Proceedings of the Computational Structural Engineering Institute Conference (한국전산구조공학회:학술대회논문집)
- 2007.04a
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- Pages.27-32
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- 2007
Dimensionality Reduced Wave Transmission Function and Neural Networks for Crack Depth Estimation in Concrete
차원 축소된 표면파 투과 함수와 인공신경망을 이용한 콘크리트의 균열 깊이 평가 기법
- Published : 2007.04.12
Abstract
Determination of crack depth in filed 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 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.
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