Sanghyo Lee1, Yonghan Ahn2, Ha Young Kim3, *
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 1-17, 2020, DOI:10.32604/cmc.2020.011104
Abstract In this study, we examined the efficacy of a deep convolutional neural network
(DCNN) in recognizing concrete surface images and predicting the compressive strength
of concrete. A digital single-lens reflex (DSLR) camera and microscope were
simultaneously used to obtain concrete surface images used as the input data for the
DCNN. Thereafter, training, validation, and testing of the DCNNs were performed based
on the DSLR camera and microscope image data. Results of the analysis indicated that
the DCNN employing DSLR image data achieved a relatively higher accuracy. The
accuracy of the DSLR-derived image data was attributed to the relatively wider range… More >
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