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Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep Convolutional Neural Network

Hyun Kyu Shin1, Yong Han Ahn2, Sang Hyo Lee3, Ha Young Kim4,*

1 Architectural Engineering, Hanyang University, ERICA, Ansan-si, 15588, Korea.
2 School of Architecture and Architectural Engineering, Hanyang University, ERICA, Ansan-si, 15588, Korea.
3 Division of Architecture and Civil Engineering, Kangwon National University, Samcheok-si, 25913, Korea.
4 Graduate School of Information, Yonsei University, Seoul-si, 03722, Korea.
* Corresponding Author: Ha Young Kim. Email: haimkgetup@gmail.com.

Computers, Materials & Continua 2019, 61(3), 911-928. https://doi.org/10.32604/cmc.2019.08269

Abstract

Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples. The experimental results indicated a root mean square error (RMSE) value of 3.56 (MPa), demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations. This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.

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APA Style
Shin, H.K., Ahn, Y.H., Lee, S.H., , H.Y.K. (2019). Digital vision based concrete compressive strength evaluating model using deep convolutional neural network . Computers, Materials & Continua, 61(3), 911-928. https://doi.org/10.32604/cmc.2019.08269
Vancouver Style
Shin HK, Ahn YH, Lee SH, HYK. Digital vision based concrete compressive strength evaluating model using deep convolutional neural network . Comput Mater Contin. 2019;61(3):911-928 https://doi.org/10.32604/cmc.2019.08269
IEEE Style
H.K. Shin, Y.H. Ahn, S.H. Lee, and H.Y.K. "Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep Convolutional Neural Network ," Comput. Mater. Contin., vol. 61, no. 3, pp. 911-928. 2019. https://doi.org/10.32604/cmc.2019.08269

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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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