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Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics

Sanghyo Lee1, Yonghan Ahn2, Ha Young Kim3, *

1 Division of Architecture and Civil Engineering, Kangwon National University, Samcheok-si, 25913, Korea.
2 School of Architecture and Architectural Engineering, Hanyang University ERICA, Ansan-si, 15588, Korea.
3 Graduate School of Information, Yonsei University, Seoul, 03722, Korea.

* Corresponding Author: Ha Young Kim. Email: email.

Computers, Materials & Continua 2020, 65(1), 1-17. https://doi.org/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 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.

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APA Style
Lee, S., Ahn, Y., Kim, H.Y. (2020). Predicting concrete compressive strength using deep convolutional neural network based on image characteristics. Computers, Materials & Continua, 65(1), 1-17. https://doi.org/10.32604/cmc.2020.011104
Vancouver Style
Lee S, Ahn Y, Kim HY. Predicting concrete compressive strength using deep convolutional neural network based on image characteristics. Comput Mater Contin. 2020;65(1):1-17 https://doi.org/10.32604/cmc.2020.011104
IEEE Style
S. Lee, Y. Ahn, and H.Y. Kim "Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics," Comput. Mater. Contin., vol. 65, no. 1, pp. 1-17. 2020. https://doi.org/10.32604/cmc.2020.011104

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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