Hyun Kyu Shin1, Yong Han Ahn2, Sang Hyo Lee3, Ha Young Kim4,*
CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 911-928, 2019, DOI: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.… More >
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