Open Access

ARTICLE

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

by Hyun Kyu Shin, Yong Han Ahn, Sang Hyo Lee, Ha Young Kim

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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Citations