Reduced surface wave transmission function and neural networks for crack evaluation of concrete structures
2007
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.
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