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

作　　者：Sanghyo Lee Yonghan Ahn Ha Young Kim 

机构地区：[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

出　　处：《Computers, Materials & Continua》2020年第10期1-17,共17页计算机、材料和连续体（英文）

基　　金：This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2018R1A2B6007333);This study was supported by 2018 Research Grant from Kangwon National University.

摘　　要：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.

关 键 词：Deep convolutional neural network(DCNN) non-destructive testing(NDT) concrete compressive strength digital single-lens reflex(DSLR)camera MICROSCOPE 

分 类 号：TB8[一般工业技术—摄影技术]