East China Jiaotong University

Liyang Institute of Smart City, Chongqing University

Jiangxi Transportation Investment Group Co., Ltd.

Baidian Li

Weixiang Shi

Xiangyu Wang

Bo Huang

Yangqing Liu

Fei Wu

Qiaoming Guo

Junbo Sun

Jianqun Wang

Yufei Wang

Provided are a quantification method for microscopic cracks inside 3D printed concrete and a system thereof. Before measuring the microscopic crack images according to relevant standards, firstly, a denoising network, improved attention-guided denoising neural network (IADNet) is adopted. IADNet can extract features from microscopic crack images from different perspectives, perceive noise from multiple levels, and perform denoising processing, greatly improving image quality and enhancing texture details, which is beneficial for training segmentation networks. The combination of IADNet and semantic segmentation algorithm has the ability to finely recognize image information, quantify microscopic crack recognition, overcome the shortcomings of measurement and analysis of microscopic cracks inside 3D printed concrete, and improve construction efficiency and quality.

Quantification method for microscopic cracks inside 3D printed concrete and system thereof

Growing community of inventors

Nanchang, China

Baidian Li