Deep learning based automated segmentation of air-void system in hardened concrete surface using three dimensional reconstructed images

•Automated segmentation of air voids from hardened concrete surface.•Three-dimensional image reconstruction method.•Deep learning strategies and process. The automated air-void detection methods specified in the ASTM C457 require the aid of contrast enhancement which is time consuming and labor inte...

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Bibliographic Details
Published in:Construction & building materials 2022-03, Vol.324, p.126717, Article 126717
Main Authors: Tao, Jueqiang, Gong, Haitao, Wang, Feng, Luo, Xiaohua, Qiu, Xin, Liu, Jinli
Format: Article
Language:English
Subjects:
3D reconstruction
Air voids
DCNN
Hardened concrete
Semantic segmentation
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Summary:•Automated segmentation of air voids from hardened concrete surface.•Three-dimensional image reconstruction method.•Deep learning strategies and process. The automated air-void detection methods specified in the ASTM C457 require the aid of contrast enhancement which is time consuming and labor intensive. This study investigated the utilization of three-dimensional (3D) reconstruction and Deep Convolution Neural Network (DCNN) methods to detect the air voids in hardened concrete surfaces without the use of contrast enhancement. The experimental results showed that the DCNN could accurately distinguish air voids from hardened concrete images with the detection accuracy of over 0.9 in only less than a minute. The accuracy rates for air content, specific surface, and spacing factor were 0.92, 0.91, and 0.89, respectively.
ISSN:0950-0618
1879-0526
DOI:10.1016/j.conbuildmat.2022.126717