Vol.65, No.1, 2020, pp.1-17, doi:10.32604/cmc.2020.011104
OPEN ACCESS
ARTICLE
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: hayoung.kim@yonsei.ac.kr.
Received 20 April 2020; Accepted 08 May 2020; Issue published 23 July 2020
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.
Keywords
Deep convolutional neural network (DCNN), non-destructive testing (NDT), concrete compressive strength, digital single-lens reflex (DSLR) camera, microscope.
Cite This Article
Lee, S., Ahn, Y., Kim, H. Y. (2020). Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics. CMC-Computers, Materials & Continua, 65(1), 1–17.
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