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Proceedings Paper

Reduced surface wave transmission function and neural networks for crack evaluation of concrete structures
Author(s): Sung Woo Shin; Chung Bang Yun; Hitoshi Furuta; John S. Popovics
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Paper Abstract

Determination of crack depth in field using the self-calibrating surface wave transmission measurement and the cutting frequency in the transmission function (TRF) is very difficult due to variations of the measurement conditions. In this study, it is proposed to use the measured full TRF as a feature for crack depth assessment. A principal component analysis (PCA) is employed to generate a basis of the measured TRFs for various crack cases. The measured TRFs are represented by their projections onto the most significant principal components. Then artificial neural network (ANN) using the PCA-compressed TRFs is applied to assess the crack in concrete. Experimental study is carried out for five different crack cases to investigate the effectiveness of the proposed method. Results reveal that the proposed method can be effectively used for the crack depth assessment of concrete structures.

Paper Details

Date Published: 10 April 2007
PDF: 8 pages
Proc. SPIE 6529, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007, 65291S (10 April 2007); doi: 10.1117/12.715901
Show Author Affiliations
Sung Woo Shin, Korea Advanced Institute of Science and Technology (South Korea)
Chung Bang Yun, Korea Advanced Institute of Science and Technology (South Korea)
Hitoshi Furuta, Kansai Univ. (Japan)
John S. Popovics, Univ. of Illinois at Urbana-Champaign (United States)


Published in SPIE Proceedings Vol. 6529:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2007
Masayoshi Tomizuka; Chung-Bang Yun; Victor Giurgiutiu, Editor(s)

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