was verified by the experimental results. This technique was optimized by investigating the inter-electrode distances, electrical current density prediction using finite element analysis. The probabilistic sensing cloud method yields improved SHM performance and efficiency for CFRPs through electrode optimization and reduction in data complexity.
Inyong Lee
"Structural health monitoring of CFRPs using piezoresistivity effect based novel structure monitoring method (Conference Presentation)", Proc. SPIE 11380, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIV, 113801L (24 April 2020); https://doi.org/10.1117/12.2557969
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Inyong Lee, "Structural health monitoring of CFRPs using piezoresistivity effect based novel structure monitoring method (Conference Presentation)," Proc. SPIE 11380, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIV, 113801L (24 April 2020); https://doi.org/10.1117/12.2557969