@Article{cmc.2020.011104,
AUTHOR = {Sanghyo Lee, Yonghan Ahn, Ha Young Kim},
TITLE = {Predicting Concrete Compressive Strength Using Deep  Convolutional Neural Network Based on Image Characteristics},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {1--17},
URL = {http://www.techscience.com/cmc/v65n1/39550},
ISSN = {1546-2226},
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 of 
the DSLR camera, which was beneficial for extracting a larger number of features. 
Moreover, the DSLR camera procured more realistic images than the microscope. Thus, 
when the compressive strength of concrete was evaluated using the DCNN employing a 
DSLR camera, time and cost were reduced, whereas the usefulness increased. 
Furthermore, an indirect comparison of the accuracy of the DCNN with that of existing 
non-destructive methods for evaluating the strength of concrete proved the reliability of 
DCNN-derived concrete strength predictions. In addition, it was determined that the 
DCNN used for concrete strength evaluations in this study can be further expanded to 
detect and evaluate various deteriorative factors that affect the durability of structures, 
such as salt damage, carbonation, sulfation, corrosion, and freezing-thawing.},
DOI = {10.32604/cmc.2020.011104}
}