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Dimensionality Reduced Wave Transmission Function and Neural Networks for Crack Depth Estimation in Concrete Structures  

Shin, Sung-Woo (한국과학기술원스마트 사회기반시설 연구센터)
Yun, Chung-Bang (한국과학기술원 건설 및 환경공학과)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.20, no.3, 2007 , pp. 247-253 More about this Journal
Abstract
Determination of crack depth in filed using the self-calibrating surface wane 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 networks (NNs) 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.
Keywords
crack depth; self-calibrating surface wave transmission; principal component analysis; neural networks;
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Times Cited By KSCI : 1  (Citation Analysis)
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